检索结果-内蒙古大学图书馆

Chebyshev polynimal dempster convolutional hearing sensitivity level detection with auditory evoked potentials

Journal of Intelligent and Fuzzy Systems 2023年第4期45卷 5353-5366页

作者： Kirthika, K.M. Paulraj, M.P. Hema, C.R. Department of Computer Science Engineering Sri Ramakrishna Institute of Technology Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering Sri Ramakrishna Institute of Technology Tamil Nadu Coimbatore India

The EEG-based HTR utilizing AEP responses of both group of participants with normal hearing and abnormal hearing are managed with the objective of detecting hearing sensitivity level using Chebyshev Recurrence Polynomial and Dempster Convolutional Neural Network (CRP-DCNN) is designed. The CRP-DCNN method is split into three sections. They are preprocessing using Chebyshev Recurrence Polynomial Filter, feature extraction by employing Orthogonalized Singular Value and Median Skewed Wavelet. Here, both Orthogonalized Singular Value Decomposition-based parametric and Median Skewness-based non-parametric modeling techniques are employed for first obtaining the hearing threshold factors and then extracting statistical features for further processing. Finally Dempster Convolutional Neural Network-based Classification for detecting hearing sensitivity level is presented. Hence, the objective to determine the significant correlations between the brain dynamics and the auditory responses and detect the hearing sensitivity level of the group of participants with normal hearing and with the group of participants with hearing loss are designed on accordance with the features of EEG signals. Simulations are performed in MATLAB to validate the features of EEG signals. © 2023 - IOS Press. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

A broad learning-based comprehensive defence against SSDP reflection attacks in IoTs

引用

Digital Communications and Networks 2023年第5期9卷 1180-1189页

作者： Xin Liu Liang Zheng Sumi Helal Weishan Zhang Chunfu Jia Jiehan Zhou College of Computer Science and Technology China University of Petroleum(East China)QingdaoChina School of Computing Lancaster University UK Nankai University TianjinChina Information Technology and Electrical Engineering University of OuluFinland

The proliferation of Internet of Things(IoT)rapidly increases the possiblities of Simple Service Discovery Protocol(SSDP)reflection *** DDoS attack defence strategies deploy only to a certain type of devices in the attack chain,and need to detect attacks in advance,and the detection of DDoS attacks often uses heavy algorithms consuming lots of computing *** paper proposes a comprehensive DDoS attack defence approach which combines broad learning and a set of defence strategies against SSDP attacks,called Broad Learning based Comprehensive Defence(BLCD).The defence strategies work along the attack chain,starting from attack sources to *** defends against attacks without detecting attacks or identifying the roles of IoT devices in SSDP reflection *** also detects suspicious traffic at bots,service providers and victims by using broad learning,and the detection results are used as the basis for automatically deploying defence strategies which can significantly reduce DDoS *** evaluations,we thoroughly analyze attack traffic when deploying BLCD to different defence *** show that BLCD can reduce the number of packets received at the victim to 39 without affecting the standard SSDP service,and detect malicious packets with an accuracy of 99.99%.

关键词： Denial-of-service DRDoS SSDP reflection Attack Broad learning Traffic detection

来源：评论

学校读者我要写书评

暂无评论

Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks

引用

International Journal of Intelligent Networks 2024年第1期5卷 255-266页

作者： Ahmad, Rizwana Anwar, Dil Nashin Bany Salameh, Haythem Elgala, Hany Ayyash, Moussa Almajali, Sufyan El-Khazali, Reyad Electronic and Electrical Engineering Department University of Cambridge United Kingdom Electrical and Computer Engineering Department University at Albany-State University of New York United States Artificial Intelligence Research Center Al Ain University Al Ain United Arab Emirates Telecommunications Engineering Department Yarmouk University Irbid Jordan Department of Computing Information and Technology Chicago State University ChicagoIL United States Department of Computer Science Princess Sumaya University for Technology Amman Jordan Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates

Advancements in unmanned aerial vehicle (UAV) technology, along with indoor hybrid LiFi-WiFi networks (HLWN), promise the development of cost-effective, energy-efficient, adaptable, reliable, rapid, and on-demand HLWN-capable indoor flying networks (IFNs). To achieve this, a unified physical layer (UniPHY) capable of simultaneous control communication, data transfer, and sensing is crucial. However, traditional block-based decoders, designed independently for LiFi and WiFi, perform poorly in complex and hybrid LiFi-WiFi-enabled UniPHY systems. In this study, we propose an end-to-end learning framework based on convolutional neural networks (CNNs) for UniPHY, which can be trained to serve hybrid LiFi-WiFi transmissions to improve error performance and simplify UAV hardware. In this work, the performance of the proposed framework is assessed and compared with that of the conventional independent block-based communication system. Furthermore, a comprehensive summary of optimal hyper-parameters for efficient training of our learning framework has been provided. It is shown that, with optimal hyper-parameters, the proposed CNN-based framework outperforms the conventional block-based approach by providing a signal-to-noise ratio gain of approximately 7 dB for the LiFi channel and 23 dB for the WiFi channel. In addition, we evaluate the complexity and training convergence for loss and accuracy. © 2024 The Authors

关键词： Data transfer

来源：评论

学校读者我要写书评

暂无评论

Enhanced and Efficient Guiding Template Design for Lamellar DSA with Graph Monomorphism

引用

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2023年第11期42卷 4329-4333页

作者： Ciou, Yi-Sian Shih, An-Jie Fang, Shao-Yun Liu, Yi-Yu National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan

Lamellar directed self-assembly (DSA) technology in combination with the self-aligned via (SAV) process has emerged as a novel and promising choice for via/contact layer fabrication, where a via between Metal- x and Metal- (x+1) is generated at the intersection of a wire segment on Metal- (x+1) and a rectangular and perpendicular guiding template. Compared to the highly investigated cylindrical DSA, lamellar DSA does not suffer from the overlay error of generated holes and thus can have better yield, and it also benefits from the better flexibility of various via pitches a single template can produce. A state-of-the-art work has addressed the guiding template design problem for the new process, and an integer linear programming (ILP) formulation that can optimally solve the problem as well as a simple heuristic approach are proposed. However, solving the ILP formulation is time consuming, and the heuristic method suffers from unignorable degradation in solution quality due to several algorithm deficiencies. In this article, we propose an enhanced and efficient template design algorithm flow for the lamellar DSA and SAV process by using graph monomorphism for pattern conflict check in multiple patterning lithography. Experimental results show that the proposed flow can greatly reduce the numbers of conflicts and short templates and is almost as efficient as the existing heuristic approach. © 1982-2012 IEEE.

关键词： Integer programming

来源：评论

学校读者我要写书评

暂无评论

Soft-Touch or Hard-Touch: Quantifying Touches through A New System Focusing on A Pool Touchpad for Measuring Timing of Swimmers 23

Soft-Touch or Hard-Touch: Quantifying Touches through A New ...

引用

10th International Conference on Networking Systems and Security, NSysS 2023

作者： Quaium, Adnan Islam, Md. Ashraful Rahman, M. Sohel Islam, A. B. M. Alim Al Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh and Department of Electrical and Electronic Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798400708787

The swimming turn or finishing presents an integral part of competitive swimming, and the time it takes by a swimmer to push off the wall is a significant factor in their ratings. Therefore, it is essential to have a reliable way to measure the timing of touch, i.e., wall-contact, over the finishing or turning points. To do so, swimming pool touchpads are widely used in swimming competitions to overcome human error in measurements by enabling touch-sensing systems at the finishing or turning points. The touch-sensing devices use their sensitive touch sensors to record time with millisecond accuracy, providing athletes with the most precise measurements possible. However, these devices might need a minimal force applied from the swimmers to sense the touch, and thus, to record the timing of the touch. As such, insufficient force applied on the touchpad during swimming could cause skipping the time log. As a result, it is critical to have a trustworthy method for measuring the extent of touch or wall-contact exerted over the touch-sensing systems. This study, for the first time in the literature to the best of our knowledge, proposes such a method for measuring the extent of touch or wall-contact for swimming pool touchpads. In the process of proposing the method, we design and develop a bi-modal sensing module to distinguish the soft-touch and hard-touch exerted over a real pool touchpad used in a swimming pool organizing national and international competitions. Our experiments show that the pool touchpad under experimentation is not always accurate in registering touches. It sometimes fails to register low-force touches while generally registering touches with high force. © 2023 ACM.

关键词： Sports

来源：评论

学校读者我要写书评

暂无评论

MAPPO based Single-hop Task Offloading with Dynamic Vehicle Prediction in RSU-assisted IoV 10

MAPPO based Single-hop Task Offloading with Dynamic Vehicle ...

引用

10th IEEE Smart World Congress, SWC 2024

作者： Zhao, Wei Yang, Dongling Weng, Tangjie Xu, Xinwei Shao, Xun Liu, Zhi Anhui University of Technology School of Computer Science and Technology China Anhui University of Technology School of Management Science and Engineering China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Japan The University of Electro-Communications Department of Computer and Network Engineering Japan

ISBN: (纸本)9798331520861

In the realm of Internet of Vehicles (IoV), Roadside Units (RSUs) play a vital role due to their advanced sensing and computing capabilities. They support Mobile Edge Computing (MEC) by enabling real-time data processing and reducing latency and energy consumption. Given the large volume of data and the limited computing capacity of RSUs, tasks are often offloaded to other nodes, such as the cloud, other RSUs, or vehicles. However, due to the environment dynamic caused by vehicle mobility, the set of neighboring nodes for each RSU frequently changes, and variations in neighboring nodes are not perceived timely. In such cases, RSUs need to predict changes in neighboring nodes to ensure effective task offloading. Moreover, since each RSU can only perceive the surrounding environment, it is difficult for a single node to make optimal offloading decisions for each task independently. To address these issues, this paper proposes a distributed task offloading scheme that incorporates vehicle trajectory prediction. First, each RSU uses the vehicle trajectories predicted by the Gated Recurrent Unit (GRU) model to determine neighboring vehicles and update the set of neighboring nodes. Then, to enable each RSU to select a suitable computing node and minimize computation delay and energy consumption for the task, we adopt a distributed approach where each RSU independently makes offloading decisions based on local observations. To achieve this goal, we establish a 01 mathematical model and transform it into a Markov Decision Process (MDP). Subsequently, we propose a solution based on Multi-Agent Proximal Policy Optimization (MAPPO). Through extensive simulation experiments, we demonstrate that the scheme effectively reduces both long-term average processing delay and energy consumption. © 2024 IEEE.

关键词： MAPPO neighboring vehicle prediction RSU task offloading

来源：评论

学校读者我要写书评

暂无评论

MEConvNN-Designing Memory Efficient Convolution Neural Network for Visual Recognition of Aerial Emergency Situations

MEConvNN-Designing Memory Efficient Convolution Neural Netwo...

引用

Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference

作者： Fatima, Unse Pyo, Junbom Ko, YeongMin Jeon, Moongu School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Korea Republic of

ISBN: (纸本)9783031397769

Unmanned aerial vehicles (UAVs) play a vital role in calamity and natural disaster management due to their remote sensing capabilities. Specifically, UAVs/drones equipped with visual sensors can have remote access to confined areas wherein human access is limited. Growing inventions in deep learning incubate the efficacy of such UAVs/drones in terms of computational capability with limited resources and lead us to effectively utilize this technology in visual recognition of emergency situations, like floods in urban areas, earthquakes or fires in forests, and traffic accidents on busy highways. This can be beneficial in mitigating the consequences of such events on the environment and people more rapidly with minimum men and material loss. However, most deep learning architectures used in this domain with high accuracy are costly regarding memory and computational resources. This motivates us to propose a framework that can be computationally efficient and can be utilized on an embedded system suitable for smaller platforms. In this work, we formalize and investigate that problem and design a memory-efficient neural network for visual recognition of emergency situations named MEConvNN. To this end, we have effectively used dilated convolutions to extract the spatial representation. The proposed method is experimentally evaluated using Aerial Image Database for Emergency Response (AIDER), showing comparative efficacy with the state-of-the-art methods. Specifically, the proposed method achieves accuracy with less than a 2% drop compared to state-of-art methods but is more memory efficient in contrast to state-of-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

引用

computers, Materials & Continua 2023年第11期77卷 2009-2030页

作者： Kalpesh Jadav Vishal Sorathiya Walid El-Shafai Torki Altameem Moustafa HAly Vipul Vekariya Kawsar Ahmed Francis MBui Department of Electronics&Communication Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt Department of Computer Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department of Electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonSK S7N5A9Canada

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking *** shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and *** of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is *** real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above *** paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle *** method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground *** to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

关键词： Change illuminations ImageJ software intelligent traffic systems mixture of Gaussian model National Institute of Health vehicle tracking

来源：评论

学校读者我要写书评

暂无评论

Short-Segment Speaker Verification Using ECAPA-TDNN with Multi-Resolution Encoder 48

Short-Segment Speaker Verification Using ECAPA-TDNN with Mul...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Han, Sangwook Ahn, Youngdo Kang, Kyeongmuk Shin, Jong Won Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Gwangju Korea Republic of

ISBN: (纸本)9781728163277

Time-domain approaches have shown the potential to improve the performance of speaker verification, but still predominant approaches utilize hand-crafted features such as the mel filterbank energies. Although these features are based on speech perception models and exhibited impressive performances, the fixed frame size does not allow good temporal and spectral resolutions at the same time and there is information loss when taking the magnitude spectrum and during frequency rescaling. In this paper, we propose to incorporate multi-resolution time-domain information into the ECAPA-TDNN speaker verification system. We construct a multi-resolution encoder to extract multiple features in different temporal resolutions, and let the extracted features drive the adapter modules. Experimental results showed that the proposed method outperformed other recently proposed approaches when the input length was 2 seconds or shorter for the VoxCeleb dataset. The proposed approach also showed superior performance on the Google Speech Commands dataset v2. © 2023 IEEE.

关键词： Signal encoding

来源：评论

学校读者我要写书评

暂无评论

A Study of Joint Framework for Robustness Against Noise on Speaker Verification System 14

A Study of Joint Framework for Robustness Against Noise on S...

引用

14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Han, Sangwook Ahn, Youngdo Kang, Kyeongmuk Shin, Jong Won School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Korea Republic of

ISBN: (纸本)9798350335385

As speaker verification (SV) technology is applied to various fields with the development of deep learning, robustness against noise is becoming more prominent. A common approach for suppressing the noise effect is to jointly train SV system along with a front-end enhancement module as pre-processing. However, in this paper, we explore a training method for a noise-robust system that focuses on enhancing the single SV system. In particular, we introduce the feature-robust loss to exploit the embeddings extracted from the pre-trained model, which is jointly trained with the enhancement module. Experimental results showed that our proposed method can mitigate the noise effect in various noisy conditions using only a single SV system, demonstrating its potential for further development. © 2023 IEEE.

关键词： Speech enhancement

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：