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6th International Conference on Intelligent computing and Signal Processing (ICSP)

作者： Pingfang Tian Deting Liu Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Institute of Scientific and Technical Information of China Beijing China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor.

关键词： Technological innovation Large language models Oral communication Signal processing Chatbots Context modeling

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real-time Dynamic Data Driven Deformable Registration for Image-Guided Neurosurgery: Computational Aspects

arXiv

引用

arXiv 2023年

作者： Chrisochoides, Nikos Fedorov, Andrey Liu, Yixun Kot, Andriy Foteinos, Panos Drakopoulos, Fotis Tsolakis, Christos Billias, Emmanuel Clatz, Olivier Ayache, Nicholas Golby, Alex Black, Peter Kikinis, Ron Center for Real-Time Computing Computer Science Department Old Dominion University NorfolkVA United States Inria French Research Institute for Digital Science Sophia Antipolis France Neuroimaging Analysis Center Department of Radiology Harvard Medical School BostonMA United States Image-guided Neurosurgery Department of Neurosurgery Harvard Medical School BostonMA United States

Current neurosurgical procedures utilize medical images of various modalities to enable the precise location of tumors and critical brain structures to plan accurate brain tumor resection. The difficulty of using preoperative images during the surgery is caused by the intraoperative deformation of the brain tissue (brain shift), which introduces discrepancies concerning the preoperative configuration. Intra-operative imaging allows tracking such deformations but cannot fully substitute for the quality of the pre-operative data. Dynamic Data Driven Deformable Non-Rigid Registration (D4NRR) is a complex and time-consuming image processing operation that allows the dynamic adjustment of the pre-operative image data to account for intra-operative brain shift during the surgery. This paper summarizes the computational aspects of a specific adaptive numerical approximation method and its variations for registering brain MRIs. It outlines its evolution over the last 15 years and identifies new directions for the computational aspects of the technique. © 2023, CC BY-SA.

关键词： Tumors

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RIPPB: A Robust and Improved PBFT Protocol for Blockchain

RIPPB: A Robust and Improved PBFT Protocol for Blockchain

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Meng Ding Heng He Rui Qiao Xiaohu Zhou School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China School of Computing Engineering and Built Environment Birmingham City University Birmingham UK

ISBN: (纸本)9781665409858

In this paper, we propose RIPPB, a robust and improved PBFT protocol for Blockchain, to improve the quality of service for blockchain applications. At RIPPB, we introduce reputation for nodes and use reputation as the voting weight for node consensus to go against Sybil attacks. And we design a novel improved logistic regression model to assign reputations to nodes based on their behavior involved in block generation. With this model, nodes performing Stake-grinding attacks and Bribery attacks, face high reputation penalties, which is unacceptable for malicious nodes to reduce the probability of launching these attacks. In addition, RIPPB adds a pre-commit phase to the PBFT algorithm to reduce the number of inter-node communications, thus improving the performance of the protocol. Cost-of-attack analysis shows the high robustness of our protocol. Experimental results show a 28% improvement in the throughput of our protocol over PBFT.

关键词： Industrial electronics Protocols Quality of service Throughput Robustness Blockchains Behavioral sciences

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Multi-year polarimetric monitoring of four CHIME-discovered repeating fast radio bursts with FAST

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science China(Physics,Mechanics & Astronomy) 2025年

作者： Yi Feng Yong-Kun Zhang Jintao Xie Yuan-Pei Yang Yuanhong Qu Dengke Zhou Di Li Bing Zhang Weiwei Zhu Wenbin Lu Jiaying Xu Chenchen Miao Shiyan Tian Pei Wang Ju-Mei Yao Chen-Hui Niu Jiarui Niu Heng Xu Jinchen Jiang Dejiang Zhou Zenan Liu Chao-Wei Tsai Zigao Dai Xuefeng Wu Fayin Wang Jinlin Han Kejia Lee Renxin Xu Yongfeng Huang Yuanchuan Zou Jinhuang Cao Xianglei Chen Jianhua Fang Dongzi Li Ye Li Wanjin Lu Jiawei Luo Jintao Luo Rui Luo Fen Lyu Bojun Wang Weiyang Wang Qin Wu Mengyao Xue Di Xiao Wenfei Yu Jianping Yuan Chunfeng Zhang Junshuo Zhang Lei Zhang Songbo Zhang Rushuang Zhao Yuhao Zhu Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Departments of Astronomy and Theoretical Astrophysics Center University of California Berkeley Department of Astronomy School of Physics Huazhong University of Science and Technology Xinjiang Astronomical Observatory Chinese Academy of Sciences Institute of Astrophysics Central China Normal University School of Astronomy and Space Science Nanjing University University of Chinese Academy of Sciences Department of Astronomy School of Physical Sciences University of Science and Technology of China Department of Astronomy Peking University Kavli Institute for Astronomy and Astrophysics Peking University Research Center for Astronomical Computing Zhejiang Laboratory Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University) Ministry of Education Department of Astrophysical Sciences Princeton University College of Physics Hebei Normal University National Time Service Center Chinese Academy of Sciences Department of Astronomy School of Physics and Materials Science Guangzhou University Institute of Astronomy and Astrophysics Anqing Normal University Shanghai Astronomical Observatory Chinese Academy of Sciences Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Institute for Astronomy School of Physics Zhejiang University National Astronomical Observatories Chinese Academy of Sciences School of Computer Science and Engineering Sichuan University of Science and Engineering South-Western Institute for Astronomy Research Yunnan University Purple Mountain Observatory Chinese Academy of Sciences Nevada Center for Astrophysics University of Nevada Department of Physics and Astronomy University of Nevada New Cornerstone Science Laboratory Department of Astronomy Tsinghua University

Fast radio bursts（FRBs） are bright,millisecond-duration radio emissions originating from cosmological *** this study,we report multi-year polarization measurements of four repeating FRBs initially discovered by the Canadian Hydrogen Intensity Mapping Experiment（CHIME）:FRBs 20190117 A,20190208 A,20190303 A,and 20190417 *** observed the four repeating FRBs with the Five-hundred-meter Aperture Spherical Radio Telescope（FAST）,detecting a total of 66 *** bursts from FRB 20190417 A exhibit a circular polarization signal-to-noise ratio greater than 7,with the highest circular polarization fraction recorded at 35.7%.While the bursts from FRBs 20190208 A and 20190303 A are highly linearly polarized,those from FRBs 20190117 A and 20190417 A show depolarization due to multi-path propagation,with σRM=2.78±0.05 and 5.19±0.09 rad m-2,*** linear polarization distributions among five repeating FRBs—FRBs 20190208 A,20190303 A,20201124 A,20220912 A,and 20240114 A—are nearly identical but show distinct differences from those of nonrepeating *** 20190117 A,20190303 A,and 20190417 A exhibit substantial rotation measure（RM） variations between bursts,joining other repeating FRBs in this *** these findings with published results,64% of repeating FRBs show RM variations greater than 50 rad m-2,and 21% exhibit RM reversals.A significant proportion of repeating FRBs reside in a dynamic magneto-ionic *** structure function of RM variations shows a power-law index of γ～（0-0.8）,corresponding to a shallow power spectrum α=-（γ+2）～-（2.0-2.8） of turbulence,if the RM variations are attributed to *** suggests that the variations are dominated by small-scale RM density *** perform K-S tests to compare the RMs of repeating and non-repeating FRBs,which reveal a marginal dichotomy in the distribution of their *** caution that the observed dichotomy may be due to the small sample size and selection biases.

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An Experimental Study of Two-way Ranging Optimization in UWB-based Simultaneous Localization and Wall-Mapping Systems

An Experimental Study of Two-way Ranging Optimization in UWB...

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International Wireless Communications and Mobile computing Conference, IWCMC

作者： Kai Li Wei Ni Bo Wei Mohsen Guizani Real-Time and Embedded Computing Systems Research Centre (CISTER) Porto Portugal Commonwealth Scientific and Industrial Research Organization (CSIRO) Sydney Australia Computer and Information Sciences Northumbria University UK Computer Science and Engineering Department Qatar University Doha Qatar

In this paper, we propose a new ultra-wideband (UWB)-based simultaneous localization and wall-mapping (SLAM) system, which adopts two-way ranging optimization on UWB anchor and tag nodes to track the target's real-time movement in an unknown area. The proposed UWB-based SLAM system captures time difference of arrival (TDoA) of the anchor nodes' signals over a line-of-sight propagation path and reflected paths. The real-time location of the UWB tag is estimated according to the real-time TDoA measurements. To minimize the estimation error resulting from background noise in the two-way ranging, a Least Squares Method is implemented to minimize the estimation error for the localization of a static target, while Kalman Filter is applied for the localization of a mobile target. An experimental testbed is built based on off-the-shelf UWB hardware. Experiments validate that a reflector, e.g., a wall, and the UWB tag can be located according to the two-way ranging measurement. The localization accuracy of the proposed SLAM system is also evaluated, where the difference between the estimated location and the ground truth trajectory is less than 1 meter.

关键词： Location awareness Estimation error Simultaneous localization and mapping Target tracking real-time systems Distance measurement Trajectory

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PerMTL: A Multi-Task Learning Framework for Skilled Human Performance Assessment

PerMTL: A Multi-Task Learning Framework for Skilled Human Pe...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Indrajeet Ghosh Avijoy Chakma Sreenivasan Ramasamy Ramamurthy Nirmalya Roy Nicholas Waytowich Department of Information Systems University of Maryland Baltimore County Center for Real-Time Distributed Sensing and Autonomy (CARDS) University of Maryland Baltimore County Department of Computer Science Bowie State University DEVCOM Army Research Laboratory United States

Intelligent and complex human motion analysis can help design the next generation IoT and AR/VR systems for automated human performance assessment. Such an automated system can help advocate the interpretability and translatability of complex human motions, intelligent motion feedback, and fine-grained motion skill assessment to design next-generation interactive human-machine teaming systems. Motivated by this, we design a wearable sensing framework for assessing the players’ performance and consider a live badminton game as our use case. Generally, the players on the field try to improve their performance by focusing on fast and synchronous coordination of their limbs’ reflex actions to have the ideal body postures to perform the desired shot. Learning the minute dissimilarities and distinctive traits from each limb of the players simultaneously can help assess the players’ performance and specific skillsets during a game. This paper proposes a multi-task learning framework, PerMTL to learn the shared features from each player’s limb. The PerMTL comprises a task-specific regressor output layer that helps to determine the dissimilarities and distinctive traits between the player’s limbs for collective inference in a body sensor network (BSN) environment. We evaluate the PerMTL framework using publicly available Badminton Activity Recognition (BAR) and Daily and Sports Activities (DSA) datasets. Empirical results indicate that PerMTL achieves R 2 Score of ≈ 82% in predicting the players’ performance.

关键词： Scalability Games Multitasking Robustness Sensors Task analysis Motion analysis

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Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

Noncontact Doppler Radar-based Heart Rate Detection on the S...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Shi, Qiuxia Hu, Bin Tian, Fuze Zhao, Qinglin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University China School of Computer and Communication Lanzhou University of Technology China Key Laboratory of Special Functional Materials and Structural Design Lanzhou University Ministry of Education Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education China

ISBN: (纸本)9781665468190

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar. © 2022 IEEE.

关键词： Singular value decomposition

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Offsite Evaluation of Localization Systems: Criteria, Systems, and Results From IPIN 2021 and 2022 Competitions

IEEE Journal on Indoor and Seamless Positioning and Navigati...

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IEEE Journal on Indoor and Seamless Positioning and Navigation 2024年 2卷 92-129页

作者： Potorti, Francesco Crivello, Antonino Lee, Soyeon Vladimirov, Blagovest Park, Sangjoon Chen, Yushi Wang, Long Chen, Runze Zhao, Fang Zhuge, Yue Luo, Haiyong Perez-Navarro, Antoni Jimenez, Antonio R. Wang, Han Liang, Hengyi De Cock, Cedric Plets, David Cui, Yan Xiong, Zhi Li, Xiaodong Ding, Yiming Franco, Fernando Javier Alvarez Polo, Fernando Jesus Aranda Rodriguez, Felipe Parralejo Moreira, Adriano Pendao, Cristiano Silva, Ivo Ortiz, Miguel Zhu, Ni Li, Ziyou Renaudin, Valerie Wei, Dongyan Ji, Xinchun Zhang, Wenchao Wang, Yan Ding, Longyang Kuang, Jian Zhang, Xiaobing Dou, Zhi Yang, Chaoqun Kram, Sebastian Stahlke, Maximilian Mutschler, Christopher Coene, Sander Li, Chenglong Venus, Alexander Leitinger, Erik Tertinek, Stefan Witrisal, Klaus Wang, Yi Wang, Shaobo Jin, Beihong Zhang, Fusang Su, Chang Wang, Zhi Li, Siheng Li, Xiaodong Li, Shitao Pan, Mengguan Zheng, Wang Luo, Kai Ma, Ziyao Gao, Yanbiao Chang, Jiaxing Ren, Hailong Guo, Wenfang Torres-Sospedra, Joaquin Pisa56124 Italy Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing100876 China Chinese Academy of Sciences Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Beijing100190 China Universitat Oberta de Catalunya Faculty of Computer Sciences Multimedia and Telecommunication Barcelona08035 Spain Arganda del Rey28500 Spain Huawei Technologies Company Ltd. Shenzhen100015 China IMEC-WAVES/Ghent University Department of Information Technology Gent9052 Belgium Nanjing University of Aeronautics and Astronautics College of Automation Nanjing210016 China University of Extremadura Badajoz06006 Spain University of Trás-os-Montes and Alto Douro Department of Engineering School of Sciences and Technology Vila Real5000-801 Portugal Universidade Do Minho Centro Algoritmi Guimarães4800-058 Portugal Ame Department of the University Gustave Eiffel Geoloc Laboratory Bouguenais44344 France Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China Wuhan University Gnss Research Center Wuhan430072 China AutoNavi Software Company Ltd. China Fraunhofer Institute for Integrated Circuits Iis Fraunhofer Iis Nuremberg90411 Germany Erlangen91058 Germany Graz University of Technology Christian Doppler Laboratory for Location-Aware Electronic Systems Graz8010 Austria Institute of Software Chinese Academy of Sciences Beijing100190 China Purple Mountain Laboratories Pervasive Communication Research Center Nanjing210000 China Beijing University of Posts and Telecommunications Beijing100876 China

Indoor positioning is a thriving research area, which is slowly gaining market momentum. Its applications are mostly customized, ad hoc installations;ubiquitous applications analogous to Global Navigation Satellite System for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the indoor positioning and indoor navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site tracks. In contrast with on-site tracks where competitors bring their systems on-site - which were impossible to organize during 2021 and 2022 - in off-site tracks competitors download prerecorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem. © 2023 CCBY.

关键词： Smartphones

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real-time Cell Sorting with Scalable In Situ FPGA-Accelerated Deep Learning

arXiv

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arXiv 2025年

作者： Islam, Khayrul Forelli, Ryan F. Han, Jianzhong Bhadane, Deven Huang, Jian Agar, Joshua C. Tran, Nhan Ogrenci, Seda Liu, Yaling Department of Mechanical Engineering Lehigh University BethlehemPA18015 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Coriell Institute for Medical Research CamdenNJ United States Department of Computer Science Lehigh University BethlehemPA18015 United States Cooper Medical School of Rowan University CamdenNJ08103 United States Center for Metabolic Disease Research Temple University Lewis Katz School of Medicine PhiladelphiaPA19122 United States Department of Mechanical Engineering and Mechanics Drexel University PhiladelphiaPA19104 United States Real-time Processing Systems Division Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Bioengineering Lehigh University BethlehemPA18015 United States

Precise cell classification is essential in biomedical diagnostics and therapeutic monitoring, particularly for identifying diverse cell types involved in various diseases. Traditional cell classification methods such as flow cytometry depend on molecular labeling which is often costly, time-intensive, and can alter cell integrity. To overcome these limitations, we present a label-free machine learning framework for cell classification, designed for real-time sorting applications using bright-field microscopy images. This approach leverages a teacher-student model architecture enhanced by knowledge distillation, achieving high efficiency and scalability across different cell types. Demonstrated through a use case of classifying lymphocyte subsets, our framework accurately classifies T4, T8, and B cell types with a dataset of 80,000 preprocessed images, accessible via an open-source Python package for easy adaptation. Our teacher model attained 98% accuracy in differentiating T4 cells from B cells and 93% accuracy in zero-shot classification between T8 and B cells. Remarkably, our student model operates with only 0.02% of the teacher model’s parameters, enabling field-programmable gate array (FPGA) deployment. Our FPGA-accelerated student model achieves an ultra-low inference latency of just 14.5 µs and a complete cell detection-to-sorting trigger time of 24.7 µs, delivering 12x and 40x improvements over the previous state-of-the-art real-time cell analysis algorithm in inference and total latency, respectively, while preserving accuracy comparable to the teacher model. This framework provides a scalable, cost-effective solution for lymphocyte classification, as well as a new SOTA real-time cell sorting implementation for rapid identification of subsets using in situ deep learning on off-the-shelf computing hardware. © 2025, CC BY.

关键词： Deep learning

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BrezeFlow: Unified Debugger for Android CPU Power Governors and Schedulers on Edge Devices

BrezeFlow: Unified Debugger for Android CPU Power Governors ...

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Design Automation Conference

作者： Alexander Hoffman Anuj Pathania Philipp H. Kindt Samarjit Chakraborty Tulika Mitra Chair of Real-Time Computer Systems Technical University of Munich School of Computing National University of Singapore Department of Computer Science University of North Carolina at Chapel Hill

ISBN: (数字)9781728110851

ISBN: (纸本)9781728158020

Power management is quintessential to the successful deployment of edge devices, such as smartphones, in power-, thermal-, and energy-constrained environments. Governors and schedulers operate system sub-routines for power management at the edge. There exist several tools for debugging power issues in Android applications. However, there exists no tool to identify and classify inevitable misdecisions by power managers, given their often inefficient underlying heuristics. In this work, we introduce the first tool - BrezeFlow - designed for unified (scheduling and frequency scaling) power debugging of CPU power managers on Android edge devices. BrezeFlow enables kernel developers to evaluate designs of their power managers retrospectively with closed-source applications in real-world scenarios based on any user-defined strategy and thereby gain insights for better future governor designs. BrezeFlow detected an average of 815 misdecisions per second for the commonly deployed duo, ondemand governor and Completely Fair Scheduler, on mobile edge devices running popular applications.

关键词： Task analysis Kernel Androids Humanoid robots Power system management Tools Debugging

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