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

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Design of a High Voltage Common Mode Resilient FlexRay Receiver in 180nm/5V CMOS Process 6

Design of a High Voltage Common Mode Resilient FlexRay Recei...

引用

6th Iranian International Conference on Microelectronics, IICM 2024

作者： Mansoury, Hamid Sadat Saeedi, Saeed Atarodi, Mojtaba Sharif University of Technology Dept. of Electrical Engineering Tehran Iran Tarbiat Modares University Dept. of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331511371

A FlexRay receiver that is capable of operating within [-60V, 60V] common mode range using a common mode cancellation method is presented in this paper. The receiver uses 5V MOSFET transistors and incorporates a digital block to reduce the required number of comparators to 2, potentially leading to lower power consumption and die area. Extensive simulations have been conducted to ensure the receiver's functionality within a temperature range of [-40°C, 150°C], which is particularly crucial for automotive applications. Furthermore, corner and mismatch simulations have been performed to ensure the circuit's process tolerance and suitability for mass production. Additionally, Electromagnetic Immunity simulations have been performed to ensure that the receiver, intended for automotive applications, meets the necessary standards. The layout of the receiver occupies an area of 153.6 × 128.7 μm2.. © 2024 IEEE.

关键词： CMOS integrated circuits

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Denial-of-Service Attack Detection and Mitigation Strategy for Synchrophasor Data Flowing in Power Transmission System 4

Denial-of-Service Attack Detection and Mitigation Strategy f...

引用

4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Nath, Soumya Ranjan Bhalja, Bhavesh R. Sidhu, Tarlochan S. Indian Institute of Technology Dept. of Electrical Engineering Roorkee India Ontario Tech University Dept. of Electrical and Computer Engineering Oshawa Canada

ISBN: (纸本)9798350383997

The coupling of the legacy power infrastructure with complex computer networks and the integration of communication devices has led to the problem of cyberattacks, which in turn may induce partial/full blackouts. In this regard, an approach based on a Decision Tree for the detection and mitigation of Denial-of-Service (DoS) attacks is proposed in this paper. Based on the patterns and effects observed during DoS attacks on the Synchrophasor data flowing from the Phasor Measurement Unit (PMU) to the Phasor Data Concentrator (PDC), various supervised machine learning models are employed, which classify healthy and attack data and detect DoS attacks. Thereafter, a recurrent deep learning network employing Gated Recurrent Unit (GRU) architecture is utilized to reconstruct the lost data, which in turn is helpful in mitigating the impacts of a DoS attack. The suggested algorithms are validated using a real-world scenario of a communication dataflow between multiple PMUs placed at different buses of a power transmission network and a PDC present at a control center, representing a wide-area network, and are incorporated using Real Time Digital Simulator and openPDC. By comparing various metrics of ML classification, the results indicate that the decision tree based model gives better accuracy along with the least detection time compared to other machine learning-based models. The suggested GRU-based mitigation algorithm can reconstruct the DoS-impacted lost data from the available pre-attack phasors. © 2024 IEEE.

关键词： Phasor measurement units

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Detection and Classification of Skin Cancer Using YOLOv8n 11

Detection and Classification of Skin Cancer Using YOLOv8n

引用

11th International Conference on electrical engineering, computer science and Informatics, EECSI 2024

作者： Riyadi, Munawar A Ayuningtias, Adela Isnanto, R Rizal Universitas Diponegoro Dept. of Electrical Engineering Semarang Indonesia Universitas Diponegoro Dept. of Computer Engineering Semarang Indonesia

ISBN: (纸本)9798350355314

Skin cancer is a disease caused by the growth of abnormal cells in skin tissues. The World Health Organization (WHO) has recorded an 88% increase in deaths due to skin cancer caused by exposure to ultraviolet rays. Currently, in the medical field, the diagnosis of skin cancer involves a biopsy process, which requires considerable time and cost. Therefore, this study aims to develop a system for detecting and classifying skin cancer based on the shape of skin lesions using the You Only Look Once version 8 nano (YOLOv8n), which can detect lesions rapidly. The dataset used is ISIC 2019, comprising of 4289 images of cancerous skin lesions divided into 9 classes: Basal Cell Carcinoma, Squamous Cell Carcinoma, Melanoma, Actinic Keratosis, Dermatofibroma, Nevus, Seborrheic Keratosis, Pigmented Benign Keratosis, and Vascular Lesion. Experimental results show that the designed system performs well in detecting and classifying the lesions, achieving an overall accuracy of 93.5%, with a Precision of 93.5%, Recall of 93.7%, and an F1-Score of 93.5%. © 2024 IEEE.

关键词： Lung cancer

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Automated Bengali Sign Language Character Classification with Deep Learning Techniques 15

Automated Bengali Sign Language Character Classification wit...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Akter, Tahmina Mahmud, Tanjim Barua, Tikle Naher, Sultana Rokeya Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

Recognition of Bengali sign language characters is crucial for facilitating communication for the deaf and hard-of-hearing population in Bengali-speaking regions, which encompass approximately 430 million people worldwide. Despite the significant number of individuals requiring this support, research on Bengali sign language character recognition remains underdeveloped. This article presents a novel approach to categorize Bengali sign language characters using the Ishara-Lipi dataset, based on convolutional neural networks (CNNs) and pretrained models. We evaluated our approach using metrics such as accuracy, precision, recall, F1-score, and confusion matrices. Our findings indicate that the CNN model achieved the highest performance with an accuracy of 98%, followed by VGG19 with 94% and ResNet variants achieving around 88%. The proposed model demonstrates robust and efficient classification capabilities, significantly bridging the gap in existing literature. This study holds substantial promise for enhancing assistive technology, thereby improving social inclusion and quality of life for Bengalispeaking deaf and hard-of-hearing individuals. © 2024 IEEE.

关键词： Bengali sign language CNNs Deaf and hard-of-hearing community Image classification

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Lightweight SNN-based White Space Detector for Cognitive Vehicular Networking

Lightweight SNN-based White Space Detector for Cognitive Veh...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Galib, Md Mehedi Hassan Younis, Mohamed University of Maryland Baltimore County Dept. of Computer Science and Electrical and Engineering BaltimoreMD21250 United States

ISBN: (纸本)9798350351255

The Dedicated Short-Range Communication (DSRC) protocol has become the de facto means for supporting vehicle ad hoc networking (VANET). Yet, the current allocated spectrum for DSRC is insufficient for handling large volumes of data, particularly during accidents or high traffic congestion, where most spectral resources are dedicated to control and emergency awareness, degrading infotainment and multimedia application services. Such a challenge motivates the pursuance of cognitive radio (CR), also known as CR-VANET, where vehicles opportunistically tap to utilize resources within the licensed spectrum as secondary users (SUs). To avoid interference with primary users (PUs), a vehicle needs to accurately sense the medium and detect white space. Given the dynamic nature of the VANET topology and varying volume of message traffic, machine-learning (ML) techniques, particularly those pursuing deep learning models, proved to be a viable option for spectrum sensing. However, the computational complexity of these techniques becomes an obstacle for vehicles. This paper opts to fill the technical gap by proposing a novel lightweight spectrum detector that employs spiking neural networks (SNN). The validation results using a field collected LTE-dataset clearly show that our proposed SNN model enables fast inference time, which ultimately expedites the spectrum sensing process and provides additional spectral resources for vehicular communication. © 2024 IEEE.

关键词： Traffic congestion

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Sensitivity Analysis for Normal and Canonical Internal Descriptions of A Notch Digital Filter

Sensitivity Analysis for Normal and Canonical Internal Descr...

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Hinamoto, Yoichi Doi, Akimitsu Dept. of Electrical and Computer Engineering National Institute of Technology Kagawa College Takamatsu761-8058 Japan Dept. of Computer Science Hiroshima Institute of Technology Hiroshima731-5193 Japan

ISBN: (纸本)9798350353464

This paper analyzes coefficient sensitivity for the internal descriptions (state-space models) of a 2nd-order recursive notch digital filter. To this end, a 2nd-order recursive notch digital filter is realized by a normal-form or canonical-form internal description. The coefficient sensitivity for each internal description is then analyzed to derive the improved l2-sensitivity measure. Moreover, the coefficient sensitivity of the normal-form internal description is compared with that of the canonical-form counterpart by means of numerical experiments. The simulation results clarify that the normal-form internal description has low coefficient sensitivity as a whole comparing to the canonical-form counterpart. ©2024 IEEE.

关键词： Notch filters

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A Medium Voltage Bidirectional Modular LLC Converter With An Extendable Stacked-Switches Leg

A Medium Voltage Bidirectional Modular LLC Converter With An...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Beshara, Maria Cheema, Muhammad Ali Masood Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada Northern Transformer Corporation Toronto Canada

ISBN: (纸本)9798350376067

This paper proposes a new stacked-switches/full-bridge (FB) structured DC/DC bidirectional LLC resonant converter for medium voltage DC grids in energy storage application. With the proposed converter configuration, the extendable stacked-switch leg that interfaces with the MV side results in low voltage stress across the switch. In addition, this leg also facilitates rectification in the reverse power flow direction. The proposed converter utilizes variable frequency control to adjust the battery voltage, with FB synchronous rectification to facilitate efficient power flow for both charging and discharging of batteries. The circuit analysis, design considerations, and the operational framework are provided. Results on a 200kW, 100kHz, 10kV/600V design are provided to demonstrate the merits of this work. © 2024 IEEE.

关键词： energy storage medium voltage Resonant converters soft-switching

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Exploring Taiwan Monkeypox Virus (MPOX/MPXV) Clade IIb by Establishing Dataset and Clustering by using Sequence and Image Similarity Comparisons 24

Exploring Taiwan Monkeypox Virus (MPOX/MPXV) Clade IIb by Es...

引用

7th International Conference on Computational Intelligence and Intelligent Systems, CIIS 2024

作者： Wang, Tzung-Wei Li, Ming-Kuang Lou, Der-Chyuan Lin, Chun-Yuan Dept. of Electronics Engineering Chang Gung University Taoyuan Taiwan Dept. of Computer Science and Information Engineering Asia University Taiwan Taichung Taiwan Dept. of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan

ISBN: (纸本)9798400717437

Based on the MPXV Clade IIb cases in Taiwan, 175 segments were identified from OPG001 to OPG210. The first focus of this paper is on sequence analysis of the MPXV cases in Taiwan, detailing the procedures, including semi-automated segmentation and dataset creation from a large number of MPXV Clade IIb nucleotide sequences found in the NCBI and GISAID databases. The second feature highlights the pioneering establishment of an MPXV Clade IIb RNA 2nd structure image dataset in Taiwan, as such a dataset does not yet exist globally. The third characteristic involves the use of unsupervised learning methods for the rapid clustering of MPXV Clade IIb RNA 2nd structure images. This approach allows not only for the quick detection and clustering of unknown MPXV Clade IIb cases but also for the rapid identification of new MPXV mutations and structural changes. Compared to traditional sequence analysis, this new hybrid systematic method significantly enhances overall efficiency, improving processing speed by at least 24 hours when handling the alignment of 10 MPXV sequences from Taiwan. Future plans include using this prototype analytical model and method to accelerate the analysis of critical lethal factors in MPXV Clade IIb, such as D14L, ultimately helping biologists reduce the time required for analysis and antiviral drug development. © 2024 Copyright held by the owner/author(s).

关键词： Nucleotides

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Content Based Classification of Short Messages using Recurrent Neural Networks in NLP

Content Based Classification of Short Messages using Recurre...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Thiagarajan, Priya Sundarakantham, K. Thiagarajar College of Engineering Dept. of Applied Mathematics and Computational Science Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350394528

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better. © 2024 IEEE.

关键词： Recurrent neural networks

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Crowd Digital Twins: High-Level Requirements and Architecture 22

Crowd Digital Twins: High-Level Requirements and Architectur...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Casadei, Roberto Delnevo, Giovanni Girau, Roberto Mirri, Silvia University of Bologna Dept. of Computer Science and Engineering Italy

ISBN: (纸本)9798331508050

Crowd management is the problem of dealing with physical crowds of humans, e.g., for safety reasons or to improve services through crowd-awareness. In previous work, the notion of a Crowd Digital Twin (CDT), namely the digital twin associated to physical gatherings of humans, was motivated and proposed as a fundamental component of crowd management systems. In this work, we better characterise the CDT notion, by investigating the functional and non-functional requirements that such a technical solution should satisfy. Then, we consider what are the fundamental components of CDT systems, and accordingly delineate a general architecture aimed at flexibly supporting the corresponding physical-digital thread. © 2025 IEEE.

关键词： Crowdsourcing

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