Given the immutability of biometric data, it is imperative to develop a biometric template protection method that guarantees the complete non-disclosure of any original biometric information while ensuring high recognition performance. Two mainstream approaches in biometric template protection—cancelable biometrics and biometric cryptosystems—have been widely adopted; however, the protected templates produced by these methods still contain some of the original biometric data, which can lead to privacy leakage. To address these challenges, we propose a novel framework named BioDeepHash that integrates deep hashing with cryptographic hash functions. In our approach, a deep hashing model generates consistent templates for similar biometric data from the same user, thereby eliminating intra-class variations. An application-specific XOR string is then used to achieve revocability, and finally these consistent templates are processed by cryptographic hash functions to produce protected templates that meet strict security standards. Our experimental results show that, compared with existing methods, BioDeepHash increases the average Genuine Acceptance Rate by 10.12% on the iris dataset and by 3.12% on the facial dataset, while achieving an extremely low False Acceptance Rate—0% for the iris dataset and only 0.0002% for the facial dataset.

Aiming at the problems encountered in object detection of UAV vessels in China’s offshore waters, this paper, based on the YOLOv11 algorithmic model, creatively proposes a new object detection algorithmic architecture, DCM-Net, based on the problems of multi-scale and complex background of the targets in the images captured by UAVs by making various improvements to the model, so as to significantly improve the performance of the UAV monitoring model. To begin with, we aim to uncover the multidimensional semantic information encapsulated within the image, we introduce the Multi-scale Dilated Attention Transformer (MSDA) module, which captures details of targets across a spectrum of scales and refining the model’s sensitivity to image subtleties. The MSDA module improves the model’s comprehension and expression of the multiscale features while maintaining the computational efficiency. Secondly, we dynamically adjust the model architecture so that DCM-Net is more sensitive to changes in the scale of ship targets, which improves the monitoring performance of the model to a greater extent. Finally, in order to optimize the model for reduced complexity and satisfy the detection task when the resources of the edge devices are insufficient, we introduce the dynamic convolution module to reduce the model computation consumption and improve the model expression ability. Through extensive experimental validation on the public datasets HRSC2016, Seaships, and Shipsdataset, our model improves 3.3%, 16%, and 50% in mAP50 and computational consumption and FPS metrics, respectively. The experimental outcomes demonstrate that our algorithm significantly enhances the model’s detection capabilities.

Prostate cancer (PCa) is a malignancy originating from epithelial cells within the prostate gland. The gold standard for diagnosing PCa is typically based on the Gleason score. However, the inherent variability in biopsy sampling and its potential discordance with radical prostatectomy outcomes can result in the misclassification of the International Society of Urological Pathology (ISUP) Gleason Group (GG). Furthermore, the employment of prostate-specific antigen (PSA) for screening, guiding biopsy decisions, and the reduction in PSA biopsy thresholds has led to a notable increase in unwarranted biopsies among patients with PCa. Consequently, developing an efficient and accurate method to predict ISUP GG is imperative. Currently, most studies focus on ISUP GG binary classification for specific GG. In this study, we leverage multiparametric Magnetic Resonance Imaging (mpMRI) images, encompassing T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC), to introduce a post-fusion based cross-attention model named PFCA-Net. The model is designed to predict ISUP GG categories, specifically GG 0/1, GG 2, GG 3, and GG 4/5. We validate our approach using three distinct deep learning classification models and a mpMRI pre-fusion classification model. Our proposed approach demonstrates exceptional performance, attaining an accuracy (ACC) of 0.9692 and an area under the curve (AUC) of 0.9986, over a dataset comprising 107 PCa patients and encompassing a total of 927 MRI images. This study emphasizes the practical value of mpMRI in distinguishing various ISUP GG categories and expediting the evaluation of PCa GG by clinicians.

This work proposes a three-step multi-resolution time-to-digital converter (TDC) architecture based on the vernier delay line (VDL). The proposed architecture uses a delay-locked loop (DLL) to control TDC with a smooth coarse-to-fine strategy. In addition, the fine TDC uses a combination of multiple resolutions to reduce the number of delay cells and flip-flops. This architecture helps to reduce the area and power consumption and maintains high resolution. We proposed architecture performs better trade-offs between power consumption, linearity, accuracy, and measurement range. The simulation results show that the 7-bit TDC based on VDL designed in 180 nm CMOS achieves 5 ps of time resolution, 0.76/-0.8 LSB DNL and 1.02/-1.39 LSB INL at 100 MHz clock frequency while consuming 3.1 mW, which corresponds to the figure of merit (FoM) of 0.242 pJ/Conv.

Medical image segmentation is an important application in medical image processing. In order to improve the efficiency of Medical image segmentation, a method to select optimal threshold by PSO algorithm with Dynamic Inertia Weight(DW-PSO) is proposed. It makes the proportion of the local and global searching ability can be effectively controlled in the whole process of optimal searching. Compared with PSO algorithm, DW-PSO gets better effects of image segmentation. The data of experiment shows that DW-PSO has obvious advantage in computing efficiency, the precision and stability of searching optimal threshold, as well as the effect of image segmentation.