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Efficient Large Scale Reverse-time Migration Imaging Computation based on Distributed Spark Cluster with GPUs 21

Efficient Large Scale Reverse-time Migration Imaging Computa...

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21st ieee International symposium on Parallel and Distributed Processing with applications, 13th ieee International Conference on Big Data and Cloud Computing, 16th ieee International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wangzhang, Suhui Yang, Ruizhang Gu, Rong Li, Bo Liu, Dingjin Wang, Zhaokang Nanjing University State Key Laboratory for Novel Software Technology China Sinopec Geophysical Research Institute China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China

ISBN: (纸本)9798350329223

The reverse-time migration (RTM) imaging algorithm, which is used for complex underground structure analysis, is known as one of the current mainstream method of high-precision seismic imaging. Nowadays, as the fast development of seismic data acquisition technology, there exists large amount of seismic data. Thus, the requirement for the efficient computation of the reverse-time migration (RTM) imaging algorithms over large-scale data have been increased greatly. To address this problem, in this paper, we propose a distributed reverse-time migration imaging algorithm based on the widely-used Spark platform first. Based on that, we design and implement a RTM computation solution based on Spark and GPU with several optimizations, including pre-shuffle&caching and load balancing. We also analyze the fault tolerance capability of the proposed distributed algorithm. We evaluate the proposed methods on the typical three-dimensional wavefild benchmark dataset. Experimental results show that the performance of the Spark based reverse-time migration imaging algorithm can scale up near linearly with computing nodes. In addition, the GPU+Spark based version has significant performance improvement on execution efficiency over the Spark-based algorithm. Moreover, the proposed optimization strategies also take effect. In addition, the method proposed in this paper has been applied in the real-world environment of one of the top oil exploration companies world wide, and has achieved significant improvement under the premise of the algorithm correctness guarantee. © 2023 ieee.

关键词： Graphics processing unit

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A real time Object Detection System for Infant Safe Sleep Based on YOLOv5 Algorithm 3

A Real Time Object Detection System for Infant Safe Sleep Ba...

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3rd International Conference on embedded and Distributed Systems, EDiS 2022

作者： Nachet, Randa Stambouli, Tarik Boudghene Signals and Images Laboratory University of Science and Technology of Oran-Mohamed Boudiaf Oran Algeria

ISBN: (纸本)9798350397543

In this paper, a real-time object detection system is proposed to reduce the risk of Sudden Infant Death Syndrome (SIDS) related to unsafe sleeping positions. The main purpose is to apply the deep learning technology and train the YOLOv5 object detection algorithm to detect and recognize whether the safest sleeping positions. Experimental results show that the proposed model is able to achieve an accuracy of more than 99%, and the inference speed has reached 2.2 ms, which makes it compatible with real-time requirements. It can be integrated into baby monitoring devices, infant safety sleep detection systems, and mobile applications. © 2022 ieee.

关键词： Object detection

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Sparse Channel Modelling in IoT Networks using Diffusion β-Divergence based Block Proportionate NLMS Algorithm 18

Sparse Channel Modelling in IoT Networks using Diffusion β-...

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18th ieee International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Sharma, Parth Pradhan, Pyari Mohan Indian Institute of Technology Roorkee Department of Electronics and Communication Engineering Roorkee India

ISBN: (纸本)9798350391725

Estimation of sparse parameters in presence of non-Gaussian noise is a challenge in Internet of Things (IoT) networks. This paper introduces a novel diffusion β-divergence based block proportionate normalized least mean squares (D β BPNLMS) algorithm tailored for distributed estimation in Internet of Things (IoT) networks. The proposed algorithm incorporates β-divergence measure to enhance robustness against non-Gaussian noise. Further, it employs block proportionate normalization to reduce computational complexity while maintaining high estimation accuracy. The algorithm also utilizes a coefficient re-ordering strategy and uniform weighting within blocks to efficiently prioritize significant coefficients. An adapt-then-combine (ATC) diffusion strategy is implemented to improve robustness and convergence speed in distributed parameter estimation. The performance of the proposed {D} β BPNLMS algorithm is validated through simulation study on two critical IoT applications: acoustic echo path estimation and urban microcell wireless channel estimation. The results demonstrate that the proposed algorithm significantly outperforms state-of-the-art algorithms, achieving lower steady-state error across various noise conditions, thereby proving its effectiveness and practical applicability in real-time IoT networks. © 2024 ieee.

关键词： Gaussian noise (electronic)

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A Comprehensive Survey on Phytopathogen Surveillance with Modern Artificial Intelligence Practices 5

A Comprehensive Survey on Phytopathogen Surveillance with Mo...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kaleeswari, G. Sundarrajan, R. Kalasalingam Academy of Research and Education Department of Computer Applications Tamilnadu Virudhunagar India Kalasalingam Academy of Research and Education Department of Information Technology Tamilnadu Virudhunagar India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

The most important aspect of modern agriculture is the detection of plant diseases with the goal of improving crop quality and output. This survey paper investigates modern approaches of deep learning algorithms, Explainable AI and Federated Learning to plant disease diagnosis. The background and broadening of plant disease detection are briefly reviewed at the outset of the study, with a focus on the importance of precise and effective identification techniques. The multivariate normal DL neural network (MNDLNN) classifier and Customized CNN are the most robust feature extraction and classification techniques among the examined technologies. Using Higher-Order Whitened Singular Value Decomposition (HOWSVD), complex data is processed in a way that highlights distinct patterns and features for easy data classification and also increases accuracy. The E-GreenNet generates disease classification features based on a MobileNetV3. The ResNet9 and Improved Vision Transformer models perform better when processing complicated visual data. Spectral Generative Adversarial Neural Network (DSGAN2) and Lite Multikernel Depthwise Convolutions architecture are suitable for real-time applications because they provide promising results with less computational overhead. These findings' implications point to a possible move toward disease detection systems that are easier to use and more effective, which would improve crop management and food security. The development of more affordable solutions for small scale farmers, the integration of embedded systems for real-time monitoring, and the improvement of these models for increased precision are some future directions. This survey highlights the transformative potential of combining deep learning with other techniques like Federated Learning in advancing agricultural practices. © 2024 ieee.

关键词： Generative adversarial networks

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ADAPTIVE LOCAL CONTEXT EMBEDDING FOR SMALL VEHICLE DETECTION FROM AERIAL OPTICAL REMOTE SENSING IMAGES

ADAPTIVE LOCAL CONTEXT EMBEDDING FOR SMALL VEHICLE DETECTION...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Liu, Shanjunyu Zhuang, Yin Dong, Hao Gao, Peng Wang, Guanqun Zhang, Tong Chen, Liang Chen, He Li, Lianlin Beijing Inst Technol Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China Peking Univ Ctr Frontiers Comp Studies Beijing 100087 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing 100087 Peoples R China Shanghai AI Lab Shanghai 200232 Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Small vehicle detection is one of the remaining challenging task because the ambiguous appearance is against complex background interference. Consequently, in order to improve the performance of small vehicle detection from aerial optical remote sensing images, a novel adaptive local context (ALC) embedding way is designed and further introduced into an anchor free detection manner which is called ALC-Net, and in ALC-Net, it can adaptively set up the effective local context feature to improve keypoint description of small vehicles and boost the detection performance without adding extra prior information. Finally, several experiments are carried out on two widely used datasets (e.g., UCAS-AOD [1] and VEDAI [2]) and the results indicate that the proposed ALC-Net can exhibit the competitive small vehicle detection performance than other detectors.

关键词： Adaptive local context embedding small vehicle detection anchor free

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SPECTRUM: A Multi-Component Pipeline for High-Quality Image Synthesis 6

SPECTRUM: A Multi-Component Pipeline for High-Quality Image ...

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6th ieee International Conference on Artificial Intelligence in Engineering and technology, IICAIET 2024

作者： Kumar, Vishal Kavitha Nair, R. Amelesh, M. Choudhary, Deepak A. Shree, Sachin Acharya Institute of Technology Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350389692

This research paper introduces a novel pipeline for image generation and enhancement, integrating advanced techniques from stable diffusion and deep neural network (DNN) super-resolution. The pipeline consists of three key components: Stable Diffusion v1.5, Stable Diffusion XL Refiner 1.0, and ESPCN (Enhanced Sub-Pixel Convolutional Network). Stable Diffusion v1.5 serves as the foundation for text-to-image generation, utilizing the innovative approaches of Self Attention Guidance (SAG) and Classifier-Free Guidance (CFG). CFG directs the model's output fidelity towards the provided prompt, ensuring faithful image generation. Meanwhile, SAG leverages internal self-attention maps to iteratively refine image details, resulting in higher-quality outputs. The Stable Diffusion XL Refiner 1.0 component enhances previously generated images, affording control over the influence of prior images on refinement. By adjusting this influence, users can tailor the refinement process to either maintain consistency with the previous image or explore new visual possibilities. ESPCN, a Deep Neural Network Upscaler embedded within OpenCV, completes the pipeline by upscaling the generated image by a factor of two. Its compact design enables real-time operations, making it suitable for various applications. Through comprehensive experimentation and evaluation, this pipeline demonstrates remarkable capabilities in generating high-quality images from textual prompts and refining them to achieve desired visual outcomes. The integration of stable diffusion techniques with DNN super-resolution presents a promising avenue for advancing image generation and enhancement methodologies. © 2024 ieee.

关键词： Deep neural networks

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Design and Implementation of a Logistic Map-Based Pseudo-Random Number Generator on FPGA

Design and Implementation of a Logistic Map-Based Pseudo-Ran...

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ieee International symposium on embedded Multicore Socs (MCSoC)

作者： T. Hui Teo Maoyang Xiang Mostafa Elsharkawy Hen Rin Leao Mateo Jalen Andrew Calderon Jun Lei Lee Syarifuddin Azhar Bin Rosli Hui Ying See En Yu Lim Engineering Product Development Singapapore University of Technology and Design Singapore Nano-Electronic Engineering and Design Singapore University of Technology and Design Singapore Chang Gung University Taiwan

ISBN: (数字)9798331530471

ISBN: (纸本)9798331530488

Chaotic systems can be employed in hardware to generate pseudo-random numbers, which is essential for a wide range of applications, including cryptography, Very Large Scale Integration (VLSI) testing, gaming, and simulations. Field-Programmable Gate Arrays (FPGAs) offer a flexible platform for implementing chaotic-based random number generators, integrating real-time data handling and visualization capabilities. This work presents the design and implementation of a pseudo-random number generator (PRNG) deployed on FPGA based on the logistic map for chaotic sequence generation. The generated pseudo-random numbers are further transformed via a Central Limit Theorem (CLT) function to produce a Gaussian distribution. The system architecture integrates several FPGA modules to support real-time interaction and data visualization: a clock generator, a Universal Asynchronous Receiver-Transmitter (UART) interface, a Xilinx Analog-to-Digital Converter (XADC), and a 7-segment display driver. These components enable the display of PRNG values directly on the FPGA and the transmission of data to a laptop for histogram analysis, confirming the Gaussian characteristics of the output.

关键词： Portable computers Data handling Data visualization Very large scale integration Generators real-time systems Cryptography Field programmable gate arrays Logistics Testing

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SGNet:A Lightweight Defect Detection Model for New Energy Vehicle Battery Current Collectors

SGNet:A Lightweight Defect Detection Model for New Energy Ve...

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2023 China Automation Congress, CAC 2023

作者： Yuan, Lei Chen, Yanrong Tang, Hai Liu, Zhengxuan Gao, Ren Wu, Wenhuan Hubei University of Automotive Technology Electrical and Information Engineering Hubei Shiyan China The 45th Research Institute of China Electronics Technology Group Corporation Beijing China

ISBN: (纸本)9798350303759

The quality of the current collector, an essential component in new energy vehicle batteries, is crucial for battery performance and significantly impacts the safety of vehicle occupants. However, detecting defects in battery current collector in real-time industrial applications with limited computational resources poses a major challenge. To address this, our paper proposes SGNet (ShuffleNet V2 + G-GFPN), a lightweight model for current collector defect detection. SGNet utilizes ShuffleNet V2 as the backbone feature extraction network and a designed G-GFPN as the feature fusion network, reducing parameter redundancy to ensure high detection accuracy while minimizing computational costs. Our experimental results demonstrate that the SGNet model achieves an impressive 90.7% detection accuracy. Boasting a compact model size of merely 3.5M and remarkably low GFLOPs of 4.1, SGNet achieves an outstanding FPS (Frames Per Second) rate of 178.6. In comparison to other sophisticated lightweight network models, SGNet surpasses them by delivering superior detection accuracy while demanding less computational resources. The successful deployment of the SGNet model on the embedded NVIDIA Jetson Nano platform paves the way for real-time defect detection. With a swift detection time of 0.073 seconds per image, the model meets the stringent requirements for accuracy and real-time performance in identifying battery collector tray defects within real-world industrial environments. © 2023 ieee.

关键词： Feature extraction

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A Reliable Routing Scheme for Vehicular AD HOC Networks using Hybrid Protocols

A Reliable Routing Scheme for Vehicular AD HOC Networks usin...

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2024 International Conference on Computer, Electronics, Electrical Engineering and their applications, IC2E3 2024

作者： Ateeq Ahmed, H. Cheelu, Dhanaraj Jawaharlal Nehru Technological University Anantapur Department of CSE Andhra Pradesh Ananthapuramu India Dr. KVSR Institute of Technology Kurnool Affiliated to JNTUA Department of CSE Andhra Pradesh India

ISBN: (纸本)9798350388534

Vehicular Ad hoc Networks (VANETs) play an important role in advancing road safety and traffic control through real-time data exchange. This research addresses critical challenges in VANETs, focusing on message broadcasting, link stability, and time synchronization. In the realm of message broadcasting, the first segment introduces the Reliable Message Broadcast Protocol (RMBP). RMBP leverages active Radio Frequency Identification (RFID) technology to enhance the accuracy of the positioning system, mitigating issues associated with GPS-based dissemination protocols. The protocol partitions the route using embedded active RFID tags, enabling location-based message broadcasting. The second segment introduces a Novel Dynamic Link Prediction Protocol (NDLP) to address the challenges posed by the dynamic nature of VANET topologies. NDLP predicts link disconnections in advance, utilizing Newton's divided difference interpolation to assess the duration of the current path's availability. By pre-emptively rerouting data packets through alternative paths, NDLP ensures uninterrupted communication. Finally, the third segment delves into the importance of precise time synchronization in large-scale VANETs. The Dynamic Message time Synchronization (DMTS) protocol is proposed to optimize timing message coordination. DMTS employs intelligent node selection to reduce the number of transmitted timing messages, periodically readjusting clock offsets for enhanced synchronization accuracy by utilizing Maximum Likelihood estimation for clock offset design. The proposed hybrid protocols RMBP, NDLP and DMTS outperforms existing protocols in simulation analyses, demonstrating superior metrics such as delay, packet delivery ratio, and throughput in terms of reliability. © 2024 ieee.

关键词： Vehicular ad hoc networks

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28th International symposium on Database Engineered applications, IDEAS 2024

28th International Symposium on Database Engineered Applicat...

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28th International symposium on Database Engineered applications, IDEAS 2024

ISBN: (纸本)9783031834714

The proceedings contain 26 papers. The special focus in this conference is on Database Engineered applications. The topics include: Enhanced Classification of embedded System Vulnerabilities Using Ensemble Embedding and BiLSTM Networks;predicting Dyskinetic Events Through Verified Multivariate time Series Classification;R2-LGBM: Sales Informative Prediction System in E-commerce Application Using Ensemble Classifier;comparing Related Languages with a Fuzzy Morphism Matching Algorithm;Enhancing LLM Code Generation Using Natural Language Processing in the Context of Machine Learning;LaSSI: Logical, Structural, and Semantic Text Interpretation;clustEm4Ano: Clustering Text Embeddings of Nominal Textual Attributes for Microdata Anonymization;about Relationships in Data Lakes;from Theory to Practice of Multidimensional Big Data Analytics over Big Healthcare Data: A real-Life Case Study;a Database Engineered System for Big Data Analytics on Tornado Climatology;siren Federate: Bridging the Gap Between Document and Relational Data Systems for Efficient Exploratory Graph Analysis;outlier-Weighted Traffic Flow Prediction Using Online Autoencoders;Speculative Query Support for RDBMS with Flexible Query Order and Benefit Verification;made to Measure: Towards Approximability of Query Evaluation Engines;DGGS-Based Continuous Trajectory Similarity Comparison;predictive Modeling of Key Performance Indicators for Greenhouse Gas Emission Reduction Using Machine Learning;validation Without Rules: A Data Integration Case Study;fairness in Group Recommender Systems Using Variational Autoencoders;a Supervised Contrastive Learning Framework for Aspect-Based Recommendations;multi-attribute Bias Mitigation in Recommender Systems;UpLIF: An Updatable Self-tuning Learned Index Framework;scheduling of Intermittent Query Processing;monitoring Environmental Factors for Optimal Health and Welfare of Broiler Chickens Using Sensor technology in Southwest Nigeria;river Levels Affecting Firefighter Inte

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