Heart rate measurement using a continuous wave Doppler radar sensor (CW-DRS) has been applied to cases where non-contact detection is required, such as the monitoring of vital signs in home healthcare. However, as a CW-DRS measures the speed of movement of the chest surface, which comprises cardiac and respiratory signals by body motion, extracting cardiac information from the superimposed signal is difficult. Therefore, it is challenging to extract cardiac information from superimposed signals. Herein, we propose a novel method based on a matched filter to solve this problem. The method comprises two processes: adaptive generation of a template via singular value decomposition of a trajectory matrix formed from the measurement signals, and reconstruction by convolution of the generated template and measurement signals. The method is validated using a dataset obtained in two different experiments, i.e., experiments involving supine and seated subject postures. Absolute errors in heart rate and standard deviation of heartbeat interval with references were calculated as $1.93\pm 1.76\mathrm{bpm}$ and $57.0\pm 28.1\mathrm{s}$ for the lying posture, and $9.72\pm 7.86\mathrm{bpm}$ and $81.3\pm 24.3\mathrm{s}$ for the sitting posture.

In this paper, the diode characteristics of our newly proposed super-steep subthreshold slope “PN-body tied (PNBT) silicon-on-insulator field-effect transistor” are presented, and compared with conventional diodes. We report that the device possesses super-steep characteristics, low leakage current, and sharp turn-on characteristics, even in the ultralow voltage range (50 mV). These indicate that the PNBT diode can potentially be used in high-efficiency rectification for energy harvesting, particularly in situations where there is ultralow input power. In addition, the hysteresis characteristics and the slight shift of the voltage at zero current are confirmed as specific characteristics of PNBT diodes.

Bacterial recognition and classification play a vital role in diagnosing disease by determining the presence of large bacteria in the specimens and the symptoms. Artificial intelligence and computer vision widely applied in the medical domain enable improving accuracy and reducing the bacterial recognition and classification time, which aids in making clinical decisions and choosing the proper treatment. This paper aims to provide an approach of 33 bacteria strains’ automated classification from the Digital Images of Bacteria Species (DIBaS) dataset based on small-scale depthwise separable convolutional neural networks. Our five-layer architecture has significant advantages due to the compact model, low computational cost, and reliable recognition accuracy. The experimental results proved that the proposed design reached the highest accuracy of $96.28\%$ with a total of 6600 images and can be executed on limited-resource devices of $3.23$ million parameters and $40.02$ million multiply–accumulate operations (MACs). The number of parameters in this architecture is seven times less than the smallest model listed in the literature.

This article proposes a low cost of USD 72.5 BoM with a small size of water level monitoring IoT sensor without Ultra-Sonic level sensors. The power consumption for sensing and RF transmission is minimized to less than 100 μW in the manner of the Beat Sensor IoT system so that power is supplied by energy harvesting power supply using a card-size solar cell. Communication range can be reached 2 km using LoRa WAN and Quasi-isotropic Ω -Antenna. The system continuously measures the water level at a pond for 21 days without a battery, and the RMSE of the water level measurement reaches 2.5 mm. The practical evaluations show that this proposal can be applicable to several IoT systems for monitoring water levels.

This paper proposed an on-chip low power Body Bias Generator (VBBGEN) for ultra low leakage at 65nm SOTB (Silicon on Thin Buried Oxide) logic circuits at sleep mode. In the results of post layout simulation, the VBBGEN can generate and apply up to -2V body bias at a supply voltage of 0.5V with a current consumption of less than 361nA. By using the VBBGEN, it is expected that sleep current of CPU on SOTB is decreased by more than two orders of magnitude. In addition, the VBBGEN also has a function that prevents thermal run away of SOTB logic circuits.