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arXiv 2024年

作者： Chen, Yun-Chih Chang, Yuan-Hao Kuo, Tei-Wei The Department of Computer Science TU Dortmund Germany The Institute of Information Science Academia Sinica Taipei115 Taiwan Department of Computer Science and Information Engineering High Performance and Scientific Computing Center Center of Data Intelligence: Technologies Applications and Systems of National Taiwan University Taiwan

To index the increasing volume of data, modern data indexes are typically stored on SSDs and cached in DRAM. However, searching such an index has resulted in significant I/O traffic due to limited access locality and inefficient cache utilization. At the heart of index searching is the operation of filtering through vast data spans to isolate a small, relevant subset, which involves basic equality tests rather than the complex arithmetic provided by modern CPUs. We introduce the Search-in-Memory (SiM) chip, which demonstrates the feasibility of performing data filtering directly within a NAND flash memory chip, transmitting only relevant search results rather than complete pages. Instead of adding complex circuits, we propose repurposing existing circuitry for efficient and accurate bitwise parallel matching. We demonstrate how different data structures can use our flexible SIMD command interface to offload index searches. This strategy not only frees up the CPU for more computationally demanding tasks, but it also optimizes DRAM usage for write buffering, significantly lowering energy consumption associated with I/O transmission between the CPU and DRAM. Extensive testing across a wide range of workloads reveals up to a 9X speedup in write-heavy workloads and up to 45% energy savings due to reduced read and write I/O. Furthermore, we achieve significant reductions in median and tail read latencies of up to 89% and 85% respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy utilization

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks

Agricultural Water Conservation through AI-Enhanced Solar Io...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： S. P. Maniraj Monisha J R. Amutha R. Kavitha P. J. Shriidhar C. Srinivasan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamilnadu India Department of Computer Science and Engineering (Cyber Security) R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering ULTRA College of Engineering and Technology Madurai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vinayaka Missions Kirupananda Variyar Engineering College Vinayaka Missions Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha Universityi Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future.

关键词： Irrigation Accuracy Food security Artificial neural networks Water conservation Real-time systems Internet of Things Sustainable development Water resources Intelligent sensors

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Regularized Multivariate Functional Principal Component Analysis

arXiv

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arXiv 2023年

作者： Haghbin, Hossein Zhao, Yue Maadooliat, Mehdi Artificial Intelligence and Data Mining Research Group ICT Research Institute Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran Department of Mathematical and Statistical Sciences Marquette University MilwaukeeWI53233 United States

Multivariate Functional Principal Component Analysis (MFPCA) is a valuable tool for exploring relationships and identifying shared patterns of variation in multivariate functional data. However, controlling the roughness of the extracted Principal Components (PCs) can be challenging. This paper introduces a novel approach called regularized MFPCA (ReMFPCA) to address this issue and enhance the smoothness and interpretability of the multivariate functional PCs. ReMFPCA incorporates a roughness penalty within a penalized framework, using a parameter vector to regulate the smoothness of each functional variable. The proposed method generates smoothed multivariate functional PCs, providing a concise and interpretable representation of the data. Extensive simulations and real data examples demonstrate the effectiveness of ReMFPCA and its superiority over alternative methods. The proposed approach opens new avenues for analyzing and uncovering relationships in complex multivariate functional *** Codes 62Hxx, 46-xx (Primary) 62H25 (Secondary) © 2023, CC BY.

关键词： Principal component analysis

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Challenges in Metaverse in Problem-Based Learning as a Game-Changing Virtual-physical Environment for Personalized Content Development 6

Challenges in Metaverse in Problem-Based Learning as a Game-...

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6th Smart Cities Symposium, SCS 2022

作者： Dutta, P.K. Bose, Manoswita Sinha, Akash Bhardwaj, Ramakant Ray, Samrat Roy, Sudipta Prakash, Kolla Bhanu School of Engineering and Technology Amity University Kolkata India School of Computer Application Amity University Kolkata India Department of Mathematics Amity University Kolkata India The Institute of Industrial Management Economics and Trade Peter the Great Saint Petersburg Polytechnic University Russia Artificial Intelligence & Data Science Jio Institute India Department of Computer Science and Engineering Konneru Lakshmaih Education Foundation India

ISBN: (纸本)9781839538544

The benefits and drawbacks of using the metaverse in the field of education are discussed, along with the possibilities and potential issues it will open up. Utilizing the idea of personalised knowledge transfer and the use of an immersive environment to match students' learning pace using horizon classroom, the Metaverse environment can add a new dimension to the field of educational technologies. We have also highlighted the virtualization-based redesign of educational environments using a personalised cloud, which is anticipated to improve learning for students by making it more affordable, collaborative, and useful. The goal is to build a metaversity that can serve as a communication tool for teachers and students anywhere in the world by providing each with an individual avatar. This platform will offer a virtual setting for students to socialise with their friends and peers and receive peer-to-peer learning, which is lacking in the current e-learning environment. The students won't need an entrance score card;instead, they will need critical learning skills to advance and mature. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Students

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Butler matrix enabled multi-beam optical phased array for two-dimensional beam-steering and ranging

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Photonics Research 2024年第5期12卷 912-920页

作者： ZUOYU ZHOU WEIHAN XU CHUXIN LIU RUIYANG XU CHEN ZHU XINHANG LI LIANGJUN LU JIANPING CHEN LINJIE ZHOU State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Laboratory of Navigation and Location ServicesShanghai Institute for Advanced Communication and Data ScienceDepartment of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu 314200China State Grid Sichuan Electric Power Company Chengdu Xinjin Power Supply Branch Xinjin 611430China

Based on the wavelength transparency of the Butler matrix(BM)beamforming network,we demonstrate a multibeam optical phased array(MOPA)with an emitting aperture composed of grating couplers at a 1.55μm pitch for wavelength-assisted two-dimensional *** device is capable of simultaneous multi-beam operation in a field of view(FOV)of 60°×8°in the phased-array scanning axis and the wavelength-tuning scanning axis,*** typical beam divergence is about 4°on both *** multiple linearly chirped lasers,multibeam frequency-modulated continuous wave(FMCW)ranging is realized with an average ranging error of 4 cm.A C-shaped target is imaged for proof-of-concept 2D scanning and ranging.

关键词： optical tuning beam

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Small errors in random zeroth-order optimization are imaginary

arXiv

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arXiv 2021年

作者： Jongeneel, Wouter Yue, Man-Chung Kuhn, Daniel École Polytechnique Fédérale de Lausanne Switzerland Musketeers Foundation Institute of Data Science Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Hong Kong

Most zeroth-order optimization algorithms mimic a first-order algorithm but replace the gradient of the objective function with some gradient estimator that can be computed from a small number of function evaluations. This estimator is constructed randomly, and its expectation matches the gradient of a smooth approximation of the objective function whose quality improves as the underlying smoothing parameter δ is reduced. Gradient estimators requiring a smaller number of function evaluations are preferable from a computational point of view. While estimators based on a single function evaluation can be obtained by use of the divergence theorem from vector calculus, their variance explodes as δ tends to 0. Estimators based on multiple function evaluations, on the other hand, suffer from numerical cancellation when δ tends to 0. To combat both effects simultaneously, we extend the objective function to the complex domain and construct a gradient estimator that evaluates the objective at a complex point whose coordinates have small imaginary parts of the order δ. As this estimator requires only one function evaluation, it is immune to cancellation. In addition, its variance remains bounded as δ tends to 0. We prove that zeroth-order algorithms that use our estimator offer the same theoretical convergence guarantees as the state-of-the-art methods. Numerical experiments suggest, however, that they often converge faster in *** Codes 65D25, 65G50, 65K05, 65Y04, 65Y20, 90C56 © 2021, CC BY.

关键词： Function evaluation

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Enhanced Bone Cancer Detection in MRI Scans via a Hybrid Quantum-Classical Convolutional Neural Network and War Strategy Optimization

Enhanced Bone Cancer Detection in MRI Scans via a Hybrid Qua...

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Ubiquitous Computing and Intelligent Information systems (ICUIS), International Conference on

作者： A. Harshavardhan S. Sudalaikani M. Saravanakumar S Saritha Pothamsetty Kasi V Rao Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India Department of Mathematics MVJ college of Engineering Whitefield Karnataka India Department of Artificial Intelligence & Data Science Nehru Institute of Engineering and tech Coimbatore Tamil Nadu India Department of Computer Science and Engineering Immaculate College for Women Cuddalore Tamil Nadu India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Bone cancer is a relatively rare and aggressive form of cancer that originates in the bones and the adjacent soft tissues; the disease is characterized by marked morbidity and mortality. Frequently, early detection is possible, and such medical conditions should be treated to enhance the result of the therapy. MRI is very useful in diagnosing bone cancer since they give clear images of bones and soft tissues. In this work, a new method of bone cancer detection from MRI images is presented with unsharp structure guided filtering for preprocessing and a CSwin transformer to extract features from the preprocessed images. This method further improves visualization and selection of MRI images by improving the representation of features in the image. The main part of the designed method is the Hybrid Quantum-Classical Convolutional Neural Network with War Strategy Optimization (HQCCNN-WSO), which combines the fundamentals of quantum computing and traditional neural networks to enhance the classification performance. Based on the parameters of the War Strategy Optimization algorithm, the model distinguishes between cancerous lesions and healthy bone tissue, and the parameters are further refined for improved performance. The suggested approach demonstrates remarkable accuracy and dependability in the identification of bone cancer, achieving over 99% across all performance criteria.

关键词： Quantum computing Accuracy Magnetic resonance imaging Neural networks Bones Transformers Feature extraction Convolutional neural networks Optimization Cancer

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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Stochastic cluster head selection model for energy balancing in IoT enabled heterogeneous WSN

引用

Measurement: Sensors 2024年 35卷

作者： Anto Pravin, R. Murugan, K. Thiripurasundari, C. Ranjith Christodoss, Prasanna Puviarasi, R. Abdul Lathif, Syed Ismail Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu India Department of Electronics and Communication Engineering KSK College of Engineering and Technology Tamil Nadu India Department of Computing Mathematics and Physics Messiah University Mechanicsburg PA United States Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu India

Energy dissipation is the most important design limitation for Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs). In order to prolong the life of WSNs, the energy of nodes must be used in an effective way. Clustering is a strategy that may effectively use the energy of the sensors, extending the life and scalability by managing the network load balance. The energy usage for network operation is reduced by using an evolutionary algorithm called Genetic Algorithm (GA). The Stochastic Cluster Head Selection Model (SCHSM) is described in the proposed protocol by taking the factors such as distance, node energy, density and capacity of nodes for developing the fitness function. The proposed protocol is designed for multiple movable sink nodes and this greatly improves the energy balancing factor in the network. For minimizing the communication gap among sensors and sinks, movable sinks can be placed carefully. Simulation results are analyzed for the system effectiveness. © 2024 The Authors

关键词： Internet of things

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Singe Image Dehazing using Deep Neural Networks

Singe Image Dehazing using Deep Neural Networks

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Nisarg Doshi Sagar Bhavsar Rajeswari D Srinivasan R Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

In dusty and hazzy circumstances, pictures caputured tend to be unclear. To tackle this challenge researchers have founded different image-dehazing approaches. Lately, high quality pictures which could be used to extract maximal data from autonomous systems are in great demand. In order to minimize the haze from the picture, this research study uses various deep learning (also known as DL) architectures to identify the information recovered and extract key details from the image. The research paper focuses on utilizing deep learning techniques to remove haze from clear *** first phase of the suggested architecture involves applying multiple pre-processing methods, including estimating the airlight, implementing contextual regularization and boundary constraints, as well as contrast enhancement techniques (CS, CLAHE, HE) and Image Fusion. The subsequent goal of this study is to pinpoint the optimal deep learning model that can produce clear images based on the resulting dehazed images. The quality of the dehazed images is evaluated by comparing them with clear images using metrics such as PSNR and SSIM. Experimental findings shows that Alex Net upon the implementation of pre-processing beats other state of the art techniques with regard to PSNR value and holds its own w.r.t SSIM values.

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