Distributed multiple-input multiple-output (MIMO) radar networks operating at millimeter-wave frequencies enable high-resolution microwave imaging but face fundamental limitations in antenna array synthesis. Sparse virtual apertures reduce hardware complexity yet introduce severe grating lobes and sidelobe artifacts, which degrade image fidelity via aliasing in the electromagnetic (EM) spatial frequency domain. To address these antenna array synthesis challenges, we propose a noisy tensor completion framework exploiting joint low-rank and sparsity constraints inherent in radar scattering. Our method reorganizes radar echoes into high-dimensional tensors with dispersed missing elements, explicitly modeling the sparse array sampling process. A key innovation is an adaptive singular-value reweighting scheme that preserves dominant EM scattering components while suppressing noise-corrupted interference. The resulting optimization is solved via an alternating direction method of multipliers (ADMM) algorithm. Extensive EM simulations and experimental validation using a prototype W-band (77 GHz) distributed MIMO radar system demonstrate superior artifact suppression and target reconstruction over state-of-the-art methods. This establishes a robust imaging solution for sparse-aperture systems by directly addressing antenna array pattern limitations through tensor-based aperture synthesis.

The rapid advancement of Internet of Things (IoT) technology, particularly in the realm of autonomous driving, has elevated the requirements for automotive radar systems to achieve precise environmental perception. This article delves into the distributed MIMO radar network model and the associated signal processing techniques that enable accurate measurement of range, velocity, and angular positions, culminating in the generation of high-resolution 4-D point clouds. These capabilities are pivotal for intelligent interactions between vehicles and their surroundings within the IoT ecosystem. We introduce a stepped-frequency frequency-modulated continuous wave (SF-FMCW) waveform that incrementally increases the starting frequency of each chirp, leading to a larger bandwidth and finer range resolution without altering the individual chirp bandwidth. Furthermore, we propose a hybrid FDM-DDM scheme to ensure orthogonality among MIMO waveforms. This scheme allows for decoding across various DDM modes through the application of overlapping binary masks, while maintaining unambiguous range-Doppler measurements, which is crucial for real-time data processing and decision-making within IoT. To enhance angular resolution, we optimize the array configuration and develop a low sidelobe direction of arrival (DOA) estimation method using phase coherence factor (PCF) techniques. Extensive simulations and experimental analyses demonstrate the superior performance of the proposed methods in resolving closely spaced targets and generating high-fidelity 4-D point clouds, even in challenging scenarios with limited angular separation. The development of these technologies is significant for intelligent perception and safe navigation of vehicles within the IoT, providing a technological foundation for seamless integration of vehicles with the IoT infrastructure.

Linear frequency modulation (LFM) radar which can measure distance of target is the key sensor in autonomous driving system in the context of Internet of Things (IoT). However, huge amount of raindrops in rainfall environment would reflect signal and result in negative influence. In order to improve the detection ability of LFM radar, echo signal model of LFM radar target in rainfall environment is established. Working principle of LFM radar, characteristics of raindrops, influence of rain clutter, and comparison with measured data in time-domain and frequency-domain distribution are given in sequence. Second, three features are defined and extracted from frequency-domain spectrum and singular-domain spectrum to recognize target echo signal, rain clutter, and mixed signal with high precision. When the rainfall rate is 3.33 mm/h, the overall recognition accuracy can still be up to 95.15%. Third, an adaptive ensemble empirical mode decomposition (EEMD) algorithm is proposed to suppress rain clutter in mixed signal. Simulation results prove the effectiveness of EEMD in improving ranging accuracy in rainfall environment. At last, outfield experiments in normal environment and rainfall environment are conducted, respectively. Measured distance obtained by proposed method in rainfall environment is still more accurate than that obtained by traditional fast Fourier transform method in normal environment, illustrating that the proposed method can ensure the high performance of LFM radar in rainfall environment. Research in this article has positive influence on improving reliability of LFM radar in inclement environment and can contribute to more stable target information for the whole in-vehicle IoT.

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.

The presence of blanket jamming, a typical form of airborne synthetic aperture radar (SAR) jamming, causes incoherent signals with strong power to enter the airborne SAR receiver, which significantly reduces the signal-to-noise ratio (SNR) of airborne SAR images and dramatically influences the imaging effect of airborne SAR. In this manuscript, an airborne SAR anti-blanket interference algorithm based on fractional order Fourier transform (FRFT) and second order blind identification (SOBI) is proposed. Firstly, a geometrical model for airborne SAR imaging under a blanket interference condition is developed. Then, a blind signal separation (BSS) algorithm using SOBI is presented. Finally, to solve the problem of separation uncertainty of the existing BSS algorithms, the FRFT is used for signal identification. This method uses the SOBI algorithm to perform the BSS algorithm. Considering that FRFT can detect linear frequency modulation (LFM) signals emitted by airborne SAR, FRFT is used to solve the disadvantages of the current BSS methods. This algorithm separates signals with a high degree of similarity and performs well in terms of blanket jamming suppression. Simulation and measured experiments prove the validity of the algorithm.

Receptor-binding is the first step of viral infection. Discovering potential virus-receptor interactions may give insight into potential strategies for treating viral infectious diseases. Most of computational methods for the virus-receptor interaction prediction are mainly based on sequence information. They neither makes effective use of structure information nor effectively handles with missing values of multiple similarities. In addition, the Link Prediction via linear optimization (LP) only uses contribution of neighbors of a node and ignores contribution of neighbors of another node on the network link. In this article, we present a virus-receptor interaction prediction method (MVLP) based on Multi-View learning and LP via contributions of all neighbors of two nodes on the network link. First, missing values of the receptor secondary structure similarity, the receptor conserved domain secondary structure similarity, the viral protein secondary structure similarity, the viral protein sequence similarity and the viral genome sequence similarity are updated by the gaussian radial basis function (GRB). To improve these similarities, we fuse updated and initial values of each similarity with multi-view learning, respectively. Next, three virus values and receptor similarities are integrated into the comprehensive virus and receptor similarity by the averaging method, respectively. Finally, LP based on contribution of neighbors of two nodes is presented for the virus-receptor interaction prediction. To evaluate the ability of MVLP, we compare MVLP with four related methods in 10 fold Cross-Validation (10CV). Computational results indicate that an average Area Under Curve (AUC) values of MVLP on viralReceptor sup and viralReceptor are 0.9427 and 0.9444, respectively, which are superior to other related methods. Furthermore, a case study also demonstrates the ability of MVLP in practice.

The carrier-free ultra-wideband (UWB) radar (impulse radar) has seen a recent surge of interest. In this letter, a novel recognition system for vehicles based on the carrier-free UWB radar is proposed, in which the sparse representation is introduced as an effective feature extraction method. Based on the original K-SVD algorithm, we provide a new dictionary learning (DL) idea. Instead of only embedding discrimination criteria in the objective function, we expand and improve the optimization procedure of the K-SVD algorithm. Moreover, to alleviate the impact of the signal diversity on the recognition performance, we propose a hierarchical code constraint (HCC) and bind it to the improved K-SVD model. In this way, signals from the same class but with different distributions will be represented by the corresponding dictionary atoms. Extensive experiments prove the improved K-SVD with an HCC-IKSVD can effectively take both reconstruction capability and discriminative power of the dictionary into consideration.

Synthetic aperture radar (SAR) may be attacked by multifarious kinds of the active-jamming, which will lead to the ineffectiveness of SAR in complex electromagnetic environment. In this paper, a novel multi-electromagnetic jamming counter-measure for the airborne SAR is proposed based on maximum signal-to-noise ratio (SNR) blind source separation. Firstly, the imaging geometry and multi-component mixed signal model of airborne SAR are established. Then, based on multi-component mixed signal matrix, a blind source separation (BSS) method based on the maximum SNR is proposed to separate the real target echo signal from the multi-electromagnetic jamming signals. After real target echo signal identification, the high resolution images of the interested target area can be achieved by the corresponding SAR imaging method. The simulated and measured data results are present to prove the feasibility and effectiveness of the proposed method.

The rapid development of flight control technology has made unmanned aerial vehicles (UAVs) widely used in high-precision strikes on the battlefield. The premise of this is to achieve accurate target recognition using UAV-based radars. Aiming at three typical ground targets, including pedestrians, wheeled vehicles, and tracked vehicles, the micro-Doppler modulation caused by the random vibration of the UAV is analyzed in this article for the first time. To improve the recognition accuracy under low signal-to-noise ratios (SNRs), Doppler signals are transformed into time–frequency images, and a deep convolutional denoising encoder (DCDE) is designed to effectively remove the noise without suppressing micro-Doppler characteristics. To avoid the complicated micro-Doppler feature extraction, deep residual learning that can reduce the burden of network training and gain higher learning efficiency compared with traditional deep convolutional neural networks (DCNNs) is adopted. Recognition results under various occasions using denoised micro-Doppler images and designed residual learning network indicate that the proposed method has higher precision and better robustness than current methods. Even when the SNR is only −16 dB, the overall recognition accuracy still exceeds 90%.

As a promising technique, dictionary learning (DL) for target recognition has seen a recent surge in recent years. Although many methods have been proposed to obtain discriminative dictionaries or coefficients via incorporating various constraints into the objective function, there are still two issues. First, it is well known that kinds of discriminative criteria on the objective function often involve substantial optimized items, increasing computation cost. Second, noises in the real world inevitably degrade the classification performance, while most DL algorithms disregard that. Aiming at these two problems, a hierarchical DL model is proposed for vehicle recognition based on the carrier-free ultrawideband (UWB) radar. With the purpose of successfully determining the identity of targets, we first learn several class-specific subdictionaries. Then, considering that the actual environment is filled with noises, we divided the learned dictionary atoms into signal and disturbance atoms in accordance with sparse coefficients to establish the signal dictionary and noise dictionary, respectively. Finally, the clean data are recovered over the corresponding signal dictionary, and meanwhile, the classification task is achieved. This hierarchical DL method takes into account both the noise-robust ability and discriminative power of the learned dictionary, in which the “atom selection” mechanism dramatically speeds up calculations. What is more, rather than imposing discriminative restraints on the objective function, we improve the K-SVD-based optimization process to complete hierarchical DL. Experimental results on the measured and synthetic data corroborate the effectiveness of the proposed method even under low signal-to-noise ratio (SNR) values. Especially, to testify to the generalization ability of the proposed method, we evaluate our algorithm on a public synthetic aperture radar (SAR) dataset (MSTAR).

Viral infectious diseases have become a serious threat to human health and global security. The receptor-binding is the first step of viral infection. Identifying potential virus-receptor interactions gives us a new perspective on understanding interaction mechanisms, and further find potential targets for prevention and therapy of viral infectious diseases. Many predicted methods have been proposed to identify potential virus-receptor interactions. They didn’t focus on fusing multiple biological features and using multiple similarity measures depending on multiple biological characteristics to improve the prediction performance. In this study, a novel predictive method (LOMCVRI) is proposed to identify potential VirusReceptor Interactions based on similarity fusion and Matrix Completion of Linear optimization. In LOMCVRI, the viral protein structure similarity, the viral protein sequence similarity and the viral genomic sequence similarity are computed based on viral secondary structure features, viral protein sequences and viral genomic sequences, respectively. The gaussian radial basis function is used to improve these viral similarities. They are further combined into a compositive viral similarity by the linear weighting average method. Second, we compute the receptor conserved domain sequence similarity, the receptor sequence similarity and the receptor protein-protein interaction network (PPI) similarity based on the receptor conserved domain sequences, the receptor amino acid sequences and the human PPI data. They are also fused into a compositive receptor similarity. Next, the K-nearest neighbor preprocessing is employed to prefill missing entries in original interaction matrix. Finally, based on updated virus-receptor interactions and compositive similarities of virus and receptor, the matrix completion of linear optimization is applied to identify potential virus-receptor interactions. 10-fold Cross-Validation (10CV) and Leave-One-Out Cross-Validation (LOOCV) experimental results show that the area under of ROC curve (AUC) values of LOMCVRI are 0.9231 and 0.9438, respectively, which consistently outperforms all other related methods. In addition, a case study further confirms the effectiveness of our method.

Complex and diverse microbial communities do not only take important roles in human health and disease, but also are clinically drug targets. Predicting potential microbe-drug associations is helpful to understand complex mechanisms of microbes in clinical treatment, drug discovery, combinations, and repositioning. But potential microbe-drug association's prediction is time-consuming and expensive by using biological experiments, while computational methods can effectively overcome these limitations. To predict Human Microbe-Drug Association, a new computational method of KATZ measure (HMDAKATZ) is proposed. As we have known so far, HMDAKATZ is the first tool to predict potential associations between microbe and drug. In our method, we firstly construct the microbe similarity network by computing the GIP kernel similarity of microbes based on known microbe-drug associations. Then, the drug similarity network is constructed by integrating the chemical structures similarity and GIP kernel similarity of drugs. We further construct the microbe-drug heterogeneous network based on two similarity networks and known microbe-drug associations. Based on the microbe-drug heterogeneous network, we apply HMDAKATZ to predict potential microbe-drug associations based on the microbe-drug heterogeneous network. The experimental result shows that HMDAKATZ has obtained an average area under the curve (AUC) value of$0.9010\pm 0.0020$in the 5-fold cross validation (5-fold CV). Furthermore, a case study also demonstrate that 100% of top 20 potential drugs of human immunodeficiency virus have been validated by existing literature, confirming the effectiveness of HMDAKATZ.

With the increase of cyberhacking activities, cyber attacks become urgent problems to system security. In this paper, cyber attacks on load frequency control (LFC) is studied. By unifying attack and detection, detection scheme considering specific attack strategies is presented. As for attack scheme design, four attack strategies are systematically analyzed with respect to their mechanism and influence on LFC performance, so that the most effective one is selected as the adopted attack scheme from the viewpoint of hackers. As for attack detection, a novel attack detection approach is proposed by analyzing differences between dynamic features of variables. Multilayer percepton classifier-based approach is used to extract the differences of area control error under attack and in normal situation, thus distinguishing compromised signals from normal ones. Simulation results show the effectiveness of the proposed detection approach.