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IEEE Conference on Wireless Communications and Networking

作者： Pengfei Shen Yulin Shao Haoyuan Pan Lu Lu Yonina C. Eldar Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization CAS Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Internet of Things for Smart City University of Macau Macau S.A.R Department of Electrical and Computer Engineering University of Macau Macau S.A.R. College of Computer Science and Software Engineering Shenzhen University China Weizmann Institute of Science Rehovot Israel

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

This paper puts forth a new metric, dubbed channel cycle time (CCT), to measure the short-term fairness of Communication networks. CCT characterizes the average duration between two consecutive successful transmissions of a user, during which all other users successfully accessed the channel at least once. In contrast to existing short-term fairness measures, CCT provides more comprehensive insight into the transient dynamics of communication networks, with a particular focus on users' delays and jitter. To validate the efficacy of our approach, we analytically characterize the CCTs for two classical commu-nication protocols: slotted Aloha and CSMA/CA. The analysis demonstrates that CSMA/CA exhibits superior short-term fairness over slotted Aloha. Beyond its role as a measurement metric, CCT has broader implications as a guiding principle for the design of future communication networks by emphasizing factors like fairness, delay, and jitter in short-term behaviors.

关键词： Jitter Media Access Protocol Time measurement Real-time systems Heterogeneous networks Delays Communication networks

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A Comprehensive Study of Real-World Bugs in Machine Learning Model Optimization

A Comprehensive Study of Real-World Bugs in Machine Learning...

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International Conference on software engineering (ICSE)

作者： Hao Guan Ying Xiao Jiaying Li Yepang Liu Guangdong Bai The University of Queensland Brisbane Australia Southern University of Science and Technology Shenzhen China UQ-SUSTech Joint Program Microsoft Software Technology Center Asia Beijing China Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems

Due to the great advance in machine learning (ML) techniques, numerous ML models are expanding their application domains in recent years. To adapt for resource-constrained platforms such as mobile and Internet of Things (IoT) devices, pre-trained models are often processed to enhance their efficiency and compactness, using optimization techniques such as pruning and quantization. Similar to the optimization process in other complex systems, e.g., program compilers and databases, optimizations for ML models can contain bugs, leading to severe consequences such as system crashes and financial loss. While bugs in training, compiling and deployment stages have been extensively studied, there is still a lack of systematic understanding and characterization of model optimization bugs (MOBs). In this work, we conduct the first empirical study to identify and characterize MOBs. We collect a comprehensive dataset containing 371 MOBs from TensorFlow and PyTorch, the most extensively used open-source ML frameworks, covering the entire development time span of their optimizers (May 2019 to August 2022). We then investigate the collected bugs from various perspectives, including their symptoms, root causes, life cycles, detection and fixes. Our work unveils the status quo of MOBs in the wild, and reveals their features on which future detection techniques can be based. Our findings also serve as a warning to the developers and the users of ML frameworks, and an appeal to our research community to enact dedicated countermeasures.

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Family of Mutually Uncorrelated Codes for DNA Storage Address Design

IEEE Transactions on Nanobioscience

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IEEE Transactions on Nanobioscience 2025年 PP卷 PP页

作者： Liu, Zhenlu Cao, Ben Shao, Qi Zheng, Yanfen Wang, Bin Zhou, Shihua Zheng, Pan Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China University of Canterbury Upper Riccarton Department of Accounting and Information Systems Christchurch8140 New Zealand

Deoxyribonucleic acid (DNA) has become an ideal medium for long-term storage and retrieval due to its extremely high storage density and long-term stability. But access efficiency is an existing bottleneck in DNA storage, especially the lack of high-quality random access address sequences. Therefore, in this paper, we report a series of approaches based on k-weakly mutually uncorrelated (k-WMU) codes to design the address sequence to improve the access efficiency of DNA storage. To address the problem of DNA sequences that are poorly scalable at the base level, we propose a 0-m-ruling coding scheme combined with k-WMU codes that can make address sequences avoid generating secondary structure with stem lengths ranging from 3 to 9. Based on the decoupled structure, We further extend the k-WMU codes with error correction function while satisfying combinatorial biological constraints. In order to investigate the performance of the designed address sequences for real-world applications, we perform simulation experiments based on thermodynamic properties and error correction capability as well as compared the minimum free energy (MFE), melting temperature (TM), and average decoding success rate (ADSR) with previous work. The results show that designed address sequences have a high MFE value and ADSR and a substantial reduction in TM-variance while satisfying the combinatorial biological constraints. As the quality of address sequences improves, this will help to achieve accurate random access as well as enhance the robustness of the DNA storage system. © 2002-2011 IEEE.

关键词： DNA sequences

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Design of A Picosecond Resolution Control Loop for Fine TDC Based on 0.11 μm CMOS Process

Design of A Picosecond Resolution Control Loop for Fine TDC ...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： Z. Li W. Hu Y. Li H. Yu H. Lao Q. Xu D. Xi Q. Xie Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Department of Biomedical Engineering Huazhong University of Science and Technology Wuhan China Shenzhen Laboratory of IC Design for IOT College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

Our group has developed a multi-voltage threshold (MVT) digitizer implemented on the application-specific integrated circuit (MVT ASIC). This new digital converter offers the advantages of low power consumption, high efficiency, and small size. In addition, it also improves the time resolution. To further enhance the performance of the MVT ASIC and silicon photomultiplier (SiPM), we propose to implement the MVT ASIC based on the 0.11 µm CMOS process. Because of the inherent delay limit of ~50 ps in this process, designing a time-to-digital converter (TDC) with 20 ps resolution is a challenge. Therefore, we propose a resolution control loop based on a two-stage phase-locked and vernier delay line structure to overcome this limitation. Through simulation, we have shown that the proposed method can achieve a ~20 ps time resolution based on a 0.11 µm process.

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New Ontology structure for intelligent controlling of traffic signals

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Procedia computer science 2022年 207卷 1201-1211页

作者： Mahmud Abdulla Mohammad Kamaran H. Manguri Taib Shamsadin Abdulsamad Abdulbasit K. Faeq Al-Talabani Akam Aziz Abdulrahman Computer Science Department College of Basic Education University of Raparin Ranya Kurdistan Region Iraq College of Science and Technology University of Human development Sulaymaniyah Kurdistan Region Iraq Department of Software Engineering Koya university Koya Kurdistan Region Iraq

This article proposes a novel ontology design for intelligent controlling of traffic signals, considering the investigated factors, crowded factors, road factors, visibility conditions, and emergency situations. Essentially, the proposed method uses video-based knowledge and key feature from a monocular video camera only, capturing footage from either a traffic signals perspective or the top of the road lane. The key factors and entities in the traffic scene are formed into an ontology, which has been evaluated using synthetic datasets to interpret challenging cases. Semantic features related to the key factors in the scene are obtained and fed to the ontology. The experimental results indicate that the proposed method is capable of controlling traffic signals more efficiently than the fixed intervals protocol.

关键词： Ontologies Traffic Light Control Transportation Semantic Segmentatio Object Detection

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Deep Learning in Lung and Colon Cancer classifications

Deep Learning in Lung and Colon Cancer classifications

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Advances in Computing, Communication & Materials (ICACCM), 2020 International Conference on

作者： Krishna Mridha MD. Iftekhar Islam Shamin Ashfaq Masrur Ahsan Priyok Dipayan Barua Computer Engineering Marwadi University Rajkot India Department of Electrical and Electronic Engineering American International University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Southeast University Software Engineering Tianjin University China Informaation Technology Marwadi Univeristy Rajkot India

ISBN: (纸本)9781665474405

The past few decades have seen revolutionary advancements in the field of Healthcare and Medicine. During this period, the underlying reasons for many fatal diseases have been unveiled, novel diagnosis techniques were invented, and medical solutions were developed. Instead of these breakthroughs, we are still vulnerable and haunted by certain diseases like cancer. Globally, cancer is the second most common cause of mortality which accounts for the death of one in every six affected individuals. Among many other types, the colon and lung are the most prevailing and life-threatening ones. Only these two variants are responsible for 25% of all cancer cases around the world. However, early lung and colon cancer detection can significantly enhance the possibility of survival. Currently, Deep Learning (DL) has facilitated the automated identification of cancer cells which is allowing us to assess more patients in a minimum time and without the help of a medical professional. The study has put forward a DL framework to classify and differentiate five types of colon and lung cancer cells. Digital Image processing (DIP) and Deep Learning (DL) algorithms are used to process and evaluate histopathological images of cancer cells. The proposed framework has obtained a maximum testing accuracy of 98.3% in identifying and classifying cancer cells. By employing the proposed model in practice, medical experts will be able to integrate an accurate and automated system for diagnosing different forms of colon and lung cancers.

关键词： Deep learning Lung Lung cancer Cancer detection Data models Colon Classification algorithms

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Automatic Video Segmentation Based on Information Centroid and Optimized SaliencyCut

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Journal of computer science & Technology 2020年第3期35卷 564-575页

作者： Hui-Si Wu Meng-Shu Liu Lu-Lu Yin Ping Li Zhen-Kun Wen Hon-Cheng Wong College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China Faculty of Information Technology Macao University of Science and TechnologyMacao 999078China

We propose an automatic video segmentation method based on an optimized SaliencyCut equipped with information centroid(IC)detection according to level balance principle in physical *** the existing methods,the image information of another dimension is provided by the IC to enhance the video segmentation ***,our IC is implemented based on the information-level balance principle in the image,and denoted as the information pivot by aggregating all the image information to a *** effectively enhance the saliency value of the target object and suppress the background area,we also combine the color and the coordinate information of the image in calculating the local IC and the global IC in the *** saliency maps for all frames in the video are calculated based on the detected *** applying IC smoothing to enhance the optimized saliency detection,we can further correct the unsatisfied saliency maps,where sharp variations of colors or motions may exist in complex ***,we obtain the segmentation results based on IC-based saliency maps and optimized *** method is evaluated on the DAVIS dataset,consisting of different kinds of challenging *** with the state-of-the-art methods are also conducted to evaluate our *** visual results and statistical comparisons demonstrate its advantages and robustness for automatic video segmentation.

关键词： automatic video segmentation information centroid saliency detection optimized SaliencyCut

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A QoE-Aware Adaptive Energy-Efficient Transmission Scheduling Method

A QoE-Aware Adaptive Energy-Efficient Transmission Schedulin...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yankun Yuan Lin Wang Chonghui Xiao Zijuan Liu Fan Dang Xu Wang Haitian Zhao Xin Miao School of Information Science and Engineering Yanshan University National Computer Network Emergency Response Technical Team/Coordination Center of China Department of Automation and BNRist Tsinghua University Global Innovation Exchange Tsinghua University School of Software and BNRist Tsinghua University

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

In this paper, we propose a dynamic data transmission strategy for smart home environments that aims to optimize the Quality of Experience (QoE) by adaptively adjusting the data upload frequency based on the predicted trends in sensor data. Using the home wireless sensors monitoring dataset, we implement a deep learning model for accurate time series forecasting. In addition, an anomaly detection mechanism is used to identify critical events, requiring more frequent data uploads when important changes are detected. The QoE is quantified through a weighted average of several influencing factors, including data timeliness, timely upload of critical events, and transmission frequency. Our optimization objective is to maximize QoE while minimizing the number of transmissions, with an emphasis on reducing energy consumption through intelligent scheduling. The results demonstrate that our approach effectively balances data timeliness, transmission efficiency, and energy savings, leading to improved user satisfaction in smart home applications.

关键词： Wireless communication Wireless sensor networks Energy consumption Data centers Time-frequency analysis Time series analysis Smart homes Quality of experience Data communication Optimization

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Distributionally Robust Control for Chance-Constrained Signal Temporal Logic Specifications

Distributionally Robust Control for Chance-Constrained Signa...

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IEEE Conference on Decision and Control

作者： Arash Bahari Kordabad Eleftherios E. Vlahakis Lars Lindemann Dimos V. Dimarogonas Sadegh Soudjani Max Planck Institute for Software Systems Kaiserslautern Germany Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science Viterbi School of Engineering University of Southern California Los Angeles USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We consider distributionally robust optimal control of stochastic linear systems under signal temporal logic (STL) chance constraints when the disturbance distribution is unknown. By assuming that the underlying predicate functions are Lipschitz continuous and the noise realizations are drawn from a distribution having a concentration of measure property, we first formulate the underlying chance-constrained control problem as stochastic programming with constraints on expectations and propose a solution using a distributionally robust approach based on the Wasserstein metric. We show that by choosing a proper Wasserstein radius, the original chance-constrained optimization can be satisfied with a user-defined confidence level. A numerical example illustrates the efficacy of the method.

关键词： Linear systems Robust control Noise Optimal control Transforms Programming Probabilistic logic Trajectory Noise measurement Optimization

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Long Short Term Memory (LSTM)-based Cuffless Continuous Blood Pressure Monitoring

Long Short Term Memory (LSTM)-based Cuffless Continuous Bloo...

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International Conference on VLSI Design

作者： Vijay Kumar Goldy Kolin Paul Mahesh Chowdhary Amar Nath and Shashi Khosla School of Information Technology Indian Institute of Technology Delhi India Department of Computer Science and Engineering Indian Institute of Technology Delhi India MEMS Software Solutions ST Microelectronics Santa Clara California United States

Continuously monitoring blood pressure (BP) is crucial for individuals at high risk of cardiac diseases. However, existing BP measurement techniques lack the ability to provide non-invasive and continuous monitoring. To address this challenge, researchers have recently explored accelerometer-based systems for BP estimation. These systems rely on signal processing algorithms that often necessitate extensive feature engineering, making updates and calibration difficult. In this paper, we propose a novel device for non-invasive continuous BP monitoring. The device consists of a patch with two inertial measurement units (IMUs) attached to the skin in the user’s neck region, specifically along the carotid artery. A control unit connected to the patch receives sensor data from these IMU units. It employs a machine learning (ML) model based on Long Short Term Memory (LSTM) to estimate BP using the sensor data. The model undergoes two general ML processes. The first ML process involves training the analysis model using a training set comprised of data from various individuals. Subsequently, the second machine learning process re-trains a portion of the analysis model using an individualized training set gathered from the specific user. This approach enhances the accuracy and personalization of the BP estimation, providing a promising solution for continuous monitoring of BP in high-risk *** BP monitoring device’s analysis model is also trained and tested on 11 volunteers. The BP monitoring device can measure an individual’s BP every 5 seconds. Additionally, the best root mean squared error (RMSE) loss obtained with our model is less than 2.932 mmHg for systolic BP and 2.231 mmHg for diastolic BP.

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