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4th IEEE International Conference on Mobile networks and Wireless Communications, ICMNWC 2024

作者： Siddiqua, Ayesha Basha, C.H. Hussaian Abbas, Haider Mohammed Sheela, P. Merlin Indhumathi, V. Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Sr University Department of Electrical & Electronics Engineering School of Engineering Warangal India The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq Department of English New Prince Shri Bhavani College of Engineering and Technology Chennai India College of Arts and Science Department of Computer Applications Sona Salem India

ISBN: (纸本)9798350352931

In recent years, the rapid growth of modern technology has improved people lives, but it also comes with a downside: increased exposure to noise from complex industrial infrastructure and machinery. To overcome this issue, the existing method namely a single-channel Deep neural network (DNN) model have developed for speech signal processing, but it led to limitations in online processing. Therefore, this research proposes a LadderNet with Hybrid Spectrogram Time-domain Audio Separation network (HSTasNet) model for reducing the noise and enhancing speech enhancement in real-time audio processing. This procedure begins with the collection of data from real-time audio signal and then preprocessed with spectral gating filter to reduce noise from signals. After that, t-distributed Stochastic Neighbor Embedding (t-SNE) technique to extract the optimal features from preprocessed signals. Finally, LadderNet and HS-TasNet techniques are employed for noise reduction and speech enhancement. As per the results, the proposed LadderNet-HSTasNet model offered outstanding results in terms of Perceptual Evaluation of Speech Quality (PESQ) of 3.847, Short-Time Objective Intelligibility (STOI) of 1.874, and Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) of 16.792 when compared with existing Dual-Path Recurrent neural network (DPRNN) model. © 2024 IEEE.

关键词： Noise abatement

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Degradation Pattern of High Speed Roller Bearings Using a Data-Driven Deep Learning Approach

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JOURNAL OF SIGNAL processing SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2022年第12期94卷 1557-1568页

作者： Rathore, Maan Singh Harsha, S. P. Indian Inst Technol Roorkee Dept Mech & Ind Engn Adv Mech Vibrat Lab Roorkee 247667 Uttarakhand India

In this paper, a data-driven approach is utilized for bearing condition monitoring involving the classification of different operating states by processing the raw vibration data. The vibration responses are analyzed and preprocessed before input to 1D-RCNN (one-dimensional residual convolutional neural network). The comparison results are based on commonly implemented evaluation indices such as precision, recall, F1-score, and ROC plots. Hence, the results revealed the superiority of the proposed methodology and its efficacy in segregating the bearing lifetime data into different operating conditions. Furthermore, t-SNE (t-distributed stochastic neighbor embedding) technique is implemented to represent the precise discriminative learning ability of different layers of the network. The overall classification accuracy values are obtained as 97.2% for 1D-RCNN, 95.31% for 1D-CNN, 86.2%, 86.42%, and 87.4% for SVM, KNN, and DNN, respectively. Hence, the proposed methodology may be effectively implemented for bearing health monitoring utilizing deep learning networks as classifiers.

关键词： Condition monitoring Residual learning Receiver operating characteristics t-distributed stochastic neighbor embedding

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distributed Distributionally Robust Optimization with Non-Convex Objectives 36

Distributed Distributionally Robust Optimization with Non-Co...

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36th Conference on neural Information processing Systems (NeurIPS)

作者： Jiao, Yang Yang, Kai Song, Dongjin Tongji Univ Shanghai Peoples R China Univ Connecticut Storrs CT USA

ISBN: (纸本)9781713871088

Distributionally Robust Optimization (DRO), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e.g., network behavior analysis, risk management, etc. However, existing DRO techniques face three key challenges: 1) how to deal with the asynchronous updating in a distributed environment;2) how to leverage the prior distribution effectively;3) how to properly adjust the degree of robustness according to different scenarios. To this end, we propose an asynchronous distributed algorithm, named Asynchronous Single-looP alternatIve gRadient projEction (ASPIRE) algorithm with the itErative Active SEt method (EASE) to tackle the distributed distributionally robust optimization (DDRO) problem. Furthermore, a new uncertainty set, i.e., constrained D-norm uncertainty set, is developed to effectively leverage the prior distribution and flexibly control the degree of robustness. Finally, our theoretical analysis elucidates that the proposed algorithm is guaranteed to converge and the iteration complexity is also analyzed. Extensive empirical studies on real-world datasets demonstrate that the proposed method can not only achieve fast convergence, and remain robust against data heterogeneity as well as malicious attacks, but also tradeoff robustness with performance.

关键词： Risk management

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Federated Recognition Architecture Based on Voting and Feedback Mechanisms for Accuracy Object Classification in distributed Edge Intelligence Environment

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IEEE SENSORS JOURNAL 2023年第22期23卷 27478-27489页

作者： Tuan, Nguyen Anh Xu, Rongxu Moe, Sa Jim Soe Kim, Dohyeun Jeju Natl Univ Dept Comp Engn Jeju 63243 South Korea Jeju Natl Univ Big Data Res Ctr Jeju 63243 South Korea

Deep learning has recently become a crucial tool to solve many complex problems and has the potential to revolutionize industries. With the widespread adoption of the Internet of Things, there are now many devices with limited computational resources that are capable of running deep learning models. It has opened up new opportunities to implement deep learning in the edge environment so that decisions can be made locally without sending data to a cloud server for processing. However, because of limited resources, the model's performance and communication overhead are challenges when deploying learning models to edge devices. The focus of this article is to investigate and propose a federated recognition architecture for object classification in a distributed edge intelligence environment. Specifically, we build an edge server that includes a voting module and a feedback module to improve the overall accuracy of object classification. The voting module aggregates predictions of multiple edge devices, whereas the feedback module sends the voting results to edge devices to adjust the local deep learning model. We build edge devices based on the EdgeX platform which makes it easy to manage data and optimize communication overheads. Because the edge server and edge nodes only exchange prediction results, our proposed architecture ensures security with sensitive data as well as deep learning model architecture. By testing on the image dataset, we evaluate the proposed architecture's performance and show that it outperforms individual local models in terms of accuracy. Furthermore, our experiments demonstrate that, with the feedback mechanism, the deep learning model is constantly updated with new data to maintain accuracy and avoid being outdated. Besides, we prove the real-time processing speed by collecting the delay time of the proposed model. The results show that our proposed architecture has the potential to be deployed in practical applications such as smart cities

关键词： Convolutional neural network (CNN) deep learning distributed edge environment EdgeX platform federated recognition Internet of Things online learning voting algorithms

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Breast Cancer Detection Using Deep Learning Technique 2

Breast Cancer Detection Using Deep Learning Technique

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2nd IEEE International Conference on distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Urabinahatti, Sachin Jayadevappa, Devappa Tatayasaheb Kore Inst of Engg & Technology Dept. of Computer Science & Engineering Warananagar India Jss Academy of Technical Education Dept of Electronics & Instrumentation Engineering Bengaluru India

ISBN: (纸本)9798350347456

One of the harmful types of cancer that can affect females is breast cancer. With the aid of images of the microscopic structure, breast cancer can be identified. This study uses mammography images to categorize various types of breast cancer. Image processing techniques can be used successfully in the classification of mammography images. Deep learning provides wonderful performance for the classification of images in many applications among various image processing algorithms. Convolutional neural network (CNN) designs like VGG19, Inception-Net, ResNet50, and others are used in effective classification. © 2023 IEEE.

关键词： Convolutional neural networks

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Federated Graph Learning for Low Probability of Detection in Wireless Ad-Hoc networks 22

Federated Graph Learning for Low Probability of Detection in...

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22nd IEEE Statistical Signal processing Workshop (SSP)

作者： Krishnan, Sivaram Park, Jihong Sagar, Subhash Sherman, Gregory Campbell, Benjamin Choi, Jinho Deakin Univ Sch Informat Technol Geelong Vic Australia Def Sci & Technol Grp Canberra ACT Australia

ISBN: (纸本)9781665452458

Low probability of detection (LPD) has recently emerged as a means to enhance the privacy and security of wireless networks. Unlike existing wireless security techniques, LPD measures aim to conceal the entire existence of wireless communication instead of safeguarding the information transmitted from users. Motivated by LPD communication, in this paper, we study a privacy-preserving and distributed framework based on graph neural networks to minimise the detectability of a wireless ad-hoc network as a whole and predict an optimal communication region for each node in the wireless network, allowing them to communicate while remaining undetected from external actors. We also demonstrate the effectiveness of the proposed method in terms of two performance measures, i.e., mean absolute error and median absolute error.

关键词： Low probability of detection federated learning wireless ad-hoc networks graph neural networks

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DENSE SHIP DETECTION GUIDED BY CENTRALITY PRIOR INFORMATION IN SAR IMAGES

DENSE SHIP DETECTION GUIDED BY CENTRALITY PRIOR INFORMATION ...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Zhang, Yu Wang, Xueqian Li, Gang Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (纸本)9798350320107

The detection of densely distributed ship targets is one of the hot issues in the context of convolutional neural network (CNN)-based synthetic aperture radar (SAR) image processing. In this case, the bounding boxes of the ships may overlap with each other. Traditional detectors do not specifically consider the processing of overlapping areas, resulting in low detection performance. To address this problem, we proposed a new SAR ship detector, where classification confidence score-based method is developed to consider the centrality prior information among the overlap areas. Then, in the shallow layers of the network, the auxiliary heads are used to guide the network to learn the features related to centers of ships. Experimental results on the open datasets with dense ships show that our method achieves the better detection performance without the obvious increase of computation burden compared with the current state-of-the-art detectors.

关键词： Ship detection synthetic aperture radar (SAR) convolutional neural network (CNN) dense target centrality

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High-performance medical data processing technology based on distributed parallel machine learning algorithm

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JOURNAL OF SUPERCOMPUTING 2022年第4期78卷 5933-5956页

作者： Liu, Ji Liang, Xiao Ruan, Wenxi Zhang, Bo Univ Sydney Sch Elect & Informat Engn Sydney NSW 2000 Australia Shanxi VC PE Fund Management Co Ltd Taiyuan 030000 Shanxi Peoples R China Taizhou Vocat Coll Sci & Technol Taizhou 318020 Peoples R China China Asset Management Co Ltd Beijing 100033 Peoples R China

The aim is to improve the efficiency of medical data processing and establish a sound medical data management system. To apply distributed parallel classification algorithms in the field of hospital intelligent guidance, a Parallel Random Forest (PRF) classification algorithm is proposed based on the Apache Spark cloud computing platform. Given sparse cluster loss in variable density distribution data sets, an Adaptive Domain Density Peak Clustering (ADDPC) method is proposed. Here, a Bilayer Parallel Training-Convolutional neural network (BPT-CNN) model based on distributed computing is proposed to detect and classify colon cancer nuclei more accurately through the large-scale parallel deep learning (DL) algorithm. Then, the performance of the proposed model is evaluated through case analysis. The results show that the PRF algorithm based on distributed cloud computing platform can independently design data-parallel tasks, thereby optimizing the data communication cost and efficiency. ADDPC algorithm can adaptively measure domain density and merge sparse clusters to prevent data loss and fragmentation. The BPT-CNN model improves the performance of the algorithm and balances the workload of each task in the algorithm. The results have a significant reference value for solving problems in medical data processing.

关键词： Adaptive density peak clustering algorithm Random forest algorithm distributed parallel classification algorithm Cloud computing

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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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distributed Computing of Functions of Structured Sources with Helper Side Information 24

Distributed Computing of Functions of Structured Sources wit...

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24th IEEE International Workshop on Signal processing Advances in Wireless Communications, SPAWC 2023

作者： Malak, Derya Eurecom Communication Systems Department France

ISBN: (纸本)9781665496261

In this work, we consider the problem of distributed computing of functions of structured sources, focusing on the classical setting of two correlated sources and one user that seeks the outcome of the function while benefiting from low-rate side information provided by a helper node. Focusing on the case where the sources are jointly distributed according to a very general mixture model, we here provide an achievable coding scheme that manages to substantially reduce the communication cost of distributed computing by exploiting the nature of the joint distribution of the sources, the side information, as well as by exploiting the symmetry enjoyed by the desired functions. Our scheme - which can readily apply in a variety of real-life scenarios including learning, combinatorics, and graph neural network applications - is here shown to provide substantial reductions in the communication costs, while simultaneously providing computational savings by reducing the exponential complexity of joint decoding techniques to a complexity that is merely linear. © 2023 IEEE.

关键词： Complex networks

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