检索结果-内蒙古大学图书馆

9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Rao, Akash K. Menon, Vishnu K. Bhavsar, Arnav Chowdhury, Shubhajit Roy Negi, Ramsingh Dutt, Varun Indian Institute of Technology Mandi Applied Cognitive Science Laboratory Mandi India Indian Institute of Technology Mandi School of Computing And Electrical Engineering Mandi India Institute of Nuclear Medicine And Allied Sciences Cognitive Control And Machine Learning Group Delhi India

ISBN: (纸本)9798350394474

Attention is the brain's mechanism for selectively processing specific stimuli while filtering out irrelevant information. Characterizing changes in attention following long-term interventions (such as transcranial direct current stimulation (tDCS)) has seldom been emphasized in the literature. To classify attention performance post-tDCS, this study uses functional connectivity and machine learning algorithms. Fifty individuals were split into experimental and control conditions. On Day 1, EEG data was obtained as subjects executed an attention task. From Day 2 through Day 8, the experimental group was administered 1mA tDCS, while the control group received sham tDCS. On Day 10, subjects repeated the task mentioned on Day 1. Functional connectivity metrics were used to classify attention performance using various machine learning methods. Results revealed that combining the Adaboost model and recursive feature elimination yielded a classification accuracy of 91.84%. We discuss the implications of our results in developing neurofeedback frameworks to assess attention. © 2024 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

A Survey of Fault Tolerance in Cloud computing

A Survey of Fault Tolerance in Cloud Computing

引用

2024 International Symposium of Systems, Advanced Technologies and Knowledge, ISSATK 2024

作者： Elhajjej, Om Kolthoum Alyaoui, Nouha Dardouri, Samia Chabir, Karim Tunisia Higher Institute of Applied Sciences and Technology Computer Science and Telecommunications Department Zarroug Gafsa Tunisia Shaqra University College of Computing and Information Technology Department of Computer Science Saudi Arabia University of Carthage InnoV'COM Laboratory-Sup'Com Tunisia

ISBN: (纸本)9798331540173

This paper is designed to provide a comprehensive overview of the latest developments in fault tolerance methods for cloud computing. Maintaining high availability and reliability of cloud environments requires fault tolerance. This paper explores fault tolerance in the context of cloud computing and discusses recent challenges and innovations in the field. Moreover, it examines the ongoing research efforts to improve fault-tolerance architectures. At the end of the paper, the paper presents system-level metrics that are relevant to fault tolerance. © 2024 IEEE.

关键词： Cloud computing architecture

来源：评论

学校读者我要写书评

暂无评论

A Comparison of Low Complexity CNN Models and Data Enhancement for Efficient Android Implementation of Leaves Recognition 13

A Comparison of Low Complexity CNN Models and Data Enhanceme...

引用

13th International Symposium on Advanced Topics in Electrical Engineering, ATEE 2023

作者： Cococi, Alin-Gabriel Dogaru, Radu University "politehnica" of Bucharest Doctoral School in Electronics Telecommunications and Information Technology Romania University "politehnica" of Bucharest Natural Computing Laboratory Dept. of Applied Electronics and Information Eng Romania

ISBN: (纸本)9798350331936

Classification of flora data is a high complexity problem due to the similarity of plants. Identification and determination of plant species is a task that most of the general public is unable to do and it can be a challenge even for the expertise of qualified individuals. This paper studies the possibility of using lightweight convolutional neural networks on resources constrained devices or platforms to accurately classify leaves of plants. Several low complexity convolutional neural networks are considered for the problem of leaves recognition in terms of accuracy, precision, latency and complexity. A novel scheme for enhancement and augmentation of the leaf's images is proposed and demonstrated as capable to improve performance. MobileNet, L-CNN and NL-CNN models with Android implementations in Tensorflow Lite1 are considered, demonstrating the capability to build a portable intelligent instrument capable to identify plants by their leaves with an accuracy of 95.3%. © 2023 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Enhanced chaos-based image compression-encryption algorithm utilizing 2D compressive sensing and genetic algorithm optimization

引用

Multimedia Tools and Applications 2024年 1-38页

作者： Nanfak, Arnaud de Dieu Nkapkop, Jean Mvogo Ngono, Joseph Tabekoueng Njitacke, Zeric Lessouga Etoundi, Christophe Magloire Effa, Joseph Yves Laboratory of Electronic Electrical Engineering Automation and Telecommunication National Higher Polytechnic School of Douala University of Douala PO Box 2701 Douala Cameroon Technology and Applied Sciences Laboratory University Institute of Technology University of Douala P.O. Box 8698 Douala Cameroon Applied Computing Laboratory Faculty of Science University of Douala P.O. Box 2701 Douala Cameroon University of Buea P.O. Box 63 Buea Cameroon Department of Physics Faculty of Science University of Ngaoundere P.O. Box 454 Ngaoundere Cameroon

This paper introduces an innovative image compression and encryption algorithm that leverages the genetic algorithm (GA), compressive sensing (CS), and a newly developed hyperchaotic map. In this algorithm, the GA is employed to optimize the compression parameters, including those related to generating the measurement matrix and performing thresholding operation. The optimization objective is centred around on the Peak-Signal-to-Noise-Ratio (PSNR) of the reconstructed image. The encryption part involves employing the Arnold transform and byte-wise XOR operator for confusion and diffusion, respectively. The generation of the measurement matrix and chaotic sequences for encryption is facilitated by a novel two-dimensional fractional-Sine–Cosine (2DFSC) hyperchaotic map introduced in this research. Simulation outcomes demonstrate dynamic parameter values adjustments through optimization, resulting in improved decompression efficiency across different compression rates and image types. Assessment of security and decompression performance confirms the effectiveness of the proposed algorithm, showing a good compression effect, a good reconstruction efficacity even for low compression ratio, good speed and high security compared to some recent similar systems. This work may therefore be suitable to compress and secure data in wireless body area network applications, including those related to the internet of medical things. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Resolution-Adaptive Representation Learning via Eliminating Abnormal Features for Cross-Resolution Person Re-Identification 14

Resolution-Adaptive Representation Learning via Eliminating ...

引用

14th International Conference on Information technology in Medicine and Education, ITME 2024

作者： Lu, Xing Lai, Fengshan Cao, Zhixiang Xia, Daoxun Guizhou Normal University School of Big Data and Computer Science Guiyang China Guizhou Normal University Key Laboratory of Information and Computing Science Guizhou Province Guiyang China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350356670

Cross-resolution person re-identification(CR-ReID) seeks to overcome the challenge of retrieving and matching specific person images across cameras with varying resolutions. Numerous existing studies utilize established CNNs and ViTs models to resize captured low-resolution (LR) images and align them with high-resolution (HR) image features. However, these methods overlook the potential feature connections between LR and HR images. Consequently, CNNs or ViTs tend to highlight outliers rather than the intended characteristics in LR image attention maps. In this work, we propose the abnormal feature elimination and reconfiguration transformer (ART) network, a novel network architecture, for robust cross-resolution person re-identification tasks. Our proposed method uses a resolution feature discriminator to learn resolution invariant features and outputs feature matrices of images with different resolutions. We utilize output feature matrices to model LR and HR image interactions through cross-resolution landmark agent attention. We aim to mitigate abnormal image features and prioritize the attention on the target person by learning representations from input images of various resolutions. Extensive evaluations on three real-world datasets confirm the excellent results of our approach. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Battery Voltage Prediction with Deep Learning: A Comparative Analysis of LSTM and Traditional Models 6

Enhancing Battery Voltage Prediction with Deep Learning: A C...

引用

6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Niakan, Masoumeh Rostam Hajihosseini, Mojtaba Madani, Seyed Saeed Ziebert, Carlos Nri Department of Electricity and Energy Economics Tehran Iran University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Renewable Energy Systems Zagreb Croatia Institute of Applied Materials-Applied Materials Physics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798350305623

The growing demand for efficient energy storage solutions has sparked increased interest in precise battery voltage prediction. In this study the Long Short-Term Memory networks, are applied in the time series forecasting, to improve battery voltage prediction compared to conventional models. The developed forecasting system includes three main parts: Pre-processing, modeling, and evaluation. In the pre-processing, the voltage and current time series are normalized, and divided to the test and train subsets. Then the windows of consecutive samples are generated for both train and test subsets. The forecasting LSTM models are learnt from training data set, in the modeling phase. Finally, the Mean Absolute Error (MAE) is used as the evaluation criterion. This model is compared to two other neural network models. The study concludes that LSTM outperforms the other models, highlighted by a significantly lower MAE. With a comprehensive methodology and successful experimental framework, this paper demonstrates the efficacy of LSTM in predicting battery voltage, indicating its superiority over simple neural networks for time series forecasting. © 2023 IEEE.

关键词： Battery voltage prediction Deep learning Long Short-Term Memory neural networks

来源：评论

学校读者我要写书评

暂无评论

LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Electricity Theft Detection Based on Blockchain

引用

IEEE Transactions on Smart Grid 2023年第5期14卷 4047-4059页

作者： Zhao, Zhiqiang Liu, Yining Zeng, Zhixin Chen, Zhixiong Zhou, Huiyu Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Fujian Key Laboratory of Financial Information Processing The Key Laboratory of Applied Mathematics of Fujian Province University Putian University Putian351100 China University of Leicester School of Computing and Mathematical Sciences LeicesterLE1 7RH United Kingdom

In most electricity theft detection schemes, consumers' power consumption data is directly input into the detection center. Although it is valid in detecting the theft of consumers, the privacy of all consumers is at risk unless the detection center is assumed to be trusted. In fact, it is impractical. Moreover, existing schemes may result in some security problems, such as the collusion attack due to the presence of a trusted third party, and malicious data tampering caused by the system operator (SO) being attacked. Aiming at the problems above, we propose a blockchain-based privacy-preserving electricity theft detection scheme without a third party. Specifically, the proposed scheme uses an improved functional encryption scheme to enable electricity theft detection and load monitoring while preserving consumers' privacy;distributed storage of consumers' data with blockchain to resolve security problems such as data tampering, etc. Meanwhile, we build a long short-term memory network (LSTM) model to perform higher accuracy for electricity theft detection. The proposed scheme is evaluated in a real environment, and the results show that it is more accurate in electricity theft detection within acceptable communication and computational overhead. Our system analysis demonstrates that the proposed scheme can resist various security attacks and preserve consumers' privacy. © 2010-2012 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

EEG and fMRI Artifact Detection Techniques: A Survey of Recent Developments

引用

SN Computer Science 2023年第5期4卷 528页

作者： Mili, Rahma Bouaziz, Bassem Maalel, Ahmed Gargouri, Faiez Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL) Faculty of Economics and Management of Sfax University of Sfax Sfax 3021 Tunisia Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL) Higher Institute of Computer and Multimedia of Sfax Sfax 3021 Tunisia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba 2010 Tunisia Higher Institute of Applied Sciences and Technology University of Sousse Sousse 4003 Tunisia

The evolution of different techniques for exploring cerebral activity and the development of signal processing and analysis methods have enabled a better understanding of the dynamic cerebral mechanisms in favor of fine diagnosis and characterization of certain diseases such as dementia and epilepsy. Integration of functional magnetic resonance imaging (fMRI) with electroencephalography (EEG) has offered the possibility of understanding new insights into neuroscientific studies because of the higher temporal and spatial measurements of brain activity when compared with the use of each technique separately. However, the experimental limitations of fMRI/EEG are mainly the numerous artifacts, mainly due to switching of magnetic field gradients and cardiac activity. To provide a comprehensive overview of this research area, a systematic mapping study has been conducted. This mapping study aims to identify the topics studied within the artifacts’ detection in EEG and fMRI data, as well as examine the adopted research methodologies, identify the gaps in the current research and point to directions for future research. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Artifacts Deep learning EEG fMRI Systematic mapping study

来源：评论

学校读者我要写书评

暂无评论

XNL-CNN: An Improved Version Of The NL-CNN Model, For Running With TPU Accelerators And Large Image Datasets 8

XNL-CNN: An Improved Version Of The NL-CNN Model, For Runnin...

引用

8th International Symposium on Electrical and Electronics Engineering, ISEEE 2023

作者： Dogaru, Radu Mirica, Adrian-Dumitru Dogaru, Ioana University 'Politehnica' of Bucharest Natural Computing Laboratory Dept. of Applied Electronics and Information Eng. Bucharest Romania University 'Politehnica' of Bucharest Doctoral School of Electronics Telecommunications and Information Technology Bucharest Romania

ISBN: (纸本)9798350301670

This paper introduces XNL-CNN, an extended version of the previously introduced, compact NL-CNN model. While the NL-CNN was designed with typical Tiny-ML constraints in order to make possible integration of various AI image recognition solutions into low power, low complexity computing platforms, the same stands for its extended version discussed herein. In addition, we consider additional layers to cope with larger image sizes, as found in most actual relevant datasets (e.g. the Cars196 or Flowers104). Moreover, since working with large image sizes requires intensive computational power, TPU accelerators readily available on Kaggle platform were used for training and evaluation of our model. In comparison to other, widely known resources-constrained architectures, such as EfficientNet or MobileNet our model offers the following advantages: i) a larger set of hyper-parameters allowing the user to do better designs (maximal accuracy for minimal resources), adapted for a wide variety of datasets;ii) has a low number of layer primitives, thus making easier their specific design for deployment on various TinyML or EdgeAI platforms, including FPGAS. © 2023 IEEE.

关键词： Image recognition

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：