In this paper, a photodiode (PD)/transimpedance (TIA) interface is presented. The cathode terminal of the PD is AC-coupled to one of the inputs of a pseudo-differential TIA while the anode terminal is DC-coupled to the other input of the TIA. The DC-coupled input provides a path for the low-frequency component of the input signal to reach the output. This component is extracted by a baseline wander compensation scheme (BWCS) and fed to the input to eliminate signal drift after long data runs. Compared with the conventional pseudo-differential receiver, the proposed interface improves the gain, the gain-bandwidth product (GBW), and the signal-to-noise ratio (SNR) by 5dB, 1.37× and 4.5dB, respectively, while maintaining 209× smaller low cutoff frequency than the AC coupled receiver. The proposed interface is compatible with any TIA topology. Post-layout simulations and analytical models validate the feasibility of the proposed technique.

This article presents a low-power, high-gain, and wide–dynamic range (DR) photoplethysmography (PPG) sensory system with a novel direct current (dc) compensation loop and light-emitting diode (LED) dimming control. The proposed PPG system exhibits a signal-aware performance, where, at strong PPG signal levels, the dc compensation loop’s capability of removing dc photocurrent is increased and the LED’s driving current is reduced. This ability allows the entire DR to be utilized by the useful alternating current (ac) component of the signal and reduces power dissipation. Fabricated in 0.35- μm standard CMOS technology, the proposed PPG sensor occupies an area of 1.744 mm2 . The proposed system exhibits a high gain and a low input-referred noise current of 19.6 MΩ and 11.23 pArms , respectively, while dissipating only 9.9 μW of power at the receiver’s analog frontend. The proposed dc compensation loop can remove up to 80 μA of dc photocurrent for 2% total harmonic distortion (THD), achieving a DR of 137 dB. An external microcontroller unit (MCU) uses a machine learning (ML) algorithm to extract blood pressure (BP) from the analog output of the implemented PPG sensor chip. Measurements from eight human subjects show a mean absolute error (MAE) and a standard deviation of 2.25 ± 2.82 mmHg for systolic BP (SBP) and 5.01 ± 2.10 mmHg for diastolic BP (DBP).

The single image super-resolution (SISR) is a computer vision task needed in many real-world applications. There are many methods developed to solve ill-posed SISR problem; however, these methods are based on attention mechanisms that need a large computing processing cost. So, these attention-based models cannot be used in real-world applications that need fast models. Thus, we propose an enhanced convolution mixer (EConvMixer) module to solve this SISR problem by using lower computing convolution layers. The EConvMixer is designed based on utilizing three convolution types, namely the dilated depthwise convolution for increasing the receptive field, the depthwise convolution for mixing spatial locations, and the pointwise convolution for mixing channel locations. Based on using this EConvMixer layer, we build a lightweight extended convolution mixer network (EConvMixN) for SR images …

Emerging pollutants, such as antibiotics and antibiotic-resistance genes, are becoming increasingly
important sources of safety and health concerns. Drinking water safety, which is closely related to
human health, should receive more attention than natural water body safety. However, minimal
research has been performed on the efficacy of existing treatment processes in water treatment plants
for the removal of antibiotics and antibiotic resistance genes. To address this research gap, this study
detected and analyzed six main antibiotics and nine antibiotic resistance genes in the treatment
processes of two drinking water plants in Wuhan. Samples were collected over three months and then
detected and analyzed using ultra-high-performance liquid chromatography-tandem mass
spectrometry and fluorescence quantitation. The total concentrations of antibiotics and antibiotic
resistance genes in the influent water of the two water plants were characterized as December > March
> June. The precipitation and filtration processes of the Zou Maling Water Plant and Yu Shidun Water
Plant successfully removed the antibiotics. The ozone-activated carbon process increased the removal
rate of most antibiotics to 100%. However, a large amount of antibiotic resistance gene residues
remained in the effluents of the two water plants. The experiments demonstrated that the existing
ozone-activated carbon processes could not effectively remove antibiotic resistance genes. This study
provides a reference for the optimization of drinking water treatment processes for antibiotics and
antibiotic resistance gene removal.