diff --git a/PROJECT_LOG.md b/PROJECT_LOG.md
index bc1571c..7694db2 100644
--- a/PROJECT_LOG.md
+++ b/PROJECT_LOG.md
@@ -1754,3 +1754,108 @@ Lab050 git add/commit и push/pull/fetch не выполнялись.
 и ускорение строгой проверки ложных кандидатов на C/500k с сохранением
 независимости RX и FEC/CRC. До доказанной скорости и свежести не выполнять
 RF, QPSK, новый видеокодек или перенос SDR на 180 МГц.
+
+# Запись 040
+
+## Дата
+
+8 сентября 2026 года
+
+## Тема
+
+Lab051 — алгоритмическое ускорение потокового BPSK-приёмника на 500 ксимв/с
+и проверка свежести JPEG при измеренной стоимости обработки
+
+## Выполнено
+
+Перед новой разработкой проверены HEAD, status, diff stat и diff check.
+По прямому разрешению текущего задания Lab050 закреплена коммитом
+60691a09f193f36983c7174c5252ed0897c559e9:
+Lab050: model fresh-frame SDR video stream.
+В показанный staged scope вошли девять файлов: код и тесты Lab050, необходимые
+изменения прежних модулей, отчёт, журнал, индекс и небольшой receiver_benchmarks.json.
+Большой result.json, JPEG-профили и посторонние файлы не добавлялись. Push не выполнялся.
+
+Lab051 начата от нового HEAD. Аудит прочитал Lab046B/C, Lab047/048/050,
+связанные функции Lab043/044/045 и convolutional_fec. Рабочая точка строго
+500000 символов/с, Fs=8000000, 16 отсчётов/символ; профиль C 128×96 RGB q25,
+1740–1893 байта. Управление ELRS/CRSF и локальная защита остаются отдельно.
+Ни один файл protocol в Lab051 не менялся.
+
+Воспроизведён прежний короткий шумный опыт Lab050: три JPEG, 24 пакета,
+seed50050, coded-bit Eb/N0=7 дБ, CFO+10 Гц, фаза0,73 и чередование полярности.
+Историческая стоимость 55,157553 с / 0,323745 с IQ =170,373647.
+Свежий инструментированный baseline занял49,614327 с, отношение153,251430;
+это отдельный замер, с влиянием настольной нагрузки/инструментации.
+Обнаружены2374 кандидата:24 valid и2350 false; средние72,967/20,099 мс.
+
+Главная стоимость ложных кандидатов — timing:41,833 с из47,232 с.
+При nominal416 и диапазоне±800 прежняя функция возвращалась к Python-перебору
+1601 смещения. Matched filter на ложных кандидатах занимал0,934 с, уточнение2,797 с.
+Ранний отказ PHY header уже существовал и не запускал тело/Viterbi/CRC.
+
+Добавлен отдельный вычислительный адаптер Lab051. Повторно используется внутренняя
+часть децимации с точным совпадением отсчётов; пересчитываются новые края.
+Общая корреляция уточнения и общий перенос/RRC обслуживают срезы кандидатов.
+Сохранены локальный DC и нулевые края. Результат извлечения кэшируется по
+уточнённому положению/длине в пределах блока. Timing вычисляет все прежние1601
+смещения через корреляцию и накопленные энергии, без сужения диапазона.
+Для неразделимых FFT-пиков и постоянного IQ сохранён исходный выбор первого максимума.
+
+Viterbi использует предвычисленные четыре пары евклидовых метрик и сравнение
+двух ветвей с прежним tie→branch0. K7,R1/2,171/133,tail6,интерливер,CRC и
+результаты декодирования сохранены. CFO-оценка прежняя; кэшируются векторы переноса.
+Новый порог отсева, GPU, multiprocessing и зависимости не добавлены.
+Фиксированный порог кандидатов0.030964473262429237 не изменялся.
+
+Финальный короткий контроль:24/24 пакета,3/3 JPEG, SHA MATCH; совпали число
+кандидатов и все причины отказов. Медиана трёх проходов1,144176 с,
+processing/RF3,534193 — ускорение48,21 раза относительно исторической базы.
+Средний valid candidate17,7675 мс, false0,187317 мс; false-медиана0,0605 мс.
+Полная стоимость с ложными кандидатами/фоном47,674 мс на физический пакет.
+
+Основной опыт:30 изображений, вход5/с, окно6 с, LATEST_ONLY, MAX_VIDEO_AGE200 мс.
+Реально поданы6,044416 с синтетического IQ блоками; receiver не получал TX-границы,
+payload или ожидаемый счётчик. Все240/240 пакетов и30/30 JPEG восстановлены точно.
+Фактическая стоимость каждого push, сборки и JPEG decode использована в событийной
+шкале CPU. Суммарно38,630132 с, processing/RF6,391045,160,959 мс/пакет.
+Получены147082 кандидата,12239 новых окон синхронизации и135057 cache hits.
+Ни один JPEG не уложился в200 мс: DISPLAYED=FRESH DISPLAYED=0,FRESH DISPLAY FPS=0.
+Цель5 свежих/с и минимальная цель>0 НЕ достигнуты.
+
+Переданы30 изображений, до TX удалено0, после decode по возрасту удалено30.
+Возраст декодированных/отброшенных JPEG min/median/max:
+276,515/15691,203/31150,009 мс. Это не измерение камеры/экрана: их задержки UNKNOWN.
+Capture lookup используется только после независимого JPEG для отчётной метрики;
+в прежнем fragment format нет capture timestamp. Live-контроль возраста не доказан.
+Видеочередь≤1, однако непрерывный IQ-источник потребовал бы внешнего backlog
+2088473848 байт при отставании32,632404 с. Такой буфер не реализован и не скрыт.
+REAL-TIME CAPABLE=NO. Поток воспроизводится офлайн с обратным давлением источнику.
+
+Пик удерживаемого IQ3177728 байт(3,031 МиБ), проверяемый предел3398912 байт.
+Инженерная оценка рабочих массивов136,161 МиБ; это не универсальный предел и не RSS.
+RSS процесса после потока154,105 МиБ, peak178,004 МиБ, включая Python/генератор/кэши.
+Длинный тест4096 блоков(33,554432 с,нулевой фон) не показал роста IQ и децимации:
+2653440 и397216 байт после прогрева. RSS156,199–156,250 МиБ. Finish освобождает буферы.
+В отдельном replay посчитана нижняя граница наблюдаемых ndarray/владельцев и
+tracemalloc peak. Внутренние C-аллокации не объявляются полностью измеренными;
+холодные общие массивы могут занимать больше памяти, чем отдельное старое окно.
+
+Сохранённая Lab047-запись20 ксимв/с повторно разобрана штатным Lab048:
+36/36,FEC/CRC PASS,JPEG SHA MATCH,19 ошибок до FEC и0 после. Исходные IQ/JPEG сохранены.
+Новый адаптер рассчитан только на500 ксимв/с; аппаратных запусков не было.
+
+38 целевых тестов PASS. Verifier независимо нашёл два FFT tie-расхождения;
+оба исправлены и повторно воспроизведены с совпадением старого результата.
+Adversary проверил malformed PHY без body/Viterbi, дубликаты/старые ID,
+лимиты объекта, усечение, восстановление подмен и арифметику возраста/backlog.
+Таблицы отчёта сверены с JSON. Документ проверен в UTF-8, ссылки существуют.
+quick_gate PASS:688 тестов,17 функциональных проверок,13 штатных пропусков.
+Полный pytest:688 passed за72,92 с,0 failed; одно предупреждение доступа к .pytest_cache.
+git diff --check PASS. Lab051 git add/commit и push не выполнялись; индекс пуст.
+RF TX=0; HackRF,180 МГц,QPSK,новый FEC/кодек и аппаратные500 ксимв/с не запускались.
+
+Следующий этап: STOP. Рекомендуемый Lab052 — отдельное программное исследование
+стоимости уникальных фоновых кандидатов в полном режиме5 изображений/с,
+с неизменными PHY/FEC/CRC и независимыми положительными/отрицательными регрессиями.
+До отдельного разрешения не выполнять RF или аппаратные500 ксимв/с.
diff --git a/data/processed/lab051/baseline_profile.json b/data/processed/lab051/baseline_profile.json
new file mode 100644
index 0000000..95c3b63
--- /dev/null
+++ b/data/processed/lab051/baseline_profile.json
@@ -0,0 +1,209 @@
+{
+  "benchmark": {
+    "symbol_rate": 500000,
+    "samples_per_symbol": 16,
+    "detection_decimation": 8,
+    "input_images": 3,
+    "tx_rf_packets": 24,
+    "accepted_rf_packets": 24,
+    "completed_images": 3,
+    "all_sha_match": true,
+    "packets_before_finish": 24,
+    "images_before_finish": 3,
+    "capture_seconds": 0.323744625,
+    "payload_airtime_seconds": 0.276768,
+    "processing_seconds": 49.61432680033613,
+    "processing_per_rf_second": 153.25143019852803,
+    "processing_per_payload_airtime_second": 179.26323419013804,
+    "coarse_seconds": 0.4982285003643483,
+    "strict_seconds": 49.04027030314319,
+    "assembly_jpeg_decode_seconds": 0.0013099998468533158,
+    "processing_seconds_per_rf_packet": 2.067263616680672,
+    "peak_iq_buffer_samples": 424309,
+    "iq_buffer_limit_samples": 424864,
+    "working_arrays_estimate_bytes": 74928288,
+    "memory_is_process_rss_measurement": false,
+    "peak_object_payload_bytes": 1821,
+    "candidate_count": 2374,
+    "peak_candidates_in_block": 2114,
+    "rejected_reasons": {
+      "PHY_HEADER_MAGIC_MISMATCH": 2298,
+      "TRUNCATED_SYMBOLS": 38,
+      "TRUNCATED_IQ_WINDOW": 14
+    },
+    "buffer_release_delay_upper_seconds": 0.052608,
+    "noisy": true,
+    "coded_bit_ebn0_db": 7,
+    "cfo_hz": 10,
+    "candidate_threshold": 0.030964473262429237,
+    "completed": [
+      {
+        "image_id": 1,
+        "sha256": "15723773a8788c9d610c874707ad2d513582322ee1d42009b0b39d3de336e10d",
+        "bytes": 1740,
+        "dimensions": [
+          128,
+          96
+        ],
+        "first_sample": 4321,
+        "last_sample": 747682,
+        "emitted_after_sample": 1025782
+      },
+      {
+        "image_id": 2,
+        "sha256": "66404b14ca9e3a6627871ad9d3b5dd4311915d7820a99dfa182fc3a20fbe9d85",
+        "bytes": 1790,
+        "dimensions": [
+          128,
+          96
+        ],
+        "first_sample": 747989,
+        "last_sample": 1491022,
+        "emitted_after_sample": 1742690
+      },
+      {
+        "image_id": 3,
+        "sha256": "166d33d72820bf3385e0d30823d991b453147c84ca75f788ab8819ad5e3c3ced",
+        "bytes": 1821,
+        "dimensions": [
+          128,
+          96
+        ],
+        "first_sample": 1491425,
+        "last_sample": 2234130,
+        "emitted_after_sample": 2496773
+      }
+    ],
+    "rf_tx": 0
+  },
+  "profile": {
+    "shared_stages": {
+      "phy_header": {
+        "calls": 24,
+        "seconds": 0.0005645002238452435,
+        "exclusive_seconds": 0.0005645002238452435
+      },
+      "coarse_localization": {
+        "calls": 36,
+        "seconds": 0.4980395002057776,
+        "exclusive_seconds": 0.4980395002057776
+      }
+    },
+    "valid": {
+      "count": 24,
+      "mean_seconds": 0.07296675831700365,
+      "median_seconds": 0.07252859993604943,
+      "total_seconds": 1.7512021996080875,
+      "stages": {
+        "refinement": {
+          "calls": 24,
+          "seconds": 0.028973900014534593,
+          "exclusive_seconds": 0.028973900014534593
+        },
+        "matched_filter": {
+          "calls": 48,
+          "seconds": 0.1906564999371767,
+          "exclusive_seconds": 0.1906564999371767
+        },
+        "timing": {
+          "calls": 48,
+          "seconds": 0.9146315004909411,
+          "exclusive_seconds": 0.0007350004743784666
+        },
+        "timing_fallback": {
+          "calls": 48,
+          "seconds": 0.9138965000165626,
+          "exclusive_seconds": 0.9138965000165626
+        },
+        "phase_diagnostics": {
+          "calls": 48,
+          "seconds": 0.019832400372251868,
+          "exclusive_seconds": 0.008994300849735737
+        },
+        "cfo_gain": {
+          "calls": 48,
+          "seconds": 0.010838099522516131,
+          "exclusive_seconds": 0.010838099522516131
+        },
+        "phy_header": {
+          "calls": 48,
+          "seconds": 0.0017112999921664596,
+          "exclusive_seconds": 0.0017112999921664596
+        },
+        "body_pipeline": {
+          "calls": 24,
+          "seconds": 0.49711450026370585,
+          "exclusive_seconds": 0.011535500641912222
+        },
+        "viterbi": {
+          "calls": 24,
+          "seconds": 0.4843809995800257,
+          "exclusive_seconds": 0.4843809995800257
+        },
+        "crc_parser": {
+          "calls": 24,
+          "seconds": 0.0002698999596759677,
+          "exclusive_seconds": 0.0002698999596759677
+        }
+      }
+    },
+    "false": {
+      "count": 2350,
+      "mean_seconds": 0.02009875251049612,
+      "median_seconds": 0.019996499991975725,
+      "total_seconds": 47.23206839966588,
+      "stages": {
+        "refinement": {
+          "calls": 2350,
+          "seconds": 2.7974041998386383,
+          "exclusive_seconds": 2.7974041998386383
+        },
+        "matched_filter": {
+          "calls": 2336,
+          "seconds": 0.9339654004434124,
+          "exclusive_seconds": 0.9339654004434124
+        },
+        "timing": {
+          "calls": 2336,
+          "seconds": 41.83316580078099,
+          "exclusive_seconds": 0.028656100272201
+        },
+        "timing_fallback": {
+          "calls": 2336,
+          "seconds": 41.80450970050879,
+          "exclusive_seconds": 41.80450970050879
+        },
+        "phase_diagnostics": {
+          "calls": 2336,
+          "seconds": 0.910528302541934,
+          "exclusive_seconds": 0.4247559007490054
+        },
+        "cfo_gain": {
+          "calls": 2336,
+          "seconds": 0.4857724017929286,
+          "exclusive_seconds": 0.4857724017929286
+        },
+        "phy_header": {
+          "calls": 2298,
+          "seconds": 0.06713570095598698,
+          "exclusive_seconds": 0.06713570095598698
+        }
+      }
+    }
+  },
+  "rf_tx": 0,
+  "array_observation": {
+    "valid": {
+      "observed_array_objects": 14361,
+      "observed_owned_arrays": 8704,
+      "observed_owned_bytes_total": 16681890,
+      "tracemalloc_retained_bytes": 1842,
+      "tracemalloc_peak_bytes": 4787123,
+      "array_count_is_lower_bound": true
+    }
+  },
+  "process_memory_after_profiling": {
+    "rss_bytes": 156041216,
+    "peak_rss_bytes": 169754624
+  }
+}
diff --git a/data/processed/lab051/benchmark.json b/data/processed/lab051/benchmark.json
new file mode 100644
index 0000000..7793fdb
--- /dev/null
+++ b/data/processed/lab051/benchmark.json
@@ -0,0 +1,419 @@
+{
+  "repeats": [
+    {
+      "symbol_rate": 500000,
+      "samples_per_symbol": 16,
+      "detection_decimation": 8,
+      "input_images": 3,
+      "tx_rf_packets": 24,
+      "accepted_rf_packets": 24,
+      "completed_images": 3,
+      "all_sha_match": true,
+      "packets_before_finish": 24,
+      "images_before_finish": 3,
+      "capture_seconds": 0.323744625,
+      "payload_airtime_seconds": 0.276768,
+      "processing_seconds": 1.2077819011174142,
+      "processing_per_rf_second": 3.7306624044103103,
+      "processing_per_payload_airtime_second": 4.363878414836304,
+      "coarse_seconds": 0.0927895000204444,
+      "strict_seconds": 0.9242713990388438,
+      "assembly_jpeg_decode_seconds": 0.0011941003613173962,
+      "processing_seconds_per_rf_packet": 0.05032424587989226,
+      "peak_iq_buffer_samples": 424309,
+      "iq_buffer_limit_samples": 424864,
+      "working_arrays_estimate_bytes": 74928288,
+      "memory_is_process_rss_measurement": false,
+      "peak_object_payload_bytes": 1821,
+      "candidate_count": 2374,
+      "peak_candidates_in_block": 2114,
+      "rejected_reasons": {
+        "PHY_HEADER_MAGIC_MISMATCH": 2298,
+        "TRUNCATED_SYMBOLS": 38,
+        "TRUNCATED_IQ_WINDOW": 14
+      },
+      "buffer_release_delay_upper_seconds": 0.052608,
+      "noisy": true,
+      "coded_bit_ebn0_db": 7,
+      "cfo_hz": 10,
+      "candidate_threshold": 0.030964473262429237,
+      "completed": [
+        {
+          "image_id": 1,
+          "sha256": "15723773a8788c9d610c874707ad2d513582322ee1d42009b0b39d3de336e10d",
+          "bytes": 1740,
+          "dimensions": [
+            128,
+            96
+          ],
+          "first_sample": 4321,
+          "last_sample": 747682,
+          "emitted_after_sample": 1025782
+        },
+        {
+          "image_id": 2,
+          "sha256": "66404b14ca9e3a6627871ad9d3b5dd4311915d7820a99dfa182fc3a20fbe9d85",
+          "bytes": 1790,
+          "dimensions": [
+            128,
+            96
+          ],
+          "first_sample": 747989,
+          "last_sample": 1491022,
+          "emitted_after_sample": 1742690
+        },
+        {
+          "image_id": 3,
+          "sha256": "166d33d72820bf3385e0d30823d991b453147c84ca75f788ab8819ad5e3c3ced",
+          "bytes": 1821,
+          "dimensions": [
+            128,
+            96
+          ],
+          "first_sample": 1491425,
+          "last_sample": 2234130,
+          "emitted_after_sample": 2496773
+        }
+      ],
+      "rf_tx": 0
+    },
+    {
+      "symbol_rate": 500000,
+      "samples_per_symbol": 16,
+      "detection_decimation": 8,
+      "input_images": 3,
+      "tx_rf_packets": 24,
+      "accepted_rf_packets": 24,
+      "completed_images": 3,
+      "all_sha_match": true,
+      "packets_before_finish": 24,
+      "images_before_finish": 3,
+      "capture_seconds": 0.323744625,
+      "payload_airtime_seconds": 0.276768,
+      "processing_seconds": 1.144175999914296,
+      "processing_per_rf_second": 3.534193038461398,
+      "processing_per_payload_airtime_second": 4.134061740932101,
+      "coarse_seconds": 0.08686010027304292,
+      "strict_seconds": 0.8724833994638175,
+      "assembly_jpeg_decode_seconds": 0.0011348994448781013,
+      "processing_seconds_per_rf_packet": 0.047673999996428996,
+      "peak_iq_buffer_samples": 424309,
+      "iq_buffer_limit_samples": 424864,
+      "working_arrays_estimate_bytes": 74928288,
+      "memory_is_process_rss_measurement": false,
+      "peak_object_payload_bytes": 1821,
+      "candidate_count": 2374,
+      "peak_candidates_in_block": 2114,
+      "rejected_reasons": {
+        "PHY_HEADER_MAGIC_MISMATCH": 2298,
+        "TRUNCATED_SYMBOLS": 38,
+        "TRUNCATED_IQ_WINDOW": 14
+      },
+      "buffer_release_delay_upper_seconds": 0.052608,
+      "noisy": true,
+      "coded_bit_ebn0_db": 7,
+      "cfo_hz": 10,
+      "candidate_threshold": 0.030964473262429237,
+      "completed": [
+        {
+          "image_id": 1,
+          "sha256": "15723773a8788c9d610c874707ad2d513582322ee1d42009b0b39d3de336e10d",
+          "bytes": 1740,
+          "dimensions": [
+            128,
+            96
+          ],
+          "first_sample": 4321,
+          "last_sample": 747682,
+          "emitted_after_sample": 1025782
+        },
+        {
+          "image_id": 2,
+          "sha256": "66404b14ca9e3a6627871ad9d3b5dd4311915d7820a99dfa182fc3a20fbe9d85",
+          "bytes": 1790,
+          "dimensions": [
+            128,
+            96
+          ],
+          "first_sample": 747989,
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+        }
+      ],
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+    }
+  ],
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+      }
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+          "exclusive_seconds": 0.09387810016050935
+        },
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+          "seconds": 0.1028753004502505,
+          "exclusive_seconds": 0.1028753004502505
+        },
+        "timing": {
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+          "exclusive_seconds": 0.04353450005874038
+        },
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+          "seconds": 0.016765999956987798,
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+        },
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+          "seconds": 0.008442100137472153,
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+        },
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+        },
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+          "seconds": 0.14421880000736564,
+          "exclusive_seconds": 0.011199799599125981
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+        "viterbi": {
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+          "exclusive_seconds": 0.00021099986042827368
+        }
+      }
+    },
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+        },
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+          "exclusive_seconds": 0.026502199470996857
+        },
+        "timing": {
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+        },
+        "phase_diagnostics": {
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+        "cfo_gain": {
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+          "seconds": 0.02638460008893162,
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+        },
+        "phy_header": {
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+          "seconds": 0.03204370115417987,
+          "exclusive_seconds": 0.03204370115417987
+        }
+      }
+    }
+  },
+  "profiled_benchmark": {
+    "symbol_rate": 500000,
+    "samples_per_symbol": 16,
+    "detection_decimation": 8,
+    "input_images": 3,
+    "tx_rf_packets": 24,
+    "accepted_rf_packets": 24,
+    "completed_images": 3,
+    "all_sha_match": true,
+    "packets_before_finish": 24,
+    "images_before_finish": 3,
+    "capture_seconds": 0.323744625,
+    "payload_airtime_seconds": 0.276768,
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+    "processing_per_rf_second": 3.5966783991655458,
+    "processing_per_payload_airtime_second": 4.207152920798105,
+    "coarse_seconds": 0.08932610007468611,
+    "strict_seconds": 0.8848743984708562,
+    "assembly_jpeg_decode_seconds": 0.0011538001708686352,
+    "processing_seconds_per_rf_packet": 0.048516887482643746,
+    "peak_iq_buffer_samples": 424309,
+    "iq_buffer_limit_samples": 424864,
+    "working_arrays_estimate_bytes": 74928288,
+    "memory_is_process_rss_measurement": false,
+    "peak_object_payload_bytes": 1821,
+    "candidate_count": 2374,
+    "peak_candidates_in_block": 2114,
+    "rejected_reasons": {
+      "PHY_HEADER_MAGIC_MISMATCH": 2298,
+      "TRUNCATED_SYMBOLS": 38,
+      "TRUNCATED_IQ_WINDOW": 14
+    },
+    "buffer_release_delay_upper_seconds": 0.052608,
+    "noisy": true,
+    "coded_bit_ebn0_db": 7,
+    "cfo_hz": 10,
+    "candidate_threshold": 0.030964473262429237,
+    "completed": [
+      {
+        "image_id": 1,
+        "sha256": "15723773a8788c9d610c874707ad2d513582322ee1d42009b0b39d3de336e10d",
+        "bytes": 1740,
+        "dimensions": [
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+          96
+        ],
+        "first_sample": 4321,
+        "last_sample": 747682,
+        "emitted_after_sample": 1025782
+      },
+      {
+        "image_id": 2,
+        "sha256": "66404b14ca9e3a6627871ad9d3b5dd4311915d7820a99dfa182fc3a20fbe9d85",
+        "bytes": 1790,
+        "dimensions": [
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+          96
+        ],
+        "first_sample": 747989,
+        "last_sample": 1491022,
+        "emitted_after_sample": 1742690
+      },
+      {
+        "image_id": 3,
+        "sha256": "166d33d72820bf3385e0d30823d991b453147c84ca75f788ab8819ad5e3c3ced",
+        "bytes": 1821,
+        "dimensions": [
+          128,
+          96
+        ],
+        "first_sample": 1491425,
+        "last_sample": 2234130,
+        "emitted_after_sample": 2496773
+      }
+    ],
+    "rf_tx": 0
+  },
+  "rf_tx": 0
+}
diff --git a/data/processed/lab051/memory.json b/data/processed/lab051/memory.json
new file mode 100644
index 0000000..efd1d17
--- /dev/null
+++ b/data/processed/lab051/memory.json
@@ -0,0 +1,67 @@
+{
+  "blocks": 4096,
+  "model_seconds": 33.554432,
+  "background": "zeros; noisy background covered by six-second integration",
+  "peak_iq_bytes": 3177728,
+  "iq_limit_bytes": 3398912,
+  "retained_after_finish": 0,
+  "checkpoints": [
+    {
+      "block": 512,
+      "iq_bytes": 2653440,
+      "decimated_bytes": 397216,
+      "rss_bytes": 163840000,
+      "peak_rss_bytes": 186650624
+    },
+    {
+      "block": 1024,
+      "iq_bytes": 2653440,
+      "decimated_bytes": 397216,
+      "rss_bytes": 163840000,
+      "peak_rss_bytes": 186650624
+    },
+    {
+      "block": 1536,
+      "iq_bytes": 2653440,
+      "decimated_bytes": 397216,
+      "rss_bytes": 163786752,
+      "peak_rss_bytes": 186650624
+    },
+    {
+      "block": 2048,
+      "iq_bytes": 2653440,
+      "decimated_bytes": 397216,
+      "rss_bytes": 163786752,
+      "peak_rss_bytes": 186650624
+    },
+    {
+      "block": 2560,
+      "iq_bytes": 2653440,
+      "decimated_bytes": 397216,
+      "rss_bytes": 163786752,
+      "peak_rss_bytes": 186650624
+    },
+    {
+      "block": 3072,
+      "iq_bytes": 2653440,
+      "decimated_bytes": 397216,
+      "rss_bytes": 163786752,
+      "peak_rss_bytes": 186650624
+    },
+    {
+      "block": 3584,
+      "iq_bytes": 2653440,
+      "decimated_bytes": 397216,
+      "rss_bytes": 163786752,
+      "peak_rss_bytes": 186650624
+    },
+    {
+      "block": 4096,
+      "iq_bytes": 2653440,
+      "decimated_bytes": 397216,
+      "rss_bytes": 163790848,
+      "peak_rss_bytes": 186650624
+    }
+  ],
+  "rf_tx": 0
+}
diff --git a/data/processed/lab051/regression20k/result.json b/data/processed/lab051/regression20k/result.json
new file mode 100644
index 0000000..ccde643
--- /dev/null
+++ b/data/processed/lab051/regression20k/result.json
@@ -0,0 +1,198 @@
+{
+  "stage": "LAB048_OFFLINE_RECEIVER_PERFORMANCE",
+  "baseline": {
+    "capture_seconds": 15.0,
+    "full_processing_seconds": 37.423910000012256,
+    "strict_stage_seconds": 32.45996900013415,
+    "average_strict_candidate_seconds": 0.47043433333527757,
+    "approximate_peak_bytes": 1545755352
+  },
+  "optimized": {
+    "capture_seconds": 15.0,
+    "full_processing_seconds": 12.978691199910827,
+    "coarse_search_seconds": 4.4797875001095235,
+    "strict_stage_seconds": 8.487128499895334,
+    "average_strict_candidate_seconds": 0.12300186231732368,
+    "approximate_peak_bytes": 1545755352,
+    "stage_measurements": {
+      "cs8_to_complex64": {
+        "calls": 113,
+        "cumulative_seconds": 1.306003100471571
+      },
+      "refine_start": {
+        "calls": 69,
+        "cumulative_seconds": 2.930933100869879
+      },
+      "matched_filter": {
+        "calls": 105,
+        "cumulative_seconds": 2.9355400999775156
+      },
+      "timing": {
+        "calls": 105,
+        "cumulative_seconds": 1.2902987005654722
+      },
+      "cfo_phase_soft": {
+        "calls": 105,
+        "cumulative_seconds": 0.039461099775508046
+      },
+      "phy_header": {
+        "calls": 105,
+        "cumulative_seconds": 0.00410620030015707
+      },
+      "post_preamble_fec_crc": {
+        "calls": 36,
+        "cumulative_seconds": 0.6347944003064185
+      },
+      "viterbi": {
+        "calls": 36,
+        "cumulative_seconds": 0.6168836002470925
+      }
+    },
+    "memory_arrays": [
+      {
+        "name": "coarse_iq_window",
+        "dtype": "complex64",
+        "shape": [
+          16000000
+        ],
+        "bytes": 128000000,
+        "mib": 122.0703125
+      },
+      {
+        "name": "coarse_decimated",
+        "dtype": "complex64",
+        "shape": [
+          800000
+        ],
+        "bytes": 6400000,
+        "mib": 6.103515625
+      },
+      {
+        "name": "coarse_downconversion_vector",
+        "dtype": "complex64",
+        "shape": [
+          800000
+        ],
+        "bytes": 6400000,
+        "mib": 6.103515625
+      },
+      {
+        "name": "coarse_correlation_scores",
+        "dtype": "float32",
+        "shape": [
+          773561
+        ],
+        "bytes": 3094244,
+        "mib": 2.9509010314941406
+      },
+      {
+        "name": "strict_full_iq_window",
+        "dtype": "complex64",
+        "shape": [
+          2306400
+        ],
+        "bytes": 18451200,
+        "mib": 17.596435546875
+      },
+      {
+        "name": "strict_cached_carrier_vector",
+        "dtype": "complex64",
+        "shape": [
+          2306400
+        ],
+        "bytes": 18451200,
+        "mib": 17.596435546875
+      },
+      {
+        "name": "strict_baseband",
+        "dtype": "complex64",
+        "shape": [
+          2306400
+        ],
+        "bytes": 18451200,
+        "mib": 17.596435546875
+      },
+      {
+        "name": "strict_matched_output",
+        "dtype": "complex64",
+        "shape": [
+          2310400
+        ],
+        "bytes": 18483200,
+        "mib": 17.626953125
+      },
+      {
+        "name": "matched_filter_taps",
+        "dtype": "float32",
+        "shape": [
+          4001
+        ],
+        "bytes": 16004,
+        "mib": 0.015262603759765625
+      }
+    ]
+  },
+  "speedup": 2.883488745018403,
+  "processing_to_capture_ratio": 0.8652460799940551,
+  "real_time_capable": true,
+  "threshold": 0.030964473262429237,
+  "threshold_changed": false,
+  "coarse": {
+    "total_local_peaks": 185828,
+    "first_stage_candidates": 32414,
+    "skipped_as_owned_event": 32345,
+    "exact_duplicates": 0
+  },
+  "strict": {
+    "candidates_processed": 69,
+    "rejected_during_refinement": 0,
+    "refinement_pass": 69,
+    "timing_pass": 69,
+    "carrier_pass": 69,
+    "phy_header_pass": 36,
+    "phy_header_fail": 33,
+    "fec_attempts": 36,
+    "fec_pass": 36,
+    "fec_fail": 0,
+    "protocol_parse_pass": 36,
+    "crc_pass": 36,
+    "crc_fail": 0,
+    "valid_frames": 36
+  },
+  "object": {
+    "valid_fragments": 36,
+    "missing_fragments": [],
+    "object_complete": true
+  },
+  "after_independent_verdict": {
+    "stage": "AFTER_COMPLETED_INDEPENDENT_VERDICT_ONLY",
+    "expected_tx_frames": 36,
+    "matched_valid_frames": 36,
+    "missed_tx_frames": [],
+    "false_crc_pass": 0,
+    "duplicate_valid_detections": 0,
+    "total_pre_fec_coded_bit_errors": 19,
+    "total_post_fec_protocol_bit_errors": 0,
+    "jpeg_size": 8724,
+    "recovered_sha256": "e913dab9ac32795250880a4be67fbbbf89a4f96536842476ab591d1acaafc02a",
+    "tx_sha256": "e913dab9ac32795250880a4be67fbbbf89a4f96536842476ab591d1acaafc02a",
+    "sha_match": true,
+    "byte_errors": 0,
+    "bit_errors": 0,
+    "jpeg_decode": "PASS",
+    "width": 320,
+    "height": 240
+  },
+  "receiver_oracles": {
+    "expected_frame_count": false,
+    "tx_coded_bits": false,
+    "tx_fragment_indexes": false,
+    "tx_frame_offsets": false,
+    "tx_image": false,
+    "tx_packets": false
+  },
+  "phy_changed": false,
+  "fec_changed": false,
+  "bpsk_changed": false,
+  "rf_tx": 0
+}
\ No newline at end of file
diff --git a/data/processed/lab051/replay.json b/data/processed/lab051/replay.json
new file mode 100644
index 0000000..ab5bb8c
--- /dev/null
+++ b/data/processed/lab051/replay.json
@@ -0,0 +1,40 @@
+{
+  "false": {
+    "reason": "PHY_HEADER_MAGIC_MISMATCH",
+    "baseline_arrays": {
+      "observed_array_objects": 2888,
+      "observed_owned_arrays": 61,
+      "observed_owned_bytes_total": 6612268,
+      "tracemalloc_retained_bytes": 1481,
+      "tracemalloc_peak_bytes": 2104129,
+      "array_count_is_lower_bound": true
+    },
+    "optimized_arrays": {
+      "observed_array_objects": 76,
+      "observed_owned_arrays": 61,
+      "observed_owned_bytes_total": 5906101,
+      "tracemalloc_retained_bytes": 1936,
+      "tracemalloc_peak_bytes": 5524392,
+      "array_count_is_lower_bound": true
+    }
+  },
+  "valid": {
+    "reason": "CRC_PASS",
+    "baseline_arrays": {
+      "observed_array_objects": 14361,
+      "observed_owned_arrays": 8704,
+      "observed_owned_bytes_total": 16681890,
+      "tracemalloc_retained_bytes": 2130,
+      "tracemalloc_peak_bytes": 5885299,
+      "array_count_is_lower_bound": true
+    },
+    "optimized_arrays": {
+      "observed_array_objects": 6594,
+      "observed_owned_arrays": 6560,
+      "observed_owned_bytes_total": 12345244,
+      "tracemalloc_retained_bytes": 2645,
+      "tracemalloc_peak_bytes": 7771675,
+      "array_count_is_lower_bound": true
+    }
+  }
+}
diff --git a/data/processed/lab051/stream.json b/data/processed/lab051/stream.json
new file mode 100644
index 0000000..c6ba443
--- /dev/null
+++ b/data/processed/lab051/stream.json
@@ -0,0 +1,7253 @@
+{
+  "scope": "continuous synthetic IQ; measured per-block CPU event replay; camera/display latency UNKNOWN",
+  "symbol_rate": 500000,
+  "samples_per_symbol": 16,
+  "input_fps": 5,
+  "input_frames": 30,
+  "observation_seconds": 6.0,
+  "iq_seconds": 6.044416,
+  "tx_images": 30,
+  "tx_physical_packets": 240,
+  "dropped_before_tx": 0,
+  "maximum_waiting_frames": 1,
+  "rx_physical_packets": 240,
+  "received_images": 30,
+  "object_jpeg_integrity": true,
+  "dropped_stale_at_display": 30,
+  "displayed": 0,
+  "fresh_displayed": 0,
+  "raw_received_fps": 0.8333333333333334,
+  "raw_display_fps": 0.0,
+  "fresh_display_fps": 0.0,
+  "drain_fresh_displayed": 0,
+  "image_age_min_seconds": 0.2765154999291971,
+  "image_age_median_seconds": 15.691202650459713,
+  "image_age_max_seconds": 31.150008799213214,
+  "fresh_image_age_seconds": [],
+  "max_video_age_seconds": 0.2,
+  "processing_seconds": 38.630131699494086,
+  "rx_processing_seconds": 38.619084799312986,
+  "assembly_seconds": 0.005451100296340883,
+  "jpeg_decode_seconds": 0.00559579988475889,
+  "processing_per_rf_second": 6.391044511081647,
+  "physical_packet_processing_seconds": 0.16095888208122536,
+  "maximum_cpu_lag_seconds": 32.63240379935919,
+  "implied_external_iq_backlog_bytes": 2088473848,
+  "live_external_queue_implemented": false,
+  "retained_iq_peak_bytes": 3177728,
+  "working_arrays_upper_estimate_bytes": 142775456,
+  "object_payload_peak_bytes": 1893,
+  "process_memory": {
+    "rss_bytes": 161591296,
+    "peak_rss_bytes": 186650624
+  },
+  "rss_samples": [
+    {
+      "samples": 4194304,
+      "rss_bytes": 163315712,
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+      "worker_end": 38.09244759940379,
+      "rx_cpu_seconds": 0.09623090003151447,
+      "packets": 0,
+      "lag_seconds": 32.06313559940379
+    },
+    {
+      "samples_received": 48300032,
+      "arrival_time": 6.037504,
+      "worker_start": 38.09244759940379,
+      "worker_end": 38.18456899934285,
+      "rx_cpu_seconds": 0.09212139993906021,
+      "packets": 0,
+      "lag_seconds": 32.14706499934285
+    },
+    {
+      "samples_received": 48355328,
+      "arrival_time": 6.044416,
+      "worker_start": 38.18456899934285,
+      "worker_end": 38.28998359933798,
+      "rx_cpu_seconds": 0.10541459999512881,
+      "packets": 0,
+      "lag_seconds": 32.24556759933798
+    },
+    {
+      "samples_received": 48355328,
+      "arrival_time": 6.044416,
+      "worker_start": 38.28998359933798,
+      "worker_end": 38.67681979935919,
+      "rx_cpu_seconds": 0.38683620002120733,
+      "packets": 0,
+      "lag_seconds": 32.63240379935919
+    }
+  ],
+  "rf_tx": 0
+}
diff --git a/docs/LABS_INDEX.md b/docs/LABS_INDEX.md
index 9830da1..a36341c 100644
--- a/docs/LABS_INDEX.md
+++ b/docs/LABS_INDEX.md
@@ -275,6 +275,39 @@
 - **Далее:** Lab051 — профиль и ускорение строгой обработки ложных кандидатов.
 - **RF:** RF TX = 0. Lab050 commit/push не выполнялись. STOP.
 
+## Lab051
+
+- **Цель:** алгоритмически ускорить независимый блочный RX на 500 ксимв/с,
+  16 отсчётов/символ, Fs=8 Мвыб/с; проверить 5 свежих JPEG C в секунду.
+- **База:** Lab050 закреплена разрешённым коммитом
+  `60691a09f193f36983c7174c5252ed0897c559e9`.
+- **Профиль:** главная стоимость Lab050 — fallback timing, 41,833 с на ложных
+  кандидатах. Полный диапазон 1601 смещения сохранён и вычисляется через FFT
+  и накопленные энергии, с исходным выбором на неразделимых пиках.
+- **Изменения:** общий front-end, точное повторное использование децимации,
+  кэш результатов уточнённого окна, короткий probe до PHY PASS, предвычисленные
+  метрики прежнего Viterbi. Порог, PHY/FEC/CRC и protocol не менялись.
+- **Короткий контроль:** 24/24 пакета, 3/3 точных JPEG; медиана трёх повторов
+  1,144176 с вместо исторических 55,157553 с. Processing/RF=3,534193.
+- **Основной поток:** 30 JPEG за 6 с, 240/240 пакетов, SHA MATCH 30/30.
+  Обработка 6,044416 с IQ заняла 38,630132 с, processing/RF=6,391045.
+  При MAX_VIDEO_AGE=200 мс все 30 JPEG просрочены; FRESH DISPLAY FPS=0.
+  Цель реального времени не достигнута, включая минимальную свежесть >0.
+- **Память:** пик удерживаемого IQ 3,031 МиБ, RSS процесса в опыте измерен.
+  Тест 33,55 с не показывает роста IQ/кэша с длительностью. Это не готовая live-
+  очередь: непрерывный источник потребовал бы около 2,09 ГБ внешнего backlog.
+- **Регрессия:** сохранённая Lab047-запись 20 ксимв/с штатным Lab048:
+  36/36, CRC PASS, JPEG SHA MATCH; 19 ошибок до FEC и 0 после.
+- **Код:** [приёмник](../experiments/lab051_streaming_receiver.py),
+  [профилировщик](../experiments/lab051_receiver_profile.py),
+  [полный поток](../experiments/lab051_receiver_performance.py),
+  [38 тестов](../tests/test_lab051_streaming_receiver.py).
+- **Отчёт:** [состояние](lab051_current_state.md),
+  [измеренный поток](../data/processed/lab051/stream.json). Журнал: запись 040.
+- **Далее:** отдельное программное исследование стоимости уникальных фоновых
+  кандидатов в полном потоке 5 кадров/с с тем же PHY/FEC/CRC.
+- **RF / Git:** RF TX=0; Lab051 git add/commit и push не выполнялись. STOP.
+
 ## Правило хранения данных
 
 Большие записи IQ, потоки `cs8`, бинарные объекты и другие аппаратные исходные
diff --git a/docs/lab051_current_state.md b/docs/lab051_current_state.md
new file mode 100644
index 0000000..f826083
--- /dev/null
+++ b/docs/lab051_current_state.md
@@ -0,0 +1,281 @@
+# Lab051 — вычислительная архитектура потокового BPSK-приёмника
+
+Дата: 8 сентября 2026 года. SOFTWARE ONLY. RF TX = 0.
+
+## Результат и граница вывода
+
+**Программный эксперимент завершён; цели реального времени и свежести не достигнуты.**
+В полном шестисекундном потоке получены 240/240 физических пакетов и 30/30 JPEG
+с точным содержимым. Обработка 6,044416 с синтетического IQ заняла **38,630132 с**,
+отношение **6,391045**. При MAX_VIDEO_AGE=200 мс **FRESH DISPLAY FPS=0**;
+не выполнен даже минимальный критерий >0.
+
+Короткий шумный сценарий Lab050 ускорен примерно в **48,21 раза** относительно
+исторического замера: 55,157553 с → медиана **1,144176 с** трёх проходов.
+Его processing/RF = **3,534193**, поэтому и этот вариант не работает в реальном времени.
+Среднее успешного кандидата — **17,7675 мс**; полная стоимость короткого опыта —
+**47,674 мс/пакет**, а длинного потока — **160,959 мс/пакет**.
+Последние величины включают обработку ложных кандидатов и фона.
+
+Модуляция BPSK, PRBS7, PHY-заголовок, фрагментация, интерливер, FEC R=1/2 K=7,
+171/133 octal, soft-метрика и CRC не менялись. GPU и multiprocessing не использовались.
+Новых зависимостей нет. Аппаратура не перечислялась и не запускалась.
+
+## Git и аудит до реализации
+
+Исходный main: `cdf5396e807b83f82a264632dc46f71428fcd616`.
+Проверены HEAD/status/diff stat/diff check, затем показан точный staged scope.
+По прямому разрешению пункта 0 задания создан коммит:
+
+`60691a09f193f36983c7174c5252ed0897c559e9`
+
+`Lab050: model fresh-frame SDR video stream`
+
+В него вошли ровно девять файлов: PROJECT_LOG.md, docs/LABS_INDEX.md,
+experiments/lab046c_offline_recovery.py, protocol/video_frame_scheduler.py,
+docs/lab050_current_state.md, experiments/lab050_streaming_receiver.py,
+experiments/lab050_video_stream.py, tests/test_lab050_video_stream.py,
+data/processed/lab050/receiver_benchmarks.json. 1769 добавлений, 12 удалений.
+Большой result.json, профили JPEG и посторонние локальные файлы не добавлялись.
+
+Lab051 начата от этого HEAD. Изучены Lab046B/C, Lab047, Lab048, Lab050,
+Lab043/044/045 и свёрточный декодер. Ядро protocol в Lab051 не менялось.
+Планировщик LATEST_ONLY и ObjectAssembler переиспользованы. Новая видеочередь не создана.
+
+## Профиль до оптимизации
+
+Рабочая точка: 500000 символов/с, Fs=8000000 отсчётов/с, 16 отсчётов/символ.
+Профиль C: 128×96 RGB, JPEG quality 25. Короткий контроль: три изображения,
+24 пакета, seed=50050, coded-bit Eb/N0=7 дБ, CFO=+10 Гц, фаза 0,73 рад,
+чередование полярности, исходное CS8-квантование. Генератор точно прежний, из Lab050.
+
+Путь кандидата: IQ → грубая нормированная корреляция → уточнение старта →
+согласованный RRC-фильтр → timing → оценка частотного рассогласования CFO →
+компенсация CFO и фазы → мягкие решения → PHY header → deinterleave →
+soft Viterbi → protocol parser → CRC. Только полный PHY/FEC/CRC PASS выдаёт пакет.
+TX-границы, ожидаемое число кадров и содержимое не входят в RX API.
+
+Историческая база Lab050: 55,157553 / 0,323745 = 170,373647.
+Свежий инструментированный baseline Lab051: **49,614327 с**, processing/RF=153,251430.
+Настольная нагрузка и инструментация влияют на время; эти замеры не подменяют друг друга.
+Все 2374 кандидата: 24 valid, 2350 false. Средние времена — **72,967 / 20,099 мс**.
+
+**Главная причина замедления — fallback timing.** Номинальное положение равно 416,
+а диапазон остался −800…+800 отсчётов. Начало выходит за ноль, и прежний быстрый
+путь возвращается к Python-перебору 1601 смещения. У ложных кандидатов timing
+занял 41,833 с из 47,232 с строгой обработки. Их matched filter — 0,934 с,
+уточнение — 2,797 с. Прежняя реализация уже останавливала кандидата при PHY FAIL:
+тело, Viterbi и CRC на таких входах не запускались.
+
+Для преамбулы и заголовка достаточно 23456 входных отсчётов и 23616 отсчётов
+после RRC. Полный физический пакет с payload 256 байт требует 92256 входных
+отсчётов. Длина тела определяется только проверенным PHY-заголовком.
+
+## Изменения вычислений
+
+1. Децимация сохраняет внутреннюю часть конечного импульсного отклика FIR.
+   При сдвиге буфера заново считаются левый нулевой край и новые правые отсчёты.
+   Коэффициенты resample_poly/kaiser(8) прежние; сравнение с исходной функцией
+   побитно точное. Среднее по всему прежнему окну сохраняется. Корреляция считается
+   только для принадлежащих блоку стартов плюс правый сосед максимума.
+2. Корреляция известного prefix для уточнения считается один раз для общего участка.
+   Кандидаты используют срезы, с прежней поправкой локального DC.
+   Постоянный probe и неразделимые FFT-пики сохраняют исходный выбор первого
+   максимума; при необходимости вызывается старое уточнение.
+3. Компенсация известного +50 кГц и RRC выполняются для общего участка.
+   Локальное вычитание среднего и оба нулевых края изолированного окна
+   восстановлены отдельно. Без этих поправок общая фильтрация меняла бы алгоритм.
+   Тело за пределами общего участка фильтруется локально только после PHY PASS.
+4. Извлечение символов кэшируется по уточнённому положению и длине внутри одного блока.
+   Усечённое окно не подменяется ранее сохранённым полным окном.
+   Кэш уничтожается вместе с блоком; история сессии не сохраняется.
+5. Timing сохраняет весь прежний диапазон 1601 смещения. Корреляция с разреженной
+   известной преамбулой и разности накопленных энергий заменяют Python-перебор.
+   Тело за последним нужным отсчётом не участвует в этом расчёте. При пиках,
+   неразделимых с точностью float32, возвращается исходный расчёт и его tie-правило.
+6. CFO по прежним преамбульным символам не изменён. Векторы переноса кэшируются;
+   вектор остаточной частоты строится на символах локального окна. Gain/фаза для
+   probe и полного кадра не объединены: у них различается локальное среднее IQ.
+7. Viterbi после устранения главного повтора остался существенной долей принятого
+   пакета. Предвычислены четыре различные пары евклидовых branch metrics;
+   две ветви сравниваются непосредственно, с прежним выбором branch 0 при равенстве.
+   Решётка, старт/финиш 0, шесть хвостовых битов и декодированные биты сохранены.
+
+Дополнительный порог преамбулы/новый критерий отсева не введён. Фиксированный
+порог первого каскада **0.030964473262429237** сохранён. В коротком опыте совпали
+число кандидатов и все причины отказа с исходным RX. Параллелизм и CUDA не добавлялись.
+
+## Профиль стадий
+
+Ячейка: **число вызовов / суммарные миллисекунды** по соответствующей группе.
+Это не длительность одного вызова. Общая работа front-end учитывается в том
+кандидате, который впервые запросил её. Фаза показана без вложенного CFO;
+время body pipeline не складывается повторно со временем Viterbi/CRC.
+
+| Стадия | Baseline valid (24) | Optimized valid (24) | Baseline false (2350) | Optimized false (2350) |
+|---|---:|---:|---:|---:|
+| Точное уточнение | 24 / 28.974 | 24 / 93.878 | 2350 / 2797.404 | 2350 / 119.743 |
+| Согласованная RRC-фильтрация | 48 / 190.656 | 48 / 102.875 | 2336 / 933.965 | 174 / 26.502 |
+| Timing: весь диапазон | 48 / 914.632 | 48 / 43.535 | 2336 / 41833.166 | 174 / 138.588 |
+| CFO и комплексное усиление | 48 / 10.838 | 48 / 8.442 | 2336 / 485.772 | 174 / 26.385 |
+| Фаза/полярность и диагностика | 48 / 8.994 | 48 / 8.324 | 2336 / 424.756 | 174 / 28.438 |
+| PHY header | 48 / 1.711 | 48 / 1.452 | 2298 / 67.136 | 2298 / 32.044 |
+| Viterbi | 24 / 484.381 | 24 / 132.119 | 0 / 0 | 0 / 0 |
+| Protocol parser / CRC | 24 / 0.270 | 24 / 0.211 | 0 / 0 | 0 / 0 |
+
+Грубая локализация — общая стадия: 36 вызовов, baseline 498,040 мс,
+optimized **89,183 мс**. На один false candidate среднее **0,187317 мс**,
+медиана **0,060500 мс**; это включает cache hits. Холодное новое окно дороже.
+
+## Массивы и память
+
+Число всех аллокаций внутри NumPy/SciPy C не измерено. Line tracer наблюдает
+массивы в локальных переменных проектного Python: число ниже — **нижняя граница**,
+включающая views. Владельцы данных выделены отдельно. Временные выражения и
+внутренние C-массивы не перечислены. Отдельный tracemalloc-проход измеряет пик
+отслеживаемой памяти. Эти медленные проходы не включены в CPU benchmark.
+
+| Кандидат / алгоритм | Наблюдаемые ndarray | Из них владельцы | Суммарные байты наблюдаемых владельцев | Пик tracemalloc, байт |
+|---|---:|---:|---:|---:|
+| false / baseline | 2888 | 61 | 6612268 | 2104129 |
+| false / optimized | 76 | 61 | 5906101 | 5524392 |
+| valid / baseline | 14361 | 8704 | 16681890 | 5885299 |
+| valid / optimized | 6594 | 6560 | 12345244 | 7771675 |
+
+Пик холодного кандидата вырос из-за общих промежуточных данных: ускорение не
+объявляется уменьшением каждого временного массива. В рабочем потоке:
+
+- Пик удерживаемых IQ: **3177728 байт = 3,031 МиБ**.
+- Проверяемый предел IQ: 3398912 байт; кэш децимации хранится отдельно.
+- Инженерная оценка рабочих массивов: **136,161 МиБ**. Использован запас прежних
+  22 размеров IQ-окна плюс 20 размеров на новые массивы и кэши. Это оценка для
+  данного опыта, не доказанный универсальный предел всех аллокаций и не RSS.
+- Windows process RSS после потока: **154,105 МиБ**; peak RSS процесса —
+  **178,004 МиБ**. В него входят Python, библиотеки, генератор, предыдущие замеры и аллокатор.
+- Текущий объект ограничен 65536 байтами / 269 фрагментами; C фактически ≤1893 байт.
+
+Длинный тест: **4096 блоков, 33,554432 с модельного времени**. Фон нулевой;
+шум отдельно проверен в основном опыте. После прогрева удерживаются 2653440 байт
+IQ и 397216 байт децимированных данных; значения не растут.
+RSS контрольных точек: **156,199–156,250 МиБ**, без роста вместе с длительностью.
+После finish эти буферы освобождены. История JSON относится к отчёту, не к RX.
+
+## Непрерывный поток 5 изображений/с
+
+30 воспроизводимых сдвигов исходного data/raw/lab011_scene.jpg, профиль C,
+1740–1893 байта. Это искусственная последовательность, не видео камеры.
+Видеопланировщик LATEST_ONLY использует время capture и завершает активное JPEG.
+Передатчик знает нижнюю задержку радио/буфера; он не получает будущую стоимость
+CPU или результаты приёма. Приёмник получает лишь IQ-блоки по 65536 отсчётов.
+
+Шум, фаза, CFO и полярность заданы, но готовность JPEG берётся из измеренного encode.
+Поэтому точное размещение пакетов и число фоновых кандидатов длинного опыта могут
+немного меняться между запусками. Короткий baseline использует точно прежний
+детерминированный сценарий Lab050.
+
+Время CPU-работника для каждого блока:
+`start=max(previous_end, block_arrival); end=start+measured_push_time`.
+Сборка/JPEG decode добавляют своё измеренное время. Генерация TX/IQ, SHA-сравнение
+с передатчиком и запись JSON исключены. Длительность RX не заменена нулём.
+Все данные реально прошли независимый PHY/FEC/CRC и JPEG decode.
+
+| Метрика | Итог |
+|---|---:|
+| INPUT FPS / FRAMES | 5 / 30 |
+| TX IMAGES / PHYSICAL PACKETS | 30 / 240 |
+| DROPPED BEFORE TX | 0 |
+| MAX WAITING VIDEO FRAMES | 1 |
+| RX PHYSICAL PACKETS / IMAGES | 240 / 30 |
+| JPEG SHA MATCH | 30 / 30 |
+| DROPPED STALE AT DISPLAY | 30 |
+| DISPLAYED / FRESH DISPLAYED | 0 / 0 |
+| RAW RECEIVED FPS в первых 6 с CPU-шкалы | 0.833333 |
+| RAW DISPLAY FPS / FRESH DISPLAY FPS | 0 / 0 |
+| Возраст декодированных JPEG: min / median / max | 276.515 / 15691.203 / 31150.009 мс |
+| MAX_VIDEO_AGE | 200 мс |
+| Строгих кандидатов | 147082 |
+| Полное время обработки / IQ | 38.630132 / 6.044416 с |
+| PROCESSING / RF TIME | 6.391045 |
+
+Возраст относится к **декодированным, затем отброшенным JPEG**. Показанных изображений нет.
+RAW DISPLAY и FRESH DISPLAY совпадают, потому что политика не выводит просроченные
+изображения; RAW RECEIVED приведён отдельно.
+
+Физическое время одного JPEG C: 8×5766/500000 = **92,256 мс**. Очередь ожидающих
+JPEG в этом режиме не задерживает готовые изображения. Время RX за опыт:
+38,619085 с; сборка 5,451 мс; JPEG decode 5,596 мс суммарно на 30 JPEG.
+RF и CPU перекрываются по событийной шкале: эти суммы нельзя просто сложить
+и назвать измеренной задержкой камеры→экрана. Camera capture latency и display
+latency **UNKNOWN**; экран в отчётной модели мгновенный.
+
+Capture lookup используется только **после** независимой сборки JPEG для метрики
+возраста/решения модели вывода. В существующем fragment format нет времени съёмки.
+Готовый независимый аппаратный контроль возраста этим не доказан; формат в Lab051
+не расширялся. ID монотонны в одной конечной сессии; wrap/restart не реализованы.
+
+Внешняя IQ-очередь также не реализована. Офлайн-источник подаёт следующий блок
+по запросу и физически не хранит многосекундный backlog. Если бы вход шёл непрерывно,
+максимальное отставание **32,632404 с** потребовало бы около **2088473848 байт**
+дополнительного complex64 IQ. Поэтому bounded RX state нельзя выдавать за готовую
+ограниченную live-систему: при таких затратах потребуются потери/переполнение.
+REAL-TIME CAPABLE = NO.
+
+В длительном опыте встречено значительно больше фоновых кандидатов, чем в коротком:
+147082 против 2374. Кэш дал 135057 повторных использований, но осталось 12239 новых
+окон синхронизации. Следующее ограничение — стоимость этих фоновых событий.
+Одного ускорения Viterbi недостаточно; подгонять отсев по TX запрещено.
+
+## Регрессии и независимая проверка
+
+38 целевых тестов: весь диапазон timing, ненулевые и нулевые ties, локальные
+DC/края общего RRC, точная децимация при движении окна, Viterbi noisy/tie/invalid,
+совпадение старого/нового блочного приёма, усечение, буферы и finally.
+Verifier нашёл два случая округления FFT (timing и refine); оба исправлены,
+воспроизведены повторно и закреплены тестами. Adversary отдельно проверил
+malformed PHY (0 body/Viterbi), дубликаты и старые ID, лимиты объекта,
+усечение и восстановление глобальных подмен.
+
+Сохранённая Lab047-запись 20 ксимв/с повторно обработана штатным Lab048:
+**36/36, FEC/CRC PASS, JPEG SHA MATCH**, 19 ошибок до FEC, 0 после.
+Исходный путь и SHA JPEG сохранены в regression20k/result.json.
+Старый режим работает прежним декодером; адаптер Lab051 принимает только 500 ксимв/с
+и не подменяет аппаратную проверку этого режима.
+
+Итог: quick_gate PASS — 688 тестов, 17 функциональных проверок, 13 штатных пропусков.
+Полный pytest — 688 passed за 72,92 с, 0 failed; одно предупреждение доступа к
+`.pytest_cache`. git diff --check PASS. Подробности — в записи 040 журнала.
+Управление ELRS/CRSF, локальная защита и ядро protocol не менялись.
+
+## Файлы и воспроизведение
+
+- [Потоковый RX](../experiments/lab051_streaming_receiver.py)
+- [Профилировщик](../experiments/lab051_receiver_profile.py)
+- [Сценарии и событийная шкала](../experiments/lab051_receiver_performance.py)
+- [Тесты](../tests/test_lab051_streaming_receiver.py)
+- [Baseline](../data/processed/lab051/baseline_profile.json)
+- [Три коротких замера и профиль](../data/processed/lab051/benchmark.json)
+- [Основной поток и все 739 блоков](../data/processed/lab051/stream.json)
+- [Длинный тест памяти](../data/processed/lab051/memory.json)
+- [Наблюдение массивов](../data/processed/lab051/replay.json)
+- [20 ксимв/с](../data/processed/lab051/regression20k/result.json)
+
+```powershell
+python experiments/lab051_receiver_profile.py
+python experiments/lab051_receiver_performance.py --stage all
+python -m pytest tests/test_lab051_streaming_receiver.py -q -p no:cacheprovider
+python tools/quick_gate.py
+python -m pytest tests/ -q
+git diff --check
+```
+
+Старая запись:
+
+```powershell
+python experiments/lab048_receiver_performance.py --run-saved-capture --capture 'C:\Users\user\Documents\SDR_Rover_Lab047_raw\20260903_115747\rx_stream.cs8' --original-result 'C:\Users\user\Documents\SDR_Rover_Lab047_raw\20260903_115747\independent_result.json' --tx-image data/processed/lab047/tx_image.jpg --output-directory data/processed/lab051/regression20k
+```
+
+Локальные replay .npy — ограниченные окна, не полная IQ-запись; в Git не добавлены.
+Новый полный CS8/IQ файл не создавался. Lab051 git add/commit и push не выполнялись.
+Следующий рекомендуемый этап — отдельное программное исследование стоимости
+уникальных фоновых кандидатов в полном режиме 5 кадров/с с тем же PHY/FEC/CRC.
+RF, QPSK, новый FEC/кодек, 180 МГц и аппаратные 500 ксимв/с не выполнялись. STOP.
diff --git a/experiments/lab051_receiver_performance.py b/experiments/lab051_receiver_performance.py
new file mode 100644
index 0000000..1fa7c07
--- /dev/null
+++ b/experiments/lab051_receiver_performance.py
@@ -0,0 +1,320 @@
+"""Lab051: измеренный блочный RX и возраст JPEG в шестисекундном потоке.
+
+Источник синтетический, из реального фото. RF не выполняется. Виртуальное
+время работника использует фактическое время каждого push/сборки/JPEG decode.
+Это офлайн-воспроизведение; отставание явно означает требуемый внешний IQ backlog.
+"""
+
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+from dataclasses import asdict
+import hashlib
+from io import BytesIO
+import json
+import math
+from pathlib import Path
+import statistics
+import sys
+import time
+
+if __package__ in (None, ""):
+    sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+import numpy as np
+from PIL import Image
+
+from experiments import lab050_video_stream as video
+from experiments import lab051_streaming_receiver as fast
+from experiments.lab051_receiver_profile import OUTPUT, ROOT, StageProfiler, array_observation, process_memory
+from protocol.video_frame_scheduler import FramePolicy, schedule_video_frames
+
+
+def write_result(name: str, result: dict) -> None:
+    OUTPUT.mkdir(parents=True, exist_ok=True)
+    (OUTPUT / name).write_text(json.dumps(result, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
+
+
+class ImageSink:
+    """Только текущий объект; никаких ожидаемых ID, размеров JPEG или TX bytes."""
+
+    def __init__(self):
+        self.object_id = -1
+        self.last_completed_id = -1
+        self.assembler = None
+        self.first_rf_sample = None
+        self.first_rx_time = None
+        self.retained_payload_peak = 0
+        self.assembly_seconds = self.jpeg_decode_seconds = 0.0
+        self.rejected = Counter()
+
+    def offer(self, packet, received_time: float) -> dict | None:
+        started = time.perf_counter()
+        fragment = video.transport.recover_fragment_from_payload(packet.payload)
+        if fragment is None or fragment.original_size > 65536 or fragment.fragment_count > 269:
+            self.rejected["invalid_fragment"] += 1
+            self.assembly_seconds += time.perf_counter() - started
+            return None
+        if fragment.image_id < self.object_id or fragment.image_id <= self.last_completed_id:
+            self.rejected["older_object"] += 1
+            self.assembly_seconds += time.perf_counter() - started
+            return None
+        if fragment.image_id > self.object_id:
+            self.object_id = fragment.image_id
+            self.assembler = video.transport.ObjectAssembler()
+            self.first_rf_sample = packet.start_sample
+            self.first_rx_time = received_time
+        self.assembler.offer(fragment)
+        self.retained_payload_peak = max(self.retained_payload_peak, fragment.original_size)
+        jpeg = self.assembler.recovered_object()
+        self.assembly_seconds += time.perf_counter() - started
+        if jpeg is None:
+            return None
+        decode_started = time.perf_counter()
+        try:
+            with Image.open(BytesIO(jpeg)) as image:
+                image.load()
+                dimensions = image.size
+        except (OSError, ValueError):
+            self.rejected["jpeg_decode"] += 1
+            return None
+        finally:
+            self.jpeg_decode_seconds += time.perf_counter() - decode_started
+        self.last_completed_id = self.object_id
+        self.assembler = None
+        return {"image_id": self.object_id, "jpeg": jpeg, "dimensions": dimensions,
+                "first_rf_sample": self.first_rf_sample, "last_rf_sample": packet.end_sample,
+                "first_rx_time": self.first_rx_time}
+
+
+def run_stream(sequence: list[video.EncodedImage]) -> dict:
+    """Вход RX — только последовательные IQ-блоки; TX-диагностика после JPEG."""
+    mode = video.phy.PhyMode(500000)
+    receiver = fast.StreamingReceiver()
+    sink = ImageSink()
+    random_generator = np.random.default_rng(50050)
+    frames = video.make_frame_groups(sequence, 5)
+    packet_time = video.FRAME_INTERVALS / mode.symbol_rate
+    bitrate = (256 + video.HEADER_SIZE) * 8 / packet_time
+    # TX использует известную нижнюю задержку радио/буфера, без предсказания
+    # будущего CPU и без знания RX verdict. Перегрузка RX измеряется ниже.
+    buffer_delay = (receiver.lookahead + 2*receiver.core_samples) / mode.sample_rate_hz
+    schedule = schedule_video_frames(frames, (), FramePolicy.LATEST_ONLY, bitrate,
+                                     max_video_age_seconds=.2, completion_latency_seconds=buffer_delay)
+    starts = {}
+    for sent in schedule.transmitted:
+        starts.setdefault(sent.item.composite_frame_id, sent.start_seconds)
+    horizon = len(sequence) / 5
+    encoded_lookup = {image.image_id: image for image in sequence}
+    capture_lookup = {frame.composite_frame_id: frame.generation_time_seconds for frame in frames}
+    pending = np.empty(0, np.complex64)
+    total_samples = tx_packets = 0
+    worker_time = rx_processing = max_lag = 0.0
+    completed = []
+    block_metrics = []
+    rss_samples = []
+
+    def consume_block(block, *, final=False):
+        nonlocal total_samples, worker_time, rx_processing, max_lag
+        total_samples += block.size
+        arrival = total_samples / mode.sample_rate_hz
+        worker_start = max(worker_time, arrival)
+        started = time.perf_counter()
+        packets = receiver.finish() if final else receiver.push_iq_block(block)
+        cpu = time.perf_counter() - started
+        rx_processing += cpu
+        worker_time = worker_start + cpu
+        for packet in packets:
+            started = time.perf_counter()
+            item = sink.offer(packet, worker_time)
+            cost = time.perf_counter() - started
+            worker_time += cost
+            if item is not None:
+                # Приём/сборка/JPEG уже завершены. TX используется только теперь.
+                jpeg = item.pop("jpeg")
+                image_id = item["image_id"]
+                capture = capture_lookup.get(image_id)
+                age = None if capture is None else worker_time - capture
+                completed.append({**item, "decode_complete_timestamp": worker_time,
+                                  "capture_timestamp": capture, "image_age_seconds": age,
+                                  "fresh": age is not None and 0 <= age <= .2,
+                                  "sha256": hashlib.sha256(jpeg).hexdigest(),
+                                  "sha_match": image_id in encoded_lookup and jpeg == encoded_lookup[image_id].jpeg})
+        lag = max(0., worker_time - arrival)
+        max_lag = max(max_lag, lag)
+        block_metrics.append({"samples_received": total_samples, "arrival_time": arrival,
+                              "worker_start": worker_start, "worker_end": worker_time,
+                              "rx_cpu_seconds": cpu, "packets": len(packets), "lag_seconds": lag})
+        if len(block_metrics) % 64 == 0:
+            rss_samples.append({"samples": total_samples, **process_memory()})
+
+    def push(values):
+        nonlocal pending
+        offset = 0
+        if pending.size:
+            needed = receiver.block_samples - pending.size
+            joined = np.concatenate((pending, values[:needed]))
+            offset = min(needed, values.size)
+            if joined.size < receiver.block_samples:
+                pending = joined
+                return
+            consume_block(joined)
+            pending = np.empty(0, np.complex64)
+        while offset + receiver.block_samples <= values.size:
+            consume_block(values[offset:offset + receiver.block_samples])
+            offset += receiver.block_samples
+        pending = values[offset:].copy()
+
+    def background(count):
+        while count:
+            amount = min(count, receiver.block_samples)
+            values = .2 * (random_generator.normal(size=amount) + 1j*random_generator.normal(size=amount))
+            push(values.astype(np.complex64))
+            count -= amount
+
+    for image in sequence:
+        if image.image_id not in starts:
+            continue
+        target = math.ceil(starts[image.image_id] * mode.sample_rate_hz)
+        background(max(0, target - total_samples - pending.size))
+        for frame in video.transport.build_object_frames(image.jpeg, image.image_id):
+            raw = video.phy.build_parameterized_tx_cs8(frame.plan, mode)
+            components = np.frombuffer(raw, np.int8).astype(np.float32)
+            values = components[0::2] + 1j*components[1::2]
+            indexes = np.arange(values.size)
+            values = values * np.exp(1j*(.73 + 2*np.pi*10*indexes/mode.sample_rate_hz)) * (-1 if tx_packets % 2 else 1)
+            energy = float(np.sum(abs(values)**2)) / frame.plan.frame_symbol_count
+            sigma = math.sqrt(energy / (2*10**(.7)))
+            values += sigma*(random_generator.normal(size=values.size) + 1j*random_generator.normal(size=values.size))
+            push(values.astype(np.complex64))
+            tx_packets += 1
+    background(max(0, math.ceil(horizon * mode.sample_rate_hz) + receiver.lookahead + receiver.core_samples
+                   - total_samples - pending.size))
+    if pending.size:
+        consume_block(pending)
+    consume_block(np.empty(0,np.complex64), final=True)
+    fresh = [row for row in completed if row["fresh"]]
+    displayed_in_window = [row for row in fresh if row["decode_complete_timestamp"] <= horizon]
+    decoded_in_window = [row for row in completed if row["decode_complete_timestamp"] <= horizon]
+    ages = [row["image_age_seconds"] for row in completed if row["image_age_seconds"] is not None]
+    fresh_ages = [row["image_age_seconds"] for row in fresh]
+    processing = rx_processing + sink.assembly_seconds + sink.jpeg_decode_seconds
+    return {
+        "scope": "continuous synthetic IQ; measured per-block CPU event replay; camera/display latency UNKNOWN",
+        "symbol_rate":500000,"samples_per_symbol":16,"input_fps":5,"input_frames":len(sequence),
+        "observation_seconds":horizon,"iq_seconds":total_samples/mode.sample_rate_hz,
+        "tx_images":len(starts),"tx_physical_packets":tx_packets,
+        "dropped_before_tx":len(schedule.dropped_frames),"maximum_waiting_frames":schedule.maximum_waiting_frames,
+        "rx_physical_packets":receiver.packet_count,"received_images":len(completed),
+        "object_jpeg_integrity":len(completed)==len(starts) and all(row["sha_match"] for row in completed),
+        "dropped_stale_at_display":len(completed)-len(fresh),"displayed":len(fresh),"fresh_displayed":len(fresh),
+        "raw_received_fps":len(decoded_in_window)/horizon,
+        "raw_display_fps":len(displayed_in_window)/horizon,"fresh_display_fps":len(displayed_in_window)/horizon,
+        "drain_fresh_displayed":len(fresh)-len(displayed_in_window),
+        "image_age_min_seconds":min(ages) if ages else None,
+        "image_age_median_seconds":statistics.median(ages) if ages else None,
+        "image_age_max_seconds":max(ages) if ages else None,
+        "fresh_image_age_seconds":fresh_ages,"max_video_age_seconds":.2,
+        "processing_seconds":processing,"rx_processing_seconds":rx_processing,
+        "assembly_seconds":sink.assembly_seconds,"jpeg_decode_seconds":sink.jpeg_decode_seconds,
+        "processing_per_rf_second":processing/(total_samples/mode.sample_rate_hz),
+        "physical_packet_processing_seconds":processing/tx_packets if tx_packets else None,
+        "maximum_cpu_lag_seconds":max_lag,
+        "implied_external_iq_backlog_bytes":math.ceil(max_lag*mode.sample_rate_hz)*8,
+        "live_external_queue_implemented":False,
+        "retained_iq_peak_bytes":receiver.peak_buffer_samples*8,
+        "working_arrays_upper_estimate_bytes":receiver.memory_estimate_bytes + 20*receiver.buffer_limit_samples*8,
+        "object_payload_peak_bytes":sink.retained_payload_peak,"process_memory":process_memory(),
+        "rss_samples":rss_samples,"candidate_count":receiver.candidate_count,
+        "rejected_reasons":dict(receiver.rejected_reasons),"front_statistics":dict(receiver.front_statistics),
+        "decimated_samples_reused":receiver.decimator.reused_samples,
+        "actual_decimator_input_samples":receiver.decimator.filtered_input_samples,
+        "images":completed,"blocks":block_metrics,"rf_tx":0,
+    }
+
+
+def run_memory_test(block_count: int = 4096) -> dict:
+    """33,55 с без истории IQ/пакетов; нулевой фон отделён от noisy IQ опыта."""
+    receiver = fast.StreamingReceiver()
+    values = np.zeros(receiver.block_samples,np.complex64)
+    checkpoints=[]
+    for index in range(block_count):
+        assert receiver.push_iq_block(values) == []
+        assert receiver.buffer.size <= receiver.buffer_limit_samples
+        if (index+1) % 512 == 0:
+            checkpoints.append({"block":index+1,"iq_bytes":receiver.buffer.nbytes,
+                                "decimated_bytes":receiver.decimator.values.nbytes,**process_memory()})
+    assert receiver.finish()==[]
+    return {"blocks":block_count,"model_seconds":block_count*values.size/8000000,
+            "background":"zeros; noisy background covered by six-second integration",
+            "peak_iq_bytes":receiver.peak_buffer_samples*8,"iq_limit_bytes":receiver.buffer_limit_samples*8,
+            "retained_after_finish":receiver.buffer.nbytes+receiver.decimator.values.nbytes,
+            "checkpoints":checkpoints,"rf_tx":0}
+
+
+def run_candidate_replay() -> dict:
+    metadata=json.loads((OUTPUT/"optimized_examples.json").read_text(encoding="utf-8"))
+    result={}
+    for kind, entry in metadata.items():
+        raw=np.load(OUTPUT/f"optimized_{kind}_window.npy")
+        candidate=fast.offline.CoarseCandidate(entry["offset"],entry["score"])
+        def call(optimized=False):
+            with fast.previous.streaming_mode(video.phy.PhyMode(500000)):
+                def decode():
+                    return fast.offline.decode_candidate_from_memmap(raw,entry["sample_count"],candidate,1,
+                        read_iq_window=lambda data,start,end:data[2*start:2*end].view(np.complex64))
+                if not optimized:
+                    return decode()
+                front=fast.BlockFrontEnd(raw.view(np.complex64),65536,Counter())
+                with fast.optimized_context(front): return decode()
+        old=call(False)
+        new=call(True)
+        assert (old.accepted,old.reason,old.refined_start_sample)==(new.accepted,new.reason,new.refined_start_sample)
+        result[kind]={"reason":new.reason,"baseline_arrays":array_observation(lambda:call(False)),
+                      "optimized_arrays":array_observation(lambda:call(True))}
+    return result
+
+
+def run_benchmark(sequence) -> dict:
+    original=video.StreamingReceiver
+    try:
+        video.StreamingReceiver=fast.StreamingReceiver
+        repeats=[video.benchmark_receiver(sequence[:3],500000) for unused in range(3)]
+        profiler=StageProfiler()
+        with profiler.measure(optimized=True): profiled=video.benchmark_receiver(sequence[:3],500000)
+        metadata={}
+        for kind,(raw,count,candidate) in profiler.examples.items():
+            np.save(OUTPUT/f"optimized_{kind}_window.npy",raw)
+            metadata[kind]={"sample_count":count,"offset":candidate.sample_offset,"score":candidate.score}
+        write_result("optimized_examples.json",metadata)
+        return {"repeats":repeats,"median_processing_seconds":statistics.median(row["processing_seconds"] for row in repeats),
+                "profile":profiler.summary(),"profiled_benchmark":profiled,"rf_tx":0}
+    finally:
+        video.StreamingReceiver=original
+
+
+def run_functional_tests() -> None:
+    assert video.maximum_jpeg_bytes(500000,5)==4148
+    assert fast.lab045.conv.CONSTRAINT_LENGTH==7
+
+
+def main() -> None:
+    parser=argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--stage",choices=("benchmark","stream","memory","replay","all"),default="all")
+    args=parser.parse_args()
+    OUTPUT.mkdir(parents=True,exist_ok=True)
+    with Image.open(ROOT/"data/raw/lab011_scene.jpg") as source:
+        sequence=video.encode_sequence(source,video.PROFILES[2],30)
+    for name,function in (("benchmark",lambda:run_benchmark(sequence)),("stream",lambda:run_stream(sequence)),
+                          ("memory",run_memory_test),("replay",run_candidate_replay)):
+        if args.stage not in ("all",name): continue
+        print(f"Lab051 {name}: START; RF TX=0",flush=True)
+        result=function()
+        write_result(f"{name}.json",result)
+        print(json.dumps({key:value for key,value in result.items() if key not in (
+            "blocks","images","repeats","profiled_benchmark","rss_samples")},ensure_ascii=False),flush=True)
+
+
+if __name__=="__main__":
+    main()
diff --git a/experiments/lab051_receiver_profile.py b/experiments/lab051_receiver_profile.py
new file mode 100644
index 0000000..7547754
--- /dev/null
+++ b/experiments/lab051_receiver_profile.py
@@ -0,0 +1,246 @@
+"""Lab051: воспроизводимое профилирование приёмника, без оборудования.
+
+Измерение времени отделено от медленного наблюдения массивов/tracemalloc.
+Классификация valid/false происходит только после штатного PHY/FEC/CRC.
+"""
+
+from __future__ import annotations
+
+from collections import defaultdict
+from contextlib import contextmanager
+from dataclasses import asdict, dataclass
+import ctypes
+from ctypes import wintypes
+import json
+from pathlib import Path
+import statistics
+import sys
+import time
+import tracemalloc
+import weakref
+
+if __package__ in (None, ""):
+    sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+import numpy as np
+from PIL import Image
+
+from experiments import lab046c_offline_recovery as offline
+from experiments import lab050_video_stream as video
+from experiments.lab050_streaming_receiver import streaming_mode
+
+
+ROOT = Path(__file__).resolve().parents[1]
+OUTPUT = ROOT / "data/processed/lab051"
+lab045 = video.phy.lab045
+lab043 = lab045.lab044.lab043
+
+
+def process_memory() -> dict:
+    """Working set Windows без psutil; чужие процессы не перечисляются."""
+    if sys.platform != "win32":
+        return {"rss_bytes": None, "peak_rss_bytes": None}
+
+    class Counters(ctypes.Structure):
+        _fields_ = [("cb", wintypes.DWORD), ("PageFaultCount", wintypes.DWORD)] + [
+            (name, ctypes.c_size_t) for name in (
+                "PeakWorkingSetSize", "WorkingSetSize", "QuotaPeakPagedPoolUsage",
+                "QuotaPagedPoolUsage", "QuotaPeakNonPagedPoolUsage", "QuotaNonPagedPoolUsage",
+                "PagefileUsage", "PeakPagefileUsage")]
+
+    kernel = ctypes.WinDLL("kernel32", use_last_error=True)
+    psapi = ctypes.WinDLL("psapi", use_last_error=True)
+    kernel.GetCurrentProcess.restype = wintypes.HANDLE
+    psapi.GetProcessMemoryInfo.argtypes = [wintypes.HANDLE, ctypes.POINTER(Counters), wintypes.DWORD]
+    counters = Counters()
+    counters.cb = ctypes.sizeof(counters)
+    if not psapi.GetProcessMemoryInfo(kernel.GetCurrentProcess(), ctypes.byref(counters), counters.cb):
+        return {"rss_bytes": None, "peak_rss_bytes": None, "error": ctypes.get_last_error()}
+    return {"rss_bytes": counters.WorkingSetSize, "peak_rss_bytes": counters.PeakWorkingSetSize}
+
+
+@dataclass
+class Stage:
+    calls: int = 0
+    seconds: float = 0.0
+    exclusive_seconds: float = 0.0
+
+
+class StageProfiler:
+    """Только последовательное профилирование; подмены возвращаются в finally."""
+
+    def __init__(self):
+        self.rows = []
+        self.current = None
+        self.shared = defaultdict(Stage)
+        self.stack = []
+        self.examples = {}
+
+    @contextmanager
+    def measure(self, *, optimized: bool = False):
+        patches = []
+        targets = (
+            (offline, "_correlation_scores", "coarse_localization"),
+            (lab043, "_refine_frame_start", "refinement"),
+            (lab043, "_matched_waveform", "matched_filter"),
+            (lab043, "_preamble_timing_offset", "timing"),
+            (lab043, "_timing_offsets", "timing_fallback"),
+            (lab043, "_estimate_cfo_gain", "cfo_gain"),
+            (lab043, "_evaluate_symbols", "phase_diagnostics"),
+            (lab045, "decode_phy_header_soft", "phy_header"),
+            (lab045, "analyze_post_preamble_soft", "body_pipeline"),
+            (lab045.conv, "viterbi_decode_soft", "viterbi"),
+            (lab045, "parse_packet", "crc_parser"),
+        )
+        if optimized:
+            from experiments import lab051_streaming_receiver as fast
+            targets += ((fast.BlockFrontEnd, "refine", "refinement"),
+                        (fast.BlockFrontEnd, "coarse_scores", "coarse_localization"),
+                        (fast.BlockFrontEnd, "matched_window", "matched_filter"),
+                        (fast.BlockFrontEnd, "timing", "timing"),
+                        (fast, "fast_viterbi", "viterbi"))
+
+        def wrap(function, name):
+            def measured(*args, **kwargs):
+                scope = self.shared if self.current is None else self.current
+                stage = scope[name]
+                started = time.perf_counter()
+                entry = [0.0]
+                self.stack.append(entry)
+                try:
+                    return function(*args, **kwargs)
+                finally:
+                    elapsed = time.perf_counter() - started
+                    self.stack.pop()
+                    if self.stack:
+                        self.stack[-1][0] += elapsed
+                    stage.calls += 1
+                    stage.seconds += elapsed
+                    stage.exclusive_seconds += elapsed - entry[0]
+            return measured
+
+        for owner, name, stage in targets:
+            original = getattr(owner, name)
+            patches.append((owner, name, original))
+            setattr(owner, name, wrap(original, stage))
+        original_decode = offline.decode_candidate_from_memmap
+
+        def candidate(raw, sample_count, coarse, number, **kwargs):
+            self.current = defaultdict(Stage)
+            started = time.perf_counter()
+            try:
+                outcome = original_decode(raw, sample_count, coarse, number, **kwargs)
+                elapsed = time.perf_counter() - started
+                kind = "valid" if outcome.accepted else "false"
+                self.rows.append({"kind": kind, "seconds": elapsed, "reason": outcome.reason,
+                                  "offset": coarse.sample_offset, "refined": outcome.refined_start_sample,
+                                  "stages": {name: asdict(row) for name, row in self.current.items()}})
+                # По одному примеру после решения, только для отдельного replay.
+                if kind not in self.examples and (kind == "valid" or outcome.reason.startswith("PHY_HEADER_MAGIC")):
+                    self.examples[kind] = (raw.copy(), sample_count, coarse)
+                return outcome
+            finally:
+                self.current = None
+
+        offline.decode_candidate_from_memmap = candidate
+        try:
+            yield self
+        finally:
+            offline.decode_candidate_from_memmap = original_decode
+            for owner, name, original in reversed(patches):
+                setattr(owner, name, original)
+
+    def summary(self) -> dict:
+        result = {"shared_stages": {name: asdict(row) for name, row in self.shared.items()}}
+        for kind in ("valid", "false"):
+            selected = [row for row in self.rows if row["kind"] == kind]
+            stages = defaultdict(Stage)
+            for row in selected:
+                for name, stage in row["stages"].items():
+                    stages[name].calls += stage["calls"]
+                    stages[name].seconds += stage["seconds"]
+                    stages[name].exclusive_seconds += stage["exclusive_seconds"]
+            times = [row["seconds"] for row in selected]
+            result[kind] = {"count": len(times), "mean_seconds": statistics.mean(times) if times else None,
+                            "median_seconds": statistics.median(times) if times else None,
+                            "total_seconds": sum(times),
+                            "stages": {name: asdict(row) for name, row in stages.items()}}
+        return result
+
+
+def array_observation(function) -> dict:
+    """Нижняя граница числа массивов в локальных переменных проектного Python.
+
+    Временные выражения и внутренности C/SciPy не видны line tracer-у.
+    tracemalloc отдельно считает пик отслеживаемой памяти, включая NumPy.
+    Эти проходы НИКОГДА не используются как измерение производительности.
+    """
+    seen = {}
+    counters = {"observed_array_objects": 0, "observed_owned_arrays": 0, "observed_owned_bytes_total": 0}
+
+    def trace(frame, event, argument):
+        filename = frame.f_code.co_filename.replace("\\", "/")
+        if not ("/experiments/" in filename or "/protocol/" in filename):
+            return None
+        if event in ("line", "return"):
+            for value in frame.f_locals.values():
+                if not isinstance(value, np.ndarray):
+                    continue
+                identity = id(value)
+                if identity in seen and seen[identity]() is value:
+                    continue
+                seen[identity] = weakref.ref(value)
+                counters["observed_array_objects"] += 1
+                if value.flags.owndata:
+                    counters["observed_owned_arrays"] += 1
+                    counters["observed_owned_bytes_total"] += value.nbytes
+        return trace
+
+    previous = sys.gettrace()
+    try:
+        sys.settrace(trace)
+        function()
+    finally:
+        sys.settrace(previous)
+    tracemalloc.start()
+    try:
+        function()
+        current, peak = tracemalloc.get_traced_memory()
+    finally:
+        tracemalloc.stop()
+    return {**counters, "tracemalloc_retained_bytes": current, "tracemalloc_peak_bytes": peak,
+            "array_count_is_lower_bound": True}
+
+
+def run_functional_tests() -> None:
+    assert video.phy.PhyMode(500_000).samples_per_symbol == 16
+
+
+def main() -> None:
+    OUTPUT.mkdir(parents=True, exist_ok=True)
+    with Image.open(ROOT / "data/raw/lab011_scene.jpg") as source:
+        sequence = video.encode_sequence(source, video.PROFILES[2], 3)
+    profiler = StageProfiler()
+    with profiler.measure():
+        benchmark = video.benchmark_receiver(sequence, 500_000)
+    result = {"benchmark": benchmark, "profile": profiler.summary(), "rf_tx": 0}
+    (OUTPUT / "baseline_profile.json").write_text(json.dumps(result, indent=2) + "\n", encoding="utf-8")
+    print(json.dumps(result["profile"], indent=2), flush=True)
+    # Небольшие ограниченные окна, не полная запись. В Git не включать.
+    metadata = {}
+    for kind, (raw, count, coarse) in profiler.examples.items():
+        np.save(OUTPUT / f"baseline_{kind}_window.npy", raw)
+        metadata[kind] = {"sample_count": count, "offset": coarse.sample_offset, "score": coarse.score}
+        with streaming_mode(video.phy.PhyMode(500_000)):
+            call = lambda: offline.decode_candidate_from_memmap(
+                raw, count, coarse, 1,
+                read_iq_window=lambda data, start, end: data[2*start:2*end].view(np.complex64))
+            result.setdefault("array_observation", {})[kind] = array_observation(call)
+    (OUTPUT / "baseline_examples.json").write_text(json.dumps(metadata, indent=2) + "\n", encoding="utf-8")
+    result["process_memory_after_profiling"] = process_memory()
+    (OUTPUT / "baseline_profile.json").write_text(json.dumps(result, indent=2) + "\n", encoding="utf-8")
+    print(json.dumps(result["array_observation"]), flush=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/lab051_streaming_receiver.py b/experiments/lab051_streaming_receiver.py
new file mode 100644
index 0000000..b30cc40
--- /dev/null
+++ b/experiments/lab051_streaming_receiver.py
@@ -0,0 +1,364 @@
+"""Lab051: общий front-end и эквивалентный timing на 500 ксимв/с.
+
+PHY, FEC, CRC и порог первого каскада заимствованы без изменения правил.
+Подмены старых функций действуют только внутри последовательного push.
+"""
+
+from __future__ import annotations
+
+from collections import Counter
+from contextlib import contextmanager
+from functools import lru_cache
+import math
+import time
+
+import numpy as np
+from scipy.signal import convolve, fftconvolve, oaconvolve, firwin, resample_poly
+
+from experiments import lab046b_continuous_iq as iq
+from experiments import lab046c_offline_recovery as offline
+from experiments import lab050_streaming_receiver as previous
+
+
+phy = previous.phy
+lab045 = phy.lab045
+lab043 = lab045.lab044.lab043
+ORIGINAL_REFINE = lab043._refine_frame_start
+ORIGINAL_TIMING = lab043._preamble_timing_offset
+ORIGINAL_MATCHED = lab043._matched_waveform
+ORIGINAL_VITERBI = lab045.conv.viterbi_decode_soft
+
+
+@lru_cache(maxsize=4)
+def timing_kernel(samples_per_symbol: int) -> np.ndarray:
+    """Те же символы преамбулы на исходной сетке отсчётов."""
+    expected = iq.radio.bpsk_modulate(lab045.lab044.preamble_bits())
+    kernel = np.zeros((expected.size - 1) * samples_per_symbol + 1, np.complex64)
+    kernel[::samples_per_symbol] = np.conj(expected)
+    return kernel[::-1].copy()
+
+
+def fast_preamble_timing(matched: np.ndarray, nominal: int, expected_symbols: np.ndarray) -> int:
+    """Все прежние 1601 смещения; отрицательные/усечённые окна имеют score=0.
+
+    Корреляция с разреженной преамбулой эквивалентна vdot каждой выборки.
+    Энергии получаются разностями сумм на каждой из 16 фаз. Диапазон не сужен.
+    """
+    samples_per_symbol = lab045.SAMPLES_PER_SYMBOL
+    offsets = np.arange(-800, 801)
+    starts = nominal + offsets
+    count = len(expected_symbols)
+    # Тело не участвует ни в одном из прежних 1601 timing-окон.
+    matched = matched[:nominal + 800 + (count - 1) * samples_per_symbol + 1]
+    valid = (starts >= 0) & (starts + (count - 1) * samples_per_symbol < matched.size)
+    scores = np.zeros(offsets.size, np.float64)
+    if np.any(valid):
+        kernel = timing_kernel(samples_per_symbol)
+        correlations = fftconvolve(matched, kernel, mode="valid")
+        padding = (-matched.size) % samples_per_symbol
+        power = np.pad(np.abs(matched) ** 2, (0, padding)).reshape(-1, samples_per_symbol)
+        cumulative = np.concatenate((np.zeros((1, samples_per_symbol)),
+                                     np.cumsum(power, axis=0, dtype=np.float64))).ravel()
+        selected = starts[valid]
+        energy = cumulative[selected + count * samples_per_symbol] - cumulative[selected]
+        scores[valid] = np.abs(correlations[selected]) / np.sqrt(count * np.maximum(energy, 0) + 1e-30)
+    largest = np.partition(scores, -2)[-2:]
+    if largest[-1] > 0 and largest[-1] - largest[-2] <= 32 * np.finfo(np.float32).eps * largest[-1]:
+        # FFT меняет последние разряды. На неразделимых пиках сохраняем
+        # исходное суммирование и его tie→первый offset, включая чистый тон.
+        return ORIGINAL_TIMING(matched, nominal, expected_symbols)
+    return int(offsets[int(np.argmax(scores))])
+
+
+@lru_cache(maxsize=8)
+def carrier_rotation(length: int) -> np.ndarray:
+    """Кэш переносов не очищается при каждом входном блоке старым адаптером."""
+    indexes = np.arange(length)
+    return np.exp(-2j * np.pi * lab043.CARRIER_OFFSET_HZ * indexes / lab045.SAMPLE_RATE_HZ).astype(np.complex64)
+
+
+@lru_cache(maxsize=8)
+def dc_response(length: int, samples_per_symbol: int) -> np.ndarray:
+    """Отклик локального окна постоянной составляющей после переноса/RRC."""
+    rotation = carrier_rotation(length)
+    return oaconvolve(rotation, lab043._matched_filter_taps(), mode="full")
+
+
+@lru_cache(maxsize=4)
+def decimation_taps(decimation: int) -> np.ndarray:
+    """Штатные коэффициенты resample_poly(up=1, window=('kaiser', 8))."""
+    return firwin(20 * decimation + 1, 1 / decimation, window=("kaiser", 8)).astype(np.float32)
+
+
+@lru_cache(maxsize=8)
+def detection_rotation(length: int, decimation: int) -> np.ndarray:
+    return np.exp(-2j * np.pi * lab045.CARRIER_OFFSET_HZ * np.arange(length)
+                  / (lab045.SAMPLE_RATE_HZ / decimation)).astype(np.complex64)
+
+
+class DecimationCache:
+    """Повторно использовать внутреннюю часть FIR, пересчитать только края.
+
+    resample_poly имеет поддержку ±10 выходных отсчётов. После сдвига окна
+    неизменная внутренняя область даёт ровно те же значения; левый нулевой
+    край и новое продолжение считаются с прежними коэффициентами заново.
+    """
+
+    def __init__(self, decimation: int):
+        self.decimation = decimation
+        self.base = self.input_count = 0
+        self.values = np.empty(0, np.complex64)
+        self.reused_samples = self.filtered_input_samples = 0
+
+    def get(self, samples: np.ndarray, base: int) -> np.ndarray:
+        stride = self.decimation
+        count = math.ceil(samples.size / stride)
+        shift = (base - self.base) // stride
+        low = max(10, -shift + 10)
+        high = min(count - 10, self.values.size - 11 - shift)
+
+        def filtered(array):
+            self.filtered_input_samples += array.size
+            return resample_poly(array, 1, stride, window=decimation_taps(stride))
+
+        if not self.values.size or (base - self.base) % stride or high <= low:
+            output = filtered(samples)
+        else:
+            output = np.empty(count, np.complex64)
+            output[low:high] = self.values[low + shift:high + shift]
+            self.reused_samples += high - low
+            prefix_end = min(samples.size, (low + 11) * stride)
+            output[:low] = filtered(samples[:prefix_end])[:low]
+            suffix_start = (high - 11) * stride
+            output[high:] = filtered(samples[suffix_start:])[11:11 + count - high]
+        self.values = output
+        self.base, self.input_count = base, samples.size
+        return output
+
+
+def fast_viterbi(soft_values: np.ndarray, information_bit_count: int) -> np.ndarray:
+    """Та же евклидова метрика и tie→branch 0, с готовыми метриками ветвей."""
+    conv = lab045.conv
+    if not isinstance(information_bit_count, (int, np.integer)) or information_bit_count <= 0:
+        return ORIGINAL_VITERBI(soft_values, information_bit_count)
+    values = np.asarray(soft_values, dtype=np.float64)
+    if (values.ndim != 1 or values.size != conv.encoded_bit_count(information_bit_count)
+            or not np.all(np.isfinite(values))):
+        return ORIGINAL_VITERBI(soft_values, information_bit_count)
+    step_count = int(information_bit_count) + conv.TAIL_BIT_COUNT
+    received = values.reshape(step_count, 2)
+    # Всего четыре различных пары BPSK-символов. Порядок арифметики исходный.
+    negative = (received + 1.0) ** 2
+    positive = (received - 1.0) ** 2
+    metrics = np.column_stack((negative[:, 0] + negative[:, 1], negative[:, 0] + positive[:, 1],
+                               positive[:, 0] + negative[:, 1], positive[:, 0] + positive[:, 1]))
+    symbols = conv._BRANCH_SYMBOLS
+    codes = (2 * (symbols[:, :, 0] > 0) + (symbols[:, :, 1] > 0)).astype(np.intp)
+    metric_zero = metrics[:, codes[:, 0]]
+    metric_one = metrics[:, codes[:, 1]]
+    predecessor_zero = conv._PREDECESSORS[:, 0]
+    predecessor_one = conv._PREDECESSORS[:, 1]
+    path = np.full(conv.STATE_COUNT, 1e18)
+    path[0] = 0.0
+    choices = np.empty((step_count, conv.STATE_COUNT), np.uint8)
+    for step in range(step_count):
+        zero = path[predecessor_zero] + metric_zero[step]
+        one = path[predecessor_one] + metric_one[step]
+        choices[step] = one < zero
+        path = np.minimum(zero, one)
+    decoded = np.empty(step_count, np.uint8)
+    state = 0
+    for step in range(step_count - 1, -1, -1):
+        decoded[step] = state >> (conv.MEMORY - 1)
+        state = int(conv._PREDECESSORS[state, choices[step, state]])
+    assert state == 0
+    return decoded[:information_bit_count]
+
+
+class BlockFrontEnd:
+    """Один ограниченный общий участок; кандидаты читают его представления.
+
+    Конечные края и локальное вычитание среднего восстановлены отдельно:
+    нельзя без этого подменять фильтрацию изолированного окна непрерывной.
+    """
+
+    def __init__(self, samples: np.ndarray, owned_end: int, statistics: Counter,
+                 *, decimator: DecimationCache | None = None, base: int = 0):
+        self.samples = samples
+        self.statistics = statistics
+        self.decimator, self.base = decimator, base
+        self.reference = lab045.lab044._preamble_reference_samples()
+        self.reference_length = (16 + lab043.PILOT_SYMBOL_COUNT + 10) * lab045.SAMPLES_PER_SYMBOL
+        self.probe_symbols = lab045.PREAMBLE_BIT_COUNT + lab045.PHY_HEADER_BIT_COUNT
+        self.probe_waveform = (self.probe_symbols + 42) * lab045.SAMPLES_PER_SYMBOL
+        self.front_length = min(samples.size, owned_end + self.probe_waveform + offline.REFINEMENT_MARGIN_SAMPLES)
+        self.owned_end = owned_end
+        self.prefix_correlation = None
+        self.changes = None
+        self.matched = None
+        self.baseband = None
+        self.soft_cache = {}
+
+    def coarse_scores(self, samples: np.ndarray, reference: np.ndarray) -> np.ndarray:
+        """Сохранить DC всего старого окна, считать корреляцию только owned+1."""
+        decimation = lab043.DETECTION_DECIMATION
+        needed = math.ceil(self.owned_end / decimation) + reference.size + 1
+        raw = (resample_poly(samples, 1, decimation, window=decimation_taps(decimation)) if self.decimator is None
+               else self.decimator.get(samples, self.base))
+        mean = np.mean(raw, dtype=np.complex128).astype(np.complex64)
+        values = raw[:needed].copy()
+        values -= mean
+        values *= detection_rotation(values.size, decimation)
+        if values.size < reference.size:
+            return np.empty(0, np.float32)
+        scores = lab043._normalized_sliding_correlation(values, reference).astype(np.float32)
+        scores[~np.isfinite(scores)] = 0
+        np.clip(scores, 0, 1, out=scores)
+        return scores
+
+    def local_start(self, array: np.ndarray) -> int:
+        return (array.ctypes.data - self.samples.ctypes.data) // self.samples.itemsize
+
+    def refine(self, samples: np.ndarray, reference: np.ndarray, coarse_start: int) -> int:
+        start = self.local_start(samples)
+        low = max(0, coarse_start - 2000)
+        high = min(samples.size, coarse_start + self.reference_length + 2000)
+        if high - low < self.reference_length or start < 0:
+            return ORIGINAL_REFINE(samples, reference, coarse_start)
+        if self.changes is None:
+            self.changes = np.empty(self.front_length, np.int64)
+            self.changes[0] = 0
+            np.cumsum(self.samples[1:self.front_length] != self.samples[:self.front_length-1],
+                      dtype=np.int64, out=self.changes[1:])
+        if self.changes[start + samples.size - 1] == self.changes[start]:
+            # После вычитания локального среднего точно нулевой сигнал.
+            # Общая FFT может оставить шум округления вместо нулевого tie.
+            return low
+        if self.prefix_correlation is None:
+            centered = self.reference[:self.reference_length] - np.mean(self.reference[:self.reference_length])
+            self.centered_reference_sum = np.sum(np.conj(centered), dtype=np.complex128)
+            self.prefix_correlation = fftconvolve(
+                self.samples[:self.front_length], np.conj(centered[::-1]), mode="valid")
+            self.statistics["shared_refinement_correlations"] += 1
+        first = start + low
+        last = start + high - self.reference_length + 1
+        correlation = self.prefix_correlation[first:last]
+        if not correlation.size:
+            return ORIGINAL_REFINE(samples, reference, coarse_start)
+        # Та же DC-поправка, что у корреляции (samples - mean(probe)).
+        correlation = correlation - np.mean(samples) * self.centered_reference_sum
+        magnitudes = np.abs(correlation)
+        if magnitudes.size > 1:
+            largest = np.partition(magnitudes, -2)[-2:]
+            if largest[-1] - largest[-2] <= 32*np.finfo(np.float32).eps*largest[-1]:
+                self.statistics["refinement_tie_fallbacks"] += 1
+                return ORIGINAL_REFINE(samples, reference, coarse_start)
+        return low + int(np.argmax(magnitudes))
+
+    def matched_window(self, samples: np.ndarray, start: int, length: int):
+        absolute = self.local_start(samples) + start
+        taps = lab043._matched_filter_taps()
+        edge = taps.size - 1
+        if absolute < 0 or absolute + length + edge > self.front_length:
+            self.statistics["local_body_matched_calls"] += 1
+            segment = samples[start:start + length] - np.mean(samples)
+            baseband = segment * carrier_rotation(length)
+            return oaconvolve(baseband, taps, mode="full"), 16 * lab045.SAMPLES_PER_SYMBOL + edge
+        if self.matched is None:
+            self.baseband = self.samples[:self.front_length] * carrier_rotation(self.front_length)
+            self.matched = oaconvolve(self.baseband, taps, mode="full")
+            self.statistics["shared_matched_calls"] += 1
+        matched = self.matched[absolute:absolute + length + edge].copy()
+        # Восстановить нулевые края исходного локального окна, без соседнего IQ.
+        matched[:edge] = convolve(self.baseband[absolute:absolute + edge], taps, method="direct")[:edge]
+        matched[-edge:] = convolve(self.baseband[absolute + length - edge:absolute + length], taps,
+                                  method="direct")[edge:]
+        phase = np.complex64(np.exp(2j * np.pi * lab043.CARRIER_OFFSET_HZ * absolute / lab045.SAMPLE_RATE_HZ))
+        matched *= phase
+        mean = np.mean(samples)
+        matched -= mean * dc_response(length, lab045.SAMPLES_PER_SYMBOL)
+        nominal = 16 * lab045.SAMPLES_PER_SYMBOL + edge
+        return matched, nominal
+
+    def timing(self, matched, nominal, expected):
+        """Локальный FFT оказался дешевле дополнительных общих timing-массивов."""
+        return fast_preamble_timing(matched, nominal, expected)
+
+    def extract(self, samples: np.ndarray, frame_start: int, symbol_count: int) -> iq.SoftWindow:
+        key = (self.local_start(samples) + frame_start, symbol_count)
+        # Усечение зависит и от доступности текущего probe, а не только от start.
+        length = (symbol_count + 42) * lab045.SAMPLES_PER_SYMBOL
+        if frame_start < 0 or frame_start + length > samples.size:
+            return iq.SoftWindow(False, np.empty(0), 0, 0, 0, {}, "TRUNCATED_IQ_WINDOW")
+        if key in self.soft_cache:
+            self.statistics["soft_window_cache_hits"] += 1
+            return self.soft_cache[key]
+        segment = samples[frame_start:frame_start + length]
+        matched, nominal = lab043._matched_waveform(segment, 0, length)
+        bits = lab045.lab044.preamble_bits()
+        expected = iq.radio.bpsk_modulate(bits)
+        timing = lab043._preamble_timing_offset(matched, nominal, expected)
+        carrier = lab043._evaluate_symbols(matched, nominal, timing, bits, lab045.PREAMBLE_BIT_COUNT)
+        if carrier["abs_h"] == 0:
+            result = iq.SoftWindow(False, np.empty(0), timing, 0, nominal, carrier, "ZERO_GAIN")
+        else:
+            symbols = matched[nominal + timing:nominal + timing + symbol_count * lab045.SAMPLES_PER_SYMBOL:
+                              lab045.SAMPLES_PER_SYMBOL].astype(np.complex128)
+            if symbols.size != symbol_count:
+                result = iq.SoftWindow(False, np.empty(0), timing, 0, nominal, carrier, "TRUNCATED_SYMBOLS")
+            else:
+                symbol_time = (np.arange(symbol_count, dtype=np.float64) + 0.5) / lab045.SYMBOL_RATE
+                normalized = symbols * np.exp(-2j * math.pi * carrier["cfo_hz"] * symbol_time)
+                normalized /= carrier["abs_h"] * np.exp(1j * carrier["phase_rad"])
+                result = iq.SoftWindow(True, normalized.real, int(timing),
+                                      int((frame_start + nominal + timing) % lab045.SAMPLES_PER_SYMBOL),
+                                      nominal, carrier, None)
+        self.soft_cache[key] = result
+        self.statistics["unique_soft_windows"] += 1
+        return result
+
+
+@contextmanager
+def optimized_context(front: BlockFrontEnd):
+    targets = ((lab043, "_refine_frame_start", front.refine),
+               (lab043, "_matched_waveform", front.matched_window),
+               (lab043, "_preamble_timing_offset", front.timing),
+               (iq, "_extract_soft_window", front.extract),
+               (lab045.conv, "viterbi_decode_soft", fast_viterbi),
+               (offline, "_correlation_scores", front.coarse_scores))
+    originals = [(owner, name, getattr(owner, name)) for owner, name, unused in targets]
+    try:
+        for owner, name, function in targets:
+            setattr(owner, name, function)
+        yield
+    finally:
+        for owner, name, function in reversed(originals):
+            setattr(owner, name, function)
+
+
+class StreamingReceiver(previous.StreamingReceiver):
+    """Совместимый API Lab050, ускорение только для рабочей точки 500 ксимв/с."""
+
+    def __init__(self, symbol_rate: int = 500_000, *, block_samples: int = 65_536):
+        if symbol_rate != 500_000:
+            raise ValueError("Lab051 исследует только 500 ксимв/с")
+        super().__init__(symbol_rate, block_samples=block_samples)
+        self.front_statistics = Counter()
+        self.decimator = DecimationCache(self.decimation)
+
+    def _scan(self, owned_end: int) -> list[previous.ReceivedPacket]:
+        front = BlockFrontEnd(self.buffer, owned_end - self.base_sample, self.front_statistics,
+                              decimator=self.decimator, base=self.base_sample)
+        with optimized_context(front):
+            return super()._scan(owned_end)
+
+    def finish(self):
+        result = super().finish()
+        self.decimator.values = np.empty(0, np.complex64)
+        return result
+
+
+def run_functional_tests() -> None:
+    receiver = StreamingReceiver()
+    assert receiver.mode.samples_per_symbol == 16
+    assert receiver.finish() == []
diff --git a/tests/test_lab051_streaming_receiver.py b/tests/test_lab051_streaming_receiver.py
new file mode 100644
index 0000000..cd581a8
--- /dev/null
+++ b/tests/test_lab051_streaming_receiver.py
@@ -0,0 +1,160 @@
+"""Регрессии вычислительной архитектуры Lab051, без файлов/оборудования."""
+
+from __future__ import annotations
+
+from collections import Counter
+from dataclasses import replace
+
+import numpy as np
+import pytest
+from scipy.signal import resample_poly
+
+from experiments import lab051_streaming_receiver as lab
+from experiments.lab050_streaming_receiver import StreamingReceiver as PreviousReceiver
+from experiments.lab050_streaming_receiver import streaming_mode
+
+
+@pytest.mark.parametrize("seed", range(8))
+def test_all_timing_offsets_preserved_including_negative_start(seed):
+    random_generator = np.random.default_rng(seed)
+    samples = (random_generator.normal(size=23616) + 1j * random_generator.normal(size=23616)).astype(np.complex64)
+    with streaming_mode(lab.phy.PhyMode(500000)):
+        expected = lab.iq.radio.bpsk_modulate(lab.lab045.lab044.preamble_bits())
+        if seed % 2:
+            samples[416 + 47:416 + 47 + len(expected)*16:16] += expected * 3
+        old = lab.ORIGINAL_TIMING(samples, 416, expected)
+        assert lab.fast_preamble_timing(samples, 416, expected) == old
+
+
+def test_zero_timing_tie_is_same_first_offset():
+    with streaming_mode(lab.phy.PhyMode(500000)):
+        expected = lab.iq.radio.bpsk_modulate(lab.lab045.lab044.preamble_bits())
+        assert lab.fast_preamble_timing(np.zeros(23616, np.complex64), 416, expected) == -800
+
+
+@pytest.mark.parametrize("value", [1, 1j, 1+1j, 44])
+def test_equal_nonzero_timing_scores_preserve_original_tie(value):
+    with streaming_mode(lab.phy.PhyMode(500000)):
+        expected = lab.iq.radio.bpsk_modulate(lab.lab045.lab044.preamble_bits())
+        samples = np.full(23616, value, np.complex64)
+        assert lab.fast_preamble_timing(samples, 416, expected) == lab.ORIGINAL_TIMING(samples,416,expected)
+
+
+@pytest.mark.parametrize("value", [1, 1j, 44])
+def test_refinement_constant_iq_preserves_zero_after_local_dc(value):
+    with streaming_mode(lab.phy.PhyMode(500000)):
+        samples=np.full(100000,value,np.complex64)
+        front=lab.BlockFrontEnd(samples,65536,Counter())
+        window=samples[1000:28456]
+        assert front.refine(window,front.reference,2000)==lab.ORIGINAL_REFINE(window,front.reference,2000)==0
+
+
+def test_refinement_equal_nonzero_peaks_preserve_first_maximum():
+    with streaming_mode(lab.phy.PhyMode(500000)):
+        samples=np.zeros(100000,np.complex64)
+        samples[1200]=samples[2200]=1
+        front=lab.BlockFrontEnd(samples,65536,Counter())
+        reference=np.zeros(front.reference_length,np.complex64)
+        reference[0],reference[1]=1,-1
+        front.reference=reference
+        window=samples[1000:28456]
+        assert front.refine(window,reference,2000)==lab.ORIGINAL_REFINE(window,reference,2000)==199
+
+
+@pytest.mark.parametrize("seed", range(5))
+def test_shared_matched_preserves_local_dc_and_both_zero_padded_edges(seed):
+    random_generator = np.random.default_rng(seed)
+    values = (random_generator.normal(size=110000) + 1j * random_generator.normal(size=110000) + 4 + 3j).astype(np.complex64)
+    with streaming_mode(lab.phy.PhyMode(500000)):
+        front = lab.BlockFrontEnd(values, 70000, Counter())
+        segment = values[5000 + seed:5000 + seed + 23456]
+        expected, nominal = lab.ORIGINAL_MATCHED(segment, 0, len(segment))
+        actual, actual_nominal = front.matched_window(segment, 0, len(segment))
+        assert actual_nominal == nominal
+        np.testing.assert_allclose(actual, expected, atol=2e-5, rtol=2e-5)
+
+
+def test_reused_decimation_is_bit_exact_for_moving_and_truncated_windows():
+    random_generator = np.random.default_rng(51001)
+    values = (random_generator.normal(size=1800000) + 1j * random_generator.normal(size=1800000)).astype(np.complex64)
+    cache = lab.DecimationCache(8)
+    for base in range(0, len(values) - 10000, 65536):
+        window = values[base:base + 400123]
+        expected = resample_poly(window, 1, 8, window=("kaiser", 8))
+        np.testing.assert_array_equal(cache.get(window, base), expected)
+        assert cache.values.size <= 50016
+    assert cache.reused_samples > 1000000
+
+
+@pytest.mark.parametrize("seed,count", [(0, 1), (1, 32), (2, 244*8), (3, 268*8), (4, 1024*8)])
+def test_viterbi_exact_bits_for_noisy_metrics_and_ties(seed, count):
+    random_generator = np.random.default_rng(seed)
+    for values in (np.zeros(lab.lab045.conv.encoded_bit_count(count)),
+                   random_generator.normal(size=lab.lab045.conv.encoded_bit_count(count)),
+                   random_generator.choice([-1., 1.], size=lab.lab045.conv.encoded_bit_count(count))):
+        np.testing.assert_array_equal(lab.fast_viterbi(values, count), lab.ORIGINAL_VITERBI(values, count))
+
+
+@pytest.mark.parametrize("values,count", [(np.array([np.nan, 0.]), 1), (np.zeros((2,2)), 1),
+                                         (np.zeros(12), 1), (np.zeros(12), 0), (np.zeros(12), 1.5)])
+def test_viterbi_rejects_same_invalid_input(values, count):
+    with pytest.raises((ValueError, TypeError)):
+        lab.fast_viterbi(values, count)
+
+
+def signal_with_unknown_offsets(seed=51):
+    random_generator = np.random.default_rng(seed)
+    plan = lab.lab045.build_transmission_plan(bytes(range(256)))
+    values = lab.phy.build_parameterized_tx_complex(plan, lab.phy.PhyMode(500000)).astype(np.complex64)
+    values *= np.exp(1j * (0.73 + 2*np.pi*10*np.arange(len(values))/8000000)).astype(np.complex64)
+    values += (random_generator.normal(size=len(values)) + 1j*random_generator.normal(size=len(values))).astype(np.complex64)
+    return np.concatenate((np.zeros(63217,np.complex64), values, np.zeros(450000,np.complex64)))
+
+
+@pytest.mark.parametrize("block", [32749, 65536])
+def test_independent_stream_packets_equal_old_decoder_across_block_boundaries(block):
+    samples = signal_with_unknown_offsets()
+    results = []
+    for receiver in (PreviousReceiver(500000), lab.StreamingReceiver()):
+        output = []
+        for start in range(0,len(samples),block):
+            output.extend(receiver.push_iq_block(samples[start:start + block]))
+        assert [item.payload for item in output] == [bytes(range(256))]
+        output.extend(receiver.finish())
+        results.append([(item.start_sample,item.end_sample,item.payload) for item in output])
+    assert results[0] == results[1]
+
+
+def test_invalid_push_and_finish_preserve_bounded_state():
+    receiver = lab.StreamingReceiver()
+    for invalid in (np.array([complex(float("nan"),0)]), np.zeros((1,2)), np.zeros(65537)):
+        with pytest.raises(ValueError): receiver.push_iq_block(invalid)
+    assert receiver.total_samples == 0
+    for unused in range(100):
+        assert receiver.push_iq_block(np.zeros(65536, np.complex64)) == []
+        assert receiver.buffer.size <= receiver.buffer_limit_samples
+        assert receiver.decimator.values.size <= (receiver.buffer_limit_samples + 7)//8
+    assert receiver.finish() == []
+    assert receiver.buffer.size == receiver.decimator.values.size == 0
+    with pytest.raises(RuntimeError): receiver.finish()
+    with pytest.raises(RuntimeError): receiver.push_iq_block(np.zeros(1,np.complex64))
+
+
+def test_truncated_frame_cannot_reach_crc_pass():
+    samples = signal_with_unknown_offsets()[:100000]
+    receiver = lab.StreamingReceiver()
+    output = []
+    for start in range(0,len(samples),65536):
+        output.extend(receiver.push_iq_block(samples[start:start+65536]))
+    assert output + receiver.finish() == []
+
+
+def test_context_restores_functions_even_on_exception():
+    originals = (lab.lab043._matched_waveform, lab.iq._extract_soft_window,
+                 lab.lab045.conv.viterbi_decode_soft, lab.offline._correlation_scores)
+    with streaming_mode(lab.phy.PhyMode(500000)):
+        front = lab.BlockFrontEnd(np.zeros(100000,np.complex64),65536,Counter())
+        with pytest.raises(RuntimeError):
+            with lab.optimized_context(front): raise RuntimeError("test")
+    assert originals == (lab.lab043._matched_waveform, lab.iq._extract_soft_window,
+                         lab.lab045.conv.viterbi_decode_soft, lab.offline._correlation_scores)