第17章 共振前端實驗(第3/4 頁)

print(f\"Robot {self.id}: Signal filtered to {filtered_signal}\")

return filtered_signal

# Amplify the signal if it's above a certain threshold

def amplify_signal(self, signal):

amplification_factor = np.random.uniform(1.5, 2.0) # Random amplification within range

amplified_signal = signal * amplification_factor

print(f\"Robot {self.id}: Amplified signal to {amplified_signal}\")

return amplified_signal

# check for cell damage and initiate repair if needed

def check_and_repair(self, signal):

if signal ＜ 0:

self.repair_mode = true

print(f\"Robot {self.id}: damaged tissue detected, initiating repair.\")

return self.perform_repair(signal)

return signal

# perform cell repair process

def perform_repair(self, signal):

print(f\"Robot {self.id}: Repairing damaged cells...\")

restored_signal = np.abs(signal) + np.random.uniform(10, 20) # Restore to positive signal range

print(f\"Robot {self.id}: Repair plete. Restored signal: {restored_signal}\")

self.repair_mode = False

return restored_signal

# Simulation of the nanorobot handling neural signals

def run_experiment:

signal_values = np.random.uniform(-5, 10, 5) # Generate random signals with possible damage indicators

robot = NanoRobot(id=204)

for signal in signal_values:

print(f\"Input signal: {signal}\")

filtered_signal = robot.capture_signal(signal)

final_signal = robot.check_and_repair(filtered_signal)

print(f\"Final processed signal: {final_signal}\\")

if __name__ == \"__main__\":

run_experiment 下面的程式碼註釋寫道：奈米機器人透過檢測體內的神經訊號，能夠對異常或受損的訊號進行捕捉和分析。首先，機器人會對神經訊號進行過濾，去除噪音和干擾，從而得到更精準的神經反饋。當訊號強度低於設定閾值時，機器人會自動跳過放