This paper presents a fully differential 2-stage operational transconductance amplifier (OTA) for a baseband transimpedance amplifier (TIA) with 360-MHz bandwidth (BW) aiming for applications in Sub-6G broadband direct-conversion receivers. The proposed OTA with folded-cascode input stage incorporates three techniques, including series-cascode-Miller compensation (SCMC), anti-pole-splitting path, and common-mode feedforward (CMFF) to ensure wideband and low distortion of the TIA with shunt feedback. The OTA’s BW is extended because the SCMC introduces a zero that cancels the non-dominant pole at the folding node of the OTA, while the anti-pole-splitting path generates a negative capacitance that neutralizes the parasitic capacitance. In addition, the CMFF circuit improves the OTA’s common-mode rejection ratio (CMRR) by about 20 dB. Designed in 28-nm CMOS with a 0.12-mm2layout area and 16.6-mW power consumption, the TIA demonstrates an SFDR of 103 dB for a 75-MHz, 250-mVpp output, an input-referred noise (IRN) of 85 µVrms, an in-band third-order input intercept point (IIP3) of 37.9 dBm, and an intermodulation-free dynamic range (IMFDR3) of 91.6 dB.

The existing commodity Wi-Fi-based human gait recognition systems mainly focus on a single subject due to the challenges of multisubject walking monitoring. To tackle the problem, we propose Wi-Diag, the first commodity Wi-Fi-based multisubject abnormal gait diagnosis system that leverages only one pair of off-the-shelf commercial Wi-Fi transceivers to separate each subject’s gait information and maintains an excellent performance when the scenario changes. It is an intelligent multisubject gait diagnosis system that can release an experienced doctor from heavy load work. Multisubject abnormal gait diagnosis is modeled as a blind source separation (BSS) issue, and multisubject walking mixed signals are efficiently separated by IC analysis (ICA) approach. This fact is verified by comprehensive theoretical derivation and experimental validation. In addition, CycleGAN is leveraged to mitigate the environmental dependency so that Wi-Diag can be robust when the scenario changes. The excellent performance of Wi-Diag is verified by extensive experiments. The average mean diagnosis accuracy with a maximum group size of four and various scenarios is 87.77%.

Practicing gymnastics activities at home with online resources has become an increasingly popular choice due to its convenience and accessibility. However, without face-to-face guidance by a trainer, a major challenge is how to assess the quality of performed gymnastics activities, effectively and fairly. Existing intrusive assessing approaches usually require live cameras or wearable sensors, which usually generate privacy and feasibility concerns. There is a lacking of accurate approaches to assess the quality of the activities. To address these challenges, a gymnastics activity assessment approach is proposed in this article, and Wi-Gym, an effective first-of-its-kind gymnastics activity assessment system is developed utilizing commodity Wi-Fi. Wi-Gym is designed to compare the activity-induced channel state information (CSI) dynamics by an exerciser and that of a trainer utilizing dynamic time warping (DTW). The comparison results are provided by a fuzzy inference system (FIS). To make Wi-Gym robust to the changes in the environment, domain adaptation is leveraged to mitigate the data distribution imbalance caused by the environment changes. Extensive experimental studies have been conducted using Wi-Gym, acoustic, and video-based sensing systems. The experimental results validate the effectiveness and robustness of the proposed approach.

The vectorization of buildings provides quantitative disaster information and building damage extraction for the accurate assessment of disasters. This study presents an automatic extraction technology for flat-roofed houses by using high-resolution remote sensing images based on the single-point extraction of such houses and the concept of stepwise image segmentation. Vegetation removal, multi-scale segmentation, k-means clustering segmentation, rectangle recognition, and morphological processing are adopted to identify the center of an assumed house in the image by considering the characteristics of remote sensing images and the main features of house recognition (i.e., spectral characteristics and shape features). Furthermore, the single-point extraction of flat-roofed houses is used to acquire the surface vector diagram of the extracted houses, and finally, achieve the automatic extraction of flat-roofed houses. Experimental result shows that the accuracy of the vectorization extraction of flat-roofed houses through the combination is 83.33%.