High-Speed Digital Detector for the Internet of Things Assisted by Signal’s Intensity Quantification

This paper proposes a high-speed digital detector for the Internet of Things (IoT) assisted by signal’s intensity quantification. The detector quantifies the amplitude of each pixel of the detected image and converts it into a digital signal, which can be directly applied to the IoT with wireless communication system. Two types of amplitude quantization algorithms, uniform quantization and non-uniform quantization, are applied to the detector, which further improves the quality of the detected image and the robustness of the image signal in a noisy environment. Related simulations have been established to verify the accuracy of the models and algorithms.

Remote Health Care Monitoring Based on the Internet Transmission System of a Patient's Essential Body Functions (Preprint)

BACKGROUND With Electrocardiogram (ECG) signals and Internet of things (IoT), we may have a remote observation for several clinical measurements, specifically pulse rate, intracranial pressure, respiration rate, and blood pressure, that indicate the state of a patient's essential body functions. Moreover, we can utilize ECG signals to analyze and identify various heart diseases from a distance, such as arrhythmias, myocardial damage etc. This study aims to propose an IoT-based transmission system for the state care of a patient's essential body functions. The system also protects personal privacy and reduces the carrying amount in ECG network transmission. First of all, we perform the proposed patients state steganography on the ECG signals. At the same time, we adopt the threshold-based compression to reduce the data amount of the ECG signals in patients information transmission. The recovery of the compressed ECG signal adopts cubic spline. In addition, the performance of the proposed steganography is enhanced by Particle Swarm Optimization (PSO). Experimental results verify the efficiency of the proposed method. OBJECTIVE In the proposed concept as shown in Fig. 3, original ECGs and patients confidential information are first combined by the proposed method (see Fig. 4 in detail) to obtain the hidden and compressed ECG. Next, the hidden and compressed ECGs are transferred to terminal equipment such as hospital server via wireless network. Finally, hospital server will extract the patients information and distribute it to different devices of doctors and nurses. METHODS This study proposes a time-domain algorithm to integrate threshold-based compression and PSO-based biomedical signal steganography. Because ECG has high requirements for accuracy, so we rewritten signal-to-noise ratio (SNR) and amplitude-quantization to performance index and constraint so that we obtain an optimization model to enhance ECG quality and robustness against attacks. In addition, the optimization model is solved by Particle Swarm Optimization (PSO). Accordingly, we perform amplitude-quantization steganography on each ECG signal to embed patient information. At the same time, we adopt the threshold-based compression technology to reduce the data amount of the embedded ECG signal. In addition, the hidden information can be extracted without the original ECG and the recovery of the compressed ECG signal adopts cubic spline. In experiments, we evaluate the appropriate threshold ε and embedding strength Q. The proposed method reduces the carrying amount of network transmission while preserving the original characteristics of ECG signals and protecting personal privacy. RESULTS Our method remains high quality for each hidden ECG signal or hidden and compressed ECG signal under sufficient hiding capacity 2048 bits no matter how the quantization size Q is increased. CONCLUSIONS The proposed method not only protect the security of the ECG transmission but also reduce the amount of ECG transmission. Moreover, the proposed method improves the drawback that the quality of each hidden ECG signal is greatly reduced as the quantization size Q is increased. In other words, our method remains high quality for each hidden ECG signal or hidden and compressed ECG signal no matter how the quantization size Q is increased. CLINICALTRIAL No trial registration.