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检索条件"任意字段=Conference on Photonic Devices and Algorithms for Computing V"

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Maximum Response Mechanism in vehicular Cooperative Caching for C-v2X Networks 4

Maximum Response Mechanism in Vehicular Cooperative Caching ...

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4th IEEE International conference on Emerging Smart computing and Informatics, ESCI 2022

作者： Qiu, Zhiwei Lin, Tianwu Xu, Zhexin Xiao, Min Fujian Normal University College of Photonic and Electronic Engineering Fuzhou China Xiamen University of Technology Xiamen China

ISBN: (纸本)9781665400732

In the existing urban traffic scenarios, in order to realize the high request response rate and low response delay of v2v communication in the highly dynamic vehicle topology and reduce the response burden of vehicles, the influencing factors of request response rate in vehicle communication are studied, and the influence laws of vehicle file cache capacity, response times and traffic density on request response rate are summarized. We proposed the maximum response mechanism in vehicle Cooperative Cache in C-v2X network, and designed the maximum response algorithm (MRA). Firstly, the base station integrates and allocates the requests and responses of vehicles, sorts them according to the number of requests, and constructs a total request table. vehicles respond according to the total request table. The whole process is coordinated by the base station and vehicle nodes. In comparison with other response algorithms, the MRA can achieve higher request response rate when the file cache capacity and response times of the vehicle are less and the traffic density is sparse, and the response burden of the vehicle is the smallest. © 2022 IEEE.

关键词： vehicles

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High-speed AI image space wavefront sensing using embedded computing: achieving 1000 frames per second 12

High-speed AI image space wavefront sensing using embedded c...

引用

2025 conference on photonic Instrumentation Engineering

作者： Baudat, Gaston Lavanchy, David Muller, Guillaume Innovat Foresight LLC 4432 Mallard Point Columbus IN 47201 USA Univ Arizona Wyant Coll Opt Sci 1630 E. Univ Blvd Tucson AZ 85721 USA Univ Appl Sci Western Switzerland HEIG VD Route Cheseaux 1Case Postale 521 CH-CH1401 Yverdon Switzerland

ISBN: (纸本)9781510684942;9781510684959

Wavefront sensing is fundamental to numerous scientific and industrial applications in optics, particularly in adaptive optics (AO). This paper presents a high-speed image space AI-powered wavefront sensing system, based on a patented technology known as AI4Wave, that eliminates the need for dedicated wavefront sensors, such as Shack-Hartmann, relying solely on fast cameras and AI edge computing devices, specifically an NvIDIA Jetson embedded module in our implementation. This module could also be utilized in the future to directly control actuators, streamlining the entire AO process. The system captures defocused images of a point source or similar, which are processed by a feedforward artificial neural network (NN) trained exclusively on synthetic data [1]. This approach enables real-time phase retrieval, overcoming the local derivative limitations of Shack-Hartmann sensors and handling large wavefront or surface errors spanning multiple wavelengths. It also provides a unique capability for field-dependent wavefront measurement from a single image of multiple sources. Yu Wu et al. [2] proposed a sub-millisecond phase retrieval method that requires two simultaneous images for optimal performance and NN training on physical data. However, this approach necessitates retraining whenever system parameters, such as f/#, wavelength, or pixel size, are altered. In contrast, AI4Wave employs synthetic, normalized data that is agnostic to most optical layout changes, enabling robust and versatile training and validation. Combined with off-the-shelf NvIDIA hardware, it offers a ready-to-use solution for industrial deployment. By integrating optimized algorithms within modern AI acceleration frameworks, such as NvIDIA TensorRT, and leveraging advanced AI edge computers, the system achieves processing speeds of 1000 frames per second or more, effectively providing sub-millisecond wavefront sensing capability. The minimal hardware setup, consisting only of a camera and an NvIDI

关键词： Cameras Education and training Monochromatic aberrations Wavefront sensors Wavefronts Phase retrieval Adaptive optics Image retrieval Point spread functions Sensors

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An Efficient DDoS Attack Detection in SDN using Multi-Feature Selection and Ensemble Learning 4

An Efficient DDoS Attack Detection in SDN using Multi-Featur...

引用

4th International conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kachavimath, Amit v. Narayan, D.G. Department of Master of Computer Applications KLE Technological University Hubballi580031 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India

Software-defined networking (SDN) has significantly enhanced network agility by decoupling network control from hardware devices. This architectural shift exposes SDN infrastructures to increased risks from Distributed Denial of Service (DDoS) attacks, which can severely disrupt network services and render them unavailable to legitimate users. Despite the existence of various techniques for detecting specific types of DDoS attacks, there remains a need for more comprehensive work capable of accurately identifying multiple attack categories. We have addressed the gap by proposing a robust DDoS attack detection method in the SDN. We utilize advanced machine learning (ML) algorithms to analyze the InSDN dataset, employing advanced feature extraction techniques to improve detection accuracy and efficiency. Three feature extraction methods, Se-lectKBest, ANOvA F-value scores, and feature importance scores from RandomForest Classifier, were used to select the best ten features from 81. This feature selection, combined with ensemble learning, achieved an accuracy of 99.9%. The Receiver Operating Characteristic (ROC) curve, confusion matrix, and K-fold cross-validation were used to evaluate the performance of the proposed model. © 2025 The Authors. Published by Elsevier B.v.

关键词： Adversarial machine learning

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Population Local Search for Single Processor Energy Efficient Scheduling Problem 4th

Population Local Search for Single Processor Energy Efficie...

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4th International conference on Numerical Computations: Theory and algorithms, NUMTA 2023

作者： Zakharova, Yulia v. Sobolev Institute of Mathematics Novosibirsk Russia Dostoevsky Omsk State University Omsk Russia

ISBN: (纸本)9783031812408

Energy consumption of computing devices is an important question at this day. One effective method for reducing energy consumption is dynamic speed scaling. The speeds are dynamically assigned to processors minimizing the total energy consumption and guaranteeing the required quality of schedules. The NP-hard offline energy-efficient scheduling of jobs on the single speed-scalable processor is considered in this work. We propose a population local search algorithm with two types of neighbourhoods. The first one is constructed based on one solution and uses swap and insert strategies with problem-specific adaptation. The second one is defined for pairs of solutions and it is based on the inheritance of values in positions of solution permutations. Experimental evaluation of instances of various structures shows that the algorithm demonstrates competitive results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Local search (optimization)

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Dynamic Emotion Regulation through Reinforcement Learning: A Neurofeedback System with EEG Data and Custom Wearable Interventions

Dynamic Emotion Regulation through Reinforcement Learning: A...

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2024 International conference on Brain Computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Joseph, Jeethu Judy, M.v. Department Of Computer Applications Kochi India

ISBN: (纸本)9798331540067

Neurofeedback, a form of biofeedback that uses brainwave activity to provide real-time feedback, is a powerful approach for emotion regulation. Recent advancements in affective computing and machine learning offer new possibilities for improving the efficacy of neurofeedback systems, particularly through adaptive algorithms for personalizing the intervention process. This study presents an innovative neurofeedback mecha-nism that uses reinforcement learning (RL) to regulate emotions. The system uses Q-learning algorithm to dynamically adjusts its intervention strategies based on sampled EEG data from the DEAP dataset that monitors brain activity associated with emotional states. The therapeutic interventions consist of a mix of music therapy sessions, mindfulness exercises, and tactile feedback provided by a custom-built wearable device. The device utilizes tactile vibration as a neurofeedback signal to help users become more conscious of and effectively regulate their emotional well-being. The system's performance was evaluated through detailed analysis of the Q-learning process and observing how various states, actions and rewards influenced effective emotional regulation. Using sampled EEG data from the DEAP dataset enabled comprehensive testing and refinement of the system's algorithms in a controlled environment, thereby setting the stage for potential future use with real time EEG devices. The results of this study highlight the power of RL-based neurofeedback to help transform emotional wellbeing, while also showcasing the potential to expand the spectrum of neurofeedback tools to be more responsive and adaptive to individual users, ultimately promoting improved mental health. © 2024 IEEE.

关键词： Reinforcement learning

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Conventional Current Reference Generation Strategies for Grid-Connected Distributed Energy Sources

Conventional Current Reference Generation Strategies for Gri...

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2023 IEEE International conference on Device Intelligence, computing and Communication Technologies, DICCT 2023

作者： Joshi, Jyoti Dubey, v.P. Kandpal, Jyoti Chamoli, vivek Graphic Era Hill University Dept. of Computer Science and Engineerring Dehradun India Graphic Era Hill University Dept. of Electronics and Communication Dehradun India

ISBN: (纸本)9781665474924

Low voltage ride through (LvRT) is the ability of a photovoltaic (Pv) system to maintain continuous operation during grid voltage dips or short-term power outages. To meet the LvRT requirements, Pv systems typically include advanced inverter technology, energy storage systems, and additional protection devices. Current reference generation is a key aspect of LvRT technology, as it allows the inverter to maintain a stable current reference during voltage dips and outages. This is achieved by using advanced algorithms to control the inverter's output current, and by incorporating voltage and frequency monitoring systems to detect grid disturbances. This research work reviews the performance of various current control systems under imbalanced grid faults and abrupt changes in irradiance. To confirm the behaviour of the current control mechanisms, simulation outcomes are shown. © 2023 IEEE.

关键词： Electric current control

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Echo Guidance: voice-Activated Application for Blind with Smart Assistive Stick Using Machine Learning and IoT

Echo Guidance: Voice-Activated Application for Blind with Sm...

引用

2024 International conference on Advances in Data Engineering and Intelligent computing Systems, ADICS 2024

作者： Abishek Kumar, G. Surya, G. Sathyadurga, v. Hindustan Institute Of Technology And Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

Navigating the world without sight presents profound challenges for millions of visually impaired individuals. Echo Guidance includes a pioneering application, addresses these obstacles by facilitating independence and mobility through voice-driven interaction and an IoT smart assistive device for obstacle detection by utilizing Ultrasonic, GPS and GSM modules powered by Arduino. This study explores the design and evaluation of Echo Guidance, highlighting its utilization of advanced algorithms like RGB888 conversion, matrix transformation, and Optical Character Recognition (OCR) for real-time object recognition. The application boasts a voice-centric interface tailored to the needs of the visually impaired, seamlessly integrating day-to-day functionalities such as weather forecasting, reminders, and a calculator accessible through voice commands. Central to Echo Guidance is its innovative object detection and recognition approach, employing the MobileNetObjDetector class powered by TensorFlow Lite for swift and accurate object detection. The Overlayview class visually represents detected objects, enhancing spatial awareness. Additionally, the integration of IoT devices, such as the smart stick with ultrasonic sensors for obstacle detection and head-level obstacle detection, coupled with SOS functionality in case of any emergency using GPS and GSM modules, further enhances Echo Guidance's capabilities. This research underscores Echo Guidance's transformative potential in improving the quality of life for visually impaired individuals, representing a significant advancement in assistive technology. Prioritizing accessibility and user experience, this paper empowers individuals worldwide with newfound independence and confidence in navigating their surroundings. © 2024 IEEE.

关键词： Linear transformations

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Integrating Mobile Sensing and Machine Learning for Early Detection and Personalized Management of Chronic Diseases 5

Integrating Mobile Sensing and Machine Learning for Early De...

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5th International conference on Advancements in computing, ICAC 2023

作者： Piyumika, G.D.T. Basnayaka, K.v.N. Hewage, H.A. Rajakaruna, R.A.R.M.S.M.S.B. Thilakarathna, Thusithanjana Perera, Jeewaka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institue of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350358131

Obesity, heart disease, diabetes, and depression have all been linked to insufficient sleep. Analysis of one's sleep habits using one's smartphone has gained popularity as a possible solution. In this paper, we provide a framework for analyzing sleep habits using mobile devices. Because of their widespread availability and extensive sensor suite, cellphones are being use in sleep research. Motion and orientation sensors and audio recorders are used in sleep analysis. Both head and body motions are tracked by the gyroscope and accelerometer, respectively. The microphone is sensitive enough to pick up noises like snoring. Approaches to sleep analysis on smartphones range widely. Sensor data may be analyzed using machine learning algorithms. These systems can distinguish between light, deep, and REM sleep by analyzing data from motion and sound sensors. Gather information about your sleep using surveys or questionnaires like the Pittsburgh Sleep Quality Index (PSQI) or the Epworth Sleepiness Scale (ESS). Analysis of sleep patterns with a mobile device has several benefits. It's a simple and non-invasive way to study how you sleep. Second, it offers continuous monitoring of sleep, which might lead to the identification and resolution of sleep disorders. Third, it may provide objective sleep data for the diagnosis of sleep disorders and the development of individualized treatment plans. Analysis of one's slumber using a smartphone might shed light on our sleeping habits and encourage healthy ones. Machine learning algorithms and questionnaires are two methods for analyzing sleep quality on a smartphone. Smartphone-based sleep studies are easy to implement and conduct, and they provide reliable, objective information on one's nightly slumber. © 2023 IEEE.

关键词： Smartphones

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A Novel Consensus Mechanism using Impression based Byzantine Fault-Tolerance for Lightweight Blockchain in IoT enabled Smart Home Autonomous System 15

A Novel Consensus Mechanism using Impression based Byzantine...

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15th International conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sahil, Mohammad Yadav, Praveen Kumar Dash, Mahesh Kumar Shahil, Md Barik, Ram Chandra C.V. Raman Global University Dept. of Comp. Science & Engg Odisha Bhubaneswar752054 India

ISBN: (纸本)9798350370249

The escalation of Internet of Things (IoT) devices accentuates the criticality of cybersecurity, given their capacity to amass confidential multimedia data including audio, imagery, and biometrics. The centralized architecture traditionally supporting IoT introduces a monolithic control over data, elevating risks of data breaches and tampering which may lead to a single point of failure that is vulnerable to attacks. Blockchain technology emerges as a transformative solution by decentralizing data validation, negating the need for a centralized authority, and thus mitigating single-point vulnerability. Classical blockchain frameworks are ill-suited for IoT, primarily due to their public data exposure with the excessive requirements for storage and processing power driven by the continuous growth of the blocks and the intensity of consensus protocols. Addressing these incongruities, this paper suggests a decentralized framework through a lightweight blockchain considering a new Impression based Byzantine Fault-Tolerance (IBFT) consensus mechanism. IBFT minimizes computational demands by utilizing an 'impression value' to elect the network's primary node, significantly enhancing performance. Additionally, integration with the Storj platform, a decentralized storage system is also proposed to ensure robust data encryption with distribution to mitigate vulnerability issues in centralized storage focusing the challenges pertaining to blockchain storage. The proposed next generation blockchain framework outperforms among recent state-of-art algorithms against emerging threats to secure IoT ecosystems. © 2024 IEEE.

关键词： Consensus IBFT IoT Lightweight Blockchain Smart Home

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Machine Learning-Driven Analysis and Optimization of Optical Ring Resonator for Integrated photonic Applications

Machine Learning-Driven Analysis and Optimization of Optical...

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Smart Systems for applications in Electrical Sciences (ICSSES), International conference on

作者： Chandrika T N Nischal S Ruqaiah Sadiya SanjayKumar B S Nithyashree M Department of ETE Siddaganga Institute of Technology Tumkur India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Ring resonators are pivotal components in photonics, enabling light manipulation for applications in telecommunications, optical sensing, and quantum computing. Integrating machine learning with photonic design opens new avenues for enhancing the performance and efficiency of these devices. In this study, we leverage the Lumerical software to create a comprehensive database of ring resonator designs and their performance metrics. Utilizing the $\mathbf{k}$ -Nearest Neighbors (KNN) regression algorithm, we develop a predictive model to optimize resonator parameters for specific applications. This approach allows for rapid exploration of the design space, uncovering innovative resonator configurations and improving sensitivity and efficiency. The proposed method demonstrates the potential of machine learning to accelerate photonic device development, offering a systematic, data-driven strategy for advancing ring resonator technology. Obtained accuracy of 82.8

关键词： Adaptation models Accuracy Computational modeling Refractive index Machine learning Nearest neighbor methods Predictive models Prediction algorithms Optical ring resonators photonics

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