Significant theoretical and practical challenges arise in the cooperative control of distributed nonlinear multi-agent systems (MAS), particularly when they involve nonstrict-feedback interconnections and unknown state-dependent control gains. Conventional neural adaptive controllers, while versatile, often operate as “black-box” models, leading to solutions that may lack physical plausibility and exhibit compromised robustness. This paper addresses this critical gap by introducing a novel Physics-Regularized Adaptive Control (PRAC) framework, implemented via neural backstepping. Central to PRAC is the design of novel Physics-Regularized Neural Networks (PRNNs), which are realized using Radial Basis Function Neural Networks (RBFNNs) as their architectural foundation in this paper. Instead of treating the neural network as a simple approximator, the PRAC methodology embeds physical priors, such as equilibrium conditions, system smoothness, and energy dissipation principles, into the PRNNs’ online adaptive laws as differentiable regularization terms. The gradients of these terms actively constrain the PRNN weight adaptation, transforming the learning process into a Lyapunov-guided constrained optimization. This enhances the physical consistency and interpretability of the learned dynamics while simultaneously improving control performance. By synergistically combining this physics-regularized architecture with Dynamic Surface Control (DSC) to manage computational complexity, the proposed scheme guarantees cooperative uniformly ultimately bounded (CUUB) tracking of a leader’s trajectory. Rigorous Lyapunov analysis substantiates the theoretical guarantees, which are further validated by comprehensive numerical simulations and a practical networked inverted-pendulum example demonstrating superior tracking accuracy and robustness over conventional neural adaptive controllers.

Coarse-grained reconfigurable array (CGRA) is a quality hardware for compute-intensive loop kernels, with its excellent balance of performance, energy efficiency, and reconfigurability. However, the efficiency of CGRA depends heavily on how the compiler maps the data flow graph (DFG) extracted from application kernels onto the target architecture. Most existing CGRA compilers encounter the challenge of long compilation times due to excessive exploration space. To reduce the exploration space and compilation time, we propose SubMap, which adaptively explores a suitable sub-CGRA for different DFGs in a target CGRA and efficiently performs the mapping process. The experimental results show that SubMap greatly reduces the compilation time compared to the latest methods while maintaining the mapping quality. On HyCube$4\times 4$, SubMap has an average performance improvement of$9.47 \times $and$11.67 \times $, respectively, compared with Morpher (Pathfinder) and Morpher (SA). As the scale of the target CGRA increases, the performance improvement of SubMap becomes more pronounced.

In this article, we proposed a novel fault-tolerant control scheme for quadrotor unmanned aerial vehicles (UAVs) based on spiking neural networks (SNNs), which leverages the inherent features of neural network computing to significantly enhance the reliability and robustness of UAV flight control. Traditional control methods are known to be inadequate in dealing with complex and real-time sensor data, which results in poor performance and reduced robustness in fault-tolerant control. In contrast, the temporal processing, parallelism, and nonlinear capacity of SNNs enable the fault-tolerant control scheme to process vast amounts of sensory data with the ability to accurately identify and respond to faults. Furthermore, SNNs can learn and adjust to new environments and fault conditions, providing effective and adaptive flight control. The proposed SNN-based fault-tolerant control scheme demonstrates significant improvements in control accuracy and robustness compared with conventional methods, indicating its potential applicability and suitability for a range of UAV flight control scenarios.

Depression is a global health issue that necessitates severity identification for effective treatment. Although early detection and monitoring of mental abnormalities are crucial for mitigating adverse conditions, standard diagnostic approaches for depression, including self-rating scales and clinician-based interviews, fall short of meeting these needs. Wearable sensors offer an opportunity for unobtrusive evaluation of patients' status throughout their daily life, but it presents challenges in feature extraction and depression severity prediction. Studies have shown that eye gaze dynamics in term of 3-D rotations are a good indicator of depression. However, their traditional representation in Euler angles is computationally inefficient and overlooks the channel-wise interrelations, thereby reducing their discriminative power in medical diagnosis. This paper proposes a novel approach to address these challenges. It uses a window-based quaternion principal component analysis algorithm to extract features of eye gaze dynamics, preserving the channel-wise interrelations and temporal information. The proposed algorithm was tested and validated using a benchmark depression dataset from previous studies. Results showed that the algorithm achieved higher accuracy in depression severity prediction with a root mean square error of 5.08, outperforming existing state-of-the-art prediction methods that only use visual data. The results were further analyzed and discussed, providing valuable insights into the effectiveness and potential applications of wearable sensor for mental health diagnostics.

Sleep posture, which affects the quality of sleep and could lead to medical conditions, such as pressure ulcers, is a key metric for sleep analysis in Internet of Medical Things (IoMT). In this article, a real-time and low-cost smart mat system for sleep posture recognition based on frequency channel selection is proposed. The system can recognize postures unobtrusively with a dense flexible sensor array. In addition, to enable real-time recognition with a relatively low-cost STM32 processor system, a lightweight algorithm that includes frequency channel selection, model pretraining, and real-time classification is proposed. Through a series of short-term and overnight experiments with 21 subjects, the feasibility and reliability of the proposed system were evaluated. Experimental results show that the accuracy of the short-term experiment is up to 95.43% and of the overnight experiment is up to 86.80% for four posture categories (supine, prone, right, and left) classification. The model size is just 56 kB which is much smaller than other methods. The runtime of the complete algorithm is about 6 ms with a low-power STM32 embedded system, which shows the system’s ability to provide real-time posture recognition. As an edge device, the proposed system could lead to the development of fast, convenient, and low-cost sleep posture recognition products for IoMT.

Recently, electroencephalography (EEG) signals have shown great potential for emotion recognition. Nevertheless, multichannel EEG recordings lead to redundant data, computational burden, and hardware complexity. Hence, efficient channel selection, especially single-channel selection, is vital. For this purpose, a technique termed brain rhythm sequencing (BRS) that interprets EEG based on a dominant brain rhythm having the maximum instantaneous power at each 0.2 s timestamp has been proposed. Then, dynamic time warping (DTW) is used for rhythm sequence classification through the similarity measure. After evaluating the rhythm sequences for the emotion recognition task, the representative channel that produces impressive accuracy can be found, which realizes single-channel selection accordingly. In addition, the appropriate time segment for emotion recognition is estimated during the assessments. The results from the music emotion recognition (MER) experiment and three emotional datasets (SEED, DEAP, and MAHNOB) indicate that the classification accuracies achieve 70-82% by single-channel data with a 10 s time length. Such performances are remarkable when considering minimum data sources as the primary concerns. Furthermore, the individual characteristics in emotion recognition are investigated based on the channels and times found. Therefore, this study provides a novel method to solve single-channel selection for emotion recognition.

Intrabody communication (IBC) is an effective way to connect various kinds of wearable devices attached on or under the surface of the body, but it is important to quantitatively evaluate the biological effects of the IBC signal on the human body before its further application. The research described in this paper analyzed the responses of HSF (human skin fibroblast) cells exposed to IBC electrical signals. A galvanic coupling IBC signal transmitting system was designed to expose the experimental samples with different amplitudes (from 0 V to 6 V or 0 mA to 4 mA), different frequencies (from 10 kHz to 1 MHz), and different duration times (12 h and 24 h). The control groups were unexcited. Cell morphology and activity were evaluated with inverted microscope and MTT assays. The cell survival rates of all the experiment groups were in the range of 90% to 110%. Then, the data was analyzed by t -tests to assess whether there were statistically significant differences. The results showed that p values were greater than 0.05, so there were no significant differences between the experimental and control groups. Therefore, it can be concluded that the IBC signals do not have a significant effect on HSF cells.

This paper presents a complete energy optimized sub-threshold standard cell library exploiting unbalanced pull-up/down (PU/PD) network, logical effort and inverse-narrow-width (INW) techniques. Individual logic cell is optimized for ultra-low-energy applications with low-to-moderate speed requirement. Three 14-tap 8-bit FIR filters are fabricated using a 0.18-μm CMOS technology, while one of them achieved the minimum energy/tap (0.0234 pJ) and 0.365 Figure-of-Merit (FoM) at 100 kHz, 0.31 V.

Ultralow-energy biomedical applications have urged the development of a subthreshold VLSI logic family in standard CMOS. This brief proposes an unbalanced pull-up/down network, together with an inverse narrow-width technique, to improve the operating speed of the individual logic cell. Effective logical efforts save both power and die area in the process of device sizing and topology optimization. Three experimental 14-tap 8-bit finite impulse response filters optimized for ultralow-voltage operation were fabricated in 0.18-μm CMOS. Measurements show that the optimized 0.45 and 0.6 V libraries achieve minimum energy operations at 100 kHz, with a figure-of-merit of 0.365 (at 0.31 V) and 0.4632 (at 0.39 V), respectively. They correspond to 35.96% and 18.74% improvements, and the overall performances are well comparable with the state of the art.

The human machine interface (HMI) is a main communication method between human and computer. Through current HMI, a machine receives and accurately responds to the commands instructed by the users. In the next generation of HMI, machines will be required to deal with more challenging problems/decisions (such as affective evaluations, ethical quandaries, and other innovations) in a self-governing manner. Thus, future HMI should be able to provide information about users' emotion to the machine for affective evaluation. In this paper, we focus on the natural connection method that can improve machines in making the acquaintance of the users. However, connecting sensors scattered on the human body poses serious problems concerning comfort and convenience. Therefore, the authors introduce the Intra Body Communication (IBC) for connecting various physiological sensors on the human body such that the physiological information can enrich the capability of the computer in cognition of the user's emotion. In addition, the authors also reported two pilot studies: using the IBC for connecting the physiological sensor on the human body and using the physiological parameters to estimate the degree of fatigue of the user.

In recent years, the increasing number of wearable devices on human has been witnessed as a trend. These devices can serve for many purposes: personal entertainment, communication, emergency mission, health care supervision, delivery, etc. Sharing information among the devices scattered across the human body requires a body area network (BAN) and body sensor network (BSN). However, implementation of the BAN/BSN with the conventional wireless technologies cannot give optimal result. It is mainly because the high requirements of light weight, miniature, energy efficiency, security, and less electromagnetic interference greatly limit the resources available for the communication modules. The newly developed intra-body communication (IBC) can alleviate most of the mentioned problems. This technique, which employs the human body as a communication channel, could be an innovative networking method for sensors and devices on the human body. In order to encourage the research and development of the IBC, the authors are favorable to lay a better and more formal theoretical foundation on IBC. They propose a multilayer mathematical model using volume conductor theory for galvanic coupling IBC on a human limb with consideration on the inhomogeneous properties of human tissue. By introducing and checking with quasi-static approximation criteria, Maxwell's equations are decoupled and capacitance effect is included to the governing equation for further improvement. Finally, the accuracy and potential of the model are examined from both in vitro and in vivo experimental results.

This paper describes a 90 nm CMOS low-noise low-power biopotential signal readout front-end (RFE). The front-stage instrumentation amplifier (IA) features a chopper; an AC-coupler and a novel chopper notch filter for minimizing the DC-offset; transistors' flicker noise and 50 Hz powerline interference concurrently. A noise-aware transistor selection (thin- and thick-oxide) in the IA enables a flexible tradeoff between noise and input impedance performances. The 2ndstage is a spike filter clocked by a parallel use of two non-overlapping clock generators, effectively tracking and suppressing the chopper spikes. The last stage is a gain-bandwidth-controllable amplifier for boosting the gain and alleviating different bio-potential signal measurements through simple digital controls. Simulation results showed that the RFE is capable of tolerating a differential electrode offset up to plusmn50 mV, while achieving 140 dB CMRR and 51.4 nV/radicHz inputreferred noise density. The notch at 50 Hz achieves 41dB rejection. The entire RFE consumes 16.55 to 35.5 muA at 3V.