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GrasOpen: Biometric Authentication via Reach-and-Grasp for Smart Door Access Using Smartwatch

IEEE Internet of Things Journal 2025年

作者： Ding, Lei Shi, Jiale Wu, Yuan Hu, Xinrong Bi, Hongliang Chen, Yanjiao Wuhan Textile University School of Computer Science and Artificial Intelligence Engineering Research Center of Hubei Province for Clothing Information 430200 China China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Zhejiang University College of Electrical Engineering Hangzhou310027 China

In the domain of smart devices, biometric identity authentication has become a leading and crucial technology, mainly because of its improved security and user convenience. Traditional methods frequently depend on complex activities, facial recognition, or passwords, which can be cumbersome and error-prone. This paper presents GrasOpen, an innovative biometric authentication system tailored for door-opening scenarios, utilizing the natural reach-and-grasp motion linked to door handles. The system employs smartwatches with accelerometers and gyroscopes to track and analyze arm movements, ensuring a seamless and intuitive user experience. GrasOpen tackles key challenges by leveraging the unique characteristics of the reach-and-grasp motion, eliminating the necessity for users to remember complex motions, and avoiding redundant actions like waiting for facial recognition. Specifically, GrasOpen initially proposes a lightweight model to discern door-opening actions from daily activities. Then, to realize the robust authentication system, GrasOpen integrates a complementary filter (CF) method to capture diversity-dependent features in door-opening actions. These features are subsequently processed through a modified ConvBoost model for precise user authentication. Experimental results reveal an impressive accuracy of 98.78% for activity recognition and 98.65% for identity authentication, highlighting GrasOpen’s excellence in function and performance. The system’s security and robustness are validated across diverse authentication environments. © 2014 IEEE.

关键词： Biometrics

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SigStyle: Signature Style Transfer via Personalized Text-to-Image Models

arXiv

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arXiv 2025年

作者： Wang, Ye Bai, Tongyuan Xie, Xuping Yi, Zili Wang, Yilin Ma, Rui School of Artificial Intelligence Jilin University China College of Computer Science and Technology Jilin University China School of Intelligence Science and Technology Nanjing University China Adobe United States Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE China

Style transfer enables the seamless integration of artistic styles from a style image into a content image, resulting in visually striking and aesthetically enriched outputs. Despite numerous advances in this field, existing methods did not explicitly focus on the signature style, which represents the distinct and recognizable visual traits of the image such as geometric and structural patterns, color palettes and brush strokes etc. In this paper, we introduce SigStyle, a framework that leverages the semantic priors that embedded in a personalized text-to-image diffusion model to capture the signature style representation. This style capture process is powered by a hypernetwork that efficiently fine-tunes the diffusion model for any given single style image. Style transfer then is conceptualized as the reconstruction process of content image through learned style tokens from the personalized diffusion model. Additionally, to ensure the content consistency throughout the style transfer process, we introduce a time-aware attention swapping technique that incorporates content information from the original image into the early denoising steps of target image generation. Beyond enabling high-quality signature style transfer across a wide range of styles, SigStyle supports multiple interesting applications, such as local style transfer, texture transfer, style fusion and style-guided text-to-image generation. Quantitative and qualitative evaluations demonstrate our approach outperforms existing style transfer methods for recognizing and transferring the signature styles. © 2025, CC BY.

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Towards Expanding Precise Timing for Collaborative Its with Deterministic Communications in 6G

Towards Expanding Precise Timing for Collaborative Its with ...

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IEEE Conference on Wireless Communications and Networking

作者： Hongxing Li Haotian Li Qianhan Gao Guochu Shou Yaqiong Liu Zhigang Guo Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The demand for precise timing continues to grow with the rapid development of smart cities, where intelligent transportation emerges as a critical application heavily dependent on accurate time distribution. Deterministic communication is anticipated to be a defining feature of nextgeneration mobile networks (6G), enabling end-to-end timecritical applications, and unlocking new possibilities for achieving end-to-end precise time synchronization as well. To cope with the challenge of delivering precise time synchronization across broader areas at a lower cost, this paper proposes a method to achieve end-to-end precise time synchronization by leveraging the deterministic characteristics of communication networks. Specially, the proposed approach adopts the Software-Defined Networking (SDN) paradigm to implement deterministic networking for the seamless integration of Time-Sensitive Networking (TSN) and cellular networks. Furthermore, to mitigate the impact of wireless channel uncertainties on synchronization accuracy, we propose a network delay measurement mechanism based on the principle of time redundancy, designed to reduce end-to-end network delay variation. Experimental results validate the effectiveness of the proposed method, demonstrating its capability to constrain the end-to-end relative time error of wired and wireless converged networks to the microsecond scale, without necessitating modifications to the existing network infrastructure.

关键词： 6G mobile communication Wireless communication Cellular networks Uncertainty Accuracy Measurement uncertainty Transportation Delays Synchronization Software defined networking

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Noise-Injected Spiking Graph Convolution for Energy-Efficient 3D Point Cloud Denoising

arXiv

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arXiv 2025年

作者： Li, Zikuan Wu, Qiaoyun Zhang, Jialin Zhang, Kaijun Wang, Jun School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics China School of Artificial Intelligence Anhui University China

Spiking neural networks (SNNs), inspired by the spiking computation paradigm of the biological neural systems, have exhibited superior energy efficiency in 2D classification tasks over traditional artificial neural networks (ANNs). However, the regression potential of SNNs has not been well explored, especially in 3D point cloud processing. In this paper, we propose noise-injected spiking graph convolutional networks to leverage the full regression potential of SNNs in 3D point cloud denoising. Specifically, we first emulate the noise-injected neuronal dynamics to build noise-injected spiking neurons. On this basis, we design noise-injected spiking graph convolution for promoting disturbance-aware spiking representation learning on 3D points. Starting from the spiking graph convolution, we build two SNN-based denoising networks. One is a purely spiking graph convolutional network, which achieves low accuracy loss compared with some ANN-based alternatives, while resulting in significantly reduced energy consumption on two benchmark datasets, PU-Net and PC-Net. The other is a hybrid architecture that combines ANN-based learning with a high performance-efficiency trade-off in just a few time steps. Our work lights up SNN’s potential for 3D point cloud denoising, injecting new perspectives of exploring the deployment on neuromorphic chips while paving the way for developing energy-efficient 3D data acquisition devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Research on sensitivity simulation and optimization of SOI pressure-sensitive chip

引用

Sensor Review 2025年

作者： Jin, Zhong He, Feng Zhou, Meng Li, Jinyu Liu, Fang-Ting Li, Junhui The State Key Laboratory of Precision Manufacturing for Extreme Service Performance and School of Mechanical and Electronical Engineering Central South University Changsha China The 48th Research Institute China Electronics Technology Group Corporation Changsha China School of Computer and Communication Engineering University of Science and Technology Beijing Haidian China

Purpose: The purpose of this study is to explore simulation-based methods for enhancing the sensitivity of pressure-sensitive chips, aiming to reduce design time and overall development costs. Design/methodology/approach: First, the factors influencing the sensitivity of Silicon On Insulator (SOI) pressure-sensitive chips are analyzed. These factors primarily include the crystal orientation of the chip material, impurity concentration, the position of the piezoresistor on the chip surface and the dimensions of the piezoresistor grid. An analysis model of the SOI pressure-sensitive chip is then established using COMSOL simulation software. Through electromechanical coupling simulations, sensitivity data under various parameters are obtained, with the factors affecting sensitivity serving as model adjustment parameters. The main parameters influencing the sensitivity of SOI pressure-sensitive chips and their respective range values are summarized. Findings: A simulation method is developed to study the influence of crystal orientation, doping concentration, piezoresistor position and size on the sensitivity. The simulation results from the typical model align well with theoretical predictions, demonstrating that the method presented in this paper can effectively optimize sensitivity design during the design phase of pressure-sensitive chips. Originality/value: This paper summarizes the factors affecting the sensitivity of SOI pressure-sensitive chips, presents a simulation model and method for optimizing pressure-sensitive chip design and simulates the optimal parameter ranges to enhance sensitivity. © 2025, Emerald Publishing Limited.

关键词： Crystal impurities

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Graph-based Group Division Network for Referring Expression Comprehension

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Ke, Jingcheng Wang, Jia Wong, Waikeung Toomey, Anne Wen, Jie Guangdong University of Technology School of Computer Science and Technology GuangZhou China Guangdong Pharmaceutical University College of Medical Information Engineering GDPU Guangdong Guangzhou China The Hong Kong Polytechnic University School of Fashion and Textiles Hong Kong Laboratory for Artificial Intelligence in Design Hong Kong Hong Kong The Royal College of Art Kensington Gore School of Design London South KensingtonSW7 2EU United Kingdom Harbin Institute of Technology School of Computer Science and Technology Shenzhen China

Referring expression comprehension (REC) aims at locating the target object described by an expression. We observe that most of the graph-based REC methods only focus on establishing relations between all objects in an image and the given expression during the graph construction while ignoring the relationships between objects in the same category. As a result, these methods are sub-optimal in locating the target object described by the expression, particularly when the target object is surrounded by objects of similar categories. Meanwhile, during reasoning, numerous irrelevant objects are considered for expression, which will introduce significant harmful noise. To address these issues, this paper proposes a new graph-based group division network (GBGDN). Different from the existing works, our work partitions the constructed graphs into several sub-graphs based on the categories of objects and expressions. In each sub-graph, the common visual features of objects will be strengthened through a feature enhancement strategy. Subsequently, the enhanced sub-graphs and expressions undergo joint processing via a filtering-based reasoning module designed to reduce the influence of unrelated nodes in each subgraph, facilitating more accurate reasoning and matching. Experimental results across various datasets, including RefCOCO /+/g, Flickr30K Entities, RefClef, and Ref-reasoning, showcase the superiority of our proposed method over existing approaches. Most importantly, our method does not need pre-training. © 1991-2012 IEEE.

关键词： Graph theory

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Geometric Feature-Driven Metric Learning for 3D Craniofacial Superimposition

Geometric Feature-Driven Metric Learning for 3D Craniofacial...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qingdong Long Junli Zhao Fuqing Duan Chengyuan Wang Xuesong Wang Lijie Geng Zhenkuan Pan Mingquan Zhou College of Computer Science and Technology Qingdao University Qingdao China School of Artificial Intelligence Beijing Normal University Beijing China National and Local Joint Engineering Research Center for Cultural Heritage Digitization School of Information Science and Technology Northwest University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Craniofacial superimposition is a crucial forensic science technique to identify human remains by matching skulls to facial images. However, this task is challenging due to significant morphological differences between skulls and faces, limited paired samples, and high data dimensionality. We proposed a geometric feature-driven metric learning method for craniofacial superimposition to address these issues. Firstly, we extracted geometric features, including depth, curvature, and elevation of 3D craniofacial data, to generate 2D maps of structured representations enriched with geometric details. Next, we novelly designed a Triplet Network for geometric feature-driven metric learning, which leverages triplet loss to learn discriminative embeddings and effectively handle the limited paired data problem. By incorporating the Sinkhorn Distance as an additional constraint, we aligned the skull and face data distributions, enhancing the matching precision. We conducted extensive experiments on a 3D craniofacial dataset, achieving a maximum accuracy of 99.45% on curvature maps, surpassing state-of-the-art methods. Our code will be available after publication at https://***/Lqd-js/cranial-superimposition.

关键词： Measurement Learning systems Three-dimensional displays Accuracy Forensics Signal processing Feature extraction Robustness Speech processing Faces

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Grasping by Spiraling: Reproducing Elephant Movements with Rigid-Soft Robot Synergy

arXiv

引用

arXiv 2025年

作者： Huang, Huishi Wang, Haozhe Fang, Chongyu Yan, Mingge Xu, Ruochen Zhang, Yiyuan Wang, Zhanchi Ying, Fengkang Liu, Jun Laschi, Cecilia Ang, Marcelo H. Advanced Robotics Centre National University of Singapore 117608 Singapore Integrative Sciences and Engineering Programme National University of Singapore Graduate School 119077 Singapore School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China 138632 Singapore

—The logarithmic spiral is observed as a common pattern in several living beings across kingdoms and species. Some examples include fern shoots, prehensile tails, and soft limbs like octopus arms and elephant trunks. In the latter cases, spiraling is also used for grasping. Motivated by how this strategy simplifies behavior into kinematic primitives and combines them to develop smart grasping movements, this work focuses on the elephant trunk, which is more deeply investigated in the literature. We present a soft arm combined with a rigid robotic system to replicate elephant grasping capabilities based on the combination of a soft trunk with a solid body. In our system, the rigid arm ensures positioning and orientation, mimicking the role of the elephant’s head, while the soft manipulator reproduces trunk motion primitives of bending and twisting under proper actuation patterns. This synergy replicates 9 distinct elephant grasping strategies reported in the literature, accommodating objects of varying shapes and sizes. The synergistic interaction between the rigid and soft components of the system minimizes the control complexity while maintaining a high degree of adaptability. Copyright © 2025, The Authors. All rights reserved.

关键词： Robotic arms

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Enhanced Sea Surface Height Estimation with Interference Rejection Using High-Frequency Fully-focused SAR Altimetry data Over Island Areas

引用

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Shi, Hongkai Wu, Yihao Shi, Yihuang He, Xiufeng Zheng, Xiangtian Andersen, Ole Baltazar Hohai University School of Earth Sciences and Engineering Nanjing211100 China Nanjing Tech University School of Geomatics Science and Technology Nanjing211816 China Nanjing Institute of Technology School of Computer Engineering Nanjing211167 China Technical University of Denmark DTU Space Lyngby2800 Denmark

The Fully-focused Synthetic Aperture Radar (FFSAR) altimetry technology has greatly enhanced the accuracy of coastal sea surface height (SSH). However, due to its pulse-limited mode in the cross-track direction, FFSAR is still susceptible to land interference. Additionally, the spatiotemporal resolution of in-situ data limits the evaluation of altimetric SSH in both coastal and open ocean regions. This study proposes a waveform interference detection and removal algorithm (WIDA), which utilizes waveform differencing and azimuthal constant false alarm rate techniques to identify and reject interference signals in island areas. Tide gauge data, particularly a seamless and high-resolution airborne gravimetric quasi-geoid model, are employed to validate the performance of WIDA, providing a new perspective for the continuous and consistent evaluation of SSH profiles. Validation of five years of data from six Sentinel-3A/B tracks over the Xisha Islands in the South China Sea demonstrates that FFSAR_WIDA (FFSAR waveforms retracked by WIDA), with 80 Hz sampling, significantly outperforms FFSAR_CORAL (Coastal Retracker for SAR Altimetry), UFSAR (Unfocused SAR), and UFSAR_WIDA within 5 km of the tide gauge station. The standard deviations (SD) for FFSAR_WIDA, FFSAR_CORAL, UFSAR, and UFSAR_WIDA are 0.153 m, 0.192 m, 0.367 m, and 0.173 m, respectively. Moreover, the evaluation with the quasi-geoid also reveals a marked improvement over FFSAR_CORAL and UFSAR within 5 km from the islands. The spatial distribution assessment shows that as the distance from the island decreases, the SD for FFSAR_CORAL (UFSAR) increases from 0.010 m (0.011 m) at 15 km to 0.061 m (0.089 m) at 3 km, while FFSAR_WIDA (UFSAR_WIDA) remains consistently below 0.011 m (0.014 m). © 1980-2012 IEEE.

关键词： Aneroid altimeters

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A Multi-Resolution Deep Learning Approach for IoT-Integrated Biomedical Signal Processing using CNN-LSTM

A Multi-Resolution Deep Learning Approach for IoT-Integrated...

引用

Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： K. Priyadharshini Ajisha Mathias R. Santhana Krishnan N Kanthimathi J. Relin Francis Raj P. Stella Rose Malar Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Electronics and Communication Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering J.P. College of Engineering Tenkasi India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The rapid advancement of Internet of Things (IoT) technology and deep learning has enabled real-time health monitoring for elderly individuals and patients. This work proposes an IoT-based health monitoring system integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to analyze physiological signals and detect anomalies effectively. A network of biomedical sensors, including ECG, EMG, EEG, heart rate, blood pressure, SpO2, temperature, accelerometer, gyroscope, and respiratory rate sensors, continuously collects health data. These sensors transmit real-time physiological signals using wireless communication protocols such as Wi-Fi, Bluetooth, LoRa, and NB-IoT, ensuring seamless data acquisition and remote monitoring. To enhance signal quality, the preprocessing stage employs Wavelet Transform (WT) for denoising biomedical signals, mitigating motion artifacts and environmental noise. Discrete Wavelet Transform (DWT) is applied to extract meaningful features while preserving critical health information. Additional preprocessing steps, including feature normalization, segmentation, and feature extraction, improve deep learning model efficiency. The proposed CNN-LSTM hybrid model leverages CNN for spatial feature extraction and LSTM for capturing temporal dependencies in time-series biomedical data. The architecture includes multiple convolutional layers with ReLU activation, batch normalization, max-pooling, bidirectional LSTM layers, dropout regularization, and an attention mechanism. Model training utilizes labeled medical datasets with an Adam optimizer and binary cross-entropy loss function. Performance evaluation considers accuracy, precision, recall, F1-score, and AUC-ROC metrics. Results demonstrate improved anomaly detection for conditions such as arrhythmias, respiratory irregularities, and early stroke indicators. Deployment in real-time health monitoring applications facilitates continuous patient assessment, fall detecti

关键词： Deep learning Medical services Feature extraction Real-time systems Convolutional neural networks Biosensors Biomedical monitoring Monitoring Medical diagnostic imaging Anomaly detection

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