检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Detection Using Deep Learning Technique 2

Breast Cancer Detection Using Deep Learning Technique

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

DENSE SHIP DETECTION GUIDED BY CENTRALITY PRIOR INFORMATION IN SAR IMAGES

DENSE SHIP DETECTION GUIDED BY CENTRALITY PRIOR INFORMATION ...

引用

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

来源：评论

学校读者我要写书评

暂无评论

LCIP: A Retargetable Framework for Optimized CNN Inference

LCIP: A Retargetable Framework for Optimized CNN Inference

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

Distributed Computing of Functions of Structured Sources wit...

引用

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

来源：评论

学校读者我要写书评

暂无评论

A Model for Predicting IoT User Behavior Based on Bayesian Learning and neural networks

引用

JOURNAL OF COMPUTER networkS AND COMMUNICATIONS 2024年第1期2024卷

作者： Xu, Xin Huang, Chengning Zhu, Yuquan Nanjing Tech Univ Pujiang Inst Sch Comp & Commun Engn Nanjing 211222 Peoples R China Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Peoples R China

To facilitate the allocation of energy and resources in the Internet of Things system, this paper presents a model for predicting user behavior in Internet of Things environments. The model is based on Bayesian learning and neural networks and is designed to provide insights into the future behavior of users, allowing for the allocation of resources in advance. In this paper, the data are preprocessed by data merging and format processing, and then the association rules are mined by association rules analysis. Finally, the data are utilized to train the behavioral prediction model of the short-duration memory network via Bayesian optimization. The experimental results showed that the average running time of the research model was 1.682 s, the average accuracy was 96.77%, the average root-mean-square error was 0.382, and the average absolute error was 0.315. The designed behavior prediction model is capable of effectively predicting the user behavior of the Internet of Things, thereby enabling the reasonable allocation of energy and resources in the Internet of Things system.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Learning Intermediate Representations using Graph neural networks for NUMA and Prefetchers Optimization 36

Learning Intermediate Representations using Graph Neural Net...

引用

36th IEEE International Parallel and distributed processing Symposium (IEEE IPDPS)

作者： TehraniJamsaz, Ali Popov, Mihail Dutta, Akash Saillard, Emmanuelle Jannesari, Ali Iowa State Univ Ames IA 50011 USA INRIA Bordeaux France

ISBN: (纸本)9781665481069

There is a large space of NUMA and hardware prefetcher configurations that can significantly impact the performance of an application. Previous studies have demonstrated how a model can automatically select configurations based on the dynamic properties of the code to achieve speedups. This paper demonstrates how the static Intermediate Representation (IR) of the code can guide NUMA/prefetcher optimizations without the prohibitive cost of performance profiling. We propose a method to create a comprehensive dataset that includes a diverse set of intermediate representations along with optimum configurations. We then apply a graph neural network model in order to validate this dataset. We show that our static intermediate representation based model achieves 80% of the performance gains provided by expensive dynamic performance profiling based strategies. We further develop a hybrid model that uses both static and dynamic information. Our hybrid model achieves the same gains as the dynamic models but at a reduced cost by only profiling 30% of the programs.

关键词： OpenMP NUMA prefetching graph neural networks LLVM Intermediate Representation

来源：评论

学校读者我要写书评

暂无评论

Remote condition monitoring of rail tracks using distributed acoustic sensing(DAS):A deep CNN-LSTM-SW based model

引用

Green Energy and Intelligent Transportation 2024年第5期3卷 70-85页

作者： Md Arifur Rahman Suhaima Jamal Hossein Taheri The Laboratory for Advanced Non-Destructive Testing In-situ Monitoringand Evaluation(LANDTIE)Department of Manufacturing EngineeringGeorgia Southern UniversityStatesboro 30458GAUSA Information Technology Department Georgia Southern UniversityStatesboro 30458GAUSA

Railroad condition monitoring is paramount due to frequent passage through densely populated *** significance arises from the potential consequences of accidents such as train derailments,hazardous materials leaks,or collisions which may have far-reaching impacts on communities and the surrounding *** a solution to this issue,the use of distributed acoustic sensing(DAS)-fiber optic cables along railroads provides a feasible tool for monitoring the health of these extended ***,analyzing DAS data to assess railroad health or detect potential damage is a challenging *** to the large amount of data generated by DAS,as well as the unstructured patterns and substantial noise present,traditional analysis methods are ineffective in interpreting this *** paper introduces a novel approach that harnesses the power of deep learning through a combination of CNNs and LSTMs,augmented by sliding window techniques(CNN-LSTM-SW),to advance the state-of-the-art in the railroad condition monitoring *** well as it presents the potential for DAS and fiber optic sensing technologies to revolutionize the proposed CNN-LSTM-SW model to detect conditions along the rail track *** insights from the data of High tonnage load(HTL)-a 4.16 km fiber optic and DAS setup,we were able to distinguish train position,normal condition,and abnormal conditions along the ***,our investigation demonstrated that the proposed approaches could serve as efficient techniques for processing DAS signals and detecting the condition of railroad infrastructures at any remote distance with DAS-Fiber optic cable ***,in terms of pinpointing the train's position,the CNN-LSTM architecture showcased an impressive 97%detection *** a sliding window,the CNN-LSTM labeled data,the remaining 3%of misclassified labels have been improved dramatically by predicting the exact locations of each type of ***,these prop

关键词： distributed acoustic sensing(DAS)-Fiber optic cable Railroad condition monitoring and anomaly detection High tonnage load(HTL) Convolutional neural network-long short-term memory-sliding window(CNN-LSTM-SW)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：