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Multiobjective adaptive car-following control of an intelligent vehicle based on receding horizon optimization

science China(Information sciences) 2023年第4期66卷 301-302页

作者： Hongbo GAO Juping ZHU Fei ZHANG Ruidong YAN Junjie ZHOU Lei JIANG Jianqiang WANG Keqiang LI Department of Automation University of Science and Technology of China Institute of Advanced Technology University of Science and Technology of China Department of Computational Mathematics School of Mathematical Sciences Anhui University School of Traffic and Transportation Beijing Jiaotong University Autonomous Driving Business Unit Chery Automobile School of College of Computer Science and Technology Zhejiang University China North Vehicle Research Institute School of Automotive Engineering Tsinghua University

Dear editor, Vehicle control is one of the key steps of intelligent driving [1]. Algorithms based on receding horizon optimization(RHO) can predict future trajectories and handle multiobjective constraint conditions; therefore, RHO-based methods have attracted considerable attention in the field of vehicle control. Although existing methods use RHO to design controllers, they do not simultaneously meet the multiobjective optimal control performance requirements on tracking, fuel economy, and ride comfort.

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Primeval Identification and Diagnosis of Alzheimer's Disease Utilizing 3D CNN Models 10

Primeval Identification and Diagnosis of Alzheimer's Disease...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sujitha, K.L. Lynsha Helena Pratheeba, H.P. Rajeshwari, R. Ramaraj, Kottaimalai Murugan, Pallikonda Rajasekaran Thilagaraj, M. Mvj College of Engineering Department of Computer Science and Engineering Bengaluru India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350384369

An early-onset neurocognitive impairment, Alzheimer's disease (AD) affects individuals. The symptoms of this illness include diminished memory, strange conduct, and linguistic difficulties. Since AD is the source of 60-80% of instances of dementia, it is the single most prevalent cause of dementia. An AD that is neurodegenerative begins with moderate cognitive impairment (MCI) and progressively worsens. It damages brain tissue, impairs ability to think and remember, and makes it difficult to carry out basic duties. The development of more sophisticated therapies for AD depends on swift detection. For this reason, stopping and managing the progression of AD depends greatly on early detection. The primary goal is to create a comprehensive framework for medical image categorization according to different phases of Alzheimer's disease and early identification of the condition. This work employs a deep learning approach, more precisely convolutional neural networks (CNN). Simple CNN architectures that handle 2D and 3D structural brain magnetic resonance imaging (MRI) form the basis of the suggested methodology. The limited quantity of datasets presents the primary obstacles for medical images. Thus, in order to optimize the size of the dataset and avoid the issue of overfitting, methods to augment data are used. Techniques for resampling, such as downsampling and oversampling are employed to address imbalanced dataset collections. © 2024 IEEE.

关键词： Convolutional neural networks

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Speech Enhancement Using U-Net-Based Progressive Learning with Squeeze-TCN 5th

Speech Enhancement Using U-Net-Based Progressive Learning wi...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Vanambathina, Sunny Dayal Nandyala, Sivaprasad Jannu, Chaitanya Sirisha Devi, J. Yechuri, Sivaramakrishna Parisae, Veeraswamy School of Electronics Engineering VIT-AP University Amaravati India Secure Systems Research Center Technology Innovation Institute Masdar City United Arab Emirates NRI Institute of Technology Vijayawada Agiripalli India Computer Science and Engineering Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9789819735228

Speech enhancement is crucial in many speech processing applications. Recently, researchers have been exploring ways to improve performance by effectively capturing the long-term contextual relationships within speech signals. Nevertheless, these investigations often neglect the significance of the time-frequency (T-F) distribution details of speech spectral elements, which are also important for improving speech quality. Moreover, accurately mapping spectral information in supervised methods requires both global and local information. This paper presents a novel speech enhancement model that utilizes long-term and time-frequency (T-F) information of speech spectral elements for enhancing target speakers. The system is composed of a feature extractor and reconstructor. The feature extractor introduces the U-Net block to enable feature recalibration from multiple scales. Furthermore, the time-frequency attention (TFA) within the U-net’s feature extractor delineates the prominent time-frequency distribution of speech through the simultaneous utilization of two branches: one for time-frame attention and another for frequency channel attention. TFA involves the assignment of distinct attention weights to individual time-frequency spectral components, empowering the models to concentrate on both the "when" (time frames) and the "what" (frequency-wise channels). In the reconstructor, several filtering modules (FMs) are stacked to facilitate the reconstruction of a spectrum in a progressive manner to obtain a coarse estimation by reducing noise in the magnitude domain. Furthermore, the intermediate outcome can be progressively improved across stages by unfolding the filtering modules repeatedly, ultimately resulting in the estimation of the spectrum. The proposed approach is tested on the Librispeech and Voicebank datasets. The findings indicate that this method surpasses earlier models and attains the state-of-the-art performance on these datasets. © The Author(s), under

关键词： Deep neural networks

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Navigating IoT Security:Insights into Architecture,Key Security Features,Attacks,Current Challenges and AI-Driven Solutions Shaping the Future of Connectivity

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computers, Materials & Continua 2024年第12期81卷 3499-3559页

作者： Ali Hassan N.Nizam-Uddin Asim Quddus Syed Rizwan Hassan Ateeq Ur Rehman Salil Bharany Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Biomedical Engineering HITEC UniversityTaxila47080Pakistan Department of Electronics Engineering University of ChakwalChakwal48800Pakistan Department of Computer Science NFC Institute of Engineering and Fertilizer ResearchFaisalabad38000Pakistan School of Computing Gachon UniversitySeongnam-si13120Republic of Korea Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia

Enhancing the interconnection of devices and systems,the Internet of Things(IoT)is a paradigm-shifting *** security concerns are still a substantial concern despite its extraordinary *** paper offers an extensive review of IoT security,emphasizing the technology’s architecture,important security elements,and common *** highlights how important artificial intelligence(AI)is to bolstering IoT security,especially when it comes to addressing risks at different IoT architecture *** systematically examined current mitigation strategies and their effectiveness,highlighting contemporary challenges with practical solutions and case studies from a range of industries,such as healthcare,smart homes,and industrial *** results highlight the importance of AI methods that are lightweight and improve security without compromising the limited resources of devices and computational *** networks can ensure operational efficiency and resilience by proactively identifying and countering security risks by utilizing machine learning *** study provides a comprehensive guide for practitioners and researchers aiming to understand the intricate connection between IoT,security challenges,and AI-driven solutions.

关键词： Internet of Things(IoT) artificial intelligence(AI) IoT architecture security attacks in IoT

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Hair Style Recommendations using CNN and AR 9

Hair Style Recommendations using CNN and AR

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Achireddy, Vummadi Sathiya, T. Ravi, Rongala Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Anurag Engineering College Department of Cse Ananthagiri Telangana Kodad India Sona College of Technology Department of Cse Salem India Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

This research begins with the collecting of a varied dataset of annotated facial photos to ensure accurate representation across populations. The photographs will be pre-processed to standardize their size, illumination, and orientation. On this dataset, we will fine-tune a pre-trained CNN, such as ResNet50, to identify various face forms before evaluating accuracy against a separate test set. Furthermore, we will create a dataset of facial photos combined with various haircuts classified by face shape, and use the CNN to extract relevant features and embeddings. A matching algorithm will match user facial features to hairdo embeddings, ranking recommendations according to similarity metrics. The AR component will leverage ARKit or ARCore for real-time face tracking, allowing users to realistically test on different haircuts overlayed on their live camera feeds. The final solution will be available on mobile and online platforms, with a user-friendly design and options for saving preferred haircuts and providing feedback. This project tackles the demand for a more engaging and individualized hairstyle picking experience, utilizing advanced AI and AR technologies to improve customer happiness and interactivity. © 2024 IEEE.

关键词： Convolutional neural networks

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Evaluation of Vehicle Sensor Performance for Vulnerable User Detection through Vehicle Speed Measurement

Evaluation of Vehicle Sensor Performance for Vulnerable User...

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2024 IEEE International Conference on Signal Processing and Advance research in Computing, SPARC 2024

作者： Rathnakar, G. Kothiyal, Arnav Shwetha, H.R. Harikeerthan, M.K. Parikh, Swapnil Maranan, Ramya JSS Science and Technology University Department of Mechanical Engineering Karnataka 570006 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand 248 002 India JNN College of Engineering Department of Electronics and Communication Engineering Shivamogga577204 India Dayananda Sagar Academy of Technology and Management Department of Civil Engineering Karnataka 560082 India Parul Institute of Engineering and Technology Department of Computer Science and Engineering Parul University Gujarat India Saveetha School of Engineering Department of Research and Innovation SIMATS Tamil Nadu 602105 India

ISBN: (纸本)9798350385199

This work involves the successful utilization of magnetic sensors for vehicle speed measurement and also emphasizes the potential of such technology in enhancing safety for vulnerable road users. The device, with its wide voltage range compatibility (3.5-60 V) and robust industrial box housing, is equipped with a wireless module, enabling versatile deployment options. The vehicle detection system using MMC5883MA and HMC1021z sensors showed higher amplitudes for nearer lanes, suggesting a need to relocate the device further from the road. Challenges included close vehicle passes and inconsistent speeds, affecting accuracy, with an average error of 5.5 km / h, reduced to 3 km / h under ideal conditions. Despite fluctuations in some vehicles like the Citroen C3, 63% of measurements had deviations under 5 km / h, and 80% under 10 km / h. © 2024 IEEE.

关键词： Highway administration

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Low-Complexity Channel Estimation for RIS-Assisted ISAC System

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China Communications 2025年第4期22卷 296-308页

作者： Chen Zhen Li Jianqing Zhang Haijun Zhang Wei The Institute of Microelectronics University of MacaoMacao 999078China School of Computer Science and Engineering Macao University of Science and TechnologyMacao 999078China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology BeijingBeijing 100083China Henan Academy of Science Applied Physics Institute Co. LtdZhengzhou 450008China

Integrated sensing and communication(ISAC),assisted by reconfigurable intelligent surface(RIS)has emerged as a breakthrough technology to improve the capacity and reliability of 6G wireless ***,a significant challenge in RIS-ISAC systems is the acquisition of channel state information(CSI),largely due to co-channel interference,which hinders meeting the required reliability *** address this issue,a minimax-concave penalty(MCP)-based CSI refinement scheme is *** approach utilizes an element-grouping strategy to jointly estimate the ISAC channel and the RIS phase shift *** previous methods,our scheme exploits the inherent sparsity in RIS-assisted ISAC channels to reduce training overhead,and the near-optimal solution is derived for our studied RIS-ISAC *** effectiveness of the element-grouping strategy is validated through simulation experiments,demonstrating superior channel estimation results when compared to existing benchmarks.

关键词： channel estimation element grouping integrated sensing and communication(ISAC) RIS

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Efficient Collaborative Rule Caching Through Pairing of P4 Switches in SDNs

Efficient Collaborative Rule Caching Through Pairing of P4 S...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Saberi, Mohammad Dolati, Mahdi Movaghar, Ali Dargahi, Tooska Khonsari, Ahmad Sharif University of Technology Department of Computer Engineering Tehran Iran Manchester Metropolitan University Manchester United Kingdom University of Tehran Department of Electrical and Computer Engineering Tehran Iran Institute for Research in Fundamental Sciences School of Computer Science Tehran Iran

ISBN: (纸本)9798350351255

Software-defined networks (SDNs) provide customizable traffic control by storing numerous rules in on-chip memories with minimal access latency. However, the current on-chip memory capacity falls short of meeting the growing demands of SDN control applications. While rule eviction and aggregation strategies address this challenge at the switch level, programmable data planes enable a more flexible approach through cooperative rule caching. However, current solutions rely on computationally intensive off-the-shelf solvers to perform rule placement across the network. In this paper, we present an efficient solution for the cooperative rule caching problem. We first present the design of a resource-efficient switch capable of caching rules for its neighbors alongside a lightweight protocol for retrieving cached rules. Then, we introduce RaSe, an approximation algorithm for minimizing rule lookup latency across the network through optimized cooperation-aware rule placement. We conduct a theoretical analysis of RaSe, followed by a P4-based proof-of-concept assessment in Mininet and a large-scale numerical evaluation using real-world network topology. In comparison with existing solver-based solutions, the proposed method obtains the solution 160 times faster and improves the average rule lookup latency by about 21% compared to several algorithmic baselines. © 2024 IEEE.

关键词： Approximation algorithms

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DEFAEK: Domain Effective Fast Adaptive Network for Face Anti-Spoofing

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Neural Networks 2023年 161卷 83-92页

作者： Lin, Jiun-Da Han, Yue-Hua Huang, Po-Han Tan, Julianne Chen, Jun-Cheng Tanveer, M. Hua, Kai-Lung Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei115201 Taiwan Department of Mathematics Indian Institute of Technology Indore Simrol453552 India

Existing deep learning based face anti-spoofing (FAS) or deepfake detection approaches usually rely on large-scale datasets and powerful networks with significant amount of parameters to achieve satisfactory performance. However, these make them resource-heavy and unsuitable for handheld devices. Moreover, they are limited by the types of spoof in the dataset they train on and require considerable training time. To produce a robust FAS model, they need large datasets covering the widest variety of predefined presentation attacks possible. Testing on new or unseen attacks or environments generally results in poor performance. Ideally, the FAS model should learn discriminative features that can generalize well even on unseen spoof types. In this paper, we propose a fast learning approach called Domain Effective Fast Adaptive nEt-worK (DEFAEK), a face anti-spoofing approach based on the optimization-based meta-learning paradigm that effectively and quickly adapts to new tasks. DEFAEK treats differences in an environment as domains and simulates multiple domain shifts during training. To further improve the effectiveness and efficiency of meta-learning, we adopt the metric learning in the inner loop update with careful sample selection. With extensive experiments on the challenging CelebA-Spoof and FaceForensics++ datasets, the evaluation results show that DEFAEK can learn cues independent of the environment with good generalization capability. In addition, the resulting model is lightweight following the design principle of modern lightweight network architecture and still generalizes well on unseen classes. In addition, we also demonstrate our model's capabilities by comparing the numbers of parameters, FLOPS, and model performance with other state-of-the-art methods. © 2023 Elsevier Ltd

关键词： Large dataset

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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