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The electrocardiogram (ECG) is one of the most extensively employed signals used to diagnose and predict cardiovascular diseases (CVDs). In recent years, several deep learning (DL) models have been proposed to improve detection accuracy. Among these, deep neural networks (DNNs) are the most popular, wherein the features are extracted automatically. Despite the increment in classification accuracy, DL models require exorbitant computational resources and power. This causes the mapping of DNNs to be slow; in addition, the mapping is challenging for a wearable device. Embedded systems have constrained power and memory resources. Therefore full-precision DNNs are not easily deployable on devices. To make the neural network faster and more power-efficient, spiking neural networks (SNNs) have been introduced for fewer operations and less complex hardware resources. However, the conventional SNN has low accuracy and high computational cost. Therefore, this paper proposes a new binarized SNN which modifies the synaptic weights of SNN constraining it to be binary (+1 and -1). In the simulation results, this paper compares the DL models and SNNs and evaluates which model is optimal for ECG classification. Although there is a slight compromise in accuracy, the latter proves to be energy-efficient.

Image processing is one of the major techniques that are used for computer vision. Nowadays, researchers are using machine learning and deep learning for the aforementioned task. In recent years, digit recognition tasks, i.e., automatic meter recognition approach using electric or water meters, have been studied several times. However, two major issues arise when we talk about previous studies: first, the use of the deep learning technique, which includes a large number of parameters that increase the computational cost and consume more power; and second, recent studies are limited to the detection of digits and not storing or providing detected digits to a database or mobile applications. This paper proposes a system that can detect the digital number of meter readings using a lightweight deep neural network (DNN) for low power consumption and send those digits to an Android mobile application in real-time to store them and make life easy. The proposed lightweight DNN is computationally inexpensive and exhibits accuracy similar to those of conventional DNNs.

In this paper, a negative high voltage DC-DC converter using a new cross-coupled charge pump structure has been proposed, which can solve the shoot-through current problem of the conventional charge pump by using a four clock phase scheme. Also, by switching the power supply to each stage based on the supply voltage, a variable voltage gain can be obtained. A complete analysis of the interaction between the power efficiency, area, and frequency have been presented. The proposed negative charge pump is designed to deliver 40μA with a widesupply range from 2.5V to 5.5V using 0.18μm high voltage LDMOS technology.

This paper proposes a novel Built-in Self Calibration (BISC) technique for a 12-bit 32MS/s successive approximation register (SAR) analog-to-digital converter (ADC) using a single input to reduce the capacitor mismatch of the digital-to-analog converter (DAC) and to compensate the comparator input offset voltage. The proposed self-calibration scheme optimize the mismatch of the DAC by changing additional auxiliary capacitor array during calibration mode. In addition, in order to minimize the offset voltage of the comparator in the SAR ADC, a simplified voltage amplifier is proposed. The controller for the proposed algorithm operates as foreground operation to achieve low power consumption during operation. Compared to the converters that use the conventional procedure, INL and DNL are reduced by about 47% and 52%, respectively. The prototype was designed using 130nm single poly 6 metal standard CMOS technology. The ADC achieves a SNDR of 65.6 dB and consumes 4.62 mW. The ADC core occupies an active area of only 240μmÍ 298 μm using 1.2V supply and the sampling rate of 50 MS/s.