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Area-NeRF: Area-based Neural Radiance Fields

Area-NeRF: Area-based Neural Radiance Fields

引用

image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Zonxin Ye Wenyu Li Peng Qiao Yong Dou National Key Laboratory of Parallel and Distributed Computing School of Computer National University of Defense Technology Changsha China

Neural Radiance Field (NeRF) has received widespread attention for its photo-realistic novel view synthesis quality. Current methods mainly represent the scene based on point sampling of ray casting, ignoring the influence of the observed area changing with distance. In addition, The current sampling strategies are all focused on the distribution of sampling points on the ray, without paying attention to the sampling of the ray. We found that the current ray sampling strategy for scenes with the camera moving forward severely reduces the convergence speed. In this work, we extend the point representation to area representation by using relative positional encoding, and propose a ray sampling strategy that is suitable for camera trajectory moving forward. We validated the effectiveness of our method on multiple public datasets.

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parallel vs distributed Edge Detection for Large Medical image Datasets

Parallel vs Distributed Edge Detection for Large Medical Ima...

引用

E-Health and Bioengineering conference (EHB)

作者： Renato Constantin Ivanescu Rodica Nagy Anca Istrate Ofiteru Cristina Comanescu Alexandru Manda Department of Computers and Information Technology University of Craiova Craiova Romania University of Medicine and Pharmacy of Craiova University of Craiova Craiova Romania

ISBN: (数字)9781665485579

ISBN: (纸本)9781665485586

The medical routine of the future is strongly influenced by medical information technology. The quality and efficiency of medicine are at higher standards due to the image-based methods and the increase in computation power. parallel and distributed computing are attractive alternatives to improve processing performance in many areas of study and this paper investigates such solutions for increasing the throughput on large datasets in medical imaging. In this research, different edge detection techniques are used for image processing, as it is the most common and challenging task in the medical domain. The results are relevant and present the effectiveness of the two processing approaches compared to a single CPU for the acceleration of medical image processing. The CUDA processing platform using a hybrid streams-based programming model is the parallel approach. The distributed system is a Linux-based cluster, which uses message passing for communication and efficient load balancing between nodes for high performance. This paper highlights parallel computing as a very good solution for processing large image datasets in the medical domain. Good results were also obtained for the distributed system.

关键词： image processing image edge detection Graphics processing units Streaming media Programming Load management Task analysis

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Algorithm and Hardware Co-Design for Efficient PMCMC Acceleration on FPGA

Algorithm and Hardware Co-Design for Efficient PMCMC Acceler...

引用

IEEE International conference on Signal and image processing (ICSIP)

作者： Fulong Wang Shiyu Peng Yuanhao Gong Minghan Li Shuanglong Liu Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control Key Laboratory of Physics and Devices in Post-Moore Era College of Hunan Province Hunan Normal University Changsha China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

The particle Markov-chain Monte Carlo (PMCMC) method is a stochastic algorithm that combines Particle Filters (PFs) and Markov-chain Monte Carlo (MCMC) techniques. This approach is widely used in Bayesian inference for high-dimensional state spaces and nonlinear, non-Gaussian dynamic systems. However, current PMCMC accelerators face significant challenges due to their intensive computational complexity and the intricate particle routing, limiting their application in real-time scenarios. To address these challenges, we propose a novel distributed PMCMC method that leverages parallel computing to enhance hardware execution speed. Additionally, our method introduces a particle exchange scheme that not only resolves the accuracy issues caused by particle routing in distributed PMCMC but also achieves faster computing speed. Our design is implemented on a Xilinx Kintex-7 xc7k480t FPGA device. Experimental results demonstrate that our accelerator is nearly 65 ×faster than CPU performance, and provides speedups up to 5× compared to existing FPGA-based accelerators.

关键词： Performance evaluation Monte Carlo methods Accuracy Particle accelerators Computer architecture Routing Hardware

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RiverMapper: Step-wisely Mapping the Surface Rivers on Optical Remote Sensing images

RiverMapper: Step-wisely Mapping the Surface Rivers on Optic...

引用

2022 International conference on Neural Networks, Information, and Communication Engineering, NNICE 2022

作者： Zhang, Peng Pan, Hengyue Yang, Ke Dou, Yong Niu, Xin National Laboratory for Parallel and Distributed Processing National University of Defense Technology 410073 China Artificial Intelligence Research Center National Innovation Institute of Defense Technology BeiJing100850 China

ISBN: (纸本)9781510655171

Accurately mapping the surface rivers is important in ecological environment monitoring and disaster prevention. The development of remote sensing technology and computer vision greatly improves the efficiency of this task. However, there are few methods that map the rivers from an image directly. The existing automatic river mapping methods usually had two successive stages: waterbody extraction and flow-path extraction, where the latter methods were very dependent on the waterbody masks generated by the former methods. Errors in waterbody masks caused breaks and redundancies in the extracted graphs. This paper proposed RiverMapper, which mapped the rivers step-wisely without dividing into two stages. Following the directions and actions predicted by the convolution neural network, RiverMapper walked along the rivers step by step and cropped the fixed-size image patches at each step for segmentation. Final river graphs were constructed by the waterbody mask patches and those tracks generated by RiverMapper. We applied RiverMapper on optical remote sensing images containing the Changjiang River and the Huanghe River. Without the degradation of the performance on waterbody extraction, RiverMapper outperformed other methods in terms of the local topological and geometrical similarity between the predicted and the ground-truth river graphs. © 2022 SPIE.

关键词： Mapping

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Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Ya Zhou Ning Zhao Guimin Huang Nanxiao Deng Qingkai Guo Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

In sentence similarity research methods, sentence similarity is often calculated from semantic aspects, however, the influence of other features is ignored. For example, the influence of sentence syntactic structure and word order on sentence similarity is not considered. Therefore, This paper proposes a method to measure the degree of similarity between sentences based on a multi-feature fusion of semantics, syntactic similarity and word order similarity, respectively. For the aspect of semantic similarity, in this paper, we first preprocess the sentence and obtain the word vectors, and then obtains sentence vector features through neural networks, so as to calculate the sentence semantic similarity; Finally, for the aspect of word order similarity, word order is considered as the auxiliary feature information to calculate sentence similarity since word order has a smaller effect on sentence similarity compared to the above information. The experimental results show that the proposed model has an accuracy of 77.4 % and an F1 value of 84.3% on the MRPC dataset.

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distributed Deep Learning-based Model for Large image Data Classification 23

Distributed Deep Learning-based Model for Large Image Data C...

引用

Proceedings of the 7th International conference on Future Networks and distributed Systems

作者： M Faisal Nurnoby Khalid A. Abu Shawarib Ayaz Ul Hassan Khan SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum and Minerals Saudi Arabia

ISBN: (纸本)9798400709036

Artificial intelligence has shown great potential in a variety of applications, from natural language models to audio visual recognition, classification, and manipulation. AI Researchers have to work with massive amount of collected data for use in machine learning, raising some challenges in effectively managing and utilizing the collected data in the training phase to develop and iterate on more accurate, and more generalized models. In this paper we conducted a review on parallel and distributed machine learning methods and challenges. We also propose a distributed and scalable deep learning model architecture which can span across multiple processing nodes. We tested the model on the MIT Indoor dataset, to evaluate the performance and scalability of the model using multiple hardware nodes, and showed the scaling characteristics of the different model using different model sizes. We find that distributed training is 80% faster using 2 GPUs than 1 GPU. We also find that the model keeps the benefits of distributed training such as speed and accuracy regardless of its size or training batch size.

关键词： Deep Learning

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DICE: Generic Data Abstraction for Enhancing the Convergence of HPC and Big Data 8th

DICE: Generic Data Abstraction for Enhancing the Convergence...

引用

8th Latin American High Performance Computing conference (CARLA)

作者： Brox, Pablo Garcia-Blas, Javier Singh, David E. Carretero, Jesus Univ Carlos III Madrid Leganes Spain

ISBN: (纸本)9783031042096;9783031042089

Today's data-intensive applications require access to multiple types of storage platforms, such as parallel file systems, distributed file systems, and in-memory data systems. In addition, many applications are demanding the processing of data streams. The goal is to develop mechanisms to integrate and hide the diversity of data sources from applications and improve data access performance. In this work, we propose the implementation of a data container-based solution for data-intensive applications, which provides a high-level programming interface to different storage systems, commonly used in both HPC and HPDA environments. Our approach, DICE, hides the complexity of dealing with data from multiple sources, reducing the effort to access items transparently to end users and developers. The abstraction is based on a series of plugins that facilitate extension to other existing file systems.

关键词： HPC Big Data Data abstraction

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Design and Implementation of a Real-Time distributed Precise Point Positioning Platform 16th

Design and Implementation of a Real-Time Distributed Precise...

引用

16th International conference on Wireless Algorithms, Systems, and Applications (WASA)

作者： Ding, Jian Wu, Dingcheng Xu, Xueyong Xu, Hongli Huang, Liusheng Univ Sci & Technol China Dept Comp Sci & Technol Hefei Peoples R China North Informat Control Res Acad Grp CO LTD Nanjing Peoples R China

ISBN: (纸本)9783030861377;9783030861360

For Global Navigation Satellite System (GNSS) data processing, voluminous real-time Continuously Operating Reference Stations (CORS) data processing is a challenging problem. There are many methods have been proposed for regional network processing, such as parallel computing. However, they are mainly used for post-processing or near real-time processing. Due to the magnitude-increased and epoch-related of large geographic area CORS data processing, it brings huge challenges for real-time reception and efficient processing. Therefore, a real-time distributed Precise Point Positioning (PPP) platform is designed based on the idea of distributed computing and message queue to solve voluminous real-time CORS data processing, and it decomposed the real-time data processing into three processes: Input/output (I/O) multiplexing for real-time stream data acquisition, parallel PPP computing, and Weight Round Robin task scheduling. The real-time data of 5 International GNSS Service (IGS) stations is processed, the results show that it generally takes 30 min to achieve accuracy within centimeter. When the platform is applied for 1414 CORS real-time data processing in China, it can perform PPP calculations with stability and high precision. Application of the real-time Precipitable Water Vapor (PWV) monitoring is also provided.

关键词： GNSS Precise Point Positioning distributed computing PWV

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Satellite image Classification using Convolutional Neural Network with Huber Loss Function for Land Cover Mapping in Forest Ecosystem

Satellite Image Classification using Convolutional Neural Ne...

引用

distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International conference on

作者： Ramy Riad Al-Fatlawy P. Raviprakash Gummagatta Yajaman Vybhavi Sripelli Jagadish S Meenakshi Sundaram Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Malla Reddy Engineering College for Women (UGC-Autonomous) Hyderabad India Department of CSE Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India School of Computer Science & Artificial Intelligence SR University Warangal India Department of CSE Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Satellite image classification for land cover involves determining high-resolution imagery to recognize and classify various types of land covers. This process assists in monitoring forest health, managing resources, and assessing biodiversity effectively. However, accurately classifying images for land cover is difficult because of diverse and complex nature of forest environments and varying image qualities. In this research, the Convolutional Neural Network with Huber Loss Function (CNN-HLF) is proposed to classify the satellite images accurately for landcover mapping in forest ecosystem. The CNN-HLF effectively manage the variability and complexity of forest environments which improves the performance of land cover. Initially, the Eurosat dataset is used to determine the CNN-HLF performance. Resizing and image sharpening is applied to adjust the size and enhance the clarity of features. DenseNet201 is employed for extracting features effectively from pre-processed images. The proposed CNN-HLF achieves a high accuracy of 99.87% using Eurosat dataset compared to existing methods like GoogleNet with pre-processing and Lightweight parallel CNN (LPCNN).

关键词： Training Accuracy Ecosystems Land surface Forestry Feature extraction Stability analysis Robustness Satellite images Convolutional neural networks

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distributed Acoustic Sensing for Vehicle Speed and Traffic Flow Estimation

Distributed Acoustic Sensing for Vehicle Speed and Traffic F...

引用

IEEE Intelligent Transportation Systems conference (ITSC)

作者： Wiesmeyr, Christoph Coronel, Carmina Litzenberger, Martin Doeller, Herbert Josef Schweiger, Hans-Bernhard Calbris, Gaetan AIT Austrian Inst Technol GmbH Vienna Austria Dr Doller Vermessung ZT GmbH Vienna Austria Febus Opt Pau France

ISBN: (纸本)9781728191423

For traffic monitoring systems it is desirable to have a low-cost, low-maintenance, and reliable system to facilitate continuous and safe traffic flow. In line with this, we introduce distributed acoustic sensing (DAS) for traffic monitoring applications. In DAS systems, series of light pulses are transmitted along a fiber-optic cable and the back-scattered light, which is affected by the mechanical strain of the fiber-optic cables due to ground vibrations, is measured and analyzed. With fiber-optic cables installed parallel to a highway where ground vibrations are induced by passing vehicles, traffic information such as vehicle flow and average speeds can be estimated from the DAS signals. In this paper, we present an algorithm based on image processing methods that estimates traffic information from image representations generated from 1-minute segments of DAS signals. We tested the results of the algorithm on DAS data recording on a real highway against reference traffic data measured by road-side sensors.

关键词： Vibrations Optical fiber sensors Roads Optical fiber cables Estimation Mechanical cables image representation

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