With the development of Internet of Things (IoT) technology, user identity recognition technology has become an important line of defense to ensure application access and privacy security. Ultra-Wideband (UWB) bio-radar, with its high range resolution and non-contact penetration detection advantages, can accurately capture the individual-specific and dynamic continuous surface micro-motion characteristics caused by cardiac structural movement through transmitted microwaves, namely Microwave HeartPrint, provides a new technological path for non-contact identity recognition. However, current radar-based non-contact cardiac motion recognition mostly treats the micro-movement of the chest wall as a single scattering point target and primarily relies on single-domain data, thereby limiting the effectiveness of the recognition mechanism. To address the aforementioned issue, this paper first employs UWB bio-radar to continuously monitor the multi-point micro-motions in the range dimension of the cardiac body surface area along the radial direction, and then achieves the selection of effective range unit echoes from the perspective of multiple range tomographic planes. Secondly, based on the echo heartbeat segmentation and combined with short-time fourier transform (STFT) time-frequency transform, the Microwave Heartprint dual-domain data corresponding to the effective range units are obtained. Finally, a multi-view dual-domain feature fusion recognition network is proposed, where the multi-view single-domain feature fusion module (MV-SFFM) is designed to effectively fuse the differential and complementary micro-motion features of effective range units on the cardiac surface. In addition, the cross-attention dual-domain feature fusion module (CA-DFFM) effectively fuses the time-domain and time-frequency domain features to further improve the feature representation capability. Actual experiments show that the recognition accuracy of this method can reach more than 95%. Ablation experiments and visualization analysis strongly demonstrates the rationality and effectiveness of comprehensively utilizing the micro-motion information of multiple effective range units of the heart and dual-domain features.

In cooperative multi-agent reinforcement learning, decentralized agents hold the promise of overcoming the combinatorial explosion of joint action space and enabling greater scalability. However, they are susceptible to a game-theoretic pathology called relative overgeneralization that shadows the optimal joint action. Although recent value-decomposition algorithms guide decentralized agents by learning a factored global action value function, the representational limitation and the inaccurate sampling of optimal joint actions during the learning process make this problem still. To address this limitation, this paper proposes a novel algorithm called Optimistic Value Instructors (OVI). The main idea behind OVI is to introduce multiple optimistic instructors into the value-decomposition paradigm, which are capable of suggesting potentially optimal joint actions and rectifying the factored global action value function to recover these optimal actions. Specifically, the instructors maintain optimistic value estimations of per-agent local actions and thus eliminate the negative effects caused by other agents' exploratory or sub-optimal non-cooperation, enabling accurate identification and suggestion of optimal joint actions. Based on the instructors' suggestions, the paper further presents two instructive constraints to rectify the factored global action value function to recover these optimal joint actions, thus overcoming the RO problem. Experimental evaluation of OVI on various cooperative multi-agent tasks demonstrates its superior performance against multiple baselines, highlighting its effectiveness.

Traditional digital image processing techniques face problems such as complex feature extraction and weak robustness when dealing with surface defects of multiple categories of electronic components. Deep learning is widely used in industrial defect detection. However, the performance of electronic component defect detection at the pixel segmentation level needs to be improved. For pixel-level defect detection, this paper constructs a defect detection model (ECSDDNet) for electronic component surface defects in computer vision engineering management system. To improve the segmentation accuracy and detection effect, three stages of experiments are conducted to address mis-segmentation problems and the shortcomings of the Unet network structure. Firstly, a classification network that can perform weight transfer is used to replace the encoding structure in the Unet network. Secondly, a simplified version of the feature fusion is proposed and added to the skip connection of the Unet network. Finally, label smoothing is used to optimize the loss and improve the generalization of the network. After the optimization experiment, some noisy contours and small defect contours that are mis-segmented are removed. Experimental results show that ECSDDNet has good segmentation effects on electronic component surface defects and can meet the segmentation and detection needs of electronic component surface defects.

In this article, an ultrawideband (UWB) radar is first employed to probe through the opaque wall media to detect behind-the-wall human motions. By employing such a radar, a high-resolution time-range map with different body parts’ reflections highly discriminable in range direction can be obtained. Second, a high-pass filter is applied to remove the wall effects in the raw time-range map. Then, with the aim of exploiting the rich range information so as to enhance their corresponding micro-Doppler features, a novel range-max enhancement strategy is proposed to extract the most significant micro-Doppler feature of each time–frequency cell along range direction for a specific motion. Finally, the effectiveness of the proposed motion feature enhancement strategy is investigated by means of onsite experiments. Comparative classifications using different convolutional neural network (CNN) structures show that the proposed approach outperforms other state-of-the-art micro-Doppler feature extraction methods. The comparison with the narrowband detection case also proves its superiority in feature enhancement in the narrowband detection scene.

A novel quantitative method to evaluate channel micro-Doppler capacity of multiple-input and multiple-output (MIMO) system is proposed here. The method is valid for ultrawideband (UWB) MIMO radar human activity systems based on time-frequency signatures, and the quality measure will be noted as a relative signal to noise ratio (RSNR). The method quantifies these signatures and evaluates the relative superiority or inferiority of these MIMO channels. Examples of micro-Doppler signature (μDs) characteristics of human activities in a channel will be considered and compared to that of all other channels. First, the MIMO UWB radar human activity signal is modeled, and its corresponding time-frequency (T-F) characteristics are analyzed to justify the rationality of using the new RSNR metric. Second, the method is evaluated using experimental data and the capability of distinguishing the μD capacity differences among channels is demonstrated. This new method clearly and accurately shows much better visible μD evaluation performance than that of the conventional signal to noise ratio in time domain (SNRt). Moreover, this evaluation method can still work well, even for signals with low signal to noise ratio (SNR) down to -4-dB level. Therefore, it can be successfully used to select the superior channels and eliminate any inferior channels or provide confidence coefficients for the collected multiple channel data of human activities. This method should lead to a significant reduction of the inferior channels' influence on further MIMO-based classification or imaging of human activities.

Postdisaster search and rescue is an important application of ultrawideband (UWB) radar systems, which mainly detect trapped victims by their respiratory-motion response. The development of a respiratory-motion detection (RMD) algorithm that can eliminate nonstationary clutter and noise is a challenging task for the application. A new algorithm is proposed to deal with the task in this letter. It uses the multichannel singular spectrum analysis (MSSA) technique to reconstruct the respiratory-motion response detected by a UWB radar. During the reconstruction, the periodicity and range interrelation characteristics of the response are exploited to adaptively identify signal subspaces. The performance of the algorithm is verified both by simulated and real data. The results show its improved performance over the reference algorithms, e.g., a singular-value-decomposition-based algorithm. The adaptive-MSSA-based RMD algorithm has great promise not only in practical use but also for future research of UWB-radar-based human being remote sensing.

Life detection radar can detect human physiological signals (respiration, heartbeat, body movement, etc.) from a long distance away by penetrating nonmetal mediums (brick walls, ruins, etc.). However, interference is often caused by respiratory movements of the radar's operator when detecting vital signs of another human target. The detection accuracy can be significantly influenced by this kind of interference. In this letter, an experimental setup with a dual-frequency continuous-wave life detection radar is investigated. The system operates with different frequencies of 5.75 and 35 GHz. An adaptive filtering method is applied to suppress the interference caused by the operator's respiratory movements. Experimental results show that this method can effectively suppress respiratory interference and improve detection accuracy.

Impulse-radio ultrawideband (IR-UWB) radar is a popular research topic in the field of post-earthquake search and rescue. By lowering center frequency, it can penetrate through earthquake rubble to detect trapped victims mainly by identifying their respiratory-motion response. Thus, low-center-frequency IR-UWB respiratory-motion response is characterized for the first time in this paper. On this basis, a novel constant false alarm rate (CFAR) algorithm that automatically identifies the response is developed. The IR-UWB respiratory-motion response has range extension and interrelation characteristics. With these characteristics, the algorithm can effectively improve the estimation accuracy of clutter energy in CFAR detection. The characteristics and the algorithm performance are verified by experiment results, which show not only great promise in practice but also significance for future research of IR-UWB radar for detection of trapped victims.

The presence of a human target and the surrounding rubble structure are both important information in postdisaster rescue. In this letter, we study the signal-processing algorithm for detecting a human target in radar echo data. Furthermore, a solution is proposed to estimate the surrounding structure-the distance between the human target and the top wall of the cavity where the human target is trapped. In the latter procedure, the human position estimated before is used as a prior knowledge. In order to verify the feasibility of the proposed method, experiments are carried out with a human subject lying under the simulated rubble structure. The results show prospects of the proposed method.

This work studies distributed linear transmission strategies for the multiple-input multiple-output (MIMO) interference channel with multiple concurrent links. We introduce the notion of interference self-mitigating beamforming (ISM-BF), where each transmitter minimizes the interference it causes to other users while satisfying its own signal-to-inter-ferenceplus-noise ratio requirement. The computation of the optimal transmit beamformers is shown to decouple into a generalized eigenvector problem. In addition, we analyze the optimality and uniqueness of the proposed distributed beamforming solution. Numerical simulations of the sum rate performance of ISM-BF show it to be a viable alternative to interference alignment-based techniques.

In this paper, we present a novel definition of connectivity for ad hoc networks, and based on this definition, we propose a scheduling algorithm that can optimize the connectivity of the network through scheduling the active links into different time slots. We also propose an adaptive beamforming algorithm that reduces the impact of the co-channel interference on the links that transmit simultaneously. Simulation results demonstrate that the choice of the number of time slots is critical in optimizing the connectivity of the network.

In this paper, we present a hybrid automatic repeat request (ARQ) scheme for wireless multicast with incremental redundancy channel coding and packet retransmission. With this scheme, we can reliably deliver the same copy of information to different users with mild delay. In addition, the design of the feedback channel for this scheme can be greatly simplified as no effort must be expended to combat cross user interference. We assume that there is always a packet for the transmitter to send whenever the channel is available. The transmitter is assumed to have a buffer of infinite length so that there is no packet-dropping. Three specific schemes are studied, including generalized slotted ALOHA (GSA), repetition time diversity (RTD), and general incremental redundancy (IR). The scaling laws of the average delay and average throughput with respect of the number of users are derived. In addition, we also derive a condition to obtain a linear scaling for both the throughput and the delay with respect to the number of users. Since every user can achieve no more than the ergodic capacity, we actually achieve the optimal scaling law in this case. Simulation results confirm our findings.

In this paper, we present a user selection scheme with a fairness constraint when zero-forcing beamforming (ZFBF) transmission is employed in a multiple-input multiple-output (MIMO) broadcast channel. This problem can be reduced to the maximization of a weighted sum rate with a power constraint. In this paper, we first derive a lower bound on the weighted sum rate. Roughly speaking, this bound is inversely proportional to the Frobenius norm of the inverse of the weighted composite channel matrix. We then present a greedy algorithm to select the users that maximize this lower bound, using the same technique in J. Wang et al. Finally, we show that this approach achieves the same scaling law as DPC does as presented in M. Sharif and B. Hassibi. Simulation suggests that this method achieves comparable or better performance than most existing schemes in the moderate to high SNR realm with comparable complexity.

In this paper, a STBC/BF hybrid technique is proposed for outdoor multipath multi-input multi-output (MIMO) channel. Depending on the amount of partial channel state information (CSI), this system evolves from pure diversity waveform signalling to pure beamforming. The performance of this technique, in terms of signal vs noise ratio (SNR), is investigated in various multi-input single-output (MISO) scenarios. Though the method is evaluated in MISO, we give a simple way to extend it to MIMO case. In addition, a beamforming scheme based on sub-space approximation is also discussed which can reduce the amount of feedback from the receiver to transmitter. Simulation results are given at the end of this paper

We derive a hybrid of beam-forming (BF) and space-time block coding (STBC), where the space-time code is transmitted over the beams generated by the steering vectors corresponding to the channel path directions. This is for the practical case where the transmit array may have adequate information on the departure angles of the dominant paths between transmitter and receiver, but unreliable information on the associated complex path gains. We compute analytically the signal-to-noise ratio (SNR) of the proposed hybrid for the specific case of a two-path channel model and using the orthogonal Alamouti code, and compare the result to the SNR of optimal linear precoding (LP) and the theoretically possible SNR of orthogonal STBC (OSTBC). Simulation results show that the performance of the BF/STBC hybrid can be very close to LP /sup n/der certain conditions - or even better in the practical case where there are phase estimation errors in the path gain estimates employed at the transmitter.

Space-time transmit structures for multi-antenna systems have received considerable interest. Circulant structures were among the first space-time coding techniques ever used for multiple-input multiple-output (MIMO) systems due to their simplicity and full rate. The fact that a circulant matrix is diagonalized by the discrete Fourier transformation matrix suggests that the circulant structure can be combined with an inverse fast Fourier transform (IFFT) at the transmitter and a fast Fourier transform (FFT) at the receiver. Using this method, the spatial mixing effect of the MIMO channel is decoupled but the diversity gain is lost. To recover the diversity advantage, we propose to spread the transmitted symbols over the diagonalized channel using the constellation rotation matrix for signal diversity designs. After spreading, every symbol experiences all the components of the frequency counterpart of the channel vector which makes our scheme provide full diversity. The proposed scheme is full rate and can be easily applied to any number of transmit antennas. Our simulation results show that the performance of our scheme is close to the performance of the ideal orthogonal space-time code and much better than the conventional circulant space-time code.

This paper presents a new temporally adaptive classification system for multispectral images. A spatial-temporal adaptation mechanism is devised to account for the changes in the feature space as a result of environmental variations. Classification based upon spatial features is performed using Bayesian framework or probabilistic neural networks (PNNs) while the temporal updating takes place using a spatial-temporal predictor. A simple iterative updating mechanism is also introduced for adjusting the parameters of these systems. The proposed methodology is used to develop a pixel-based cloud classification system. Experimental results on cloud classification from satellite imagery are provided to show the usefulness of this system.