Despite the fact that biometric systems won many desirable advantages such as high security and convenience, biometric data are vulnerable to various attacks. Once the biometric data are compromised during any point of the authentication process, they are lost forever. In this paper, we propose a security information management approach to address privacy concerns emerging from the management of biometric data in remote Photoplethysmogram (rPPG) biometric authentication systems. In our design, Fully Homomorphic Encryption (FHE) is introduced to protect PPG signals in the transmission and storage phases. We further design an efficient FHE-based Convolutional Neural Network (CNN) model and encrypt the key model parameters of the CNN to realize secure inference. In addition, Principal Component Analysis, Max-Relevance and Min-Redundancy, and Relative Mutual Information methods are employed in the feature extraction stage to screen out the most representative PPG features, thus improving the accuracy of PPG authentication. In the experiments, we adopt eight publicly PPG/rPPG databases and our own rPPG dataset collected via a remote camera to evaluate our proposed biometric authentication system. The experimental results show that our proposed remote PPG biometric authentication system outperforms the state-of-the-art related work in terms of balancing authentication accuracy and biometric privacy protection.

In the natural world, energy and information are deeply entwined, mutually constraining and complementing each other. To exploit this natural merit, this paper proposes a FEI strategy: Fusion processing of sensing Energy and Information for infrared smart vision system. The proposed Information-Power-Coupler (IPCp) takes the ability of simultaneous energy harvesting and low power inpixel computing, which utilizes in-situ coupled energy to process the containing information on the same focal plane. Furthermore, a self-adaptive Intelligent-Power- Controller (IPCtrl) capable of scheduling the harvested energy to complete low power neural network inference is introduced. The implementation of IPC2 system utilizes a software-hardware co-design strategy to exploit the layer-wise characteristic of the computation process and circuit topology, achieving energy-efficient self-sustainable fusion processing of sensing energy and information. Simulation results show that the IPCtrl could supply 594.68nW with the power conversion efficiency of 93.38%, when the harvested energy from the IPCp is 636.84nW. The performance validates the self-sustainability of the system with the self-powered image recognition of a complete network running at 4fps with an accuracy of 99.4%.

Bistatic Synthetic Aperture Radar (BiSAR) enables highly flexible configuration, offering broad application prospects. However, existing BiSAR imaging algorithms neglect the complex scattering characteristics of the target, resulting in the loss of target structural information in the imaging results. To enhance the target structural information in imaging results and improve the interpretability of BiSAR images, we first establish the BiSAR echo model based on the target scattering model and analyze the target’s imaging characteristics by incorporating the imaging mechanisms. Subsequently, based on the imaging characteristics, we propose a structure-driven multi-stage BiSAR trajectory planning method (SMTP). This method solves a multi-stage multi-objective optimization problem driven by target scattering characteristics, thereby fully presenting all discernible structural features in the imaging results. Numerical simulation experiments validate the proposed method, demonstrating its ability to recover target structural information. This approach addresses the gap where BiSAR mission planning has largely overlooked target-specific characteristics.

Simultaneous Localization and Mapping (SLAM) has a broad application prospect in robot navigation, autonomous driving, and augmented reality. While visual SLAM systems have made significant progress, they still face challenges in complex environments due to the complexity and time variability of the real environment. Traditional methods have limitations in feature extraction, matching, and mapping, and the effect of localization and mapping can be greatly reduced due to scene changes. In response to these challenges, this paper proposes an improved visual SLAM method based on deep learning. We use a lightweight Mobilenetv2 network for feature extraction and introduce the Hardswish activation function to improve the performance further. Meanwhile, the ECA-Net attention mechanism is introduced into the feature extraction network to enhance its ability to capture key image features accurately. In addition, we use multi-task distillation to train our network to reduce the complexity of the model. Extensive experimental evaluation on multiple datasets reveals that the proposed method has significantly improved accuracy and robustness, particularly in challenging environments, demonstrating superior performance. Index Terms—SLAM, ECA-Net, deep learning, feature extraction

As the third-generation neural networks, spiking neural networks (SNNs) have great potential on neuromorphic hardware because of their high energy efficiency. However, deep spiking reinforcement learning (DSRL), that is, the reinforcement learning (RL) based on SNNs, is still in its preliminary stage due to the binary output and the nondifferentiable property of the spiking function. To address these issues, we propose a deep spiking Q -network (DSQN) in this article. Specifically, we propose a directly trained DSRL architecture based on the leaky integrate-and-fire (LIF) neurons and deep Q -network (DQN). Then, we adapt a direct spiking learning algorithm for the DSQN. We further demonstrate the advantages of using LIF neurons in DSQN theoretically. Comprehensive experiments have been conducted on 17 top-performing Atari games to compare our method with the state-of-the-art conversion method. The experimental results demonstrate the superiority of our method in terms of performance, stability, generalization and energy efficiency. To the best of our knowledge, our work is the first one to achieve state-of-the-art performance on multiple Atari games with the directly trained SNN.

MOTIVATION: Pyrosequencing technology provides an important new approach to more extensively characterize diverse sequence populations and detect low frequency variants. However, the promise of this technology has been difficult to realize, as careful correction of sequencing errors is crucial to distinguish rare variants (∼1%) in an infected host with high sensitivity and specificity. RESULTS: We developed a new approach, referred to as Indel and Carryforward Correction (ICC), to cluster sequences without substitutions and locally correct only indel and carryforward sequencing errors within clusters to ensure that no rare variants are lost. ICC performs sequence clustering in the order of (i) homopolymer indel patterns only, (ii) indel patterns only and (iii) carryforward errors only, without the requirement of a distance cutoff value. Overall, ICC removed 93-95% of sequencing errors found in control datasets. On pyrosequencing data from a PCR fragment derived from 15 HIV-1 plasmid clones mixed at various frequencies as low as 0.1%, ICC achieved the highest sensitivity and similar specificity compared with other commonly used error correction and variant calling algorithms. AVAILABILITY AND IMPLEMENTATION: Source code is freely available for download at http://indra.mullins.microbiol.washington.edu/ICC. It is implemented in Perl and supported on Linux, Mac OS X and MS Windows.

UNLABELLED: ViroBLAST is a stand-alone BLAST web interface for nucleotide and amino acid sequence similarity searches. It extends the utility of BLAST to query against multiple sequence databases and user sequence datasets, and provides a friendly output to easily parse and navigate BLAST results. ViroBLAST is readily useful for all research areas that require BLAST functions and is available online and as a downloadable archive for independent installation. AVAILABILITY: http://indra.mullins.microbiol.washington.edu/blast/viroblast.php.

The ability of human immunodeficiency virus type 1 (HIV-1) to develop high levels of genetic diversity, and thereby acquire mutations to escape immune pressures, contributes to the difficulties in producing a vaccine. Possibly no single HIV-1 sequence can induce sufficiently broad immunity to protect against a wide variety of infectious strains, or block mutational escape pathways available to the virus after infection. The authors describe the generation of HIV-1 immunogens that minimizes the phylogenetic distance of viral strains throughout the known viral population (the center of tree [COT]) and then extend the COT immunogen by addition of a composite sequence that includes high-frequency variable sites preserved in their native contexts. The resulting COT(+) antigens compress the variation found in many independent HIV-1 isolates into lengths suitable for vaccine immunogens. It is possible to capture 62% of the variation found in the Nef protein and 82% of the variation in the Gag protein into immunogens of three gene lengths. The authors put forward immunogen designs that maximize representation of the diverse antigenic features present in a spectrum of HIV-1 strains. These immunogens should elicit immune responses against high-frequency viral strains as well as against most mutant forms of the virus.