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Enhancing Oil Spill Detection Using Synthetic Aperture Radar With Dual Attention U-Net Model

Enhancing Oil Spill Detection Using Synthetic Aperture Radar...

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International Conference on computing and Networking Technology (ICCNT)

作者： M Phani Shanmukh S Baghavathi Priya Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Oil spills present a critical hazard in oceans, requiring advanced monitoring systems to rapidly detect and contain environmental damage. this work proposes a deep learning approach combining a UNet encoder-decoder architecture with a fully convolutional network in a dual attention model for accurate and efficient oil spill identification from SAR data. the model leverages the strengths of UNet and FCN for robust feature extraction and segmentation, while incorporating a dual attention mechanism to focus on the most salient spatial and channel-wise features. the dual attention model is compared against popular architectures like DeepLabV3, AlexNet, and PSPNet on public SAR datasets capturing oil spills. Results demonstrate the superior performance of the proposed dual attention model, achieving an impressive 97.5% accuracy in detecting oil spills outperforming all benchmark models evaluated. the high accuracy, coupled with the model’s computational efficiency, highlights its potential for real-time autonomous oil spill monitoring and disaster management. the study underscores the efficacy of integrating attention mechanisms in deep learning models for SAR-based oil spill detection tasks.

关键词： Deep learning Accuracy Oils Computational modeling computer architecture Predictive models Feature extraction Radar polarimetry Convolutional neural networks Synthetic aperture radar

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Combining Cache and Refresh to Optimize SSD Read performance Scheme 15th

Combining Cache and Refresh to Optimize SSD Read Performance...

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15th International symposium on Advanced Parallel Processing Technologies, APPT 2023

作者： Chen, Jinli Li, Peixuan Xie, Ping College of Computer of Qinghai Normal University Xining810016 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810016 China The Key Laboratory of Internet of Things of Qinghai Province Xining810016 China Academy of Plateau Science and Sustainability Xining810016 China

ISBN: (纸本)9789819978717

In the era of continuous advances in flash technology, the storage density of NAND flash memory is increasing, but the availability of data is declining. In order to improve data availability, low-density parity check codes (LDPC), which have been highly corrected in recent years, are used in flash memory. However, although LDPC can solve the problem of low data availability, it also brings the problem of long decoding time. Moreover, the LDPC decoding delay time is related to its decoding level, and the higher the LDPC decoding level, the longer the delay time. Long decoding delays can have an impact on the read performance of the flash. therefore, in order to improve the speed of reading data in flash memory, this paper proposes a scheme combining cache and flushing, the main idea of which is to use the cache to reduce LDPC decoding time, and at the same time refresh the pages with high latency replaced by the cache, so that the pages with high latency can be restored to the state of low latency pages Experimental results show that this scheme can significantly reduce LDPC decoding delay and improve data availability with less overhead, and optimize the read performance of flash memory. the experimental results show that compared with the original strategy, the average response time is reduced by 24%, and the average IOPS value is increased by 32%. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decoding

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Enhancing Scalability: A Complete Tree Sharding architecture Towards IoT 18th

Enhancing Scalability: A Complete Tree Sharding Architecture...

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18th International Conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Zhang, Luyi Wang, Yujue Ding, Yong Liang, Hai Yang, Changsong Zheng, Haibin Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Hangzhou Innovation Institute of Beihang University Hangzhou310052 China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen518055 China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783031714665

With the rapid proliferation of integration of blockchain and IoT, traditional centralized architectures and single-node processing have been effectively solved, but scalability of blockchain have become inadequate to meet the demands of high-load environments. Sharded blockchain brings an innovative approach to tackle the scalability of blockchain and has the potential to greatly enhance the performance of IoT based on blockchain. this paper introduces CTS towards IoT – complete tree sharding that overlaps shards to enable a little shards to retain records of other shards, facilitating the validation of cross-shard transactions as intra-shard transactions. A tree sharding consensus mechanism that relies on the complete tree architecture is proposed to ensure consistency among interconnected shards during the execution of cross-shard transactions. A prototype for CTS towards IoT is simulated and the findings demonstrate that CTS can enhance transaction throughput by a factor of 1.3 when compared to state of the art layered sharding systems observed in a system consisting of 15 shards and 1500 nodes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distributed ledger

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Scalable Data Aggregation for Cloud-based Internet of things Applications

Scalable Data Aggregation for Cloud-based Internet of Things...

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International Conference on computing and Networking Technology (ICCNT)

作者： Muktha Eti Santosh Kumar Singh Meenakshi Dheer Department of Electrical and Electronics Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Biotechnology ARKA JAIN University Jamshedpur Jharkhand India Department of Agriculture Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Scalable records aggregation is a critical component of cloud-primarily based net of things (IoT) programs. Facts aggregation permits the evaluation and optimization of offerings by means of permitting aggregated views of information from an expansion of various disbursed assets. this procedure is often tough because of the high volume of information and variety of information resources associated with IoT packages. To cope with those challenges, new procedures to statistics aggregation ought to be evolved that focus on scalability, availability, and performance. this paper explores modern-day procedures to scalable information aggregation for cloud-primarily based IoT programs and recommends techniques for similarly studies in the region. Capability answers discussed consist of self-contained databases, disbursed storage structures, hierarchical facts aggregation, and the usage of flow processing frameworks. Moreover, the paper provides an architecture and algorithm for efficient and scalable statistics aggregation tailored specifically for cloud-primarily based IoT applications. the proposed structure and set of rules are then evaluated in two unique eventualities and a number of metrics to apprehend the differences between them and their scalability performance. Outcomes show that the proposed answer is able to supplying pretty appearing and scalable statistics aggregation for cloud-primarily based IoT applications.

关键词： Measurement Cloud computing Information resources Databases Scalability computer architecture Data aggregation Computational efficiency Internet of things Optimization

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A Novel Adaptive Computation Offloading Strategy for Collaborative DNN Inference over Edge Devices 20

A Novel Adaptive Computation Offloading Strategy for Collabo...

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20th IEEE Int symposium on Parallel and Distributed Processing with Applicat / 15th IEEE Int Conf on Social Comp and Networking / 12th IEEE Int Conf on Big Data and Cloud Comp / 12th IEEE Int Conf on Sustainable Comp and Commun

作者： Zhang, Yansong Liu, Xiao Xu, Jia Yuan, Dong Li, Xuejun Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Deakin Univ Sch Informat Technol Geelong Vic Australia Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia

ISBN: (纸本)9781665464970

With the breakthrough of deep learning technology and the rapid growth of Internet of things (IoT), a fast-increasing number of artificial intelligence (AI) applications are being widely used in many smart IoT systems. Taking the UAV last mile delivery system as an example, UAVs require the assistance of various AI applications such as object detection and route planning during the delivery process. However, provisioning AI services over the cloud computing environment are facing many challenges such as network congestion and high latency. To tackle these challenges, edge computing has emerged as a new computing paradigm where computing resources are provisioned closer to the end devices from the network edge. In this paper, combined with the edge computing-based UAV last mile delivery scenario, we focus on the computation offloading problem for deep learning-based applications at the inference stage. A novel computation offloading strategy for multi-device collaborative pipelining processing of deep neural networks (DNNs) tasks called CoDNN is proposed. First, when the DNN inference task is generated, CoDNN will predict the execution time for each layer of the DNN and model the process of DNN inference. Second, CoDNN will use the offloading partitioning algorithm based on Particle Swarm Optimization (PSO) to determine the target computing resources and assign them to appropriate workloads. Specifically, we explore the impact of different computing environments on the performance of CoDNN and show its advantages over other representative computation offloading strategies.

关键词： edge computing computation offloading deep neural networks particle swarm optimization

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Using Logging-on-Write to Improve Non-Volatile Memory Checkpoints via Processing-in-Memory

Using Logging-on-Write to Improve Non-Volatile Memory Checkp...

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International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Kleber Kruger Ricardo Pannain Rodolfo Azevedo Institute of Computing State University of Campinas Campinas Brazil

NVM architectures must keep consistent data in case of failures, a property called crash consistency. A common way to do so is by checkpoint mechanisms. However, most of the strategies developed have performance and usability problems. Among main limitations are non-software-transparent strategies, the addition of logging operations in the critical execution path, and increased writes to NVM, resulting in significant bandwidth usage between processor and memory. DONUTS solves these problems by a hardware mechanism that provides crash consistency via checkpoints integrated into cache replacement policy using Processing-in-Memory to perform logging operations. Its approach reduces writes from the processor's memory controller and the NVM external bandwidth usage but generates unnecessary log entries. this paper expands DONUTS to a multi-core scenario evaluating two processing-in-memory strategies. the first logs during read operations, and the second uses a new lazy strategy to log data exclusively on write operations when these operations are effectively needed. Finally, we compare runtime performance, log rate, energy consumption, and memory space. Results show that our new logging-on-write strategy maintained the DONUTS runtime performance but reduced energy consumption in multi-core applications by around 33% due to an average reduction of 42% in log operations. Also, the new strategy generates a checkpoint size 5x smaller than the previous system, maximizing the use of NVM. Compared to other systems, DONUTS presented an average overhead of 1% to 3% against up to 7% of previous software-transparent better-performing projects.

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Design and Implementation of a Real-time IoT Solution for Smart Meter Data Analysis in Microsoft Azure

Design and Implementation of a Real-time IoT Solution for Sm...

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International Conference on computing and Networking Technology (ICCNT)

作者： Sai Deepa BhavaniPeri Abhishek Ravi M Supriya Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

this research presents an end-to-end IoT solution designed as a project, not a traditional lab setup. It simulates high-velocity data generation from smart meters and focuses on real-time data visualization within Azure’s cloud ecosystem. An integral part of this project is an AI-based spike and dip anomaly detection system. the project creates an IoT ecosystem that replicates smart meter telemetry data. It utilizes Microsoft Azure’s robust services to cover the entire IoT data lifecycle, from data ingestion to real-time visualization. Key project components involve Azure resource provisioning, IoT Hub setup, and the deployment of a Device Provisioning Service. the project implements a lambda architecture, filtering and processing a subset of telemetry data in real-time. this data undergoes immediate visualization, showcasing Azure’s agility in handling high-velocity streams. this paper is a valuable reference for interests in implementing IoT solutions with Microsoft Azure, particularly emphasizing real-time data processing and visualization within a lambda architecture while incorporating AI-based anomaly detection for spikes and dips.

关键词： Data analysis Ecosystems Data visualization computer architecture Real-time systems Smart meters Internet of things Telemetry Anomaly detection Streams

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Neural Network Based Light Field Predictors: An Evaluation

Neural Network Based Light Field Predictors: An Evaluation

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IEEE Latin American symposium on Circuits and Systems (LASCAS)

作者： Matheus Jahnke Ítalo D. Machado Lucas Ikenoue Bruno Zatt Video Technology Research Group (ViTech) Graduate Program in Computing (PPGC) Federal University of Pelotas (UFPel) Pelotas Brazil

Processing and storing the 4D structure of light fields can be challenging and expensive due to the high-dimensional data and its unique characteristics. there are plenty of works employing convolutional neural networks (CNNs) for light field prediction and encoding. Nonetheless, to the best of our knowledge, the literature lacks an efficiency evaluation of different CNN architectures as well as 4D neural networks for these purposes. therefore, this paper presents an experimental study that assesses the performance of pipeline and U-net convolutional neural networks for light field block prediction in both spatial and angular dimensions. Additionally, we compare these architectures with a novel 4D network that aims to exploit the light field data structure. the results of the study show that U-net and 4D networks outperform classical CNN architectures in terms of prediction accuracy and residue generation. Furthermore, the spatial dimension prediction provides more valuable information for the networks to learn, improving their prediction by 5dB.

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JANUS: A Simple and Efficient Speculative Defense using Reinforcement Learning

JANUS: A Simple and Efficient Speculative Defense using Rein...

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International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Pavlos Aimoniotis Stefanos Kaxiras Uppsala University Uppsala Sweden

ISBN: (数字)9798350356168

ISBN: (纸本)9798350356175

Speculative execution and the emergence of Spectre attacks have forced architects to rethink how microprocessors are designed. Several approaches aim to close this security vulnerability while trying to minimize performance degradation, often involving complex and sophisticated mechanisms. these strategies typically entail substantial modifications to the processor core and memory hierarchy, which ultimately inhibit their adoption in real *** this work, we leverage two of the simplest speculative defense ideas, NDA and DoM, that can co-exist in the same core, and we apply a simple form of Reinforcement Learning (RL) to select the most effective mechanism, as the underlying processor defense, for a window of execution. NDA forbids the propagation of a potential secret to subsequent instructions while DoM prohibits the creation of observable timing differences in the cache. We observe that their impact on different applications can vary significantly, but, often, they can complement each other within the same application. However, our investigation also reveals vulnerabilities in previous proposals that try to combine these secure speculation schemes into one. We demonstrate an attack scenario that violates the security of the combined scheme and we present the conditions that must hold to safely combine them. Lastly, while the cost and complexity of reinforcement learning may seem inordinately high for microarchitectural implementations, we build on recent research that demonstrates remarkably lightweight solutions, provided that the action space is *** present JANUS, a lightweight architecture leveraging an RL agent based on a two-armed bandit algorithm. JANUS selects the optimal, performance-wise, defense mechanism that protects the processor within a specific time window. We evaluate JANUS on SPEC2017 benchmark suite and find that it outperforms NDA by +4.9%, STT (a more sophisticated and complex scheme that uses taint tracking) by +1%, and DoM by +2.6

关键词： threat modeling Reinforcement learning computer architecture Side-channel attacks Benchmark testing Timing Security Proposals Protection Optimization

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Adaboost-based Graph Convolutional Networks with Channel Attention for Intrusion Detection in Internet-of-things 21

Adaboost-based Graph Convolutional Networks with Channel Att...

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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： Long, Jing Luo, Cuiting Chen, Ruxin Liang, Wei Qin, Zheng Hunan Normal University College of Information Science and Engineering Changsha410081 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

ISBN: (纸本)9798350329223

With the increasing proliferation of Internet of things (IoT) and industrial big data technologies, while IoT scenarios offer intelligent services, cyberspace faces various types of threats and attacks. Despite Graph Convolutional Networks (GCN) having been demonstrated as a potent tool for learning graph data representations, many current IoT intrusion detection methods tend to overlook the adoption of graph-related techniques, given the non-graph-structured nature of most IoT traffic data. this leads to the model design overlooking the potential correlations among variables, thus limiting the accuracy of intrusion detection. In this study, we introduce an integrated intrusion detection model that fuses graph learning with a dual attention mechanism(A-GCA). Initially, our research adopts a combination of GCN and Convolutional Neural Networks (CNN) to deeply capture intricate interactions between data nodes. To further enhance the model's feature-learning capability, we incorporate a channel attention mechanism for emphasizing features with pivotal information. Additionally, a multi-head attention mechanism is adopted to amalgamate a wealth of contextual information, ensuring more vivid feature representations. Regarding the training strategy, we employ a variant of Adaboost which uniquely steers weak classifiers from focusing on high-precision fitting of raw data towards precise binary classification tasks, aiming to augment the model's overall performance in intrusion detection. Evaluations and performance analyses using real-world datasets UNSW-NB15 and CICIDS2017 indicate that the proposed method achieves improvements of 2.5% and 3.7% over the best baseline model in the accuracy and F1-score, respectively, relative to the baseline, surpassing other approaches. © 2023 IEEE.

关键词： Adaptive boosting

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