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

共 1067 条记录，以下是161-170 订阅

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SiGe Qubit Biasing with a Cryogenic CMOS DAC at mK Temperature 49

SiGe Qubit Biasing with a Cryogenic CMOS DAC at mK Temperatu...

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IEEE 49th European Solid-State Circuits conference (ESSCIRC)

作者： Schreckenberg, Lea Otten, Rene vliex, Patrick Xue, Ran Tu, Jhih-Sian Seidler, Inga Trellenkamp, Stefan Schreiber, Lars R. Bluhm, Hendrik van Waasen, Stefan Forschungszentrum Julich Cent Inst Engn Elect & Analyt Elect Syst ZEA 2 D-52428 Julich Germany Rhein Westfal TH Aachen JARA Inst Quantum Informat D-52074 Aachen Germany Forschungszentrum Julich D-52074 Aachen Germany Forschungszentrum Julich Helmholtz Nano Facil HNF D-52428 Julich Germany Univ Duisburg Essen Fac Engn Commun Syst D-47057 Duisburg Germany

ISBN: (纸本)9798350304206

For running advanced algorithms on a universal quantum computer, millions of qubits are required. To make use of quantum effects, state-of-the-art solid-state qubit devices have to be cooled to mK temperatures, which limits the systems' scalability with room temperature (RT) electronics. We present the direct co-integration of a scalable, fully integrated, eight channel Bias-DAC designed in a 65-nm bulk CMOS technology and a Si/SiGe spin qubit device at the mixing chamber (MC) of a dilution refrigerator operating below 45mK MC temperature. As a full proof of principle, the bias of a single electron transistor used as a sensing dot, as well as a single and double quantum dot bias of the qubit device is reported. The slow drift of the DAC S&H output circuit of 0.96 mu v/ s leads to a calculated prospective power consumption of 64.5pW/ch for DC qubit bias voltages generated at low temperature.

关键词： CMOS cryogenic temperature quantum computing spin qubit cryogenic CMOS DAC

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17th International conference on Computational Intelligence in Security for Information Systems, CISIS 2024 and the 15th International conference on EUropean Transnational Education, ICEUTE 2024

17th International Conference on Computational Intelligence ...

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17th International conference on Computational Intelligence in Security for Information Systems, CISIS 2024 and the 15th International conference on EUropean Transnational Education, ICEUTE 2024

ISBN: (纸本)9783031750151

The proceedings contain 33 papers. The special focus in this conference is on Computational Intelligence in Security for Information Systems. The topics include: An Approach Based on LLMs for Forensic Threat Detection in Autonomous Systems;smart Water Meter Network Anomaly and Failure Detection System;Design and Implementation of a Fast, Platform-Adaptive, AIS-20/31 Compliant PLL-Based True Random Number Generator on a Zynq 7020 SoC FPGA;A Comparison of Frameworks for Heterogeneous computing Using High-Performance Pattern-Matching for DNA Biometrics and Digital Forensics;Fuzzing Robotic Software Using HPC and LLM;performance Impact of Strengthening the Accountability and Explainability System in Autonomous Robots;Generation of Industrial Protocol-Traffic via Enhanced Wasserstein GAN;cyber Security in Hospitals: A Network-Oriented Model for Behavioural Learning of Employees During Phishing Simulations;Augmenting API Security Testing with Automated LLM-Driven Test Generation;Exploring the Use of LLMs to Understand Network Traces;securing v2X Communication: A Hybrid Deep Learning Approach for Misbehavior Detection;Use of GAN Models and CNN Heatmaps in Malware;Evaluating Sniffers, IDS, and IPS: A Systematic Literature Mapping;Performance Analysis of NTT algorithms;exploring the Landscape of Honeypots in the Fight Against Cyber Threats: A Systematic Mapping of Literature;phishing and Spam Prevention Powered by Jetson Nano;decentralized Edge-Based Detection of Label Flipping Attacks in Federated Learning;analysis of Post-Quantum Cryptographic algorithms: A Systematic Literature Review;A Comparison of AI-Enabled Techniques for the Detection of Attacks in IoT devices;understanding Malware Dynamics in IoT Networks: Dataset Construction Using Mathematical Epidemiology and Complex Networks;Quality of Service Enhancement for IoT-Based Smart Office System Using Ad Hoc On-Demand Distance vector-Smart Control Ration Control Algorithm (AODv-SRCA);cyberattack Detector for Real-Time

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Botnet Attack Classification with Deep Learning Models 5th

Botnet Attack Classification with Deep Learning Models

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5th International conference on Smart computing and Informatics, SCI 2021

作者： Pradeepthi, K.v. Saxena, Ashutosh C R Rao Advanced Institute of Mathematics Statistics and Computer Science Hyderabad India C M R Technical Campus Hyderabad India

ISBN: (纸本)9789811697043

Most devices, ranging from big data servers to the IoT in household device, are accessible on the Internet. Their functioning almost partly or wholly depends on the Internet connectivity. These innumerable devices on the Internet are opportunity for the attackers, to compromise any device and use it for their own benefit. Botnets attack detection and identification is an important security concern. In this paper, we apply various deep learning algorithms to UNBS-NB 15 dataset for the classification of botnet traffic. From our analysis, we understand that deep learning algorithms with their intricate structure are able to perform better than the machine learning algorithms in botnet detection. Also, the hybrid models have an added advantage of canceling out the negatives of the parent algorithms and harnessing only the positives. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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Improving the Cloud Healthcare Services by Introducing AI for Better Performance and Quality 7

Improving the Cloud Healthcare Services by Introducing AI fo...

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7th International Youth conference on Radio Electronics, Electrical and Power Engineering, REEPE 2025

作者： Hamedi, Aisha Nassr, Mohammad Anbar, Mohammad Ali vlasova, vita v. Zykina, Alena A. Sin, Tan K. University of Tarous Information Technology Engineering Deparment Tartous Syrian Arab Republic Information and Communication Technology Engineering College Communication Technology Engineering Department Tartous Syrian Arab Republic Alayen Iraqi University Auiq Computer Technology Engineering Department Nasiriyah Iraq Bauman Moscow State Technical University Ai Research Institute Moscow Russian Federation Bauman Moscow State Technical University Moscow Russian Federation Multimedia University Faculty Of Business (FOB) Melaka Bukit Beruang Malaysia

ISBN: (纸本)9798331531836

Cloud computing has gained widespread popularity in various fields, especially in healthcare field where the cloud provide medical services without having to own or maintain the medical devices. This research offers an effective system to ensure the availability of cloud healthcare services by detecting faults in the cloud system to ensure the availability of resources, and making accurate predictions over a short period of time to improve the quality of service and to avoid waste and improve security and privacy. The methodology has been applied by using OpenStack platform for fault injection and Google Colab for data preprocessing. A variety of machine learning algorithms have been used to improve the accuracy of prediction: the Logistic Regression, Support vector Classification, Random Forest, k-Nearest Neighbors and decision Tree. The comparison results showed that the use of the SvC classification gave the best accuracy to predict faults, reaching 98%. © 2025 IEEE.

关键词： artificial intelligence cloud computing cloud services fault healthcare services machine learning algorithms

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ML Based Multi-Faceted Health Monitoring devices: Enhancing Elderly Well-Being

ML Based Multi-Faceted Health Monitoring Devices: Enhancing ...

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Recent Trends in Microelectronics, Automation, computing and Communications Systems (ICMACC), International conference on

作者： Sneha Priya Chaudhry Aayushi Malode O.v.Gnana Swathika School of Electrical Engineering Vellore Institute of Technology Chennai India Centre for Smart Grid Technologies Vellore Institute of Technology Chennai India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

This research paper explores the application, implementation, and assessment of health parameters in the elderly healthcare sector using Random Forest and Support vector Machine algorithms in Machine Learning. These algorithms increase the accuracy and predictive capabilities of health monitoring devices that analyze heart rate, fall risks, stress levels, and hypertension in the elderly in real time. The integration of IoT technology into the system ensures continuous and efficient monitoring with reliable communication and connectivity. The sensors are located to pick up essential health data, which provides the baseline for a deep analysis of health trends. This paper elaborates on how machine learning predicts and monitors health parameters in the elderly population.

关键词： Support vector machines Hypertension Machine learning algorithms Human factors Prediction algorithms Reliability Older adults Biomedical monitoring Monitoring Random forests

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Implementation of YOLOv9 in Agricultural AI for Enhanced Weed Detection

Implementation of YOLOv9 in Agricultural AI for Enhanced Wee...

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2024 International conference on Intelligent computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Duraisamy, Premkumar Deepika, A. Niranjani, v. Jeevitha, R. Sibishree, M. Oviya, A. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350369175

Weeds significantly hinder agricultural productivity by reducing crop yields and increasing production costs. Leveraging artificial intelligence (AI) is critical for equipping farmers with early detection capabilities to implement effective weed management strategies. Among AI technologies, deep learning (DL) techniques are especially effective for analyzing agricultural field images to identify weed species. This paper reviews state-of-the-art DL methodologies, with a focus on the YOLOv9 model for weed detection. Our findings highlight YOLOv9's superior effectiveness, achieving an accuracy of $\mathbf{9 0 \%}$, due to its robust architecture and advanced feature extraction. The model's proficiency in accurately identifying weeds is evident from our thematic analysis. We also explore various image acquisition devices, including robots, drones, and mobile phones. Comparative evaluations show that YOLOv9 consistently outperforms other DL techniques, achieving remarkable accuracy and speed. This study includes a comparative analysis of several algorithms like SSD, Mask R-CNN, and Fast R-CNN, underscoring YOLOv9's superior performance. This paper serves as a valuable resource for researchers and practitioners, guiding future efforts in weed management and precision agriculture by emphasizing YOLOv9's transformative potential. © 2024 IEEE.

关键词： Agricultural productivity Deep learning Image acquisition object detection Weed detection YOLOv9 algorithm

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An Internet of Things (IoT) Assisted Forest Fire Detection Mechanism Using Hybrid Classification and Learning Model

An Internet of Things (IoT) Assisted Forest Fire Detection M...

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2024 International conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Balachandran, G. Govindasamy, Suresh Sankar, R. vidhyasree, v. Sunheriya, Neeraj Attar, H. Jeppiaar Engineering College Department of Ece Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India S.A. Engineering College Department of Master of Computer Applications Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Department of Energy Engineer zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

For all the primary and secondary advantages they provide, forests are an indispensable part of humanity's natural resource arsenal. Forest fires and other natural catastrophes significantly affect both the rate of global warming and the viability of Earth's biota. Consequently, studying how to automatically detect forest fires is crucial for reducing the frequency and severity of such catastrophes. Determinants of fire prevention and suppression strategies can benefit from early detection as well. In this study, we use computer vision algorithms based on artificial intelligence to identify fire and smoke in photographs. This paper presents a new hybrid methodology called Enhanced Learning with Fire Classification Model (ELFCM). It uses learning in conjunction with classification norms to accurately report fire and smoke details. To test how well the proposed scheme works, it is cross-validated with the conventional Random Forest Classifier (RFC). According to the findings of the experiments, some of the unreported or incorrectly detected fires that ELFCM reported may be confirmed by using IoT devices. The fire department receives this recorded report for their records. In the end, we tested the fire classification findings by comparing our method's performance to that of other previously announced fire detection methods that employed commonly used performance matrices. © 2024 IEEE.

关键词： Premixed flames

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Integrating AI-Driven IoT Solutions for Enhanced Predictive Analytics in Healthcare a Comprehensive Study on Chronic Disease Management

Integrating AI-Driven IoT Solutions for Enhanced Predictive ...

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2024 IEEE International conference on Intelligent computing and Emerging Communication Technologies, ICEC 2024

作者： Sreenivasu, S.v.N. Katta, Siva Koteswararao Auguskani, Jerlin Priya Lovelin Priya, Dumpa vamsi Jagadish, v. Raghunath, vedaprada Narasaraopeta Engineering College Dept of Computer Science and Engineering Narasaraopet India Kitew Department of Mechanical Kakinada India Jazan University Department of Nursing Saudi Arabia Vignan Institute of Information and Technology Visakhapatnam India Department of Cse Hyderabad India Visveswaraya Technological University India

ISBN: (纸本)9798331508432

The high incidence of chronic non-communicable diseases (NCDs) places a significant financial strain on public health systems. Brazil is projected to see a rise in mortality rates caused by non-communicable diseases (NCDs) by 2030. These include cancer, cardiovascular disease, respiratory illness, and diabetes. The unique challenge of managing chronic and integrated care for NCDs is well-documented. This study presents an AI-based framework for managing chronic diseases and providing digital health services globally. The suggested platform is constructed and supported by healthcare 4.0 technologies, which enable the implementation of a smart healthcare system. Among these innovations are AI-enabled cloud solutions, the internet of medical things, and wearable technology. Also included in the publication is a case study of diabetes prediction that demonstrates the platform's viability. An initial dataset was created for the research case with many attributes, including bio-impedance, skin impedance, skin temperature, pulse rate, and oxygen concentration. In this study, we investigate how the IoT and Artificial Intelligence (AI) are changing the face of illness detection in connected healthcare systems. AI has quickly become an indispensable tool in the healthcare industry, providing advanced algorithms for evaluating medical data and facilitating forecasting and decision-making. The internet of things (IoT) improves upon this by allowing web-enabled devices, such as implanted sensors and wearables, to continuously gather data. By integrating AI and IoT, smart healthcare systems improve medical procedures, patient experiences, and operational operations. Rapid and reliable disease diagnosis is made possible by combining AI-driven procedures with IoT data streams. This paper aims to overcome the limitations of traditional methods, which are typically influenced by human biases. © 2024 IEEE.

关键词： Diagnosis

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Design of Optimized CNN for Image Processing using verilog 4

Design of Optimized CNN for Image Processing using Verilog

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4th IEEE Global conference for Advancement in Technology, GCAT 2023

作者： veena, M.B. Deodurg, Rohan Shrinidhi, v. Soundarya, S. Dept of Electronics and Communication BMS College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798350305258

This paper presents the implementation of Optimized CNN for Image Processing using verilog. The main emphasis lies in harnessing the capabilities of CNNs (Convolutional Neural Networks) for image processing tasks, such as image compression and accentuating descriptor points within an image. The fundamental component of the suggested approach is designing and developing an architecture capable of successfully carrying out CNN-based image processing activities. verilog is used as the hardware description language to model and simulate the hardware components. Synthesis tools are employed to convert the verilog code into a gate-level representation suitable for implementation on FPGA or ASIC platforms. The verilog- based optimized CNN design enable faster and efficient execution of image processing algorithms compared to traditional software implementations. Real-time performance can be accomplished by offloading the computing task to specialized hardware, making it appropriate for applications with strict latency constraints. The image size is decreased by the image compression method used in this work. while preserving important visual information. verilog allows the effective implementation of image scaling algorithms in hardware, enabling CNNs like vGG to operate effectively on devices with the limited resources. In this article, we will explore the integration of vGG model with verilog to achieve image size reduction and improve CNN's performance based applications in resource constrained environments. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) visual Geometry Group (vGG)

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An FPGA Implementation of A Portable DNA Sequencing Device Based on RISC-v 20

An FPGA Implementation of A Portable DNA Sequencing Device B...

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20th IEEE Interregional NEWCAS conference (IEEE NEWCAS)

作者： Wu, Zhongpan Hammad, Karim Beyene, Abel Dawji, Yunus Ghafar-Zadeh, Ebrahim Magierowski, Sebastian York Univ Dept Elect Engn & Comp Sci Toronto ON Canada

ISBN: (数字)9781665401050

ISBN: (纸本)9781665401050

Miniature and mobile DNA sequencers are steadily growing in popularity as effective tools for genetics research. As basecalling algorithms continue to evolve, basecalling poses a serious challenge for small computing devices despite its increasing accuracy. Although general-purpose computing chips such as CPUs and GPUs can achieve fast results, they are not energy efficient enough for mobile applications. This paper presents an innovative solution, a basecalling hardware architecture based on RISC-v ISA, and after validation with our custom FPGA verification platform, it demonstrates a 1.95x energy efficiency ratio compared to x86. There is also a 38% improvement in energy efficiency ratio compared to ARM. In addition, this study also completes the verification work for subsequent ASIC designs.

关键词： DNA Sequencing RISC-v ISA energy-efficiency Oxford nanopore basecalling viterbi FPGA ASIC

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