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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Overview_Oracle_Platform_Powering_Institutional_Tokenization_copy.pdf filter=lfs diff=lfs merge=lfs -text

Andrew-Lee-Cruz-main.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9251334009f6ee7d4493d6aea97197057233474be92dab0a88457d30281dcfb5
+size 90680

BCI Hardware Emulator

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+{"cells":[{"cell_type":"code","source":"import numpy as np\nimport time\nimport threading\nimport queue\nimport json\n\n# Base class for BCI device emulation\nclass BCIDeviceEmulator:\n    def __init__(self, name, channels, sample_rate):\n        self.name = name\n        self.channels = channels\n        self.sample_rate = sample_rate\n        self.running = False\n        self.data_queue = queue.Queue()\n\n    def generate_eeg_data(self):\n        # Simulate EEG: Sine waves (delta-gamma freqs) + Gaussian noise\n        # This mirrors the \"Cruz Algebra\" signed motion logic in signal form\n        t = np.linspace(0, 1, self.sample_rate)\n        frequencies = np.random.uniform(1, 40, self.channels)  # 1-40 Hz\n        amplitudes = np.random.uniform(0.5, 2.0, self.channels)\n        noise = np.random.normal(0, 0.1, (self.sample_rate, self.channels))\n        signals = np.array([amplitudes[i] * np.sin(2 * np.pi * frequencies[i] * t) for i in range(self.channels)]).T + noise\n        return signals\n\n    def start(self):\n        self.running = True\n        def stream():\n            while self.running:\n                data = self.generate_eeg_data()\n                self.data_queue.put(data)\n                time.sleep(1)  # 1s chunks\n        threading.Thread(target=stream, daemon=True).start()\n\n    def stop(self):\n        self.running = False\n\n    def get_data(self):\n        try:\n            return self.data_queue.get_nowait()\n        except queue.Empty:\n            return None\n\n# Specific Hardware Profiles [cite: user prompt]\nclass MuseEmulator(BCIDeviceEmulator):\n    def __init__(self):\n        super().__init__(\"Muse S (Gen 2)\", channels=7, sample_rate=256)\n\nclass EmotivEmulator(BCIDeviceEmulator):\n    def __init__(self):\n        super().__init__(\"Emotiv Insight/EPOC X\", channels=14, sample_rate=128)\n\nclass PiEEGEmulator(BCIDeviceEmulator):\n    def __init__(self):\n        super().__init__(\"PiEEG\", channels=16, sample_rate=250)\n\nclass NeuroSkyEmulator(BCIDeviceEmulator):\n    def __init__(self):\n        super().__init__(\"NeuroSky MindWave Mobile 2\", channels=1, sample_rate=512)\n\nclass BITalinoEmulator(BCIDeviceEmulator):\n    def __init__(self):\n        super().__init__(\"BITalino/OpenEEG\", channels=4, sample_rate=1000)\n\nclass TelepathyEmulator(BCIDeviceEmulator):\n    def __init__(self):\n        super().__init__(\"Neuralink Telepathy (Sim)\", channels=1024, sample_rate=20000)\n\n# Implementation\ndevices = {\n    \"muse\": MuseEmulator(),\n    \"emotiv\": EmotivEmulator(),\n    \"pieeg\": PiEEGEmulator(),\n    \"neurosky\": NeuroSkyEmulator(),\n    \"bitalino\": BITalinoEmulator(),\n    \"neuralink\": TelepathyEmulator()\n}\n\ndef sample_all():\n    samples = {}\n    for name, dev in devices.items():\n        data = dev.get_data()\n        if data is not None:\n            # FFT Transformation to extract bands for the Sovereign Hub\n            freqs = np.fft.fftfreq(len(data), 1/dev.sample_rate)\n            fft = np.abs(np.fft.fft(data, axis=0))\n            samples[name] = {\n                \"delta\": float(np.mean(fft[(freqs > 0) & (freqs < 4)])),\n                \"theta\": float(np.mean(fft[(freqs >= 4) & (freqs < 8)])),\n                \"alpha\": float(np.mean(fft[(freqs >= 8) & (freqs < 12)])),\n                \"beta\": float(np.mean(fft[(freqs >= 12) & (freqs < 30)])),\n                \"gamma\": float(np.mean(fft[(freqs >= 30) & (freqs < 100)]))\n            }\n    return samples\n\nif __name__ == \"__main__\":\n    print(f\"Starting BCI Virtual Emulation for Andrew Lee Cruz...\")\n    for dev in devices.values(): dev.start()\n    \n    try:\n        while True:\n            time.sleep(2)\n            results = sample_all()\n            # Output can be piped to dashboard or JSON\n            print(json.dumps(results, indent=2))\n    except KeyboardInterrupt:\n        for dev in devices.values(): dev.stop()","outputs":[],"execution_count":null,"metadata":{}}],"metadata":{"colab":{"from_bard":true},"kernelspec":{"display_name":"Python 3","name":"python3"}},"nbformat":4,"nbformat_minor":0}

Overview_Oracle_Platform_Powering_Institutional_Tokenization_copy.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e534a14afbb1fb0158517aa71ae9266e59066c94a3bc63060dde040fa165a56
+size 4101015

stripe_backup_code.txt

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+Always verify you are on dashboard.stripe.com before entering this code, and never share it with anyone (including Stripe support or Stripe employees).
+
+Your code is: gkvh-lxkl-dnlt-rjwv-rieh