检索结果-内蒙古大学图书馆

Proceedings of the IEEE Dallas Circuits and Systems Workshop (DCAS)

作者： Sonia Akter Kasem Khalil Magdy Bayoumi The Center for Advanced Computer Studies University of Louisiana at Lafayette LA USA Department of Electrical and Computer Engineering University of Mississippi Mississippi USA Department of Electrical Engineering Assiut University Assiut Egypt Department of Electrical and Computer Engineering University of Louisiana at Lafayette LA USA

ISBN: (数字)9798350349535

ISBN: (纸本)9798350349542

Random Number Generators (RNGs) are substantially used in many security domains, providing a fundamental source of unpredictability essential for tasks such as cryptography, simulations, and statistical analyses. The efficiency and quality of an RNG directly impact the reliability and security of diverse applications, making advancements in RNG design, as explored in this study, of significant importance for enhancing computational processes. This paper presents an innovative Pseudo-Random Number Generator (PRNG) that leverages the efficiency of two carefully selected Linear Feedback Shift Registers (LFSRs) and a connecting XOR gate. The investigation of five polynomials identified an optimal pair, resulting in a notable improvement of over 200X in the length of random bit sequences compared to a single LFSR-based PRNG. The Basys3 FPGA board with the xc7a35tcpg236-1 FPGA chip was used to implement and synthesize the proposed design. Two significant findings emerge from this research. Firstly, using variable polynomials demonstrates a huge enhancement in the duration of randomness, outperforming the impact of variable seeds. A noteworthy observation is that employing the same polynomials in different branches does not result in optimal results. Secondly, managing more seeds is associated with an increased area cost, underscoring the efficiency of handling two polynomials.

关键词： Costs Statistical analysis Linear feedback shift registers Logic gates Reliability engineering Polynomials Generators

来源：评论

学校读者我要写书评

暂无评论

Deep fake Image Detection Based on Deep Learning Using a Hybrid CNN-LSTM with Machine Learning Architectures as Classifier

Deep fake Image Detection Based on Deep Learning Using a Hyb...

引用

Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Omar Alfarouk Hadi Hasan Al-Dulaimi Sefer Kurnaz Department of Electrical & Computer Engineering Institute of Graduate Studies Altinbas University Istanbul Turkey

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

One of the most important and difficult subjects in social communication is detecting deepfake images and videos. Deepfake techniques have developed widely, making this technology quite available and proficient enough so that there is worry about its bad application. Considering this issue, discovering fake faces is very important for ensuring security and preventing sociopolitical issues on a private and general level. Deep learning provides higher performance than typical image processing approaches when it comes to deepfake detection. This work presents construction of an artificial intelligence system, which is capable of detecting deepfake from more than one dataset. This study proposes neural network models based on deep learning using random forest (RF) and support vector machines (SVM) as classifier for deepfake detection. The use of two classifiers (RF) and (SVM) and their combination with a convolutional neural network is the first study of its kind in the field of deepfake detection in images from three open-source datasets (FaceForensics++, FaceAntiSpoofing, and iFakeFaceDB). This proposed method shows an accuracy of 96%, 87% and 52% in iFakeFaceDB, CelebA-Spoof, FaceForensics++ and respectively.

关键词： Support vector machines Deep learning Training Deepfakes Soft sensors Data models Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Evaluating Machine Learning Algorithms for Early Detection of GDM

Evaluating Machine Learning Algorithms for Early Detection o...

引用

Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Osamah Al-Azzawi Sefer Kurnaz Department of Electrical & Computer Engineering Institute of Graduate Studies Altinbas University Istanbul Turkey

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

Through the use of the Gestational Diabetes Mellitus (GDM) Data Set, this research conducts an in-depth analysis of machine learning methods for the early diagnosis of GDM. The effectiveness of various algorithms is evaluated by the use of metrics such as recall, accuracy, precision, and F1 score. There are several algorithms that fall under this category, such as Logistic Regression, Long Short-Term Memory (LSTM), Gradient Boosting Classifier, Random Forest, and K-Nearest Neighbors. It is necessary to do meticulous data preparation in order to find the best models for GDM prediction. Some examples of this preparation include feature scaling and principal component analysis (PCA). Should this research prove to be effective, it has the potential to provide insight on how machine learning may be used to enhance the diagnosis and management of GDM.

关键词： Machine learning algorithms Predictive models Prediction algorithms Data models Classification algorithms Reliability Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Chiral Damping of Magnons

引用

Physical Review Letters 2024年第15期133卷 156704页

作者： Dae-Yun Kim Imane Berrai T. S. Suraj Yves Roussigné Shuhan Yang Mohamed Belmeguenai Fanrui Hu Guoyi Shi Hui Ru Tan Jifei Huang Anjan Soumyanarayanan Kyoung-Whan Kim Salim Mourad Cherif Hyunsoo Yang Department of Electrical and Computer Engineering Samsung Advanced Institute of Technology LSPM CNRS UPR 3407 F-93430 Villetaneuse France Department of Physics Institute of Materials Research & Engineering Center for Spintronics

Chiral magnets have garnered significant interest due to the emergence of unique phenomena prohibited in inversion-symmetric magnets. While the equilibrium characteristics of chiral magnets have been extensively explored through the Dzyaloshinskii-Moriya interaction (DMI), nonequilibrium properties like magnetic damping have received comparatively less attention. We present the inaugural direct observation of chiral damping through Brillouin light scattering (BLS) spectroscopy. Employing BLS spectrum analysis, we independently deduce both the DMI and chiral damping, extracting them from the frequency shift and linewidth of the spectrum peak, respectively. The resulting linewidths exhibit clear odd symmetry with respect to the magnon wave vector, unambiguously confirming the presence of chiral damping. Our study introduces a novel methodology for quantifying chiral damping, with potential ramifications on diverse nonequilibrium phenomena within chiral magnets.

关键词： Dzyaloshinskii-Moriya interaction Magnons Chiral magnets Brillouin scattering & spectroscopy

来源：评论

学校读者我要写书评

暂无评论

Empowering Supply Chain Transparency and Risk Management through Hybrid CNN and BiGRU Approach 1

Empowering Supply Chain Transparency and Risk Management thr...

引用

1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Guhan, K. Lakshmi, S. Verma, Virendra Kumar Sai, M K Satya Sharma, Amit Kumar, B. Suresh Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India Bharati Vidyapeeth Management Studies and Research Department of Management Business Administration Navi Mumbai India Symbiosis Institute of Business Management Bengaluru India Vignana Bharathi Institute of Technology Department of Mechanical Engineering Hyderabad India Lovely Professional University School of Computer Application Phagwara India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronic Engineering Telangana Hyderabad India

ISBN: (纸本)9798350352931

Everything from needs to luxuries has been impacted by the COVID-19 pandemic in the manufacturing sector, and that includes the automotive industry. Thus, there is an immediate need for research into the automotive supply chain's sustainability, transparency, and competency development practices. For the automotive supply chains to successfully apply the Transparency for Sustainability (TfS) architecture in the long run, it also specifies a set of skills that are necessary. The three steps that comprise this proposed method are preprocessing, feature selection, and model training. The data had to be preprocessed in order for it to be ready for analysis. The feature selection procedure is comprised of the LDA topic model, term frequency, and correlation analysis. A GRU-DT was utilized for the model's training. The suggested method outperforms GRU and DT with an average accuracy of 96.29%. © 2024 IEEE.

关键词： Automotive industry

来源：评论

学校读者我要写书评

暂无评论

Optimized Energy Efficient Strategy for Data Reduction Between Edge Devices in Cloud-IoT

引用

computers, Materials & Continua 2022年第7期72卷 125-140页

作者： Dibyendu Mukherjee Shivnath Ghosh Souvik Pal D.Akila N.Z.Jhanjhi Mehedi Masud Mohammed A.AlZain Department of Computer Science and Engineering Brainware UniversityBarasat700125India Department of Computer Science and Engineering Global Institute of Management and TechnologyKrishnanagar741102India Department of Information Technology School of Computing SciencesVels Institute of ScienceTechnology&Advanced StudiesChennai600117India School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Numerous Internet of Things(IoT)systems produce massive volumes of information that must be handled and answered in a quite short *** growing energy usage related to the migration of data into the cloud is one of the biggest *** computation helps users unload the workload again from cloud near the source of the information that must be handled to save time,increase security,and reduce the congestion of ***,in this paper,Optimized Energy Efficient Strategy(OEES)has been proposed for extracting,distributing,evaluating the data on the edge *** the initial stage of OEES,before the transmission state,the data gathered from edge devices are supported by a fast error like reduction that is regarded as the largest energy user of an IoT *** initial stage is followed by the reconstructing and the processing *** processed data is transmitted to the nodes through controlled deep learning *** entire stage of data collection,transmission and data reduction between edge devices uses less *** experimental results indicate that the volume of data transferred decreases and does not impact the professional data performance and predictive *** consumption of 7.38 KJ and energy conservation of 55.57 kJ was found in the proposed OEES *** accuracy is 97.5 percent,data performance rate was 97.65 percent,and execution time is 14.49 ms.

关键词： Energy efficient internet of things transmission performance cloud computing edge devices

来源：评论

学校读者我要写书评

暂无评论

Object Detection Enhancement Based on Super-Resolution Mapping

Object Detection Enhancement Based on Super-Resolution Mappi...

引用

Iranian Conference on electrical engineering (ICEE)

作者： Danial Abyazi Dadfar Abyazi Mehran Yazdi Department of Computer Engineering Institute for Advanced Studies in basic Science University Zanjan Iran Department of Electrical and Computer Engineering Shiraz University Shiraz Iran School of Electrical and Computer Engineering Shiraz Uniersity Shiraz Iran

Object detection, while being a key step in many applications, has remained challenging, mainly due to the different resolutions of objects in an image. On the other hand, Super-Resolution (SR) approaches have recently been developed to improve the resolution of an image. In this paper, we utilize SR to enhance the quality of images before applying them to a detection model and propose a structure where the detection performance is improved. Furthermore, we remove the image noises by SR methods. Comparisons depict the superior visual performance of the proposed method compared to the conventional detection models. We evaluate our proposed method for detection of solar panels in two countries. To this aim, we created a dataset of satellite images needed for implementing detection models. As a result, we can simply evaluate the usage of solar panels in countries.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Spiking Neural Network-Based Control of an Unmanned Aerial System Implemented on a Customized Neural Flight Simulation Environment

Spiking Neural Network-Based Control of an Unmanned Aerial S...

引用

2024 American Control Conference, ACC 2024

作者： Garcia A, Omar A. Arana, Diego Chavez Espinoza, Eduardo S. Scola, Ignacio Rubio Garcia Carrillo, Luis Rodolfo Sornborger, Andrew T. Center for Research and Advanced Studies of the National Polytechnic Institute in Mexico City Mexico Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM United States Center for Research and Advanced Studies of the National Polytechnic Institute in Mexico City Conahcyt Mexico Conicet and National University of Rosario Department of Industrial Products Engineering of the National Institute of Industrial Technology Argentina Los Alamos National Laboratory Los AlamosNM United States

ISBN: (纸本)9798350382655

A prototyping environment for the development of Spiking Neural Networks (SNN) is integrated with a physics-based flight simulator with the objective of stabilizing a quad rotorcraft Unmanned Aerial System (UAS) via neuromorphic controllers. Making use of the Neural engineering Framework (NEF), SNN-based Proportional+Derivative (PD) controllers are designed for the translational and rotational dynamics of the UAS. An online Model in the Loop (MIL) evaluation scenario was implemented, showing that the proposed neuromorphic controllers are capable of stabilizing the quad rotorcraft UAS in both regulation and trajectory tracking tasks. © 2024 AACC.

关键词： Flight simulators

来源：评论

学校读者我要写书评

暂无评论

An Automated Detection System of Autism Spectrum Disorder Using Optimal Feature Selection-Based Adaptive RNN with Attention Mechanism

An Automated Detection System of Autism Spectrum Disorder Us...

引用

International Conference on computer and Communications Technologies (ICCCT)

作者： J. Jegan Amarnath S. Meera Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Chennai Tamilnadu India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

The research introduces an automated system for ASD detection through the implementation of an adaptive RNN combined with optimal features selection together with an attention mechanism. The system operates to detect ASD early through a data-driven methodology by analysing behavioural, visual and auditory indicators that mark symptoms of ASD. This solution provides accurate diagnosis and personalized care that doctors can use because it operates across many scales. The system accepts multiple forms of data from facial expressions together with speech patterns and motor behavioural data. A combination of feature selection methods identifies essential data attributes within raw information to decrease complex dimensions that improves system operational speed. The adaptive RNN model extracts temporal relationships through its learning process and the attention mechanism allows the model to select matching features to detect subtle changes in ASD behaviour properly. The framework adapts automatically because it uses available data to refine itself with a purpose of refining detection accuracy. Research outcome shows that adding the attention mechanism enables the model to select important attributes leading to better detection precision. The feature selection system adds to model performance optimization because it enables the model to concentrate on important signals while reducing potential overfitting problems. The system can apply in real time through its diagnostic excellence which minimizes both false positive and negative results. The proposed automated system represents a major step forward for early ASD detection since it delivers clinicians an effective instrument for early intervention. The precise results of this model stem from its adaptive learning system as well as its attention-based focus which leads to better ASD diagnosis and treatment planning outcomes.

关键词： Adaptation models Autism Visualization Attention mechanisms Recurrent neural networks Adaptive systems Accuracy Computational modeling Variable speed drives Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Improved Deepmaxoutmodel for Disease Prediction in Healthcare IoT Cloud System

Improved Deepmaxoutmodel for Disease Prediction in Healthcar...

引用

IEEE Electron Devices Kolkata Conference (EDKCON)

作者： Preetha P A. Packialatha Department of Computer Science Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamilnadu

ISBN: (数字)9798350374643

ISBN: (纸本)9798350374650

The Internet of Medical Things IoMT has grown rapidly due to the growing popularity of wearable technology and its applications in health monitoring systems. The IoMT significantly reduces the death rate by facilitating early sickness identification. Predicting cardiac disease is among the most significant issues with clinical dataset analysis. Finding the key components of heart disease prediction is the goal of the suggested investigation, which makes use of machine learning techniques. Despite several studies on the issue, the accuracy of the findings on the diagnosis of heart illness is low. The purpose of this paper is to provide an innovative framework for healthcare monitoring that includes the following phases. Data preprocessing, feature extraction, acquisition of data, and prediction are the primary stages. The process of acquiring data is at the IoTs layer. The medical data is considered in this case (benchmark datasets of different illnesses). The data pre-processing operations of data cleaning and data filtering at the cloud layer are applied to the data obtained from the IoT sensors (considering the dataset), where the data normalization process will take place. Subsequently, statistical features and raw features are extracted. The multi-layered Deepmaxout model will predict the diseases during in the prediction phase. Lastly, the evaluation of the suggested approach is compared to existing methods using several types of metrics.

关键词： Measurement Heart Filtering Data preprocessing Data acquisition Predictive models Feature extraction Sensors Monitoring Diseases

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：