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检索条件"机构=Dept. of Electrical and Computer Engineering and Computer Science University of Cincinnati"

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10 Gbps Data-dependent Jitter Measurement Circuit for Authentication Over Lossy Wireline Channels

10 Gbps Data-dependent Jitter Measurement Circuit for Authen...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Sydney Lang Tawfiq Musah Dept. Electrical and Computer Engineering The Ohio State University Columbus OH USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

This paper presents a relative jitter measurement circuit for use in wireline authentication receivers. Design considerations and trade-offs are discussed for a 10 Gbps realization in a 28nm CMOS process. The results show high accuracy jitter measurements over different channel profiles, with the errors less than 1.25% of the bit period. Simulated power consumption indicates minimal overhead to adding the proposed jitter measurement circuit for authentication in a wireline receiver.

关键词： Power measurement Power demand Accuracy Circuits and systems Measurement uncertainty Authentication Receivers

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An All-Analog Sampled-Line VSWR Sensor

An All-Analog Sampled-Line VSWR Sensor

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IEEE Topical Conference on Power Amplifiers for Wireless and Radio Applications (PAWR)

作者： Grace Gomez Devon T. Donahue Robert Macfarland Taylor W. Barton Electrical Computer and Energy Engineering Dept. University of Colorado Boulder Boulder USA

A sampled-line reflectometer is presented that uses all-analog computation to produce a voltage proportional to the square of the load reflection coefficient, intended to be integrated into an RF front-end within an array. The novel approach for analog computation, and its dependence on sampler placement along the line is discussed. Experimental measurements of a varying load impedance are demonstrated using a microstrip line with diode samplers, operating at 3 GHz.

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Reducing the Computational Complexity of the Volterra Series Using Permutation/Product Maps

Reducing the Computational Complexity of the Volterra Series...

引用

Canadian Conference on electrical and computer engineering (CCECE)

作者： Aaron J. Fonseca Andrew K. Bolstad Julie A. Dickerson Dept. of Electrical and Computer Engineering Iowa State University Ames United States

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

The Volterra series is often used to model nonlinear systems in the fields of system identification and adaptive filtering. One means of computing the response of Volterra series-based filters is via input product vector generation. This method is beneficial for applications which utilize the input product vector in addition to the filter’s response. However, the problem of input vector generation as well as the more general problem of Volterra series filtering is computationally complex—as the number of terms within the kernel grows exponentially with the model’s memory and order. This paper proposes a process of Volterra input product vector generation which utilizes the minimal number of multiplication instructions required. This is done by computing permutation and product maps which relate terms in the current iteration’s input product vector to terms within the previous iteration. A MATLAB implementation of the proposed method is applied to the task of Volterra filtering. Its performance is then compared to (a) the MATLAB implementation of a similar, reduced (though not-minimal) multiplication method of input product generation and (b) a "Fast Volterra Filtering" script listed at the MATLAB Central File Exchange. The results show that the proposed method performs comparably to or outperforms both of the other methods for increasing memories and orders of the underlying Volterra kernel.

关键词： Filters Filtering Computational modeling Vectors Mathematical models System identification Kernel

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Carotid plaque stroke risk assessment using multiscale AM-FM analysis based on DoG filterbanks

Carotid plaque stroke risk assessment using multiscale AM-FM...

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International Conference on Digital Signal Processing (DSP)

作者： Kyriacos P. Constantinou Ioannis P. Constantinou Marios S. Pattichis Efthyvoulos Kyriacou Constantinos S. Pattichis Dept. of Computer Science University of Cyprus Nicosia Cyprus Istognosis Ltd Nicosia Cyprus Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA Dept. of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

The objective of this work was the investigation of multiscale Amplitude Modulation - Frequency Modulation (AM-FM) analysis based on Difference of Gaussians (DoG) filterbanks representations in order to predict the risk of stroke by analysing carotid plaques ultrasound images of individuals with asymptomatic carotid stenosis. We computed the instantaneous amplitude, instantaneous phase and the magnitude of instantaneous frequency to extract histogram features on each plaque region. The Support Vectors Machine classifier was implemented to classify asymptomatic versus symptomatic plaques. A dataset of 100 carotid plaque images (50 asymptomatic and 50 symptomatic) were tested, and showed that the AM-FM features based on DoG filterbanks and simple histograms performed better than the traditional AM-FM features. Best results were obtained when an eight scale filterbank with a combination of scales was used reaching the accuracy of 75%.

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A Pulse Width Modulation-Based Spike Generator to Eliminate Timing Errors in Spiking Neural Networks

A Pulse Width Modulation-Based Spike Generator to Eliminate ...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Jungkook Jo Keonhee Sim Sangwoong Sim Jaehoon Jun dept. of electrical and computer engineering and program in semiconductor convergence Inha University Incheon Republic of Korea dept. of electrical engineering Inha University Incheon Republic of Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This paper proposes a novel spike generator for processing in memory (PIM) technology. Most of the electronics today utilize a von Neumann architecture. The von Neumann architecture suffers from the separation of memory and processor. This architecture delays data transfer between memory and processor. To overcome the issues, we utilize the spiking neural network (SNN) that combines memory and processor. SNN can be classified into voltage-based, current-based, and time-based architectures, each with its own pros and cons. Time-based SNN suffers from timing issues. To address the timing sensitivity of time-based neuron SNN, this paper proposes a PWM-based SNN. The PWM-based SNN utilizes pulse width-based logic to overcome timing sensitivity.

关键词： Sensitivity Image edge detection Neurons Spiking neural networks Data transfer Generators Delays Logic Next generation networking Clocks

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Cybersecurity for Industrial IoT: A Review on Vulnerabilities and Attacks 8

Cybersecurity for Industrial IoT: A Review on Vulnerabilitie...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kumari, Reetu Kumar, Sunil Amandeep Dutta, Kamlesh Kumari, Aparna Guru Jambheshwar University of Science and Technology Dept. of Computer Science & Engineering Haryana Hisar India Guru Jambheshwar University of Science and Technology Dept. of AI & Data Science Haryana Hisar India National Institute of Technology Hamirpur Dept. of Computer Science & Engineering Himachal Pardesh India Institute of Technology Nirma University Dept. of Computer Science & Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798331520762

Internet has evolved from a network of connecting people to a network of connecting things, leading to a more complex and sophisticated network of Industrial Things, known as Industrial IoT (IIoT) today. This evolution has revolutionized the traditional Industrial system into real-time smarter systems that enhanced overall efficiency and productivity. Despite its benefits, the interconnected nature of IIoT, expanded the attack surface, exposing the systems to new crucial and complex cybersecurity threats. Several real time attacks on various industries as mentioned in this paper and the increasing cybersecurity cost over the years, clearly states the need of robust security solutions. To deeply understand the cybersecurity aspect, we provided a comprehensive study of attacks and vulnerabilities in this paper. In this paper, we did a indept. study of various IIoT related severe attacks and aspects related to these attacks, like vulnerabilities, impacts, potential solutions etc. We also provided future research directions to help the research community, to work upon and develop more robust and efficient cybersecurity solutions. © 2025 IEEE.

关键词： attacks cybersecurity etc IIoT Industrial IoT IoT vulnerabilities

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Peer-to-Peer Electric Vehicles Energy Trading through Reinforcement Learning and Multilateral Bargaining

Peer-to-Peer Electric Vehicles Energy Trading through Reinfo...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Nicholas Kemp Md Sadman Siraj Eirini Eleni Tsiropoulou Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The establishment of Peer-to-Peer (P2P) Electric Vehicles (EVs) energy trading markets holds significant importance for optimizing energy exchange among EVs. Despite previous research, challenges remain in enabling distributed pairing of EVs and determining the optimal energy exchange among them. This paper introduces a reinforcement learning mechanism for Charging EVs (CEVs) to autonomously select optimal Discharging EVs (DEVs) and a multilateral bargaining game-theoretic model for determining the optimal procured energy amounts. These innovations enable DEVs to optimize the service provision and profit, thus, contributing to the advancement of P2P EVs energy trading markets. Experimental validation demonstrates the operational characteristics, scalability, and adaptability of the proposed P2P energy trading market model.

关键词： Adaptation models Technological innovation Energy exchange Scalability Reinforcement learning Electric vehicles Peer-to-peer computing Global communication

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Investigating the Use of Generative ai in M&S Education

Investigating the Use of Generative ai in M&S Education

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Simulation Winter Conference

作者： James F. Leathrum Yuzhong Shen Masha Sosonkina Dept. of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Large Language Models (LLMs) are rapidly creating a place for themselves in society. There are numerous reports, both good and bad, of their use in business, academia, government and society. While some organizations are trying to limit, or eliminate, their use, it appears that it is inevitable they will become a common “tool”. In education, there is a fear that students will not acquire critical thinking in the future, but we argue that LLMs will become a tool to assist students with critical thinking, giving guidance, feedback, and assessment. This paper investigates how the current state of LLMs can be integrated into modeling and simulation (M&S) education. Example cases for modeling and simulation development are presented showing how an LLM can assist M&S design and education in anticipation of LLMs becoming a common tool for M&S practitioners. Current limitations are also highlighted, and where possible, short-term solutions are proposed.

关键词： Graphics Hands Large language models Computational modeling Education Government Lead Chatbots Solids Prompt engineering

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Vehicle Movement Status Network on Drone-Perspective View with Adaptive Adversarial Learning

Vehicle Movement Status Network on Drone-Perspective View wi...

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Annual Conference of Industrial Electronics Society

作者： Youlkyeong Lee Jehwan Choi Kanghyun Jo Dept. of Electrical Electronic and Computer Engineering University of Ulsan Ulsan Republic of Korea

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

The rapidly developing autonomous driving field now needs a more secure transportation system through information between multiple mobility. Deep learning that can judge traffic conditions by convergence of various sensor data and in particular, research on the convolutional neural network using computer vision are being actively conducted. In addition, recognizing many objects at once in a large area through drone images and understanding the movement of the object is used as safe traffic assistance information. In this study, an image classification study is conducted to determine the status of the vehicle on the road through drone flight image data. The goal is to build a new image classification model robust to the proposed image classification network by applying the weighted adversarial learning method. Weight adversarial learning is a method of securing robust performance in image classification of various statuses while disturbing the model by forcibly reflecting the slope value in reverse when updating the network through the reverse gradient layer. In the experiment, model performance is evaluated through the collected drone flight data set.

关键词： Adaptation models Adaptive systems Object oriented modeling Roads Transportation Feature extraction Adversarial machine learning Reflection Image classification Drones

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MeQANet: Deep Learning-based Framework for Classifying Medical Query Answering in Bengali

MeQANet: Deep Learning-based Framework for Classifying Medic...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Fairooz Tasnia Mohammed Moshiul Hoque Dept. of Computer Science and Engineering Chittagong University of Engineering & Technology Chattagram Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

The automatic classification of medical QA pairs is crucial for improving healthcare efficiency, allocating specialized care, optimizing resource distribution, and facilitating robust data analysis. Bengali, the 7 th most widely spoken language globally, has a growing volume of health-related queries. However, unlike English, there is no benchmark large-scale medical QA dataset, pre-trained language model, or human baseline score for medical QA in Bengali. This paper introduces a deep learning-based framework called MeQANet that leverages CNN and BiLSTM techniques for classifying medical QA texts in Bengali. It also presents a novel medical QA corpus in Bengali (BnMedQA), consisting of approximately 4500 medical QA pairs across eight domains: Orthopedic, ENT, Gastroenterology and Hepatology, Gynecology, Pediatrics, General Medicine, Dermatology, and Cardiology. Various techniques (including LR, SVM, RF, MNB, AdaBoost (AB), CNN, BiL-STM, and CNN+BiLSTM) are employed to perform the classification tasks. Experimental results indicate that the CNN+BiLSTM approach achieved the highest F1-score of 0.85 among all employed techniques.

关键词： Support vector machines Radio frequency Data analysis Natural languages Dermatology Gynecology Medical services Gastroenterology Data models Resource management

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