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Controllable chaos in the coupled waveguide-optomechanical system with phase shifter

science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 26-34页

作者： Deng-Wei Zhang Pei-Qi Qin Li-Li Zheng Zhi-Ming Zhan Xin-You Lü Department of Mathematics and Physics Luoyang Institute of Science and TechnologyLuoyang 471023China School of Computer and Information Engineering School of SoftwareLuoyang Institute of Science and TechnologyLuoyang 471023China School of Artificial Intelligence Jianghan UniversityWuhan 430056China School of Physics Huazhong University of Science and TechnologyWuhan 430074China

We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.

关键词： cavity optomechanics chaos exceptional point

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Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

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computers, Materials & Continua 2024年第2期78卷 1957-1975页

作者： Liya Yue Pei Hu Shu-Chuan Chu Jeng-Shyang Pan Fanli Business School Nanyang Institute of TechnologyNanyang473004China School of Computer and Software Nanyang Institute of TechnologyNanyang473004China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by *** number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition *** proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum ***,we use the information gain and Fisher Score to sort the features extracted from ***,we employ a multi-objective ranking method to evaluate these features and assign different importance to *** with high rankings have a large probability of being ***,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local *** random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification *** results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

关键词： Speech emotion recognition filter-wrapper high-dimensional feature selection equilibrium optimizer multi-objective

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IoT-Supported Indoor Navigation System Using Machine Learning Technique 12th

IoT-Supported Indoor Navigation System Using Machine Learnin...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Bhuvana, S. Vidhya, K. Sudharsan, S. Suhita, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819788354

Indoor Navigation System (INS) supports seamless movement of objects within confined spaces in smart environments. In this paper, a novel INS that relies on ESP32-based Received Signal Strength Indication (RSSI) measurements for precise estimation of distance using K-Nearest Neighbour (K-NN) algorithm is proposed. The system architecture includes three ESP32 nodes functioning as clients, and ESP8266 combined with ESP32 acting as a server. To overcome the limitations of Global Positioning System (GPS) within an indoor environment, Machine Learning (ML)-based prediction models are employed to estimate distance between nodes and predict user locations along with the distance between source and destination. This approach not only addresses the challenges posed by GPS in indoor settings, but also shows the potential of ESP32 devices and predictive algorithms for enhancing navigation accuracy and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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PULSE: Progressive Utilization of Log-Structured Techniques to Ease SSD Write Amplification in B-epsilon-tree 25

PULSE: Progressive Utilization of Log-Structured Techniques ...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Peng, Huai-De Chen, Yi-Shen Chen, Tseng-Yi Chang, Yuan-Hao Department of Computer Science and Information Engineering National Central University Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan Institute of Information Science Academia Sinica Taiwan

ISBN: (纸本)9798400706356

During the unprecedented expansion of global data, efficient storage solutions are essential for processing massive datasets stored on modern storage devices. B-epsilon-tree (Bϵ-tree) is one of the most well-known techniques that provides a write-optimized structure for database file systems. With the excellent access performance and high energy efficiency of solid-state drives (SSDs), they are expected to yield promising outcomes for large-scale data computation. However, their integration into storage systems has the challenges of write amplification, which impacts SSD endurance and reliability. This work identifies significant write amplification issues with Bϵ-tree implementations on SSDs due to the complicated management of key-value pairs. To mitigate the impact of write amplification, we propose PULSE, a novel scheme that rethinks Bϵ-tree designs by leveraging log-structured techniques optimized for SSDs. Moreover, PULSE integrates auxiliary indexing and a dual flush selector to minimize write amplification. Experimental results demonstrate that PULSE significantly mitigates write amplification by more than 62.6% on SSDs for the representative benchmarks compared to the Bϵ-tree. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Flash-based SSDs

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Efficient Collision-Free Motion Planning in Distributed Multi-Robot Systems 17

Efficient Collision-Free Motion Planning in Distributed Mult...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Sharma, Om Chakraborty, Nilotpal India Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798331531195

In distributed multi-robot systems, ensuring collision-free motion planning is a complex challenge, especially in dynamic environments where multiple robots are operating simultaneously. Traditional path-planning algorithms are effective for individual robot navigation but struggle when dealing with the unpredictable movements of other robots in the system. To overcome these limitations, this research focuses on advanced motion planning techniques that ensure safe and efficient robot coordination. We model the overall motion planning problem into a two-dimensional grid and our objective is to minimize both the time and the energy taken by the robots to reach their destinations from their respective sources. We enhance standard path-finding algorithms such as A∗ search, along with Deep Reinforcement Learning (DRL) with suitable modifications to adapt to real-time changes. DRL allows robots to learn from their environment and make optimal decisions, improving their ability to avoid collisions and navigate efficiently in uncertain conditions. By integrating these algorithms and conducting a comparative analysis, we propose a framework that enables robots to dynamically adjust their paths, ensuring collision-free movement and optimal coordination. © 2025 IEEE.

关键词： Robot programming

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ASL-OOD:Hierarchical Contextual Feature Fusion with Angle-Sensitive Loss for Oriented Object Detection

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computers, Materials & Continua 2025年第2期82卷 1879-1899页

作者： Kexin Wang Jiancheng Liu Yuqing Lin Tuo Wang Zhipeng Zhang Wanlong Qi Xingye Han Runyuan Wen Northwest Institute of Mechanical and Electrical Engineering Xianyang712099China School of Information Engineering Chang’an UniversityXi’an710064China School of Computer Science and Technology Xidian UniversityXi’an710071China

Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object *** frameworks for oriented detection modules are constrained by intrinsic limitations,including excessive computational and memory overheads,discrepancies between predefined anchors and ground truth bounding boxes,intricate training processes,and feature alignment *** overcome these challenges,we present ASL-OOD(Angle-based SIOU Loss for Oriented Object Detection),a novel,efficient,and robust one-stage framework tailored for oriented object *** ASL-OOD framework comprises three core components:the Transformer-based Backbone(TB),the Transformer-based Neck(TN),and the Angle-SIOU(Scylla Intersection over Union)based Decoupled Head(ASDH).By leveraging the Swin Transformer,the TB and TN modules offer several key advantages,such as the capacity to model long-range dependencies,preserve high-resolution feature representations,seamlessly integrate multi-scale features,and enhance parameter *** improvements empower the model to accurately detect objects across varying *** ASDH module further enhances detection performance by incorporating angle-aware optimization based on SIOU,ensuring precise angular consistency and bounding box *** approach effectively harmonizes shape loss and distance loss during the optimization process,thereby significantly boosting detection *** evaluations and ablation studies on standard benchmark datasets such as DOTA with an mAP(mean Average Precision)of 80.16 percent,HRSC2016 with an mAP of 91.07 percent,MAR20 with an mAP of 85.45 percent,and UAVDT with an mAP of 39.7 percent demonstrate the clear superiority of ASL-OOD over state-of-the-art oriented object detection *** findings underscore the model’s efficacy as an advanced solution for challenging remote sensing object detection tasks.

关键词： Oriented object detection transformer deep learning

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OFDM-structure based waveform designs for integrated sensing and communication

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science China(information sciences) 2025年第5期68卷 267-284页

作者： Jiaqi LIU Ping YANG Ke JIANG Jiarong ZHAO Wei XIANG Jianping WEI Ruixiang DUAN Saviour ZAMMIT Tony Q.S.QUEK National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China School of Computing Engineering and Mathematical Sciences La Trobe University Meituan Academy of Robotics Department of Communications and Computer Engineering University of Malta Information Systems Technology and Design Pillar Singapore University of Technology and Design

The future sixth-generation(6G) paradigm aims to seamlessly integrate communication and environmental sensing capabilities into a single radio signal, promising improved efficiency and cost-effectiveness through simultaneous data communications and environmental perception. At the core of this evolution, orthogonal frequency division multiplexing(OFDM) and its advanced waveforms emerge as pivotal for integrated sensing and communications(ISAC). This study introduces a concise and unified ISAC waveform design framework based on orthogonal multicarriers. This framework supports versatile applications of OFDM and its derivative waveforms within a generalized ISAC system, marking a significant leap in integrating communication and sensing capabilities. A distinguishing feature of this framework is its adaptability,allowing users to intelligently select modulation strategies based on their specific environmental needs. This adaptability optimizes performance across diverse scenarios. Central to our innovations is the proposal of discrete Fourier transformspread OFDM with index modulation(DFT-S-OFDM-IM). This framework is paired with newly proposed signal processing methods for single-input single-output and multiple-input multiple-output(MIMO) systems. Extensive evaluations highlight DFT-S-OFDM-IM's superiority, including dramatically reduced peak-to-average power ratios(PAPRs), competitive communication performance, and exceptional sensing capabilities, striking an elegant balance between communication capacity and environmental sensing precision.

关键词： DFT-S-OFDM-IM index modulation integrated sensing and communication OFDM

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A Bilateral Assessment of Human Activities Using PSO-Based Feature Optimization and Non-linear Multi-task Least Squares Twin Support Vector Machine

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SN computer science 2024年第3期5卷 315页

作者： Thakur, Ujwala Vidyarthi, Ankit Prajapati, Amarjeet Department of Computer Science Engineering and Information Technology Jaypee Institute of Information Technology Noida Sector-62 Uttar Pradesh Noida 201309 India

Human activity recognition (HAR) is an essential part of many applications, including smart surroundings, sports analysis, and healthcare. Accurately categorizing intricate actions from sensor data is still difficult, though. This method infers an individual’s actions using a variety of sensor data, including magnetometers, gyroscopes, and accelerometers. This work suggests a unique method for classifying human activities by combining a non-linear multi-task least squares twin support vector machine (NMtLSSVM) with particle swarm optimization (PSO) for feature optimization. Utilizing the advantages of both approaches, the suggested strategy achieves excellent resilience and accuracy in activity identification. The suggested method reduces dimensionality and computing costs while maintaining pertinent information using PSO to extract the most important features from the raw sensor data. On the other hand, NMtLSSVM is used to construct a multi-task learning framework that learns several related tasks at the same time and shares information among them. As a result, generalization and resilience can be enhanced beyond single-task models. Two datasets that are available to the public, WISDM, and UCI-HAR, were used to assess the suggested method. The testing findings show that the suggested approach works noticeably better than the most advanced techniques already in use. Overall activities, the outcome achieves an average classification accuracy of 97.8% (UCI-HAR dataset) and 98.5% (WISDM dataset). Furthermore, the PSO-based feature optimization decreased the feature count by 60% without sacrificing efficiency. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Human action recognition Intelligent computing Machine learning classification Performance comparison Sensory data analysis

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A top-down character segmentation approach for Assamese and Telugu handwritten documents

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3275-3287页

作者： Dutta, Prarthana Muppalaneni, Naresh Babu Department of Computer Science and Engineering National Institute of Technology Assam Silchar786003 India Department of Computer Science and Engineering The Assam Kaziranga University Assam Jorhat785006 India Indian Institute of Information Technology Design and Manufacturing Kurnool Andhra Pradesh Kurnool India

Digitization offers a solution to the challenges associated with managing and retrieving paper-based documents. However, these paper-based documents must be converted into a format that digital machines can comprehend, as they primarily understand alphanumeric text. This transformation is achieved through Optical Character Recognition (OCR), a technology that converts scanned image documents into a format that machines can process. A novel top-down character segmentation approach has been proposed in this work, involving multiple stages. Our approach began by isolating lines from handwritten documents and using these lines to segment words and characters. To further enhance the character segmentation, a Raster Scanning object detection technique is employed to isolate individual characters within words. Thus, the character segmentation results are integrated from the results of the vertical projection and raster scanning. Recognizing the significance of advancing digitization of handwritten documents, we have chosen to focus on the regional languages of Assam and Andhra Pradesh due to their historical and cultural importance in India’s linguistic diversity. So, we have collected datasets of handwritten texts in Assamese and Telugu languages due to their unavailability in the desired form. Our approach achieved an average segmentation accuracy of 93.61%, 85.96%, and 88.74% for lines, words, and characters for both languages. The key motivation behind opting for a top-down approach is two-fold: firstly, it enhances the accuracy of character recognition, and secondly, it holds the potential for future use in language/script identification through the utilization of segmented lines and words. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Object detection

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An Examination of Load Carrying Gait Kinematics in Healthy Individuals 6

An Examination of Load Carrying Gait Kinematics in Healthy I...

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6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Singhal, Prateek Yadav, Rakesh Kumar Department of Computer Science and Engineering Maharishi University of Information Technology Uttar Pradesh India

Concerns have been expressed about how frequently people (including kids, men, and women) use backpacks. The goal of this study to examines the movement patterns and spinal alignment when carrying heavy backpack loads. One of the key focuses of the study is to evaluate the alignment of the spine during load carriage. Carrying heavy backpack loads can potentially affect spinal alignment due to the additional weight and its distribution on the body. Video recordings were made from a side perspective and used for the study. We looked at three distinct load weights: 4.1 kg, 6.8 kg, and 10.5 kg. The study discovered that while the angles at the ankle and knee remained constant, the hip's initial and maximum flexion angles rose. The weight of the backpacks was balanced with respect to the centre of mass (COM) by bending the spine forward. As a result, important variations were mostly seen around the hip level and in the spine's forward bending. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Kinematics

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