This paper proposed a simple but highly accurate feature extraction method for epilepsy detection from electroencephalogram (EEG) signals. Based on the combination of Discrete Wavelet Transform (DWT) and the newly proposed features Local Binary Pattern Transition Histogram (LBPTH) and Local Binary Pattern Mean Absolute Deviation (LBPMAD), our proposed feature extraction method can efficiently extract features from EEG signals for machine learning classification of epilepsy, achieving high classification accuracy with a feature size of only 18 for each signal. Tested on the publicly available University of Bonn Epilepsy EEG Dataset using a signal length of 4097 data points (23.61 seconds), the proposed method achieved larger than 99.6% accuracy results for Support Vector Machine (SVM) and k-Nearest Neighbor (KNN) classification of ictal (set E) against any non-ictal (set A, B, C, or D) or combinations of non-ictal (set $\text{A}+\text{B}$ , set $\text{C}+\text{D}$ , or set $\text{A}+\text{B}+\text{C}+\text{D}$ ) EEG signals, which is among the best of currently published works. Our method can maintain high classification accuracy even with short input signals, achieving more than 99.1% SVM classification accuracy when input signal length is reduced to 512 data points (2.95 seconds). The high accuracy, small feature size, ability to work with short input signals and low computing requirements made the proposed method suitable for mobile, low power, and low-cost wearable medical devices.

The battery monitoring system (BMoS) is crucial to monitor the condition of the battery in supplying and absorbing the energy when operating and simultaneously determine the optimal limits for achieving long battery life. All of this can be done by measuring the battery parameters and increasing the state of charge (SoC) and the state of health (SoH) of the battery. The battery dataset from NASA is used for evaluation. In this work, the gradient vector is employed to obtain the trend of the energy supply pattern from the battery. In addition, a support vector machine (SVM) is adopted for an accurate battery accuracy index. This is in line with the use of polynomial regression; hence, points V1 and V2 are obtained as the boundaries of the normal-usage phase. Furthermore, testing of the time length distribution is also carried out on the length of time the battery was successfully extracted from the classification. All these stages can be used to calculate the rate of battery degradation during use so that this strategy can be applied in real situations by continuously comparing values. In this case, using the voltage gradient, SVM method, and the suggested polynomial regression, MAPE (%), MAE, and RMSE can be obtained against the battery value graph with values of 0.3%, 0.0106, and 0.0136, respectively. With this error value, the dynamics of the SoC value of the battery can be obtained, and the SoH problem can be resolved with a shorter usage time by avoiding the voltage-drop phase.

In this paper, we proposed the implementation of a simple and lightweight Atrial Fibrillation detection based on an improved Variable Step Dynamic Threshold Local Binary Pattern algorithm. Using feature selection based on correlation and statistical significance, we can reduce the input feature size to just 44 features without significantly degrading classification accuracies. Tested on 15-second signal segments from the MIT-BIH Atrial Fibrillation Database and combined with a support vector machine classifier, the proposed method can achieve 99.14% sensitivity, 99.12% specificity, and 99.13% accuracy. When the input signal length is 60 seconds, the sensitivity, specificity, and accuracy are 99.49%, 99.46%, and 99.47%, respectively. The reduced input feature size results in a machine learning model size as small as 132.86kB when the input signal length is 60 seconds. When implemented on an Arm Cortex M4-based STM32F413ZHT3 microprocessor with 100MHz clock frequency, the proposed method can achieve similar performance as a PC-based system with an average current consumption of just 27mA. The embedded C program can fit in as small as 114.46kB of flash memory and complete one SVM inference as fast as 11.28ms. The results presented in this paper show that it is possible to do highly accurate machine learning classification that can detect Atrial Fibrillation from ECG signals on a low-power, constrained resource microcontroller. Our results will make developing a high quality, low cost, and low power wearable smart medical electronic device for detecting atrial fibrillation from ECG signal much easier.

This article proposes a new authentication method for vehicular monitoring Internet of Things (IoT) systems. Based on parallel hash chains, the proposed method is suitable for low-power and low-cost IoT devices. Encryption keys are continuously generated on identical parallel hash chains on both the IoT device and the server, removing the need to transmit secret keys over the network. The proposed method only needs two transmission handshakes for each authentication and data transmission. It does not need random number generation on the IoT device, reducing hardware requirements and potential security vulnerability. It minimizes power consumption and data transmission cost while providing mutual authentication of both the server and the device. Other merits of our proposed methods include: it provides device anonymity, allows message authentication, provides database integrity verification, is resistant to transmission loss, and is dynamically adaptable to disturbances in transmission network quality.

A new method for reducing input‐referred thermal noise of dynamic comparator circuit without increasing load capacitance as commonly used in the conventional method is proposed. An implementation circuit with selectable low‐noise mode operation is presented, which enable both low‐noise mode and standard mode operation by a single circuit. Simulation in 180 nm CMOS process technology shows that the proposed new method and circuit topology can achieve up to 90% increase in gain of comparator first stage, resulting in up to 40% decrease in input‐referred thermal noise voltage, compared with a conventional circuit with similar load capacitance. The proposed circuit is also able to operate with similar performance as the conventional circuit when low‐noise mode operation is not necessary.