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Frontiers in Optics + Laser science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser science 2023

作者： Shandilya, Pradyoth H. Moille, Gregory D'Aguanno, Giuseppe Srinivasan, Kartik Menyuk, Curtis R. Department of Computer Science and Electrical Engineering University of Maryland Baltimore United States Microsystems and Nanotechnology Division NIST Gaithersburg United States

We show that 3-wave equations can be used to accurately model the multicolor solitons that appear in dual-pumped microresonators. These equations can be used to analyze the soliton properties. © Optica Publishing... 详细信息

ISBN: (纸本)9781957171296

关键词： Fourier transforms Laser pumping Microcavities Phase shift Phase velocity Solitons

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Human Action Recognition Using Difference of Gaussian and Difference of Wavelet

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Big Data Mining and Analytics 2023年第3期6卷 336-346页

作者： Gopampallikar Vinoda Reddy Kongara Deepika Lakshmanan Malliga Duraivelu Hemanand Chinnadurai Senthilkumar Subburayalu Gopalakrishnan Yousef Farhaoui Department of Computer Science and Engineering(AI&ML) CMR Technical CampusHyderabad 501401India. Department of Information Technology Kakatiya Institute of Technology and ScienceWarangal 506015India. Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women(Autonomous)Hyderabad 500100India. Department of Computer Science and Engineering S.A.Engineering College(Autonomous)Thiruverkadu 600077India. Department of Electronics and Communication Engineering Saveetha Institute of Medical and Technical Sciences(SIMATS)Chennai 602105India. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India. IDMS Team STI LaboratoryFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsErrachidia 25003Morocco.

Human Action Recognition(HAR)attempts to recognize the human action from images and *** major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments.A novel action descriptor is proposed in this study,based on two independent spatial and spectral *** proposed descriptor uses a Difference of Gaussian(DoG)filter to extract scale-invariant features and a Difference of Wavelet(DoW)filter to extract spectral *** create a composite feature vector for a particular test action picture,the Discriminant of Guassian(DoG)and Difference of Wavelet(DoW)features are *** Discriminant Analysis(LDA),a widely used dimensionality reduction technique,is also used to eliminate duplicate ***,a closest neighbor method is used to classify the *** and UCF 11 datasets were used to run extensive simulations of the suggested strategy,and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform *** average accuracy of DoG+DoW is observed as 83.6635%while the average accuracy of Discrinanat of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 80.2312%and 77.4215%,*** average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG+DoW is observed as 62.5231%while the average accuracy of Difference of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 60.3214%and 58.1247%,*** the above accuracy observations,the accuracy of Weizmann is high compared to the accuracy of UCF 11,hence verifying the effectiveness in the improvisation of recognition accuracy.

关键词： human action recognition difference of Gaussian difference of wavelet linear discriminant analysis Weizmann UCF 11 accuracy

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Online 3D Deformable Object Classification for Mobile Cobot Manipulation 56

Online 3D Deformable Object Classification for Mobile Cobot ...

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56th International Symposium on Robotics, ISR Europe 2023

作者： Nguyen, Khang Dang, Tuan Huber, Manfred Learning and Adaptive Robotics Laboratory Department of Computer Science and Engineering University of Texas at Arlington ArlingtonTX76013 United States

ISBN: (纸本)9783800761418

Vision-based object manipulation in assistive mobile cobots essentially relies on classifying the target objects based on their 3D shapes and features, whether they are deformed or not. In this work, we present an auto-generated dataset of deformed objects specific for assistive mobile cobot manipulation using an intuitive Laplacian-based mesh deformation procedure. We first determine the graspable region of the robot hand on the given object’s mesh. Then, we uniformly sample handle points within the graspable region and perform deformation with multiple handle points based on the robot gripper configuration. In each deformation, we identify the orientation of handle points and prevent self-intersection to guarantee the object’s physical meaning when multiple handle points are simultaneously applied to the mesh at different deformation intensities. We also introduce a lightweight neural network for 3D deformable object classification. Finally, we test our generated dataset on the Baxter robot with two 7-DOF arms, an integrated RGB-D camera, and a 3D deformable object classifier. The result shows that the robot is able to classify real-world deformed objects from point clouds captured at multiple views by the RGB-D camera. The source code is available at https://***/mkhangg/deformable_cobot. © VDE VERLAG GMBH Berlin Offenbach.

关键词： 3D vision deformable object manipulation

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Build to Win: electrical Hardware Opportunities for STEM Education

Build to Win: Electrical Hardware Opportunities for STEM Edu...

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IEEE Integrated STEM Education Conference (ISEC)

作者： Daniel Monagle Eric A. Ponce Steven B. Leeb Erik K. Saathoff Dylan E. Brooks Thomas C. Krause Daisy H. Green Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Department of Electrical and Computer Engineering University of Hawai'i Manoa

ISBN: (数字)9798350352801

ISBN: (纸本)9798350352818

engineering outreach and introductory courses are essential for motivating and training the next generation of capable engineers. Accessibility and portability of the infrastructure for a STEM course is critical for spreading STEM education and motivation efforts. The ubiquity of student personal computers and laptops provides a convenient platform for introducing computer programming, but the relatively cumbersome equipment deemed necessary for a hands-on electronics learning environment contributes to the lack of electronic circuit hardware exposure for many K-12 students and university undergraduates. This paper presents the relatively low-cost, versatile, electronics platform we have developed at MIT upon which our new, hands-on introductory STEM electrical engineering course has been built and taught in recent years. This paper focuses its discussion on the power supply and measurement systems we have produced that allow students and instructors to engage in hands-on electric circuit exercises and projects without the requirement for expensive laboratory infrastructure. Example demonstrations of introductory laboratory exercises using our platform are shown.

关键词： Training Power measurement Portable computers Power supplies Hardware Mathematics Programming profession

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Evaluating the Impact of Different Features on the Efficiency of Emotion Recognition Algorithms in Text 2

Evaluating the Impact of Different Features on the Efficienc...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Sharma, Sunil Ezhilarasan, G. Ghosh, Rajkumari Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India ARKA JAIN University Department of English Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

The effectiveness today's emotion recognition algorithms in texts has become increasingly more critical in current years today's its programs in supporting users gauge actual-time reactions from text-based totally conversations. On this paper, we advise a fixed state-of-the-art experiments to assess the effect that different functions, along with syntactic and semantic functions, have at the efficiency contemporary emotion reputation algorithms in textual content. We use a manually annotated dataset from the automatic have an effect on recognition in text (AART) task to create characteristic units consultant of various ranges latest functions. Those feature units are then used to assess the overall performance latest emotion recognition algorithms - Recursive Neural Networks and Convolutional Neural Networks. The results display that incorporating syntactic and semantic functions can improve the accuracy present day emotion popularity algorithms at a charge ultra-modern 6-10% while as compared with the baseline overall performance. From the findings modern this test, we conclude that incorporating syntactic and semantic features can drastically enhance the overall performance trendy emotion popularity algorithms in text-based totally conversations. © 2024 IEEE.

关键词： Semantics

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Design and Qualification of a High-Speed Low-Power Comparator in 40 nm CMOS Technology 32

Design and Qualification of a High-Speed Low-Power Comparato...

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32nd International Scientific Conference Electronics, ET 2023

作者： Zaki, Alireza Mohammad Du, Yutong Nihtianov, Stoyan Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Department of Microelectronics Mekelweg 5 DelftCD 2628 Netherlands

ISBN: (纸本)9798350302004

This paper presents the design methodology, test setup and experimental qualification results of a high-speed low-power threshold comparator in 40 nm CMOS technology intended for the registry of particles landing on a PIN-detector surface in particle detector readout electronics. The operation of the designed comparator is experimentally qualified for ideal digital pulses and analog signals generated by the preceding stages in a targeted potential application. © 2023 IEEE.

关键词： Comparators (optical)

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A Type of EEG-ITNet for Motor Imagery EEG Signal Classification

A Type of EEG-ITNet for Motor Imagery EEG Signal Classificat...

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17th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Titkanlou, Maryam Khoshkhooy Monjezi, Ehsan Mouček, Roman Department of Computer Science and Engineering University of West Bohemia Plzen306 14 Czech Republic Department of Electrical Engineering Shahid Chamran University Golestan Blvd Ahvaz Iran

The brain-computer interface (BCI) is an emerging technology that has the potential to revolutionize the world, with numerous applications ranging from healthcare to human augmentation. Electroencephalogram (EEG) motor imagery (MI) is among the most common BCI paradigms used extensively in healthcare applications such as rehabilitation. Recently, neural networks, particularly deep architectures, have received substantial attention for analyzing EEG signals (BCI applications). EEG-ITNet is a classification algorithm proposed to improve the classification accuracy of motor imagery EEG signals in a noninvasive braincomputer interface. The resulting EEG-ITNet classification accuracy and precision were 75.45% and 76.43%, using a motor imagery dataset of 29 healthy subjects, including males aged 21-26 and females aged 18-23. Three different methods have also been implemented to augment this dataset. © 2024 by SCITEPRESS–science and Technology Publications, Lda.

关键词： Electroencephalography

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DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations 24

DualFed: Enjoying both Generalization and Personalization in...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Guogang Liu, Xuefeng Niu, Jianwei Tang, Shaojie Wu, Xinghao Zhang, Jiayuan State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Department of Management Science and Systems University at Buffalo BuffaloNY United States

ISBN: (纸本)9798400706868

In personalized federated learning (PFL), it is widely recognized that achieving both high model generalization and effective personalization poses a significant challenge due to their conflicting nature. As a result, existing PFL methods can only manage a trade-off between these two objectives. This raises an interesting question: Is it feasible to develop a model capable of achieving both objectives simultaneously? Our paper presents an affirmative answer, and the key lies in the observation that deep models inherently exhibit hierarchical architectures, which produce representations with various levels of generalization and personalization at different stages. A straightforward approach stemming from this observation is to select multiple representations from these layers and combine them to concurrently achieve generalization and personalization. However, the number of candidate representations is commonly huge, which makes this method infeasible due to high computational costs. To address this problem, we propose DualFed, a new method that can directly yield dual representations correspond to generalization and personalization respectively, thereby simplifying the optimization task. Specifically, DualFed inserts a personalized projection network between the encoder and classifier. The pre-projection representations are able to capture generalized information shareable across clients, and the post-projection representations are effective to capture task-specific information on local clients. This design minimizes the mutual interference between generalization and personalization, thereby achieving a win-win situation. Extensive experiments show that DualFed can outperform other FL methods. Code is available at https://***/GuogangZhu/DualFed. © 2024 ACM.

关键词： Federated learning

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Global Floorplanning via Semidefinite Programming 23

Global Floorplanning via Semidefinite Programming

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Wei Li Fangzhou Wang José M. F. Moura R. D. Blanton Electrical and Computer Engineering Department Carnegie Mellon University Department of Computer Science and Engineering The Chinese University of Hong Kong

ISBN: (纸本)9798350323481

A major task in chip design involves identifying the location and shape of each major design block/module in the layout footprint. This is commonly known as floorplanning. The first step of this task is known as global floorplanning and involves identifying a location for each module that minimizes wire length and leaves sufficient area for each module. Existing global floorplanning methods either have a non-convex problem formulation, or have trivial global solutions with no guarantee on the quality of the result. Here, we model the global floorplanning as a Semi-Definite Programming (SDP) problem with a rank constraint. We replace the rank constraint with a direction matrix and convexify the problem, whose solution is shown to be a global optimum if an appropriate direction matrix is chosen. To calculate the direction matrix, a convex iteration algorithm is used where the problem is decomposed into two SDP sub-problems. Furthermore, we introduce a series of techniques that enhance the flexibility, accuracy, and efficiency of our algorithm. Design experiments demonstrate that our proposed method reduces the average wirelength up to 20% for different benchmarks and outline aspect ratios.

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Panel: Faculty Leadership 53

Panel: Faculty Leadership

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Karam, Lina Zissis, Georges Yao, Jason Nelson, Jill Jo, Minho Sliz, Rafal Nogueira, Michele Watkins, Steve Wang, Huihui School of Electrical Computer and Energy Engineering Arizona State University TempeAZ United States Univestité Toulouse Iii - Paul Sabatier Science and Engineering Toulouse France East Carolina University Department of Engineering GreenvilleNC United States George Mason University Department of Electrical and Computer Engineering FairfaxVA United States Korea University Department of Computer Convergence Software Sejong City Korea Republic of University of Oulu Optoelectronics and Measurement Techniques Unit Oulu90570 Finland Federal University of Minas Gerais Department of Computer Science MG Belo Horizonte Brazil Missouri University of Science and Technology Department of Electrical and Computer Engineering RollaMO United States St. Bonaventure University Department of Computer Science and Cybersecurity St. Bonaventure United States

ISBN: (纸本)9798350336429

This faculty leadership panel will be held jointly by the IEEE Education Society (IEEE EdSoc) and IEEE Educational Activities Board (EAB) Faculty Resources Committee (FRC). The panel intends to provide participants introductory knowledge and skills. Two moderators and four panelists will focus on discussing their various academic leadership experiences and practices. The panel will be held in an interactive way so that the participants will have enough time to ask questions or communicate with the panelists. It is expected that this joint panel may potentially help to build up networks and mentorships. © 2023 IEEE.

关键词： faculty leadership leadership knowledge leadership skills mentorship

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