Motion capture of the human body potentially holds great significance for exoskeleton robots, human-computer interaction, sports analysis, rehabilitation research, and many other areas. Dielectric elastomer sensors (DESs) are excellent candidates for wearable human motion capture systems because of their intrinsic characteristics of softness, light weight, and compliance. In this paper, DESs were applied to measure all component motions of the wrist joints. Five sensors were mounted to different positions on the wrist, and each one is for one component motion. To find the best position to mount the sensors, the distribution of the muscles is analyzed. Even so, the component motions and the deformation of the sensors are coupled; therefore, a decoupling method was developed. By the decoupling algorithm, all component motions can be measured with a precision of 5°, which meets the requirements of general motion capture systems.

This paper focuses on waveform design for multi-input multi-output (MIMO) dual-function radar-communication (DFRC) systems, particularly tailored for environments with multiple single-antenna downlink user equipments (UEs). Our approach leverages orthogonal frequency division multiplexing (OFDM) technology to address the challenges of frequency-selective fading. To mitigate the peak-to-average power ratio (PAPR) issues inherent in OFDM signals, the desired low-PAPR property is also incorporated into the design of the waveforms. For enhanced radar sensing functionality, we introduce an advanced metric, the weighted peak or integrated sidelobe level (WPISL), meticulously crafted to measure and minimize low-range sidelobes. On the communication front, we integrate constructive interference (CI) techniques to significantly enhance quality of service (QoS) in data transmission. To address the intricate optimization challenges presented by our design objectives, we have developed an efficient algorithm anchored in the majorization-minimization (MM) framework. The numerical experiments demonstrate that this algorithm notably surpasses existing state-of-the-art benchmarks in reducing range sidelobe interference. Furthermore, our CI-based approach yields enhanced performance compared to traditional least squares (LS) methods, achieving lower symbol error rates (SER) and higher average achievable sum rates.

Exposure to malachite green (MG) may pose great health risks to humans; thus, it is of prime importance to develop fast and robust methods to quantitatively screen the presence of malachite green in water. Herein the application of extractive electrospray ionization mass spectrometry (EESI-MS) has been extended to the trace detection of MG within lake water and aquiculture water, due to the intensive use of MG as a biocide in fisheries. This method has the advantage of obviating offline liquid-liquid extraction or tedious matrix separation prior to the measurement of malachite green in native aqueous medium. The experimental results indicate that the extrapolated detection limit for MG was ~3.8 μg·L⁻¹ (S/N = 3) in lake water samples and ~0.5 μg·L⁻¹ in ultrapure water under optimized experimental conditions. The signal intensity of MG showed good linearity over the concentration range of 10–1000 μg·L⁻¹. Measurement of practical water samples fortified with MG at 0.01, 0.1 and 1.0 mg·L⁻¹ gave a good validation of the established calibration curve. The average recoveries and relative standard deviation (RSD) of malachite green in lake water and Carassius carassius fish farm effluent water were 115% (6.64% RSD), 85.4% (9.17% RSD) and 96.0% (7.44% RSD), respectively. Overall, the established EESI-MS/MS method has been demonstrated suitable for sensitive and rapid (<2 min per sample) quantitative detection of malachite green in various aqueous media, indicating its potential for online real-time monitoring of real life samples.

Active sensing systems prefer probing waveforms with good auto-correlation properties. Faced with the difficulty in getting individual sequences with impulse-like aperiodic auto-correlation functions, we resort to the temporal waveform diversity based on complementary sets of sequences (CSS). It seems an ideal solution, but has rarely been used due to the extreme sensitivity of CSS to Doppler shifts. This paper is devoted to applying numerical methods to the design of Doppler resilient complementary waveforms (DRCW) whose ambiguity functions are almost free of range sidelobes along modest Doppler shifts. We formulate the problem as minimizing the worst-case peak sidelobe level (PSL) with low peak-to-average power ratio (PAR) constraints, and then approximate the min-max problem by a lp-norm minimization problem. A hierarchical strategy is developed to tackle the phase optimization and the amplitude-phase optimization respectively. Correspondingly, two algorithms based on phase-gradient and Majorization-Minimization are proposed, both of which can be efficiently computed via FFT. Numerical experiments show our method is effective and superior to existing ones. The DRCWs thus obtained have their range sidelobes at all lags suppressed below -60dB, and are expected to bring significant performance improvements in active sensing. Two application examples, detecting weak targets in heavy clutter and estimating the high-resolution range profile of an extended target by active radars, are given.

In modern electronic warfare, cognitive radar with knowledge-aided waveforms would show significant flexibility in anti-interference. In this paper, a novel method, named particle swarm-assisted projection optimization (PSAP), is introduced to design phase-coded waveforms with multi-level low range sidelobes, which mainly considers the stability for randomized initialization under the unimodular constraint. Firstly, the mathematical problem corresponding to avoid the range sidelobe masking from multiple non-cooperative targets or interference is formulated by giving different threat levels. Then, based on the alternating direction decomposition idea, the original problem is divided into triple-variable ones where these non-linear approximations can be solved via alternating projections along with FFT. Furthermore, the PSAP method with swarm intelligence, learning factor, and particle-assisted projection could ensure the optimization convergence in a parallel way, which could relax the non-convex constraint and enhance the global exploiting performance. Finally, simulations for several typical scenarios and numerical results are all provided to assess the waveforms generated by PSAP and other prevalent ones.

Human activity recognition plays a vital role in many applications, such as body falling surveillance and healthcare for elder’s in-home monitoring. Instead of using traditional micro-Doppler signals based on time-frequency distribution, we turn to another way and use the Relax algorithm to process the radar echo so as to obtain the required parameters. In this paper, we aim at the multi-view idea in which two radars at different views work synchronously and fuse the features extracted from each radar, respectively. Furthermore, we discuss the common estimated time-frequency features and time-varying spatial features of multi-view radar-echo and then formulate the parameters matrix via principal component analysis, and finally transform them into the machine learning classifiers to make further comparisons. Simulations and results show that our proposed multi-view parameter fusion idea could lead to relative-high accuracy and robust recognition performance, which would provide a feasible application for future human–computer monitoring scenarios.