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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems III"

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Evaluation of NVIDIA Xavier NX Platform for Real-Time image processing for Fusion Diagnostics 28

Evaluation of NVIDIA Xavier NX Platform for Real-Time Image ...

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28th International conference on Mixed Design of Integrated Circuits and System (MIXDES)

作者： Jablonski, Bartlomiej Makowski, Dariusz Perek, Piotr Lodz Univ Technol Dept Microelect & Comp Sci Lodz Poland

ISBN: (纸本)9788363578190

Real-time image processing is the core component of image plasma diagnostics. Efficient algorithms enable machine protection, contributing to future steady-slate operation in nuclear fusion devices. The paper evaluates the applicability of the newest low-power NVIDIA Jetson Xavier NX platform for fusion diagnostics. This embedded NVIDIA Tegra System-on-a-Chip (SoC) integrates a Graphics processing Unit (GPU) and Central processing Unit (CPU) on a single chip. General-Purpose computing on Graphics processing Units (GPGPU) provides high parallelism that is advantageous in image-based calculations. The hardware differences in comparison to the previous NVIDIA Jetson TX2 based on Pascal architecture, including innovations introduced in the Volta architecture for NVIDIA Tegra, are signified. The evaluation is performed on the Wendelstein 7-X (W7-X) stellarator experimental data. Implemented algorithms detect and analyse thermal events in real-time utilising the embedded GPU. Investigated thermal events are strike-lines, overload hotspots, reflections and surface layers. Their detection allows the automated real-time risk evaluation incorporated in the feedback plasma control and interlock systems in the W7-X. The speedup resulting from the upgrade to the Xavier NX platform is presented in the paper, along with techniques pertaining to key hardware differences and programming aspects specific to the NVIDIA Tegra facilitating real-time computing on the low-power embedded device.

关键词： Graphics processing Unit General-Purpose Computing on Graphics processing Units image processing Plasma Diagnostics Fusion Diagnostics Embedded systems

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A Statistical Approach for Improving image Quality and Cell Quantification from Imaging Mass Cytometry

A Statistical Approach for Improving Image Quality and Cell ...

引用

Communication, image and Signal processing (CCISP), International conference on

作者： Xu Xiao Yating Lin Lei Zhang Wenxian Yang Rongshan Yu School of Informatics Xiamen University Xiamen China Aginome Scientific Xiamen China

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

Imaging Mass Cytometry (IMC), a multiplexed imaging technology, has become a valuable tool in biomedical research due to its capability to measure over 100 markers theoretically. However, the presence of noise in IMC images can affect cell phenotyping and the precision of clinical analysis. To address this challenge, we propose IMCell, a highly effective and generalizable method that enhances image quality and cell quantification by leveraging biological priors. IMCell begins by generating decoy cells using Monte Carlo sampling and incorporates cell shape information for each protein channel. Subsequently, it estimates background noise from the expression of decoy cells by fitting Gaussian mixture models with the EM algorithm. Additionally, it identifies positive cell expressions by comparing distribution patterns between real cells and decoy cells. Based on the analysis results from IMCell, we further introduce an image quality index for comprehensive image quality assessment by combining noise and positive cell expressions. Experiment results demonstrate the efficacy of our proposed approach in enhancing image quality and facilitating downstream analysis. By improving the quality of IMC images, IMCell holds the potential to enhance our understanding of complex biological systems and disease mechanisms.

关键词： image quality Proteins Multiplexing Protein engineering Monte Carlo methods Shape Noise Signal processing algorithms Clustering algorithms Background noise

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On Momentum Acceleration for Randomized Coordinate Descent in Matrix Completion

On Momentum Acceleration for Randomized Coordinate Descent i...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Matthew Callahan Trung Vu Raviv Raich School of EECS Oregon State University Corvallis OR USA Department of CSEE University of Maryland Baltimore County MD USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Matrix completion plays an important role in machine learning and signal processing, with applications ranging from recommender systems to image inpainting. Many approaches have been considered to solve the problem and some offer computationally efficient solutions. In particular, a highly-efficient random coordinate descent approach reduces the per-epoch computation dramatically. This paper is concerned with further improvement of computational efficiency to expand the range of problem sizes and conditions that can be solved. Momentum acceleration is a well-known method to improve the efficiency of iterative algorithms, but applying it to random coordinate descent methods without increasing the computational complexity is non-trivial. To address this challenge, we introduce a momentum-accelerated randomized coordinate descent for matrix completion approach that does not increase computational complexity by accelerating at the level of epochs. Additionally, we propose an analysis-driven, tuning-free method for step size selection. To that end, we offer a convergence rate analysis for the algorithm. Using numerical evaluations, we demonstrate the competitiveness of the method and verify the theoretical analysis.

关键词： Closed-form solutions Shape Signal processing algorithms Machine learning Signal processing Computational efficiency Computational complexity Speech processing Recommender systems Convergence

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A Review of Intrinsic image Decomposition

A Review of Intrinsic Image Decomposition

引用

image processing and Media Computing (ICIPMC), International conference on

作者： Siyuan Liu Xiaoyue Jiang Letian Liu Zhaoqiang Xia Sihang Dang Xiaoyi Feng School of Electronics and Information Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386677

Intrinsic image decomposition (IID) aims to extract intrinsic components from natural images, namely shading and reflectance, which is widely applied in computer vision and image processing tasks for the better understanding of the objects and scenes. However, IID is a severely ill-posed problem, extra constraints should be applied to address the decomposition problem. In this paper, we reviewed recent papers about intrinsic image decomposition. The extra cues from lighting, shape, color and textures are used to constrain the ill-posed problem. These papers can be mainly classified into two categories: physics-based methods and learning-based methods. Physics-based methods mainly introduced extra constraints into an optimization framework to solve the IID problem. While learning-based methods mainly depend on an encoder-decoder network with the explicit constraint from the labeled intrinsic images. Additionally, extra priors are introduced into the network by the form of network structure or specific feature. In order to make the IID problem more practical, researchers may explore how to achieve fast and accurate image decomposition with lower computing resources through optimization algorithms and model design.

关键词： Learning systems Accuracy image color analysis Shape Computational modeling image decomposition Real-time systems

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Optimization of a MIMO Antenna using the API metaheuristic algorithm

Optimization of a MIMO Antenna using the API metaheuristic a...

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International conference on image and Signal processing and their Applications (ISPA)

作者： Bitat Hiba Ines Maamri Fouzia Khelfaoui Fatima Djellab Hanane Messai Yacine Aouissi Feriel SATIT Laboratory University Abbes Laghrour Khenchela Khenchela Algeria University Alarbi TBESSI Tebessa Tebessa Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Antenna technology has undergone significant advancements with the integration of innovative techniques such as MIMO (Multiple Input Multiple Output) and phased array systems. By utilizing multiple antennas for transmission and reception, MIMO enhances data rates, spectral efficiency, and overall system performance. Phased array antennas, on the other hand, introduce beamforming capabilities, allowing for dynamic control and steering of radio wave beams without physical movement. The goal of this work is to optimize the control of the antenna network (MIMO) from the perspective of directivity and secondary lobe reduction using the API (pachycondyla apicalis algorithm) metaheuristic algorithm. Specifically, the API algorithm will be employed to determine the optimal element spacing for an N=16 element linear array operating at the LTE frequency of 2.5 GHz, and phase shift Ψ=2.3 radians with the objective of achieving the best value of the inter element spacing. while minimizing the secondary lobes.

关键词： Phased arrays Array signal processing Heuristic algorithms System performance Metaheuristics Signal processing algorithms Space exploration

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Kid Activity Recognition: A Comprehensive study of kid activity recognition with monitoring activity level using YOLOv8s algorithms 3

Kid Activity Recognition: A Comprehensive study of kid activ...

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3rd International conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Bisla, Tiya Shukla, Ritik Dhawan, Mansi Islam, Md. Rahatul Koyasu, Takao Teramoto, Keisuke Kataoka, Yui Horio, Keiichi Dronacharya Group of Institution Dept. of Computer Science & Engineering Greater Noida India Kyushu Institute of Technology Graduate School of Life Science and Systems Engineering Fukuoka Japan Kyushu Women's Junior College Fukuoka Japan Aichi University of Education Aichi Japan Fukuoka Kodomo Junior College Fukuoka Japan

ISBN: (纸本)9798350372120

Recognizing human activities and behavior is a cutting-edge field of research because of the complexity and limited availability of data. Our research involves the use of the trending YOLOv8s deep learning algorithm for the classification of active states (sitting, standing, running, laying, and jumping). The research focuses on the detection of a child's activities in the kindergarten playroom. To train the model, we created the KAR (Kids Action Recognition) dataset using our own sources. We labeled over 6,489 images in the dataset using ***. Each image is annotated with activity labels and the location of the student (in a room or on a mat). This assisted in presenting insights on the student's vitality level and physical involvement over a period of approximately an hour. We analyzed the whole activity and compared it with the times. Standing in the room and mat, standing in the room, and jumping in the room and mat are most of the activities of the kid. The model achieved an impressive overall accuracy of 88.7 %. We conducted all the training in a Google collaborative environment with A-100 GPU specifications. The results demonstrate that the model can identify and classify each activity with high precision. © 2024 IEEE.

关键词： Graphics processing unit

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Quick and Efficient Large Scale Approximate Nearest Neighbor Search on High-Dimensional Data

Quick and Efficient Large Scale Approximate Nearest Neighbor...

引用

International conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

作者： Hu Zhibing University Of Electronic Science and Technology Of China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Large scale approximate nearest neighbor search on high-dimensional data is always a critical research question, including recommendation systems and image search. Graph-based ANNS algorithms have received more and more attention due to its accuracy and efficiency. Nevertheless, the graph-based ANNS algorithms faces the following challenges: computation latency and memory requirements. In this work, we present a novel graph-based similarity search algorithm called HNSWQ, which optimizes the HNSW algorithm by building quick and comprehensive uppers without using compression. Those uppers can bring high-quality entry point to accelerate query processing by reducing the number of points to be accessed during the search and shorten the whole computation latency. At the same time, we consider the parallelism of GPU and take advantage of GPU to shorten the build and search time of the novel HNSWQ algorithm. Finally, we verify the presented algorithm on several representative datasets. Compared with the state-of-the-art graph-based algorithm, our novel method can acquire great accuracy and search speed.

关键词： Accuracy High dimensional data Query processing Memory management Graphics processing units Nearest neighbor methods Parallel processing Media Approximation algorithms Recommender systems

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RADCI: A Synchronized Radar-RGBT Object Detecting-Tracking Dataset And A Benchmark

RADCI: A Synchronized Radar-RGBT Object Detecting-Tracking D...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Heng Yu Ruiheng Zhang Haoyang Sun Zhe Cao Biwen Yang Jin Zhang Guanyu Liu Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

High-quality perception is crucial in autonomous driving and monitoring systems, where millimeter-wave radar and infrared cameras play important roles due to their robustness and reliability under harsh conditions. Both technologies can serve as low-cost supplements to optical image detection, improving overall system robustness. However, there is currently a lack of widely applicable feature-level fusion methods and multimodal datasets to effectively integrate visible light with these two heterogeneous data types for multiple tasks. In this work, we collect a new multimodal dataset, RADCI8, which synchronizes data from a camera, an infrared camera, and a radar for target detection and tracking. The dataset includes 2D image annotations, radar RAD tensor data with distance, angle, and Doppler information, as well as target ID annotations in both data formats. In addition, to address the incomplete use of radar data in previous fusion algorithms, we propose a detection method that fuses image and radar features using feature concatenation and an attention mechanism. Our proposed algorithm achieves 51.5% AP with an IOU of 50:95 on 2D bounding box prediction, significantly improving average detection accuracy over vision-based methods and maintaining robustness even when a single sensor degrades.

关键词： Tensors Radar detection Signal processing algorithms Radar imaging Radar tracking Cameras Prediction algorithms Robustness Doppler radar Synchronization

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Comprehensive image Classification and Object Detection Using Convolutional Neural Networks

Comprehensive Image Classification and Object Detection Usin...

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Disruptive Technologies (ICDT), International conference on

作者： Archana Kumari Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In the modern era, Deep Learning (DL) methodology is used extensively in multiple areas and Convolutional Neural Network (CNN) is majorly used for addressing the problems in real-time for computer vision activities due to its capability of enabling acquisitions with higher accuracy. The concept at the back of this DL technique was inspired from architecture of biotic visual systems. In the current time, it is applied extensively in multiple areas and industries like automotive, medical and healthcare, space, aviation, agriculture etc. Moving forward, numerous algorithms on CNN were proposed to address the requirement of multiple areas, with Natural Language processing (NLP) and computer vision as the major fields for application. The activities in computer vision with major application of CNN includes image categorization, object identification, recognition of face, estimation of human pose, recognition of action, scene labelling along with document analysis. This paper delves into modern techniques of CNN for classification of images and detection of objects.

关键词： Deep learning Computer vision image recognition Face recognition Medical services Real-time systems Natural language processing Agriculture Convolutional neural networks Biomedical imaging

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Brain Tumor Segmentation of MRI images Based on K-Means and White Shark Optimizer 24

Brain Tumor Segmentation of MRI Images Based on K-Means and ...

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24th International Arab conference on Information Technology, ACIT 2023

作者： Braik, Malik Al-Hiary, Heba Al-Betar, Mohammed Azmi Al-Balqa Applied University Department of Computer Science Al-salt Jordan Al-Balqa Applied University Department of Computer Information Systems Al-salt Jordan Department of Information Technology Ajman United Arab Emirates Al-Balqa Applied University Irbid Jordan

ISBN: (纸本)9798350384307

Due to the intricacy of the process involved in segmenting and extracting the tumor regions in magnetic resonance imaging (MRI), it is quite difficult to successfully detect the diseases. Due to its critical significance in subsequent image processing phases, this creates a requirement for a suitable clustering algorithm. In this study, a clustering method is presented that may address a variety problems for MRI brain tumor segmentation. One of the most well-known conventional clustering methods that is simple to use, quick to process, and ensures convergence is k-means (KM). Its weakness, which is defined by its susceptibility to the initial center's random initialization, is present. Because of this, the KM was optimized in this study using the white shark optimizer (WSO) to address this weak side of the KM. The KM would set the starting point for WSO's final position. Evaluation results of WSO-based KM were judged on a publicly available datasets in terms of several relevant evaluation methods and contrasted with outcomes of KM, fuzzy c-means (FCM), and other meta-heuristics when practiced to cluster the same datasets. The WSO-based KM clustering approach outperformed the standard KM and other competing algorithms-based KM. © 2023 IEEE.

关键词： FCM image segmentation K-means White shark optimizer

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