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D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

SN computer Science 2021年第1期2卷 48页

作者： Demiray, Bekir Z. Sit, Muhammed Demir, Ibrahim Department of Computer Science University of Iowa Iowa City United States Interdisciplinary Graduate Program in Informatics University of Iowa Iowa City United States Department of Civil and Environmental Engineering University of Iowa Iowa City United States Department of Electrical and Computer Engineering University of Iowa Iowa City United States

Digital elevation model (DEM) is a critical data source for variety of applications such as road extraction, hydrological modeling, flood mapping, and many geospatial studies. The usage of high-resolution DEMs as inputs in many application areas improves the overall reliability and accuracy of the raw dataset. The goal of this study is to develop a machine learning model that increases the spatial resolution of DEM without additional information. In this paper, a GAN based model (D-SRGAN), inspired by single image super-resolution methods, is developed and evaluated to increase the resolution of DEMs. The experiment results show that D-SRGAN produces promising results while constructing 3 feet high-resolution DEMs from 50 feet low-resolution DEMs. It outperforms common statistical interpolation methods and neural network *** study shows that it is possible to use the power of artificial neural networks to increase the resolution of the DEMs. The study also demonstrates that approaches from single image super-resolution can be applied for DEM super-resolution. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Deep learning DEM DEM reconstruction Generative adversarial networks (GANs) Super-resolution

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Predicting Human Activity with LSTM Face Detection on Server Surveillance System

Predicting Human Activity with LSTM Face Detection on Server...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Alexander Nurenie Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Indonesia

Surveillance server technology was growth with new technology, effective, extra new features, human friendly, and human deals with big amount data, can't view and collect the data in the short time, and took time to analyze, playback video/picture to determine machine, human, vehicle or environment issues or performance, Surveillance Server Systems now which has the ability to face recognition, face detection, human detection, motion detection, license plate recognition, The authors perform this study that still new this research has never been done before to determine the efficacy of the LSTM in predicting human behavior (Long Short Term Memory) Face Detection on Server surveillance system, by taking log view data with a total of 91501 Face detection data downloaded from 10/18/2022~11/9/2022, the data will be processed using Python programming and training so that it can be used to predict the future regarding human activities that vary utilizing time series prediction LSTM include the number of daily activities, the highest and lowest numbers of days, and the maximum and minimum numbers of days. from the results of this study it was found to help to find out the days with the lowest number of humans and the days with the highest number of human activities, so that the owner can predict with sequence of the data the service would be provided when human activity is high in certain area or certain day, it can also can find out the maximum or minimum amount human counting day by day, and compare able some different date and location, the author will continue to do more in-depth research the others data related with prediction with deep learning server surveillance machine system interaction with human, vehicle behavior in the future studies.

关键词： Training Surveillance Time series analysis programming Behavioral sciences Servers Face detection

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Comparison of Algebraic Reconstruction Technique Methods and Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography (MIT)

Comparison of Algebraic Reconstruction Technique Methods and...

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2021 International Conference on biomedical engineering, ICoBE 2021

作者： Lubis, A.J. Nasir, N.F. Mohd Zakaria, Z. Jusoh, M. Azizan, M.M. Zakaria, M.Z. Kazim, M.N.F.M. Rahman, S. Faculty of Engineering and Computer Science Universitas Harapan Medan Medan Indonesia Computer Engineering Program Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Biomedical Electronic Engineering Program Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Universiti Malaysia Perlis Perlis Malaysia Department of Electrical and Electronic Department Faculty of Engineering and Built Environment Universiti Sains Islam Malaysia Negeri Sembilan Malaysia Faculty of Mechanical Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Mechatronic Engineering Program Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Perlis Malaysia

Magnetic induction tomography (MIT) is a technique used for imaging electromagnetic properties of objects using eddy current effects. The non-linear characteristics had led to more difficulties with its solution especially in dealing with low conductivity imaging materials such as biological tissues. Two methods that could be applied for MIT image processing which is the Generative Adversarial Network (GAN) and the Algebraic Reconstruction Technique (ART). ART is widely used in the industry due to its ability to improve the quality of the reconstructed image at a high scanning speed. GAN is an intelligent method which would be able to carry out the training process. In the GAN method, the MIT principle is used to find the optimum global conductivity distribution and it is described as a training process and later, reconstructed by a generator. The output is an approximate reconstruction of the distribution's internal conductivity image. Then, the results were compared with the previous traditional algorithm, namely the regularization algorithm of BPNN and Tikhonov Regularization method. It turned out that GAN had able to adjust the non-linear relationship between input and output. GAN was also able to solve non-linear problems that cannot be solved in the previous traditional algorithms, namely Back Propagation Neural Network (BPNN) and Tikhonov Regularization method. There are several other intelligent algorithms such as CNN (Convolution Neural Network) and K-NN (K-Nearest Neighbor), but such algorithms have not been able to produce the expected image quality. Thus, further study is still needed for the improvement of the image quality. The expected result in this study is the comparison of these two techniques, namely ART and GAN to get the best results on the image reconstruction using MIT. Thus, it is shown that GAN is a better candidate for this purpose. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Generative adversarial networks

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Temperature Dependent Anomalous Threshold Voltage Modulation of a-IGZO TFT by Incorporating Variant Gate Stresses

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ECS Journal of Solid State Science and Technology 2024年第6期13卷 065008-065008页

作者： Aslam, Muhammad Chang, Shu-Wei Chen, Yi-Ho Lee, Yao-Jen Li, Yiming Lee, Wen-Hsi Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan 701401 Taiwan Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan Institute of Pioneer Semiconductor Innovation Industry Academic Innovation School National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan

Amorphous indium gallium zinc oxide (a-IGZO) has recently made significant advancement as a key material for electronic component design owing to its compatibility with complementary metal oxide semiconductor technologies. A comprehensive analysis of reliability-related issues is required to determine the true potential of a-IGZO-based devices for next-generation electronics applications. To address this objective, we electrically characterize scaled-channel a-IGZO thin film transistors (TFTs) under positive bias (temperature) stress (PB(T)S). Both PBS and PBTS are characterized by positive and negative V_th shift, respectively, during the various gate stresses. In particular, the negative V_th shift is explained by the generation of donor-like traps stimulated by ionization of oxygen vacancy/hydrogen at elevated temperature. The TFTs exhibit relatively decent stability during the PBS operation. The analysis of devices with variant channel dimensions implies that long-channel devices exhibit relatively higher stability and performance compared to the short-channel ones. We also observe that the V_th can be controllably adjusted by employing the top gate (TG) with bottom gate sweep. Moreover, the stress-induced partial recovery mechanism is experimentally observed owing to detrapping of charges. Generally, the reported results infer a perceptive understanding of scaled-channel a-IGZO-TFTs which helps with shaping performance-enhancement strategies. © 2024 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited

关键词： amorphous IGZO-TFT electrical instabilities hydrogen-impact scaling impact on reliability

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Deep and shallow data science for multi-scale optical neuroscience

arXiv

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arXiv 2024年

作者： Mishne, Gal Charles, Adam Halıcıoğlu Data Science Institute Department of Electrical and Computer Engineering the Neurosciences Graduate Program UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Biomedical Engineering Kavli Neuroscience Discovery Institute Center for Imaging Science Department of Neuroscience Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD21287 United States

Optical imaging of the brain has expanded dramatically in the past two decades. New optics, indicators, and experimental paradigms are now enabling in-vivo imaging from the synaptic to the cortex-wide scales. To match the resulting flood of data across scales, computational methods are continuously being developed to meet the need of extracting biologically relevant information. In this pursuit, challenges arise in some domains (e.g., SNR and resolution limits in micron-scale data) that require specialized algorithms. These algorithms can, for example, make use of state-of-the-art machine learning to maximally learn the details of a given scale to optimize the processing pipeline. In contrast, other methods, however, such as graph signal processing, seek to abstract away from some of the details that are scale-specific to provide solutions to specific sub-problems common across scales of neuroimaging. Here we discuss limitations and tradeoffs in algorithmic design with the goal of identifying how data quality and variability can hamper algorithm use and dissemination. © 2024, CC BY.

关键词： Neuroimaging

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Multimodal Surface Sensing based on Hybrid Flexible Triboelectric and Piezoresistive Sensor

Multimodal Surface Sensing based on Hybrid Flexible Triboele...

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2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable computers, UbiComp/ISWC 2022

作者： Lin, Zenan Lei, Kai Chong Mu, Shilong Song, Ziwu Dai, Yuan Ding, Wenbo Zhang, Xiao-Ping Tsinghua-Berkeley Shenzhen Institute Shenzhen International Graduate School Tsinghua University China Tencent Robotics X Lab China Tsinghua-Berkeley Shenzhen Institute Shenzhen International Graduate School Tsinghua University RISC-V International Open Source Laboratory China Shenzhen International Graduate School Tsinghua University Department of Electrical Computer and Biomedical Engineering Ryerson University China

ISBN: (纸本)9781450394239

Sensing the surface properties through touch, as the most natural perceptual way of humans, has become an important and practical method for human-machine interactions (HMI) and robot manipulations. In this paper, we design a fingertip hybrid flexible tactile sensor for multimodal surface sensing, based on the triboelectric and piezoresistive mechanisms. A real-time tactile sensing system is implemented on a 3D-printed robot finger together with a wireless data acquisition board. A virtual data generation method is proposed to expand the model adaptability under different compression force levels. Moreover, considering the characteristics of data generated by our sensors, a novel deep learning model with a residual structure is developed, named parallel residual convolutional neural network (PR-CNN). Our model outperforms the state-of-the-art models, i.e., Res-CNN, LSTM-FCN and InceptionTime, with over 96% accuracy, on three classification tasks, including textures (13 types), materials (10 types), and combinations of textures and materials (18 types). The proposed system has broad applications in service robots, industrial sorting robots, and HMI. © 2022 Owner/Author.

关键词： Textures

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Measurement of Water Diffusion Coefficient in Cross-Linked Gelatins Using the Terahertz Time-Domain Spectroscopy Technique

Measurement of Water Diffusion Coefficient in Cross-Linked G...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Walden, D. Chakraborty, D. Cheng, J. Straksys, A. Stirke, A. Melo, W.C.M.A. Jukna, A. Komissarov, I. Sobolewski, Roman Department of Physics and Astronomy University of Rochester RochesterNY14627 United States Laboratory for Laser Energetics University of Rochester RochesterNY14623-1299 United States Materials Science Graduate Program University of Rochester RochesterNY14627 United States State Research Institute Center for Physical Sciences & Technology Vilnius Lithuania Department of Physics Vilnius Gediminas Technical University Vilnius Lithuania Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States

A novel method has been developed to evaluate the water diffusion dynamics in gelatin-based gels, based on transient terahertz time-domain spectroscopy measurements. Such gels are widely used models for human skin. &#... 详细信息

ISBN: (纸本)9781957171395

关键词： Transient analysis

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Performance Analysis of Interleaved Bidirectional DC-DC Converters for Battery Control in Islanded Microgrid

Performance Analysis of Interleaved Bidirectional DC-DC Conv...

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IEEE General Meeting Power& Energy Society

作者： Lindemberg Roberto de Lima Pedro A. de Alcântara Luana C. S. Soares Camila M. V. Barros Luciano S. Barros Mathematics and Computational Modeling Graduate Program João Pessoa Brazil Electrical Engineering Graduate Program Federal University of Paraíba João Pessoa Brazil Computer Systems Department Federal University of Paraíba João Pessoa Brazil

In this paper, considering photovoltaic/battery sourced microgrids with grid-forming droop control in the load interface converter for power management, two alternative converter topologies for the battery are tested: dual boost interleaved converter and three-phase interleaved converter. These topologies are based on the conventional bidirectional buckboost converter and their employment is to allow a reduction in component sizing, improvement in power quality, and higher output voltage. Analysis of the operation of these converters in different operation modes of the microgrid is carried out, including battery normal charging, battery normal discharging, battery charging limit, and maximum state-of-charge, through simulations in MATLAB/Simulink environment. Results suggest advantages such as reduced stress on switches and battery charging current ripple for the two interleaved converters tested and the potential implementation of higher output voltage for the dual boost converter.

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Stratifying High-Risk Thyroid Nodules Using a Novel Deep Learning System

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Experimental and Clinical Endocrinology & Diabetes 2023年第10期131卷 508-514页

作者： Chia-Po Fu Ming-Jen Yu Yao-Sian Huang Chiou-Shann Fuh Ruey-Feng Chang Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan Division of Endocrinology and Metabolism Department of Medicine Taichung Veterans General Hospital Taichung Taiwan Department of Medicine Chung Shan Medical University Taichung Taiwan Department of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Department of Computer Science and Information Engineering National Changhua University of Education Changhua County Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Swin-T classifier can be a useful tool in helping shared decision-making between physicians and patients with thyroid nodules, particularly in those with high-risk characteristics of sonographic patterns.

关键词： thyroid nodule, sonography, deep learning, ResNeSt50, Swin T

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Measurement of Water Diffusion Coefficient in Cross-Linked Gelatins Using the Terahertz Time-Domain Spectroscopy Technique

Measurement of Water Diffusion Coefficient in Cross-Linked G...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics