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23rd International Vacuum Electronics Conference, IVEC 2022

作者： Bhattacharya, Ranajoy Shih, Pao-Chuan Palacios, Tomas Browning, Jim Boise State University Dept. of Electrical & Computer Engineering BoiseID United States Massachusetts Institute of Technology Dept. of Electrical Engineering and Computer Science CambridgeMA United States

ISBN: (纸本)9781665443258

GaN field emitter arrays are being studied for use as vacuum channel transistors (VCTs). In this work, arrays of 150 x 150 GaN field emitters were characterized before and after UV exposure. Collector voltage was kept at 200V DC, and the gate voltage was swept from 0 to 60V. From the I-V measurements, a jump in emission current, a form of conditioning, was observed after a few voltage sweeps resulting in a stable emission current. After UV exposure at 185 nm with 350μW/cm2, 5 times increase in current was observed, reaching a maximum field emission current of 6μA. © 2022 IEEE.

关键词： III-V semiconductors

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Genetic Effects of Covid 19 on the Development of Neurodegenerative Diseases 5

Genetic Effects of Covid 19 on the Development of Neurodegen...

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5th International Conference on Electrical Information and Communication Technology, EICT 2021

作者： Podder, Nitun Kumar Shill, Pintu Chandra Rana, Humayan Kabir Omit, Shudeb Babu Sen Mehedee Hasan Al Shahriar, M. Shafiul Azam, M. Khulna University of Engineering Technology Dept. of Computer Science Engineering Khulna Bangladesh Green University of Bangladesh Dept. of Computer Science Engineering Dhaka Bangladesh Z. H. Sikder University of Science and Technology Dept. of Computer Science Engineering Shariatpur Bangladesh Pabna University of Science and Technology Dept. of Computer Science Engineering Pabna Bangladesh

ISBN: (纸本)9781665409063

The coronavirus disease (COVID-19) caused by SARS-COV-2, a highly infectious pathogen, genetically similar to SARS-COV is an unprecedented worldwide health crisis. Rapidly accumulating clinical research revealed that COVID-19 is manifested by various neurological symptoms and also affects the brain in many ways including direct infection to systemic inflammation. Which indicates it may substantially increase the incidence of developing neurodegenerative diseases (NDGDs). To discourse this issue we studied the computable frameworks to address the gene expression association of COVID-19 and NDGDs to identify the link among them. We analyzed GEO microarray datasets from COVID-19 and NDGDs including Epilepsy, Stroke, Multiple Sclerosis, Alzheimer's disease, and Parkinson's disease. We constructed disease-gene relationship networks and identified dysregulated pathways, ontological pathways, protein-protein interaction (PPI) network, and protein-drug interaction (PDI) network. We observed that COVID-19 associated genes share 19, 26, 20, 19, 22 differentially expressed genes with Epilepsy, Stroke, Multiple Sclerosis, Alzheimer's disease, and Parkinson's disease respectively. Gene expression dysregulation, PPI and PDI relationship networks, different pathways suggest that COVID-19 may have a significant link to the development of these NDGDs. This analysis may help to develop therapeutic strategies and raise awareness about the influence of COVID-19 on the progression of NDGDs. © 2021 IEEE.

关键词： Proteins

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Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems for Next-Generation Wireless Networks

Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems f...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Satya Prakash Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This study explores the optimisation of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems through the use of phased arrays and advanced beamforming methods. With a focus on overcoming the challenges that come with 5G wireless communication, like high data rate needs, interference resistance, and efficient spectrum usage, this work presents a novel approach that combines dynamic beam steering and adaptive precoding to significantly enhance system performance. Through thorough modelling, we demonstrate considerable improvements in bit error rates (BER), throughput, and spectrum efficiency compared to traditional MIMO-OFDM configurations. The results demonstrate a 25% improvement in throughput and a 40% reduction in BER in densely populated metropolitan environments, underscoring the potential of phased arrays and beamforming to further improve 5G network performance. This work not only validates the efficacy of the proposed enhancements but also establishes a foundation for future advancements in wireless communication systems by highlighting the critical roles that phased arrays and beamforming will play in achieving the maximum potential of 5G technology.

关键词： Phased arrays Beam steering Adaptive systems Array signal processing 5G mobile communication Precoding Wireless networks Bit error rate Interference Throughput

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Monocular Microscope to CT Registration using Pose Estimation of the Incus for Augmented Reality Cochlear Implant Surgery

arXiv

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arXiv 2024年

作者： Zhang, Yike Davalos, Eduardo Su, Dingjie Lou, Ange Noble, Jack H. Dept. of Computer Science Vanderbilt University United States Dept. of Electrical and Computer Engineering Vanderbilt University United States

For those experiencing severe-to-profound sensorineural hearing loss, the cochlear implant (CI) is the preferred treatment. Augmented reality (AR) aided surgery can potentially improve CI procedures and hearing outcomes. Typically, AR solutions for image-guided surgery rely on optical tracking systems to register pre-operative planning information to the display so that hidden anatomy or other important information can be overlayed and co-registered with the view of the surgical scene. In this paper, our goal is to develop a method that permits direct 2D-to-3D registration of the microscope video to the pre-operative Computed Tomography (CT) scan without the need for external tracking equipment. Our proposed solution involves using surface mapping of a portion of the incus in surgical recordings and determining the pose of this structure relative to the surgical microscope by performing pose estimation via the perspective-n-point (PnP) algorithm. This registration can then be applied to pre-operative segmentations of other anatomy-of-interest, as well as the planned electrode insertion trajectory to co-register this information for the AR display. Our results demonstrate the accuracy with an average rotation error of less than 25 degrees and a translation error of less than 2 mm, 3 mm, and 0.55% for the x, y, and z axes, respectively. Our proposed method has the potential to be applicable and generalized to other surgical procedures while only needing a monocular microscope during intra-operation. Copyright © 2024, The Authors. All rights reserved.

关键词： Augmented reality

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Hardware Implementation to Detect Brain Tumor using AI ML based Convolution Neural Networks CNN in the Field of Bio Medical engineering 15

Hardware Implementation to Detect Brain Tumor using AI ML ba...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Ravindranath, C. Gopalaiah Adithya, T.G. Pavithra, G. Manjunath, T.C. Department of Civil Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India Department of Electronics & Instrumentation Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India Dept. of Computer Science & Engineering PES University Hoskerehalli 100 Feet Ring Road Karnataka Bangalore560085 India Dept. of Electronics & Communication Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798331300579

A tumor is nothing but excess cells growing in an uncontrolled manner. Brain tumor cells grow in a way that they eventually take up all the nutrients meant for the healthy cells and tissues, which results in brain failure. Currently, doctors locate the position and the area of brain tumor by looking at the MR Images of the brain of the patient manually. This results in inaccurate detection of the tumor and is considered very time consuming. We are using a Deep Learning architectures CNN (Convolution Neural Network) generally known as NN (Neural Network) Transfer learning for detecting brain tumors. The performance of the model is predicting image tumor is present or not in image. If the tumor is present, it returns yes otherwise return no. Here the location of the tumor is identified by K-means clustering using Raspberry pi. The Arduino controller is used for the movement of the robotic wheels to move in the exact location and determines the position of the tumor. © Grenze Scientific Society, 2024.

关键词： Convolution

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ML Assisted Web Application Firewall

ML Assisted Web Application Firewall

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Pranav Kalariya Manthan Jethva Yasser Alginahi Electrical and Computer Engineering Dept. University of Windsor Windsor Canada Computer Science Dept. Adrian College MI USA

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

This study presents the development and implementation of a sophisticated Web Application Firewall (WAF) empowered by machine learning techniques to bolster cybersecurity measures. Traditional WAFs primarily rely on rule-based systems, which may struggle to adapt to the evolving nature of web-based threats. In contrast, our proposed solution leverages machine learning algorithms to dynamically analyze and respond to emerging cyber threats, providing a more proactive and adaptive defense mechanism. The core functionality of the system involves the continuous monitoring of incoming web traffic, extracting relevant features, and utilizing a machine learning model to classify the traffic as either benign or malicious. The model is trained on historical data to recognize patterns and behaviors indicative of various cyber threats, including SQL injection, cross-site scripting, and other common attack vectors. Through this learning process, the system becomes adept.at discerning malicious activities and adapting its defense strategies accordingly. The proposed model helps achieve higher precision in identifying the threat requests from normal requests.

关键词： Adaptation models Machine learning algorithms Firewalls (computing) Machine learning SQL injection Feature extraction data models

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Low Frequency Oscillator Circuit Using Si-Gated Field Emitter Arrays 23

Low Frequency Oscillator Circuit Using Si-Gated Field Emitte...

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23rd International Vacuum Electronics Conference, IVEC 2022

作者： Bhattacharya, Ranajoy Hay, Robert Cannon, Mason Rughoobur, Girish Karaulac, Nedeljko Akinwande, Akintunde I. Browning, Jim Boise State University Dept. of Electrical & Computer Engineering BoiseID United States Massachusetts Institute of Technology Dept. of Electrical Engineering and Computer Science CambridgeMA United States

ISBN: (纸本)9781665443258

Si-gated field emitter arrays (Si-GFEAs) are strong candidates for nano vacuum channel transistors (NVCTs). In this work, Si-GFEA die with 1000 X1000 arrays were used to create a Colpitts oscillator circuit. First, transfer and output characteristics sweeps were carried and based on those data, a LTSpice vacuum transistor (VT) model was developed. Then the model was used to develop a Colpitts oscillator circuit. The circuit was experimentally tested where the collector voltage was kept at 200 V DC and the gate voltage was kept at 50V DC. From the experiment, an oscillation frequency of 152 kHz was observed with a maximum amplitude of of Vpp=4.38 V. © 2022 IEEE.

关键词： Timing circuits

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Exploring the Capabilities and Limitations of Generative AI Applications, Challenges, and Future Directions

Exploring the Capabilities and Limitations of Generative AI ...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Singh, Gulbir Meenakshi Sharma, Poonam Bhardwaj, Anjul Graphic Era Hill University Department of Computer Science and Engineering Haldwani Campus Uttarakhand Nanital India Mmict&bm Mullana Haryana Ambala India Divine Institute of Management & Studies Dept. of Computer Application Ziradei Bihar Siwan India

ISBN: (纸本)9798331508685

Unprecedented capabilities for content generation, predictive analytics, and automation are made available by the introduction of Generative Artificial Intelligence (AI) technologies, which uses in a new age of industrial innovation. The purpose of this study is to investigate the transformational potential of generative AI from the perspective of exploring its capabilities and limitations and highlighting its role in the development of future technological developments. With the help of this study, we want to highlight important unsolved issues in contemporary generative AI paradigms. These challenges need to be addressed in order to further improve the capabilities, variety, and dependability of these concepts. Our goal is to give researchers with significant insights that will allow them to explore successful research areas, which will ultimately lead to the creation of generative AI systems that are more robust and accessible. © 2025 IEEE.

关键词： Generative adversarial networks

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Distributionally robust optimization as a scalable framework to characterize extreme value distributions 24

Distributionally robust optimization as a scalable framework...

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Patrick Kuiper Ali Hasan Wenhao Yang Yuting Ng Hoda Bidkhori Jose Blanchet Vahid Tarokh Dept. of Electrical and Computer Engineering Duke University Durham North Carolina Dept. of Management Science and Engineering Stanford University Stanford California Computational and Data Sciences Dept. George Mason University Fairfax Virginia

The goal of this paper is to develop distributionally robust optimization (DRO) estimators, specifically for multidimensional Extreme Value Theory (EVT) statistics. EVT supports using semi-parametric models called max-stable distributions built from spatial Poisson point processes. While powerful, these models are only asymptotically valid for large samples. However, since extreme data is by definition scarce, the potential for model misspecification error is inherent to these applications, thus DRO estimators are natural. In order to mitigate over-conservative estimates while enhancing out-of-sample performance, we study DRO estimators informed by semi-parametric max-stable constraints in the space of point processes. We study both tractable convex formulations for some problems of interest (e.g. CVaR) and more general neural network based estimators. Both approaches are validated using synthetically generated data, recovering prescribed characteristics, and verifying the efficacy of the proposed techniques. Additionally, the proposed method is applied to a real data set of financial returns for comparison to a previous analysis. We established the proposed model as a novel formulation in the multivariate EVT domain, and innovative with respect to performance when compared to relevant alternate proposals.

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Course Recommendation System for Company Job Placement Using Collaborative Filtering and Hybrid Model 13

Course Recommendation System for Company Job Placement Using...

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13th International Conference on data science, Technology and Applications, data 2024

作者： Park, Jaeheon Lee, Suan Lee, Woncheol Kim, Jinho Kangwon Joint Program of Software Convergence Course Kangwon National University Chuncheon Korea Republic of School of Computer Science Semyung University Jecheon Korea Republic of SeedsSoft Co. Ltd. Chuncheon Korea Republic of Dept. of Computer Science and Engineering Kangwon National University Chuncheon Korea Republic of

ISBN: (纸本)9789897587078

This study introduces a novel recommendation system aimed at enhancing university career counseling by adapting it to more accurately align with students' interests and career trajectories. Recognizing the challenges students face in selecting courses that complement their career goals, our research explores the efficacy of employing both collaborative filtering and a hybrid model approach in the development of this system. Uniquely, this system utilizes a company-course recommendation method, diverging from the traditional student-course paradigm, to generalize company-course relationships, thereby enhancing the system's recommendation precision. Through meticulous feature engineering, we improved the performance of the NeuMF model. Our experiments demonstrate that the proposed method outperforms other models by 10% to 79% based on the mAP metric, suggesting that the proposed model can effectively recommend courses for employment. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Collaborative learning

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