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作者： Mohammadzadeh, M. Shahabadi, M. Soltanian-Zadeh, H. Tavakoli, A. Rafiei, B. Javidnia, D. Oghabian, M.A. Department of Physics Amir-Kabir University of Technology Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI United States Department of Electrical Engineering Amir-Kabir University of Technology Tehran Iran Medical Imaging Center Imam Khomeyni Hospital Tehran Iran Research Center for Science and Technology in Medicine Tehran University of Medical Sciences Tehran Iran

In this paper, design principles and application of a thin and flexible intravascular top hat monopole probe with increased signal-to-noise ratio (SNR) and improved longitudinal and radial coverage are described and compared with the conventional monopole probe. The top hat probe is constructed by removing a segment of the shield of a coaxial cable. A flexible thin copper foil in contact with the inner conductor of the cable is added to the end section of the cable. The copper foil acts as an antenna top hat load increasing the uniformity of the current distribution on the probe and establishes a uniform region of SNR distribution around the probe. Improvement of the SNR distribution of the top hat probe relative to the conventional monopole probe is evaluated using quantitative indices. Experimental results show that for the top hat probe, the ratio of the maximum image SNR to its relative standard deviation (SR) is 2.73 times higher than that of a conventional probe. © 2006 IOP Publishing Ltd.

关键词： Medical imaging

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GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph

arXiv

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arXiv 2024年

作者： Liu, Shaonan Chen, Wenting Liu, Jie Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China Department of Electrical Engineering City University of Hong Kong Hong Kong AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians’ ocular movements during image interpretation, thereby elucidating their visual attention patterns and information-processing strategies. In this paper, we initially define the context-aware gaze estimation problem in medical radiology report settings. To understand the attention allocation and cognitive behavior of radiologists during the medical image interpretation process, we propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns throughout the image interpretation process. It consists of a context-awareness module, visual behavior graph construction, and visual behavior matching. Within the context-awareness module, we achieve intricate multimodal registration by establishing connections between medical reports and images. Subsequently, for a more accurate simulation of genuine visual search behavior patterns, we introduce a visual behavior graph structure, capturing such behavior through high-order relationships (edges) between gaze points (nodes). To maintain the authenticity of visual behavior, we devise a visual behavior-matching approach, adjusting the high-order relationships between them by matching the graph constructed from real and estimated gaze points. Extensive experiments on four publicly available datasets demonstrate the superiority of GEM over existing methods and its strong generalizability, which also provides a new direction for the effective utilization of diverse modalities in medical image interpretation and enhances the interpretability of models in the field of medical imaging. https://***/Tiger-SN/GEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Eye movements

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Raspberry Pi-Based Monitoring System for Grid-Connected PV Systems using Deep Learning Technique*

Raspberry Pi-Based Monitoring System for Grid-Connected PV S...

引用

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Zahra Yahyaoui Wajdi Saadaoui Mansour Hajji Majdi Mansouri Mohamed Nounou Kais Bouzrara Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The evolution of embedded systems has demonstrated their reliability as a solution for monitoring and controlling industrial systems, particularly in renewable energy conversion systems like photovoltaic (PV) energy. The increasing adoption of PV systems highlights the critical need for effective fault diagnosis to ensure their reliable operation. In this paper, we present a novel fault diagnosis approach utilizing Long Short-Term Memory (LSTM) networks optimized through Bayesian optimization techniques. Our methodology is implemented on a Raspberry Pi platform, demonstrating the feasibility of deploying sophisticated fault diagnosis algorithms in resource-constrained environments. Through extensive experiments, we demonstrate the effectiveness of our approach to accurately diagnose faults in grid-connected photovoltaic systems, thereby improving the reliability and efficiency of integrated environmental monitoring *** obtained results highlight the potential of combining advanced deep learning techniques with embedded systems to address complex diagnostic challenges, as demonstrated by achieving a 100% accuracy rate.

关键词： Fault diagnosis Deep learning Photovoltaic systems Embedded systems Accuracy Economic indicators Bayes methods Environmental monitoring Long short term memory Optimization

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Suspicious face detection based on key frame recognition under surveillance video

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2016年 9712 LNCS卷 645-652页

作者： Zheng, Xiaohui Ning, Yi Chen, Xianjun Zhan, Yongsong Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Experiment Center of Information Science Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology GuilinGuangxi541004 China

ISBN: (纸本)9783319409993

Surveillance video is characterized by large amount of data and redundancy, which makes the suspicious face detection to be a problem. To solve the problem above we proposed suspicious face detection based on key frame. Surveillance video has the type of fixed background, so this paper used the frame difference method with low computational complexity and small computation to extract the key frame. We proposed a new method combined DPM with skin color detection to detect the suspicious face in the key frames. To solve the speed bottleneck of the traditional DPM, we proposed to use the fast HOG LUT feature extraction and the near optimal cost sensitive decision making improving the traditional method. Meanwhile we used YCrCb + otsu skin color segmentation. Since the otsu is easily affected by the illumination, we proposed an improved skin color detection. Experiment results show that the proposed algorithms are robust and accurate for real-time. © Springer International Publishing Switzerland 2016.

关键词： Face recognition

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image-based material characterization of complex microarchitectured additively manufactured structures

arXiv

引用

arXiv 2019年

作者： Korshunova, N. Jomo, J. Lékó, G. Reznik, D. Balázs, P. Kollmannsberger, S. Chair for Computation in Engineering Technische Universität München Arcisstr. 21 München80333 Germany Research and Technology Center Siemens AG Corporate Technology Siemensdamm 50 Berlin13629 Germany Department of Image Processing and Computer Graphics University of Szeged Árpád tér 2 SzegedH-6720 Hungary

Significant developments in the field of additive manufacturing (AM) allowed the fabrication of complex microarchitectured components with varying porosity across different scales. However, due to the high complexity of this process, the final parts can exhibit significant variations in the nominal geometry. computer tomographic images of 3D printed components provide extensive information about these microstructural variations, such as process-induced porosity, surface roughness, and other undesired morphological discrepancies. Yet, techniques to incorporate these imperfect AM geometries into the numerical material characterization analysis are computationally demanding. In this contribution, an efficient image-to-material-characterization framework using the high-order parallel Finite Cell Method is proposed. In this way, a flexible non-geometry-conforming discretization facilitates mesh generation for very complex microstructures at hand and allows a direct analysis of the images stemming from CT-scans. Numerical examples including a comparison to the experiments illustrate the potential of the proposed framework in the field of additive manufacturing product simulation. Copyright © 2019, The Authors. All rights reserved.

关键词： 3D printers

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An Anisotropic Diffusion Filtering Implementation to Execute in Parallel Distributed Systems

An Anisotropic Diffusion Filtering Implementation to Execute...

引用

IEEE International Conference on Computational Science and Engineering Workshops (CSEWORKSHOPS)

作者： Antonio C. Sobieranski Leandro Coser M.A.R. Dantas Aldo v. Wangenheim Eros Comunello LAPIX-Image Processing and Graphic Computing Lab The Cyclops Group Federal University of Santa Catarina Brazil LaPeSD-Distributed Systems Research Laboratory Federal University of Santa Catarina Brazil Graduate Program Applied Computer Science Universidade do Vale do Itajaí Brazil

ISBN: (纸本)9780769532578

In this paper, we present a parallelization of a filtering algorithm related to non-linear anisotropic diffusion, used to enhance the performance of an application in a parallel distributed system. The anisotropic diffusion is a well-established technique for image enhancement by means of diffusivity functions, which act as border attenuators. However, it requires a high computational cost when a large amount of data is used. The proposed implementation was parallelized considering both point-to-point and collective communications, adopting the MPI paradigm. Results from both approaches indicate that the proposed algorithm has reached interesting levels of performance (81% and 93% of efficiency, respectively) when compared to the execution of one process in a single computer node. In addition, our results indicate an enhancement of around 21% utilizing the collective communication strategy when compared to point-to-point communication.

关键词： Filtering Magnetic separation Anisotropic magnetoresistance Ions Kernel Maximum likelihood detection Pixel

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FPGA based emulated-digital CNN-UM implementation with GAPU

FPGA based emulated-digital CNN-UM implementation with GAPU

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： Zsolt Voroshazi Andras Kiss Zoltan Nagy Peter Szolgay Department of Image Processing and Neurocomputing University of Pannonia Veszprem Hungary Faculty of Information Technology Pázmány Péter Catholic University Hungary Cellular Sensory and Wave Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

The paper addresses the issue of implementing a GAPU (global analogic programming unit) on the emulated-digital CNN-UM (cellular neural/nonlinear networks universal machine) architecture. It has been embedded with a flexible Xilinx MicroBlaze soft-core processor to take full advantage of the joint computing power of high-speed arithmetics and programmability. The GAPU implementation accelerates the processing performance of the Falcon architecture by decreasing the I/O communication and it also provides a stand-alone operation, which is capable of controlling complex sophisticated analogic CNN algorithms similar to various visual microprocessors, such as the ACE16k and the Q-Eye in the Bi-i, and eye-RIS cellular vision systems. Our primary aim was to implement an FPGA-based emulated-digital CNN-UM with embedded GAPU for computation intensive, real image-processing tasks.

关键词： Field programmable gate arrays computer architecture Process control Random access memory Vector processors Mathematical model Computational modeling

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A standalone FPGA based emulated-digital CNN-UM system

A standalone FPGA based emulated-digital CNN-UM system

引用

IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： Zsolt Voroshazi Andras Kiss Zoltan Nagy Peter Szolgay Department of Image Processing and Neurocomputing University of Pannonia Veszprem Hungary Faculty of Information Technology Pázmáany Péter Catholic University Hungary Cellular Sensory and Wave Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

The Falcon emulated-digital CNN-UM (cellular neural/nonlinear networks universal machine) architecture has been extended by an embedded GAPU (global analogic programming unit) using the flexible Xilinx MicroBlaze soft-core processor to take full advantage of the joint computing power of high-speed distributed arithmetics and programmability. The implemented GAPU provides a stand-alone operation, which is capable of controlling complex sophisticated CNN analogic algorithms similar to various visual microprocessors, such as the ACE4k, ACE16k, and Bi-i vision systems. The quality of the embedded GAPU implementation is demonstrated by analogic algorithms, mainly in which sequences of template operations are required.

关键词： Field programmable gate arrays computer architecture image processing Mathematical model Organizing computers Cellular neural networks

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Neuron segmentation using 3D wavelet integrated encoder-decoder network

arXiv

引用

arXiv 2021年

作者： Li, Qiufu Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shen zhen University Shenzhen518060 China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen518060 China

Motivation: 3D neuron segmentation is a key step for the neuron digital reconstruction, which is essential for exploring brain circuits and understanding brain functions. However, the fine line-shaped nerve fibers of neuron could spread in a large region, which brings great computational cost to the neuron segmentation. Meanwhile, the strong noises and disconnected nerve fibers bring great challenges to the task. Results: In this paper, we propose a 3D wavelet and deep learning based 3D neuron segmentation method. The neuronal image is first partitioned into neuronal cubes to simplify the segmentation task. Then, we design 3D WaveUNet, the first 3D wavelet integrated encoder-decoder network, to segment the nerve fibers in the cubes;the wavelets could assist the deep networks in suppressing data noises and connecting the broken fibers. We also produce a Neuronal Cube Dataset (NeuCuDa) using the biggest available annotated neuronal image dataset, BigNeuron, to train 3D WaveUNet. Finally, the nerve fibers segmented in cubes are assembled to generate the complete neuron, which is digitally reconstructed using an available automatic tracing algorithm. The experimental results show that our neuron segmentation method could completely extract the target neuron in noisy neuronal images. The integrated 3D wavelets can efficiently improve the performance of 3D neuron segmentation and reconstruction. © 2021, CC BY.

关键词： Neurons

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Enhancing Fault Diagnosis of Uncertain Grid-Connected Photovoltaic Systems using Deep GRU-based Bayesian optimization

引用

IFAC-PapersOnLine 2024年第4期58卷 449-454页

作者： Zahra Yahyaoui Mansour Hajji Majdi Mansouri Abdelmalek Kouadri Kais Bouzrara Hazem Nounou Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine 1200 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M Hamed Bougara of Boumerdes Boumerdes Algeria Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir Tunisia

The efficacy of photovoltaic systems is significantly impacted by electrical production losses attributed to faults. Ensuring the rapid and cost-effective restoration of system efficiency necessitates robust fault detection and diagnosis (FDD) procedures. This study introduces a novel interval-gated recurrent unit (I-GRU) based Bayesian optimization framework for FDD in grid-connected photovoltaic (GCPV) systems. The utilization of an interval-valued representation is proposed to address uncertainties inherent in the systems, the GRU is employed for fault classification, while the Bayesian algorithm optimizes its hyperparameters. Addressing uncertainties through the proposed approach enhances monitoring capabilities, mitigating computational and storage costs associated with sensor uncertainties. The effectiveness of the proposed approach for FDD in GCPV systems is demonstrated using experimental application.

关键词： Bayesian optimization fault detection fault diagnosis gated recurrent units grid-connected PV systems interval-data representation uncertainties

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