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JSAP-OSA Joint Symposia, JSAP 2021

作者： Bao, J. Peraca, N. Marquez Tay, F. Kritzell, T.E. Li, X. Noe, G.T. Katayama, I. Takeda, J. Nojiri, H. Yamahara, H. Tabata, H. Baydin, A. Kono, J. Department of Physics and Astronomy Rice University HoustonTX United States Department of Electrical and Computer Engineering Rice University HoustonTX United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX United States Department of Physics Graduate School of Engineering Science Yokohama National University Yokohama Japan Institute for Materials Research Tohoku University Sendai Japan Department of Electrical Engineering and Information Systems Graduate School of Engineering University of Tokyo Japan Smalley-Curl Institute Rice University HoustonTX United States Department of Materials Science and NanoEngineering Rice University HoustonTX United States

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Low-Frequency Noise in “Graphene-Like” Exfoliated Thin Films of Topological Insulators

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MRS Online Proceedings Library 2011年第1期1344卷 1-5页

作者： M. Z. Hossain S. L. Rumyantsev K. M. F. Shahil D. Teweldebrhan M. Shur A. A. Balandin Nano-Device Laboratory Department of Electrical Engineering and Materials Science and Engineering Program University of California Riverside USA Department of Electrical Computer and Systems Engineering and Center for Integrated Electronics Rensselaer Polytechnic Institute Troy USA Ioffe Institute Russian Academy of Sciences St. Petersburg Russia

We report results of the study of the low-frequency noise in thin films of bismuth selenide topological insulators, which were mechanically exfoliated from bulk crystals via “graphene-like” procedures. From the resistance dependence on the film thickness, it was established that the surface conduction contributions to electron transport were dominant. It was found that the current fluctuations have the noise spectral density SI ∞ 1/ f (where f is the frequency) for the frequency range up to 10 kHz. The obtained noise data are important for transport experiments with topological insulators and for any proposed device applications of these materials.

关键词： devices electrical properties thin film

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A Novel Blockchain-Based Strategy for Energy Conservation in Cognitive Wireless Sensor Networks

A Novel Blockchain-Based Strategy for Energy Conservation in...

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Blockchain Technology and Information Security (ICBCTIS), International Conference on

作者： Khemraj Sharma Rohit Goyal Sukesh Kumar Bhagat Sheenu Agarwal Girish Singh Bisht Mohemmed Hussien Department of Business Administration Doctoral Program in Management Sciences Nanhua University Dalin Taiwan School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India Department of Computer Science & Engineering Uttaranchal University Dehradun India Department of Electronics and Communication Engineering Uttarakhand Technical University Dehradun India Department of Computer Science and Engineering Tula's Institute Dehradun India Dehradun India The Islamic university Najaf Iraq

ISBN: (数字)9798331528324

ISBN: (纸本)9798331528331

Internet of Things (IoT) industrial applications have increased in internet-connected devices. As a result, accessibility, scalability, connectivity, and adaptability have become major challenges. It is possible to create connections between loT devices over the wireless medium, but efficiently utilizing scarce spectrum is the biggest concern. The paper proposes a novel method for selecting sensing nodes with the goal of reducing energy usage and increasing sensing efficiency. In order to ensure that node trust values conform to each other without any ambiguity, cryptography encrypts all information that relates to a node in a central node (CN). Nodes participating in CSS are selected based on their performance. Based on a comparison of various algorithms using varying numbers of nodes, the proposed algorithm achieves a 10% sensing efficiency.

关键词： Wireless communication Wireless sensor networks Adaptation models Machine learning algorithms Heuristic algorithms Scalability Sensors Blockchains Internet of Things Reliability

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AI-Powered Precision: Revolutionizing Skin Cancer Detection with Advanced Machine Learning

AI-Powered Precision: Revolutionizing Skin Cancer Detection ...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Mariam M. Abd-elaziz Basma M. Elshafie Hagar Abdullah Karen Y. Gerges Ingy A. Ghanem Khaled M. Fouad Amir El-Ghamry Ibrahim F. Moawad Sara El-Metwally Faculty of computer science and engineering New Mansoura university Dakahlia Egypt Faculty of Engineering Computer and Communication program Faculty of Computer Science and Engineering Mansoura University New Mansoura University Information Systems Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Mansoura University New Mansoura University Egypt School of Engineering and Computer Science University of Hertfordshire GAF Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Artificial Intelligence College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN architecture, combined with Keras for model lifecycle management and TensorFlow for real-time inference, forms the foundation of this study. Using 10,015 dermatoscopic pictures from seven different classifications of skin cancer in the HAM10000 dataset, the methodology includes several key stages: image preprocessing to correct illumination and adjust resolution, data augmentation to balance the dataset, and model training using Transfer Learning. The MobileNet model was trained over 50 epochs with a comprehensive architecture incorporating multiple specialized layers. By combining large-scale data analysis and adaptive learning, this methodology demonstrates The revolutionary possibilities of AI and ML in improving skin cancer diagnostics and public health outcomes. Keywords Skin Cancer Detection, Dermatoscopic Images, Machine Learning, Deep Learning

关键词： Training Deep learning Adaptation models Image resolution Transfer learning Lighting Real-time systems Image preprocessing Public healthcare Skin cancer

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EXPPO: EXecution Performance Profiling and Optimization for CPS Co-simulation-as-a-Service

EXPPO: EXecution Performance Profiling and Optimization for ...

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International Symposium on Object-Oriented Real-Time Distributed Computing

作者： Yogesh D. Barve Himanshu Neema Zhuangwei Kang Hongyang Sun Aniruddha Gokhale Thomas Roth Department of Electrical Engineering and Computer Science Vanderbilt University Nashville TN USA Smart Grid and Cyber-Physical Systems Program Office Engineering Laboratory National Institute of Standards and Technology Gaithersburg MD USA

ISBN: (数字)9781728169583

ISBN: (纸本)9781728169590

A co-simulation may comprise several heterogeneous federates with diverse spatial and temporal execution characteristics. In an iterative time-stepped simulation, a federation exhibits the Bulk Synchronous Parallel (BSP) computation paradigm in which all federates perform local operations and synchronize with their peers before proceeding to the next round of computation. In this context, the lowest performing (i.e., slowest) federate dictates the progression of the federation logical time. One challenge in co-simulation is performance profiling for individual federates and entire federations. The computational resource assignment to the federates can have a large impact on federation performance. Furthermore, a federation may comprise federates located on different physical machines as is the case for cloud and edge computing environments. As such, distributed profiling and resource assignment to the federation is a major challenge for operationalizing the co-simulation execution at scale. This paper presents the Execution Performance Profiling and Optimization (EXPPO) methodology, which addresses these challenges by using execution performance profiling at each simulation execution step and for every federate in a federation. EXPPO uses profiling to learn performance models for each federate, and uses these models in its federation resource recommendation tool to solve an optimization problem that improves the execution performance of the co-simulation. Using an experimental testbed, the efficacy of EXPPO is validated to show the benefits of performance profiling and resource assignment in improving the execution runtimes of co-simulations while also minimizing the execution cost.

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Process monitoring via independent components

Process monitoring via independent components

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IEEE International Conference on systems, Man and Cybernetics

作者： T.T. Shannon D. Abercrombie J. McNames Systems Science Program Portland State University Portland OR USA LSI Logic Inc. Gresham OR USA Electrical and Computer Engineering Department Portland State University Portland OR USA

We propose a method for process monitoring of a semiconductor manufacturing process. Independent component analysis (ICA) is applied to characterize E-test parameter data. We calculate angular confidence intervals for the model, eliminate marginally significant components and implement control charts for significant components of interest. Alarms are generated off of deviations in the charted components. Alarms are easily used in process diagnosis based on the interpretation of the independent components.

关键词： Independent component analysis Manufacturing processes Process control computerized monitoring Semiconductor device manufacture Production Circuit testing Random variables Mutual information Principal component analysis

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An algorithm for conditional-fault local diagnosis of multiprocessor systems under the MM⁎ model

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Theoretical computer science 2024年 987卷

作者： Lv, Yali Lin, Cheng-Kuan Hsu, D. Frank Fan, Jianxi School of Information Technology Henan University of Chinese Medicine Zhengzhou China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology NDU Taoyuan Taiwan Department of Computer and Information Science Fordham University New York United States School of Computer Science and Technology Soochow University Suzhou China

Diagnosis is a crucial subject for maintaining the reliability of multiprocessor systems. Under the MM⁎ diagnosis model, Sengupta and Dahbura proposed a polynomial-time algorithm with time complexity O(N5) to diagnose a system with N processors. In this paper, we propose a (α,β)-trees combination S(u,X,α,β) and give an algorithm to identify the fault or fault-free status of each processor for conditional local diagnosis under the MM⁎ model. According to our results, a connected network with a (α,β)-trees combination S(u,X,α,β) for a node u is conditionally locally (α+2β−3)-diagnosable at node u and the time complexity of our algorithm to diagnose u is O(α2β+αβ2). As an application, we show that our algorithm can identify the status of each node of n-dimensional star graph Sn if the faulty node number does not exceed 3n−8. Compared with existing algorithms, our algorithm allows more faulty nodes in a multiprocessor system. © 2024 Elsevier B.V.

关键词： Computational complexity

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Light Trapping Shape Optimization of Organic Solar Cells for Maximum Conversion Efficiency

Light Trapping Shape Optimization of Organic Solar Cells for...

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IEEE Nanotechnology Materials and Devices Conference (NMDC)

作者： Muath Bani Salim Reza Nekovei Amit Verma Sustainable Energy Systems Engineering Program Texas A&M University –Kingsville Kingsville TX USA Department of Electrical Engineering and Computer Science Texas A&M University –Kingsville Kingsville TX USA

ISBN: (数字)9781728126371

ISBN: (纸本)9781728126388

This paper explores the light-trapping shape optimization to maximize the organic photovoltaic cells (OPV) conversion efficiency (PCE). The optimization aims to find the best light-trapping profile of the hole transfer layer that can absorb more light and minimize reflection. The optimization is done using Genetic Algorithm and by considering the PCE as the primary objective function. The results show that the PCE of P3HT:PCBM increases from 3.96% to 4.45% by using the optimized shape. Also, the same optimized shape can be used with other OPV materials like PTB7:PC71BM at which the PCE increases from 8.2% to 8.9%.

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Advancing Precision Aquaculture Through Big Data Analytics and Machine Learning in Canadian Fish Farming

Advancing Precision Aquaculture Through Big Data Analytics a...

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Bravo, Francisco Amorim, Joana Amirkandeh, Melika Besharati Bodorik, Peter Cerqueira, Vitor Gomes, Nuno R.C. Korus, Jennie Oliveira, Mariana Parent, Marianne Pimentel, João Reilly, Derek Sclodnick, Tyler Grant, Jon Filgueira, Ramón Whidden, Christopher Universidad Adolfo Ibañez Facultad de Ingenieria y Ciencias Santiago Chile Dalhousie University Faculty of Computer Science Halifax Canada University of Porto Faculty of Engineering Portugal Barcelona Spain Innovasea Marine Systems Bedford Canada Charlottetown Canada Dalhousie University Department of Oceanography Halifax Canada Marine Affairs Program Dalhousie University Halifax Canada

ISBN: (纸本)9798331540081

The aquaculture industry faces significant challenges related to sustainability, productivity, and fish welfare. Key issues include managing environmental conditions, disease, pests, and data integration from various sensors and monitoring systems. The BigFish project aims to address these challenges through advanced analytics and machine learning, focusing on three case studies in Atlantic salmon farms: predicting oxygen levels, reducing sea lice infestations, and improving data interaction and visualization. Predictive models for oxygen levels and sea lice infestation, as well as natural language interfaces for data visualization, demonstrate the potential for improved decision-making and management practices in aquaculture. Early results indicate the effectiveness of these approaches, highlighting the importance of data-driven solutions in enhancing industry sustainability and productivity. © 2024 IEEE.

关键词： Aquaculture

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Speech Emotion Recognition using Convolutional Neural Network with Audio Word-based Embedding

Speech Emotion Recognition using Convolutional Neural Networ...

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International Symposium on Chinese Spoken Language Processing

作者： Kun-Yi Huang Chung-Hsien Wu Qian-Bei Hong Ming-Hsiang Su Yuan-Rong Zeng Department of Computer Science and Information Engineering National Cheng Kung University Taiwan PhD Program for Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan

ISBN: (纸本)9781538656280;9781538656273

A complete emotional expression typically contains a complex temporal course in a natural conversation. Related research on utterance-level, segment-level and multi-level processing lacks understanding of the underlying relation of emotional speech. In this work, a convolutional neural network (CNN) with audio word-based embedding is proposed for emotion modeling. In this study, vector quantization is first applied to convert the low level features of each speech frame into audio words using k-means algorithm. Word2vec is adopted to convert an input speech utterance into the corresponding audio word vector sequence. Finally, the audio word vector sequences of the training emotional speech data with emotion annotation are used to construct the CNN- based emotion model. The NCKU-ES database, containing seven emotion categories: happiness, boredom, anger, anxiety, sadness, surprise and disgust, was collected and five-fold cross validation was used to evaluate the performance of the proposed CNN-based method for speech emotion recognition. Experimental results show that the proposed method achieved an emotion recognition accuracy of 82.34%, improving by 8.7% compared to the Long Short Term Memory (LSTM)- based method, which faced the challenging issue of long input sequence. Comparing with raw features, the audio word-based embedding achieved an improvement of 3.4% for speech emotion recognition.

关键词： Emotion recognition Feature extraction Speech recognition Acoustics Training Databases Neural networks

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