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Multi-scale Frequency Attention Fusion network for Optical and SAR Image Registration 17

Multi-scale Frequency Attention Fusion Network for Optical a...

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17th IEEE International conference on Signal processing, ICSP 2024

作者： Hu, Xin Wu, Yan Li, Zhikang Yang, Zhifei Li, Ming Xidian University School of Electronic Engineering Xi'an China Xidian University National Key Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9798350387384

Image registration is a prerequisite for remote sensing image fusion and classification, and registration accuracy affects the performance of these tasks. However, there are significant nonlinear radiation differences between optical and synthetic aperture radar (SAR) images and speckle noise in SAR, which limits the ability of pure convolutional neural networks (CNNs) to obtain modality-shared features. To solve these problems, this paper proposes a Multi-scale Frequency Attention Fusion network for Optical and SAR image Registration (MFAF-OS-RegNet), which includes a Multi-scale Frequency Channel Attention Fusion (MFCAF) module and a Momentum Contrastive Learning (MCL) strategy. Firstly, we extract repeatable keypoints distributed on the edges and develop a novel MFCAF module to obtain modality-shared features. In the MFCAF module, multi-scale feature fusion is used to improve the positioning accuracy of matching points, and frequency channel attention is used to mine more favorable features. Secondly, we design a MCL strategy to reduce the interference of speckle noise in network training. The momentum teacher corrects handcrafted labels by learning the essential characteristics of the data for robust matching. Finally, we filter outliers and estimate transformation parameters based on the initial matching correspondence output by MFAF-OS-RegNet. Experimental results on a publicly available OS-dataset show that the proposed method outperforms other state-of-the-art methods. © 2024 IEEE.

关键词： Image fusion

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2nd International conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

2nd International Conference on Next-Generation Computing an...

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2nd International conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

ISBN: (纸本)9781032738659

The proceedings contain papers. The special focus in this conference is on . The topics include: Impact of artificial intelligence on high-penetration renewable physical infrastructure;collaborative attacks and defense;building a safe and secure metaverse;distributed-computing based versatile healthcare services framework for diagnostic markers;supervised context-aware Latent Dirichlet allocation-based drug recommendation model;combination kernel support vector machine based digital twin model for prediction of dyslexia in distributed environment;Time series analysis of vegetation change using remote sensing, GIS and FB prophet;advances in smart farming for precision agriculture: Green-IoT and machine learning as a solution;Towards a serverless computing and edge-intelligence architecture for the Personal-Internet-of-Things (PIoT);IOT and developed deep learning based road accident detection system and societal knowledge management;Healthcare CAD model for hierarchical processing of surgical navigation system;applicability of eye tracking technology in virtual keyboard for human-computer interactions;a digital twin framework for smart contract-based DeFi applications in the metaverse: Towards interoperability, service scaleup & resilience;a novel approach to glass identification using ensemble learning for forensics;deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification;Multi-class instance segmentation for the detection of cervical cancer cells using modified mask RCNN;dilated convolution model for lightweight neural network;artificial intelligence based Monte Carlo model for epidemic forecasting for societal aspect;efficient data visualization through novel recurrent neural network-based dimension reduction;An optimized CNN-based model for pneumonia detection;EEG-based emotion recognition: Leveraging CNNs for precision;A seven layer DNN approach for social-media multilevel image-based text classification.

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Direct image classification using an on-chip photonic deep neural network 27

Direct image classification using an on-chip photonic deep n...

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conference on Integrated Optics - Devices, Materials, and Technologies XXVII

作者： Ashtiani, Farshid Geers, Alexander J. Aflatouni, Firooz Univ Penn Dept Elect & Syst Engn 200 S 33rd St Philadelphia PA 19104 USA Nokia Bell Labs 600 Mt Ave Murray Hill NJ 07974 USA

ISBN: (纸本)9781510659537;9781510659544

Artificial neural networks have been a major driving force in a wide variety of technologies from pattern recognition and natural language reprocessing to medical diagnosis. neural networks can be trained to perform a certain set of tasks and are typically formed by the interconnection of multiple layers of neurons to perform linear and nonlinear computations. Highly reconfigurable clock-based digital signal processors have been the main hardware platform to implement such networks. However, clock frequency and memory access time are two main contributors in limiting the processing speed. Integrated photonics benefits from the large available bandwidth at optical frequencies and low-loss interconnects, and has proved to be a promising candidate to address the challenges of electronic systems. Most of the demonstrated photonic neural networks have been either using bench-top setups, or the partial integration of some of the linear and/or nonlinear computation blocks. A scalable solution that can integrate all computation blocks has been an open problem. Here, we demonstrate the first end-to-end photonic deep neural network (PDNN) for direct image classification. On-chip image formation of the target object using a 2D array of grating couplers and direct processing of the received optical signals through linear (optical) and nonlinear (opto-electronic) computation, enable an end-to-end classification time of 570 ps. Using a uniformly distributed supply light enables scaling the PDNN to networks with more layers of neurons. The PDNN chip is used to demonstrate classification of 2- and 4-class datasets of handwritten letters and the system achieves accuracies higher than 93.8% and 89.8%, respectively.

关键词： Photonic neural network Silicon photonics Integrated photonics Image classification

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A Real-Time Load Forecasting Approach Based on Graph Convolution neural network in a distributed IoT Energy System

A Real-Time Load Forecasting Approach Based on Graph Convolu...

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2023 IEEE International conference on Dependable, Autonomic and Secure Computing, 2023 International conference on Pervasive Intelligence and Computing, 2023 International conference on Cloud and Big Data Computing, 2023 International conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Pan, Li Gan, Jixiang Liu, Qi Zhang, Yonghong Liu, Xiaodong School of Software Nanjing University of Information Science and Technology Nanjing China School of Automation Nanjing University of Information Science and Technology Nanjing China School of Computing Edinburgh Napier University Edinburgh United Kingdom

ISBN: (纸本)9798350304602

With the development of edge devices, smart sockets are now capable of handling various power load data, which provide a new solution for edge computing and real-time load forecasting. In this paper, a distributed system that can centrally process load data is proposed, and a KGLSTM network based on graph neural networks and long short-term memory networks is introduced. In our proposed system, smart sockets are used for the collecting and processing of load data. The proposed KGLSTM network is based on a dual-branch structure. One branch extracts medium and short-term load data features by using LSTM, and the other branch extracts medium and long-term load data features by using graph neural networks. In terms of the training of model, a dynamic weighted loss function is proposed to guide the training, which improves the load prediction performance for peak load intervals. Finally, by comparing with advanced models, the effectiveness of KGLSTM and weighted KGLSTM for load prediction is validated, and the proposed two models also have a great performance in peak forecasting. © 2023 IEEE.

关键词： Long short-term memory

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Secure CNN Training and Inference based on Multi-key Fully Homomorphic Encryption 29

Secure CNN Training and Inference based on Multi-key Fully H...

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29th IEEE International conference on Parallel and distributed Systems, ICPADS 2023

作者： Qin, Hong He, Debiao Feng, Qi Luo, Min Wuhan University School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350330717

Convolutional neural network (CNN) has attracted increasing attention and been widely used in imaging processing, bioinformatics and so on. As the cloud computing and multiparty computing are booming, the training and inference data of convolutional neural network often comes from diverse users. These users tend to jointly perform the computation but reluctantly share original data with others. Multi-key fully homomorphic encryption (MKFHE) supports homomorphic computation on ciphertexts encrypted with different keys, which is especially suitable for this scenario. In this paper, we firstly propose secure convolution, matrix multiplication, comparison and maximum protocols based on MKFHE. Then we design the secure CNN training and inference framework, outsourcing almost all computations to cloud server. To improve the efficiency, we use key switching technique for ciphertext transformation. We prove that the proposed frameworks are secure and feasible. The theoretical and experimental analysis show that our framework achieves the trade-off between security, efficiency and scalability. © 2023 IEEE.

关键词： Cloud computing Convolutional neural network Multi-key fully homomorphic encryption Privacy protection

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Delay-Aware Backpressure Routing Using Graph neural networks 48

Delay-Aware Backpressure Routing Using Graph Neural Networks

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48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Zhao, Zhongyuan Radojicic, Bojan Verma, Gunjan Swami, Ananthram Segarra, Santiago Rice University United States University of Novi Sad Serbia Us Army's Devcom Army Research Laboratory United States

ISBN: (纸本)9781728163277

We propose a throughput-optimal biased backpressure (BP) algorithm for routing, where the bias is learned through a graph neural network that seeks to minimize end-to-end delay. Classical BP routing provides a simple yet powerful distributed solution for resource allocation in wireless multi-hop networks but has poor delay performance. A low-cost approach to improve this delay performance is to favor shorter paths by incorporating pre-defined biases in the BP computation, such as a bias based on the shortest path (hop) distance to the destination. In this work, we improve upon the widely-used metric of hop distance (and its variants) for the shortest path bias by introducing a bias based on the link duty cycle, which we predict using a graph convolutional neural network. Numerical results show that our approach can improve the delay performance compared to classical BP and existing BP alternatives based on pre-defined bias while being adaptive to interference density. In terms of complexity, our distributed implementation only introduces a one-time overhead (linear in the number of devices in the network) compared to classical BP, and a constant overhead compared to the lowest-complexity existing bias-based BP algorithms. © 2023 IEEE.

关键词： Backpressure routing bias graph neural networks independent set scheduling duty cycle shortest path

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Proceedings - 2023 IEEE International conference on Parallel and distributed processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and networking, ISPA/BDCloud/SocialCom/SustainCom 2023

Proceedings - 2023 IEEE International Conference on Parallel...

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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

ISBN: (纸本)9798350329223

The proceedings contain 153 papers. The topics discussed include: transaction data management optimization based on multi-partitioning in blockchain systems;semi-asynchronous federated learning optimized for NON-IID data communication based on tensor decomposition;HKTGNN: hierarchical knowledge transferable graph neural network-based supply chain risk assessment;DQR-TTS: semi-supervised text-to-speech synthesis with dynamic quantized representation;deep reinforcement learning-based network moving target defense in DPDK;iNUMAlloc: towards intelligent memory allocation for AI accelerators with NUMA;and predictive queue-based low latency congestion detection in data center networks.

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Adaptive ResNet Architecture for distributed Inference in Resource-Constrained IoT Systems 19

Adaptive ResNet Architecture for Distributed Inference in Re...

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19th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC)

作者： Khan, Fazeela Mazhar Baccour, Emna Erbad, Aiman Hamdi, Mounir Hamad Bin Khalifa Univ Qatar Fdn Coll Sci & Engn Doha Qatar

ISBN: (纸本)9798350333398

As deep neural networks continue to expand and become more complex, most edge devices are unable to handle their extensive processing requirements. Therefore, the concept of distributed inference is essential to distribute the neural network among a cluster of nodes. However, distribution may lead to additional energy consumption and dependency among devices that suffer from unstable transmission rates. Unstable transmission rates harm real-time performance of IoT devices causing low latency, high energy usage, and potential failures. Hence, for dynamic systems, it is necessary to have a resilient DNN with an adaptive architecture that can downsize as per the available resources. This paper presents an empirical study that identifies the connections in ResNet that can be dropped without significantly impacting the model's performance to enable distribution in case of resource shortage. Based on the results, a multi-objective optimization problem is formulated to minimize latency and maximize accuracy as per available resources. Our experiments demonstrate that an adaptive ResNet architecture can reduce shared data, energy consumption, and latency throughout the distribution while maintaining high accuracy.

关键词： optimization distributed inference neural networks resilience ResNet

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LCIP: A Retargetable Framework for Optimized CNN Inference

LCIP: A Retargetable Framework for Optimized CNN Inference

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conference on Real-Time Image processing and Deep Learning

作者： Pan, Lei Zhang, Yan Bhattacharyya, Shuvra S. Univ Maryland College Pk MD 20742 USA

ISBN: (数字)9781510661714

ISBN: (纸本)9781510661707;9781510661714

Deep neural networks (DNNs) are adopted in numerous application areas of signal and information processing with Convolutional neural networks (CNNs) being a particularly popular class of DNNs. Many machine learning (ML) frameworks have evolved for design and training of CNN models, and similarly, a wide variety of target platforms, ranging from mobile and resource-constrained platforms to desktop and more powerful platforms, are used to deploy CNN-equipped applications. To help designers navigate the complex design spaces involved in deploying CNN models derived from ML frameworks on alternative processing platforms, retargetable methods for implementing CNN models are of increasing interest. In this paper, we present a novel software tool, called the Lightweight-dataflow-based CNN Inference Package (LCIP), for retargetable, optimized CNN inference on different hardware platforms (e.g., x86 and ARM CPUs, and GPUs). In LCIP, source code for CNN operators (convolution, pooling, etc.) derived from ML frameworks is wrapped within dataflow actors. The resulting coarse grain dataflow models are then optimized using the retargetable LCIP runtime engine, which employs higherlevel dataflow analysis and orchestration that is complementary to the intra-operator performance optimizations provided by the ML framework and the back-end development tools of the target platform. Additionally, LCIP enables heterogeneous and distributed edge inference of CNNs by offloading part of the CNN to additional devices, such as onboard GPU or network devices. Our experimental results show that LCIP provides significant improvements in inference throughput on commonly-used CNN architectures, and the improvement is consistent across desktop and resource-constrained platforms.

关键词： Convolutional neural networks distributed Inference Dataflow Modeling Edge Computing Heterogeneous Computing

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EdgeShield: Hybrid Real-Time Attack Detection in IOTA Tangle via Edge Devices 15

EdgeShield: Hybrid Real-Time Attack Detection in IOTA Tangle...

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15th International conference on Computing Communication and networking Technologies, ICCCNT 2024

作者： Arunodhai, V. Susan, Leena P. Kannimoola, Jinesh M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350370249

The Internet of Things (IoT) revolutionizes technology interaction by enabling seamless connectivity and automation, but it introduces significant security challenges. A distributed ledger technology (DLT) called IOTA Tangle, a utilizing a Directed Acyclic Graph (DAG), enables secure and feeless transactions among IoT devices. Despite its advantages, the IOTA Tangle faces threats such as replay attacks and double spending, which highlight vulnerabilities in distributed ledger technologies. To address these security concerns, we initially developed EdgeGuard, a rule-based system for spam detection within the IOTA ecosystem. However, its reliance on predetermined rulesets limited its adaptability. We have now enhanced our approach with EdgeShield, incorporating a neural network for superior threat *** enhances adaptability over EdgeGuard in threat detection for the IOTA Tangle by integrating machine learning. EdgeShield analyzes network packets to identify threats, leveraging machine learning to adapt to evolving risks. Trained using the MQTTSet dataset, it detects network-based threats such as denial-of-service (DoS) attacks and packet flooding. Tested with simulated IoT data, EdgeShield demonstrates effectiveness across various domains, particularly in healthcare IoT. Its decentralized processing on edge devices like the Raspberry Pi 4 enables immediate network traffic analysis, reducing reliance on centralized servers and improving system resilience. Additionally, real-time alerts empower administrators to proactively secure IoT deployments and maintain transaction integrity within the IOTA Tangle. This paper traces our research evolution from EdgeGuard to EdgeShield, highlighting advancements and demonstrating the enhanced security of IoT transactions on the IOTA Tangle. © 2024 IEEE.

关键词： and Decentralised Security Measures distributed Ledger Edge Computing Edge Devices Internet of Things (IoT) IOTA Tangle Machine Learning network Packet Analysis Real-Time Security

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