In radar target detection, noise estimation bias and sidelobe interference from target spectral leakage degrade detection performance. This paper proposes a CLEAN framework-based truncated sidelobe suppression constant false alarm rate (TSS-CFAR) detector, featuring a novel dual-stage adaptive truncation strategy. First, background noise is extracted by a robust Gaussian truncation threshold constructed via the median and median absolute deviation (MAD). Second, a primary Rayleigh distribution threshold derived from quantile function integrates adaptive censoring. Based on the linear superposition of these dual-mode noise statistics, a dynamic detection threshold is formulated to precisely separate targets from clutter. Furthermore, the Candan fine-frequency estimator is employed to refine target parameters, enabling the CLEAN framework to iteratively deduct reconstructed signals for closed-loop sidelobe suppression. Experimental results demonstrate the robustness of TSS-CFAR across varying signal strengths. It improves the target recall rate by 15.2% over SS-CFAR at a practical SNR of 15 dB, while achieving a 2.06-fold precision improvement over conventional methods at 30 dB by effectively suppressing sidelobes.

With the rapid increase of the aging population, fall detection, as a key technology of intelligent medical treatment, has attracted extensive attention. Compared with the poor comfort of wearable devices and the light sensitivity of visual methods, the wireless sensing scheme based on channel state information (CSI) shows unique advantages with its non-contact and privacy friendly. However, the existing CSI-based methods have the problem of insufficient long-range modeling ability, and fail to establish the deep semantic correlation between time-domain and frequency-domain in the fall process. Therefore, this paper proposes a fall detection method based on hybrid Mamba and Transformer, namely HMT-Fall. Different from the existing methods that simply parallel the time-domain network and the frequency-domain network, HMT-Fall creatively constructs a multi-domain modeling mechanism with clear functional division and collaborative design. Firstly, Mamba network is introduced to process the raw CSI sequences, and its selective state space mechanism is used to achieve efficient long-range timing modeling. Secondly, the short-time Fourier transform (STFT) spectrum of CSI is analyzed by Swin Transformer, and the frequency domain representation with both local details and global context is efficiently extracted through the shifted window self-attention mechanism. Furthermore, a bidirectional cross-attention fusion module is designed to achieve dynamic alignment and mutual constraint between time-domain features and frequency-domain features at the semantic level, rather than simple static splicing or weighting, so as to form a physically consistent and more discriminative joint representation. The experimental results on the self-built dataset HMT-HAR and public datasets show that the detection performance of HMT-Fall is significantly better than that of the existing representative methods, achieving over 99% accuracy, which verifies the effectiveness and superiority of the proposed method.

Unmanned Aerial Vehicle (UAV) swarms Cooperative Spectrum Sensing (CSS) denotes the technology in which UAVs serve as sensing nodes to detect, monitor, and analyze wireless spectrum resources in target regions through collaborative clustering. Compared with conventional CSS systems, this framework demonstrates advantages in wide-area coverage and flexible deployment, enabling dynamic spectrum sharing in complex electromagnetic environments. However, the on-demand scalability of UAV swarms introduces dynamic variations in signal quality and node availability, which significantly degrade the efficiency and reliability of spectrum sensing. Existing CSS approaches largely neglect these time-varying characteristics, leading to suboptimal sensing performance in dynamic swarm environments. To address this issue, a novel adaptive node screening CSS approach (ANS-CSS) is proposed in this paper, which integrates a node screening strategy based on quality evaluation and a temporal-spatial feature extraction mechanism. Meanwhile, an Adaptive Node Screening Net (ANSNet) is constructed on the basis of ANS-CSS. Specifically, the sensing signals from the UAV nodes are evaluated and screened in real time to form high-quality signal sets. The temporal and spatial features are then extracted from these sets, enabling the network to achieve efficient and reliable spectrum detection in dynamic node scenarios. Simulation results demonstrate that the proposed approach exhibits superior performance in dynamic scenarios of UAV swarms, particularly under low Signal-to-Noise Ratio (SNR) conditions. The detection probability of ANSNet reaches 97.7% when SNR=-16dB, outperforming existing CSS methods.

In recent years, intelligent human behavior sensing based on channel state information (CSI) has garnered significant attention from researchers, serving as a pivotal application of contactless health monitoring. However, the feature extraction networks used in existing perception schemes have significant limitations in terms of global context perception, computational complexity, and only consider features in one direction. To address these issues, this article proposes a novel bidirectional velocity Mamba (BVMamba) model and constructs an intelligent behavior sensing system, named IMRadar. The system first analyzes the velocity information that better characterizes the human motion state from CSI data, and uses the BVMamba model to extract global deep behavioral features from both forward and reverse directions. The BVMamba model includes forward velocity Mamba block (FVMamba), reverse velocity Mamba block (RVMamba), and bidirectional velocity feature fusion block (FUBlock), which can comprehensively capture the dynamic characteristics of complex behaviors. Experiments have shown that IMRadar exhibits excellent recognition performance on both publicly available datasets (ARIL, Widar) and self-built dataset (IM-HAR), with accuracy rates exceeding 98% for all datasets, providing an efficient and robust solution for non-contact behavior perception technology.

This work presents a radio-frequency (RF) blocker-tolerant multi-band (0.5 to 2GHz) receiver to achieve both wide passband bandwidth (BW) and sharp out-of-band (OB) attenuation at near-by frequency. At RF front-end, it features the combination of the 4th-order N-path low-noise transconductance amplifier (LNTA) and the bottom-plate N-path filter, while at baseband (BB) it introduces the 3rd-order N-path lowpass filter and 2nd-order BB trans-impedance amplifier (TIA). Besides, a negative-feedback frequency-translational loop is created to achieve the input-impedance matching condition. Designed in 65nm CMOS technology, our receiver achieves -220dB/decade near-by roll-off slope for a 200MHz RF-BW at 2GHz. With 3xRF-BW offset, the receiver achieves 36.5dBm OB-IIP3. The noise figure (NF) is simulated from 2.4 to 3.8dB and the power consumption is 27.6 to 37.8mW. The chip area is 0.68mm2.

With the widespread sharing of personal face images on the Internet, systems based on face recognition encounter the real threat of being breached by potential adversaries who are able to access individuals’ face images and use them to intrude the systems. In this article, we propose a novel privacy protection method in the sparsified multiscale feature subspaces to protect sensitive facial features, taking care of the influence or weight-ranked subspace coefficients on the privacy budget, named “ranked differential privacy (RDP).” After the multiscale subspaces’ decomposition, the lightweight Laplacian noise is added to the dimension-reduced sparsified subspaces’ coefficients according to the geometric superposition method. Then, we rigorously prove that the RDP satisfies$\varepsilon _{0}$-differential privacy. After that, the nonlinear Lagrange multiplier method (LM) is formulated for the constraint optimization problem of maximizing the utility of protected face images of high-visualization quality with sanitizing noise, under a given privacy budget$\varepsilon _{0}$. Then, two methods are proposed to solve the nonlinear Lagrangian multiplier method (LM) problem and obtain the optimal noise scale parameters: 1) the analytical normalization approximation (NA) method with identical average noise scale parameter for real-time online applications and 2) the LM optimization method via gradient descent (LMGD) to obtain the nonlinear solution through iterative updating for more accurate offline applications. Experimental results on two real-world datasets show that our proposed RDP outperforms other state-of-the-art methods: at a privacy budget of$\varepsilon _{0} = 0.2$, the peak signal-to-noise ratio (PSNR) of the optimized RDP is about ~10 dB higher than (10 times as high as) the highest PSNR of all state-of-the-art methods compared.

The autoregressive context model has been proven effective in point cloud attribute compression. However, it suffers from unbearable decoding latency due to the limitations of serial decoding and the large scale of point clouds. In this paper, we propose a rich, parallelizable context model for point cloud attribute compression to speed up the decoding process. To further improve rate-distortion (RD) performance, we propose cross-coordinate and intra-coordinate attention modules to reduce the spatial redundancy of the latent representations. We validate our method on the large-scale Moving Picture Experts Group (MPEG) point cloud benchmarks, and demonstrate that our model achieves much lower decoding time than previous autoregression-based methods while maintaining similar RD performance.

Cross correlations are ubiquitous in time-series IoT data sets such as trajectories from smartphones and smart meters data in smart grids. Conventional privacy methods have difficulty to protect cross correlation privacy within such correlated data set. Here we propose a novel Correlated noise mechanism for Cross-correlated Data Privacy (CCDP). Because the Fourier coefficients of the cross correlation of two data records are the linear product of those of the two data records, the sanitizing Fourier coefficients noise is used for efficient optimization. Also, the noise is added via the Geometric sum method, which is proved to provide the required Laplace distribution. We perform rigorous mathematical analysis of the CCDP and prove that it satisfies ε-Pufferfish privacy. We also prove that the CCDP can achieve the optimal data utility for a given privacy budget ε. What's more important, we further derive the mathematical procedure to obtain the optimal Laplace noise scale parameter to achieve better data utility. Simulations show that the proposed CCDP outperforms the independent Fourier coefficients noise mechanism, as well as two other state-of-the-art time-domain privacy mechanisms in the literature, for three types of data sets: computer-generated data, real-world trajectory data, and smart meter data.

New privacy implications are induced to individuals and families because of the time-series data classification problem in the Internet of Things such as appliance classifications in the smart grid. To prevent the adversary from inferring the household appliance classification used in the smart grid, a singular spectrum analysis (SSA) has been applied to the local differential privacy (SSA-LDP). First, the Fourier spectrum noise has been added via the geometric sum which has been proved to achieve the Laplace noise distribution. Furthermore, we have proved that the sanitized data through the SSA-LDP is ε -deferentially private for the adversary inference attack. In addition, to achieve a better data utility, a formula has been obtained for the optimal Fourier spectrum noise by decomposing it into the superposition of power spectra of the dominant SSA eigenfilters. Finally, experiments have been performed with a computer-generated data set and a real-world smart-meter data set. Comparisons to other privacy approaches show that the optimized SSA-LDP does achieve a better data utility for a given data privacy.

A mutual correlation between trajectories of two users is very helpful to real-life applications such as product recommendation and social media. While providing tremendous benefits, the releasing of correlated trajectories may leak sensitive social relations, due to potential links between mutual correlations and social relations. To the best of our knowledge, we take the first step to propose a mathematically rigorous n-body Laplace framework, satisfying "-differential privacy, which efficiently prevents a social relation inference through the mutual correlation between n-node trajectories of two users. The problem is mathematically formulated by defining a trajectory correlation score to measure the social relation between two users. Then, under the n-body Laplace framework, we propose two Lagrange Multiplier-based Differentially Private (LMDP) approaches to optimize the privacy budgets, for the data utility measured by location distances and the data utility measured by location correlations, i.e., UD-LMDP and UC-LMDP. Also, we present detailed analyses of privacy, data utility, adversary knowledge and the constrained optimizations. Finally, we perform experimental studies with real-life data. Our experimental results show that our proposed approaches achieve better privacy and data utility than the existing approaches.

In this paper, we present a novel passive millimeter-wave (PMMW) imaging system designed using compressive sensing principles. We employ randomly encoded masks at the focal plane of the PMMW imager to acquire incoherent measurements of the imaged scene. We develop a Bayesian reconstruction algorithm to estimate the original image from these measurements, where the sparsity inherent to typical PMMW images is efficiently exploited. Comparisons with other existing reconstruction methods show that the proposed reconstruction algorithm provides higher quality image estimates. Finally, we demonstrate with simulations using real PMMW images that the imaging duration can be dramatically reduced by acquiring only a few measurements compared to the size of the image.

We present observations of microwave scattering from ambient room air ionized with a negative ion generator. The frequency dependence of the radar cross section of ionized air was measured from 26.5 to 40 GHz (Ka-band) in a bistatic mode with an Agilent PNA-X series (model N5245A) vector network analyzer. A detailed calibration scheme is provided to minimize the effect of the stray background field and system frequency response on the target reflection. The feasibility of detecting the microwave reflection from ionized air portends many potential applications such as remote sensing of atmospheric ionization and remote detection of radioactive ionization of air.