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Performance Evaluation of Nonlinear Effects in Frequency-Splitting SWIPT Signals

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Performance Evaluation of Nonlinear Effects in Frequency-Spl...

IEEE Conference on Vehicular technology (VTC)

作者： Akashkumar Rajaram Rui Dinis João Madeira Dushantha Nalin K. Jayakody Marko Beko Faculty of Science and Technology New University of Lisbon Portugal School of Computer Science and Robotics National Research Tomsk Polytechnic University Russia Copelabs Universidade Lusòfona Lisbon Portugal

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

Simultaneous wireless information and power transmission (SWIPT) is one of the popular technique used in radio frequency energy harvesting (EH). Frequency splitting based SWIPT (FS-SWIPT) is a novel SWIPT technique that can be adopted with frequency domain multiple access signal transmission protocol for the near field EH application in biomedical sensors. In this paper, we implement FS-SWIPT in orthogonal frequency division multiplexed (OFDM) signal, the OFDM signal has separate sub carriers for the data symbols and the energy carrying symbols. At the receiver, the energy and information symbols are based on their sub carriers by using FS-SWIPT technique. The amplification of high energy OFDM signal at transmitter by using a high power amplifier are susceptible to non linear distortion (NLD), which degrades the bit error rate (BER) performance. We analyze the impact of NLD on the BER performance of FS-SWIPT and validate the analytical results by using simulations.

关键词： OFDM Frequency-domain analysis Wireless communication Energy harvesting Nonlinear distortion Bit error rate

Search is All You Need for Few-shot Anomaly Detection

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arXiv 2025年

作者： Wang, Qishan Guo, Jia Gao, Shuyong Wang, Haofen Xiong, Li Hu, Junjie Guo, Hanqi Zhang, Wenqiang Academy for Engineering and Technology Fudan University Shanghai China School of Biomedical Engineering Tsinghua University Beijing China School of Computer Science Fudan university Shanghai China College of Design & Innovation Tongji University Shanghai China School of Physics and Mechanical & Electrical Engineering Hexi University Gansu China Engineering Research Center of AI&Robotics Ministry of Education Academy for Engineering&Technology Fudan university Shanghai China

Few-shot anomaly detection (FSAD) has emerged as a crucial yet challenging task in industrial inspection, where normal distribution modeling must be accomplished with only a few normal images. While existing approaches typically employ multi-modal foundation models combining language and vision modalities for prompt-guided anomaly detection, these methods often demand sophisticated prompt engineering and extensive manual tuning. In this paper, we demonstrate that a straightforward nearest-neighbor search framework can surpass state-of-the-art performance in both single-class and multi-class FSAD scenarios. Our proposed method, VisionAD, consists of four simple yet essential components: (1) scalable vision foundation models that extract universal and discriminative features;(2) dual augmentation strategies - support augmentation to enhance feature matching adaptability and query augmentation to address the oversights of single-view prediction;(3) multi-layer feature integration that captures both low-frequency global context and high-frequency local details with minimal computational overhead;and (4) a class-aware visual memory bank enabling efficient one-for-all multi-class detection. Extensive evaluations across MVTec-AD, VisA, and Real-IAD benchmarks demonstrate VisionAD’s exceptional performance. Using only 1 normal images as support, our method achieves remarkable image-level AUROC scores of 97.4%, 94.8%, and 70.8% respectively, outperforming current state-of-the-art approaches by significant margins (+1.6%, +3.2%, and +1.4%). The training-free nature and superior few-shot capabilities of VisionAD make it particularly appealing for real-world applications where samples are scarce or expensive to obtain. Code is available at ***/Qiqigeww/VisionAD. Copyright © 2025, The Authors. All rights reserved.

关键词： Normal distribution

EXPRESS: An Energy-Efficient and Secure Framework for Mobile Edge Computing and Blockchain based Smart Systems

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EXPRESS: An Energy-Efficient and Secure Framework for Mobile...

IEEE International Conference on Automated Software Engineering (ASE)

作者： Jia Xu Xiao Liu Xuejun Li Lei Zhang Yun Yang School of Computer Science and Technology Anhui University Hefei China School of Information Technology Deakin University Geelong Australia Antwork Robotics Co. Ltm. Hangzhou China School of Software and Electrical Engineering University of Technology Melbourne Australia

ISBN: (数字)9781450367684

ISBN: (纸本)9781728172811

As most smart systems such as smart logistic and smart manufacturing are delay sensitive, the current mainstream cloud computing based system architecture is facing the critical issue of high latency over the Internet. Meanwhile, as huge amount of data is generated by smart devices with limited battery and computing power, the increasing demand for energy-efficient machine learning and secure data communication at the network edge has become a hurdle to the success of smart systems. To address these challenges with using smart UAV (Unmanned Aerial Vehicle) delivery system as an example, we propose EXPRESS, a novel energy-efficient and secure framework based on mobile edge computing and blockchain technologies. We focus on computation and data (resource) management which are two of the most prominent components in this framework. The effectiveness of the EXPRESS framework is demonstrated through the implementation of a real-world UAV delivery system. As an open-source framework, EXPRESS can help researchers implement their own prototypes and test their computation and data management strategies in different smart systems. The demo video can be found at https://***/r3U1iU8tSmk.

关键词： Systems architecture Blockchain Unmanned aerial vehicles Energy efficiency Edge computing Software engineering Smart manufacturing

Application of Graph-Curvature Features in Auxiliary Diagnosis for Histopathological Image Identification of Gastric Cancer

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SSRN

SSRN 2023年

作者： He, Ruilin Li, Chen Yang, Xinyi Yang, Jinzhu Jiang, Tao Grzegorzek, Marcin Sun, Hongzan Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang China School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information Technology Chengdu China Institute for Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Shengjing Hospital of China Medical University Shenyang China School of Computer Science and Engineering

Background: Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors, but it has some drawbacks. This paper explores a computer-aided diagnostic method that can identify benign and malignant gastric cancer with histopathology images. Method: This article obtains the most suitable process through multiple experiments, compared multiple methods and features for classification. Firstly, the U-net is applied to segment the image. Next, the nucleus is extracted from the segmented image and the Minimum Spanning Tree (MST) diagram structure is drawn. The third step is to extract the graph-curvature features of histopathology image according to the MST image. Finally, by inputting graph-curvature features into the classifier, the recognition results for benign or malignant can be obtained. Result: During the experiment, we use various methods for comparison. In the image segmentation stage, U-net, watershed algorithm and Otsu threshold segmentation methods are used respectively. Combined with multiple indicators, we find that the U-net method is the most suitable for segmentation of histopathology images. In the feature extraction stage, in addition to extracting graph-edge feature and graph-curvature feature, several basic image features are also extracted, including Red, Green, Blue feature, Gray-Level Co-occurrence Matrix feature, Histogram of Oriented Gradient feature, and Local Binary Pattern feature. In the classifier design stage, we experimented with various methods, such as Support vector machine (SVM), Random forest, Artificial Neural Network, K Nearest Neighbors, VGG-16 and Inception-V3. Through the comparison and analysis, the classification results with an accuracy of 98.57% can be obtained by inputting the graph-curvature feature into SVM classifier. Conclusion: This paper has created a unique feature, graph-curvature feature based on MST to represent and analyze histopathology images. This graph-based feature can be used

关键词： Local binary pattern

Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge

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arXiv 2024年

作者： Ma, Jun Zhang, Yao Gu, Song Ge, Cheng Wang, Ershuai Zhou, Qin Huang, Ziyan Lyu, Pengju He, Jian Wang, Bo The Department of Laboratory Medicine and Pathobiology University of Toronto Peter Munk Cardiac Center UHN AI Hub University Health Network Vector Institute Toronto Canada Lenovo Research Beijing China The Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Ocean University of China Qingdao China Department of Research and Development ShenZhen Yorktal DMIT Co. LTD. Shenzhen China Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Jiao Tong University Shanghai AI Laboratory Shanghai China City University of Macau China Hanglok-Tech Co. Ltd. Hengqin China The Department of Nuclear Medicine Nanjing Drum Tower Hospital Nanjing China The Peter Munk Cardiac Center University Health Network Department of Laboratory Medicine and Pathobiology Department of Computer Science University of Toronto Vector Institute UHN AI Hub University Health Network Toronto Canada

Organ and cancer segmentation in abdomen Computed Tomography (CT) scans is the prerequisite for precise cancer diagnosis and treatment. Most existing benchmarks and algorithms are tailored to specific cancer types, limiting their ability to provide comprehensive cancer analysis. This work presents the first international competition on abdominal organ and pan-cancer segmentation by providing a large-scale and diverse dataset, including 4650 CT scans with various cancer types from over 40 medical centers. The winning team established a new state-of-the-art with a deep learning-based cascaded framework, achieving average Dice Similarity Coefficient (DSC) scores of 92.3% for organs and 64.9% for lesions on the hidden multi-national testing set. The dataset and code of top teams are publicly available, offering a benchmark platform to drive further innovations https://***/competitions/12239. © 2024, CC BY-NC-ND.

关键词： Lung cancer

An Intelligent Age of Information Based Self-Energized Uav-Assisted Wireless Communication System

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SSRN

SSRN 2022年

作者： Jeganathan, Anandpushparaj Dhayabaran, Balaji Jayakody, Dushantha Nalin K. Muthuchidambaranathan, P. Ranchagodage Don, Sanjaya Arunapriya National Institute of Technology Tiruchirappalli India Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli620015 India Senior Member Technical Staff Baseband Algorithm Development Team Saankhya Labs Private Limited Bengaluru560001 India Centro de Investigaçä em Tecnologias - Autónoma TechLab Universidade Autónoma de Lisboa Portugal School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk Russia Centre for Telecommunication Research School of Engineering Sri Lanka Technological Campus Padukka10500 Sri Lanka

Internet-of-things (IoT) is an enabling technology in the fourth generation industrial revolution. An unified performance metric named age of information (AoI) is introduced to quantify the freshness of data and its applications. Thus, this paper study the combination of AoI and full-duplex enabled unmanned aerial vehicle (FD-UAV) as a promising solution to improve latency and spectrum efficiency of a wireless powered sensor network. A sensor node (SN), located ina transport infrastructure, harvests energy from radio frequency signals transmitted by the FD-UAV. This uses to transmit real time sensor observations to the data sink via FD-UAV. The SN generates an update after replenished the battery and transmits by using the harvested energy. A closed-form expression for average AoI (AAoI) is derived as a function of time allocated for energy harvesting (EH). The optimal time allocation for EH that maximizes the freshness of data update is identified. Deep learning algorithms such as Long-Short Term Memory (LSTM) is used to predict the AAoI. Simulation results demonstrate the usefulness performance bounds in-terms of freshness of data updates. © 2022, The Authors. All rights reserved.

关键词： Sensor nodes

Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing

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arXiv 2021年

作者： Kong, Zhe Zhang, Wentian Liu, Feng Luo, Wenhan Liu, Haozhe Shen, Linlin Ramachandra, Raghavendra College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus Guangdong Shenzhen518107 China Norwegian University of Science and Technology Gjøvik2818 Norway

Biometric systems are vulnerable to Presentation Attacks (PA) performed using various Presentation Attack Instruments (PAIs). Even though there are numerous Presentation Attack Detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem. In this work, we empirically prove that the initialization of the PAD model is a crucial factor for the generalization, which is rarely discussed in the community. Based on such observation, we proposed a self-supervised learning-based method, denoted as DF-DM. Specifically, DF-DM is based on a global-local view coupled with De-Folding and De-Mixing to derive the task-specific representation for PAD. During De-Folding, the proposed technique will learn region-specific features to represent samples in a local pattern by explicitly minimizing generative loss. While De-Mixing drives detectors to obtain the instance-specific features with global information for more comprehensive representation by minimizing interpolation-based consistency. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both face and fingerprint PAD in more complicated and hybrid datasets when compared with state-of-the-art methods. When training in CASIA-FASD and Idiap Replay-Attack, the proposed method can achieve an 18.60% Equal Error Rate (EER) in OULU-NPU and MSU-MFSD, exceeding baseline performance by 9.54%. The source code of the proposed technique is available at https://***/kongzhecn/dfdm. Copyright © 2021, The Authors. All rights reserved.

关键词： Supervised learning

***: A Julia Package for Spin Dynamics

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arXiv 2025年

作者： Dahlbom, David Zhang, Hao Miles, Cole Quinn, Sam Niraula, Alin Thipe, Bhushan Wilson, Matthew Matin, Sakib Mankad, Het Hahn, Steven Pajerowski, Daniel Johnston, Steve Wang, Zhentao Lane, Harry Li, Ying Wai Bai, Xiaojian Mourigal, Martin Batista, Cristian D. Barros, Kipton Neutron Scattering Division Oak Ridge National Laboratory United States Department of Physics and Astronomy University of Tennessee United States Theoretical Division CNLS Los Alamos National Laboratory United States Kodiak Robotics School of Physics Georgia Institute of Technology United States Department of Physics and Astronomy Univeriy of California Los Angeles United States Department of Physics and Astronomy Louisiana State University United States X-Computational Physics Division Los Alamos National Laboratory United States Computer Science and Mathematics Division Oak Ridge National Laboratory United States Institute for Advanced Materials and Manufacturing Unversity of Tennessee United States Center for Correlated Matter School of Physics Zhejiang University China Department of Physics and Astronomy University of Manchester United Kingdom The University of Manchester at Harwell University of Manchester United Kingdom School of Physics and Astronomy University of St Andrews United Kingdom Computer Computational and Statistical Sciences Division Los Alamos National Laboratory United States

Sunny is a Julia package designed to serve the needs of the quantum magnetism community. It supports the specification of a very broad class of spin models and a diverse suite of numerical solvers. These include powerful methods for simulating spin dynamics both in and out of equilibrium. Uniquely, it features a broad generalization of classical and semiclassical approaches to SU(N) coherent states, which is useful for studying systems exhibiting strong spin-orbit coupling or local entanglement effects. Sunny also offers a well-developed framework for calculating the dynamical spin structure factor, enabling direct comparison with scattering experiments. Ease of use is a priority, with tools for symmetry-guided modeling and interactive visualization. © 2025, CC BY.

关键词： Spin dynamics

Bacteria Flagella-Mimicking Polymer Multilayer Magnetic Microrobots

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SSRN

SSRN 2024年

作者： Wu, Zhiguang Lu, Liang Bai, Shuang Shi, Jiaqi Zhang, Zhuotao Pan, Yunlu Wang, Wei Wu, Wenfeng Sun, Shoubin Li, Xu Hou, Gang Huang, Tianyun Jia, Yuxin Zuo, Kaiwen Granovsky, Alexander Wu, He Nikolai, Perov School of Medicine and Health Harbin Institute of Technology Harbin China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China The First Affiliated Hospital of Harbin Medical University Harbin China National Center for Respiratory Medicine State Key Laboratory of Respiratory Health and Multimorbidity National Clinical Research Center for Respiratory Diseases Institute of Respiratory Medicine Chinese Academy of Medical Sciences Department of Pulmonary and Critical Care Medicine Center of Respiratory Medicine China-Japan Friendship Hospital Beijing China College of Science Sichuan Agricultural University Chengdu China The Fourth Affiliated Hospital of Harbin Medical University Harbin China Department of Ophthalmology The Second Hospital Jilin University Changchun China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Department of Mathematics Imperial College London United Kingdom Department of Computer Science University of Warwick Coventry United Kingdom Magnetism Department Faculty of Physics Lomonosov Moscow State University Moscow Russia Harbin Medical University China

Learning from natural bacteria flagellum, we demonstrate a magnetic polymer multilayer conical microrobot that bestow the controllable propulsion upon external rotating magnetic field with uniform intensity. The magnetic microrobots were constructed by template-assisted Layer-by-Layer technique technique and subsequent functionalization of magnetic particle onto the big opening of the microrobots. Geometric variables of the polymer microrobots, such as the diameter and wall thickness, can be controlled by selection of porous template and layers of assembly. The microrobots perform controllable propulsion through the manipulation of magnetic field. The comparative analysis of the movement behavior reveals that the deformation of microrobots may attributed to the propulsion upon rotating magnetic field, which is similar as that of natural bacteria. The influence of actuation frequencies and frequencies on the velocity of the microrobots is studied. Such use of polymer polymer multilayer tubular micro/nanoarchitectures results in significant savings in the processing costs and provides a simple and cost-effective fabrication route for the mass production of magnetic polymer microrobots. © 2024, The Authors. All rights reserved.

关键词： Bacteria