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Fusion Methods in Motor Imagery Classification 7

Fusion Methods in Motor Imagery Classification

引用

2022 7th international conference on Electronic technology and information Science, ICETIS 2022

作者： Xu, Ziyan Department of Information Science and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9783800757794

Motor Imagery (MI) based Brain-computer Interface system (BCI) has various applications including prosthesis control, virtual reality control, motor rehabilitation, etc. Yet, such systems still face many challenges such as low accuracy in MI classification. there has been a lot of research on improving motor imagery performance, but most of them focus on studying single signal or single technique. In this paper, we explore the feasibility of improving the accuracy using fusion methods. First, we will explain some terminology regarding to motor imagery and brain computer interface. then, we will discuss several categories of fusion according to the level at which the fusion is conducted. Finally, we discuss different situations and different fusion methods specifically. this review summarizes typical technology and state-of-the-art progress in the fusion methods of motor imagery classification over the last decades. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Brain computer interface

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Design of Intentional Eye-blink Signal Acquisition and Control System Based on Pressure Sensor 7

Design of Intentional Eye-blink Signal Acquisition and Contr...

引用

7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Shao, Xueqi Ning, Haonan Yang, Wenjun Niu, Yafeng Southeast University School of Mechanical Engineering Department of Industrial Design Nanjing China National Key Laboratory of Science and Technology on Aircraft Control Xi'an China

ISBN: (纸本)9781665478571

Eye-blink control means that users blink intentionally to realize human-computer interaction. An intentional eye-blink signal acquisition and control system based on flexible pressure sensor is designed, which aims to control the object through simple eye movements such as blinking and winking. the system uses two pressure sensors to collect the blink signals at the orbicularis oculi muscles of the left and right eyes respectively. the software part realizes the acquisition and recognition of six intentional eye-blink signals, and transmits blink-control instruction signals through Bluetooth, so as to realize the control of the remote-controlled vehicle. the advantage of this system is that it is not affected by illumination and does not need GUI interface for control. the availability of the system is qualified after system test. © 2022 IEEE.

关键词： Human computer interaction

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NIN-DSC: A Network Traffic Anomaly Detection Method Based on Deep Learning 7

NIN-DSC: A Network Traffic Anomaly Detection Method Based on...

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7th international conference on Signal and Image processing, ICSIP 2022

作者： Li, Yanmiao Kong, Xuan Hou, Jiangang Li, Xin Zhao, Kun Liang, Wei Jiang, Tongqing Xin, Yang Liu, Zhi Beijing University of Posts and Telecommunications Center of Information Science Beijing China Shandong University School of Information Science and Engineer Qingdao China Beijing Institute of Technology School of Computer Science & Technology Beijing China Inspur Electronic Information Industry Co. Ltd Jinan China Qilu University of Technology School of Information and Automation Jinan China Shandong Gentle Tecchnology Co. Ltd Jinan China Beijing University of Posts and Telecommunications Centre of Information Security Beijing China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Network traffic anomaly detection plays an important role in maintaining network space security and is usually modeled as a traffic classification problem. With the rapid change of the Internet, the network traffic environment is increasingly complex, and the traditional classification method is no longer applicable. In this article, we propose a new traffic classification framework for network anomaly detection, which operates directly on the original traffic. the NIN-DSC network was used to extract the features of the data, and the validation was carried out in the public data set. © 2022 IEEE.

关键词： Deep neural networks

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Research on Remote and Near Integration Control System of Solenoid Valve for Agriculture 7

Research on Remote and Near Integration Control System of So...

引用

7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Liu, Yong Liu, Sijia Xu, Shipu Wu, Yingjing Liu, Chang Wang, Yunsheng Hu, Wenwen Liu, Guoqing Research Institute of Information of Agricultural Science and Technology Shanghai Academy of Agricultural Sciences Shanghai China Shanghai Institute of Technology Department of Railway Transportation Shanghai China School of Information Engineering Nanchang Hangkong University Nanchang China Technology Center Information Technology Laboratory Fujian Wuyi Tobacco Limited Company Fujian China

ISBN: (纸本)9781665478571

In order to solve the problem of the combination of the traditional on-site control method of the valve in the current agricultural production and the remote control method after the introduction of the agricultural Internet of things technology, this paper studies a remote-near integrated agricultural solenoid valve control system. the system introduces WI-FI technology to connect the valve controller to the Internet to realize remote control. In order to adapt to the usage habits of agricultural production personnel and as a backup of the remote control method, the system realizes the near-field control function. Near-field control uses industrial touchscreens to replace traditional physical buttons or switches. Industrial touch screens are generally used in conjunction with PLCs. Considering the sensitivity of agricultural production to cost, this article uses a lower-priced STM32 microcontroller to replace the PLC. the system finally realizes the real-time synchronization of the control states of the remote control and the near-field control, and truly realizes the integration of the remote control and the near-field control. © 2022 IEEE.

关键词： Solenoid valves

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Optical Character Recognition System of Telugu Language Characters Using Convolutional Neural Networks 7th

Optical Character Recognition System of Telugu Language Char...

引用

7th international conference on Emerging Research in Computing, information, Communication and Applications, ERCICA 2022

作者： Charan, K.V. Pramod, T.C. Department of Computer Science and Engineering Shridevi Institute of Engineering and Technology Karnataka Tumkur India Department of Computer Science and Engineering Siddaganga Institute of Technology Karnataka Tumkur India

ISBN: (纸本)9789811954818

In pattern recognition, recognition of characters plays a crucial role. Optical Character Recognition (OCR) is a challenging problem for many decades. Identifying handwritten characters is an easy task for humans, but it is hard for a computer. Exploration in character recognition is very popular for many applications such as in banks, processing of languages, library automation, and designs of multi-media. An OCR system contains an optical scanner to scan the text and advanced software for interpreting images. Recognition of Handwritten Telugu Characters is strenuous as discrete handwritten scripts may have discrete styles of writings, slants, etc. the main purpose of optical character recognition is to make editable documents or images. In this paper, we have used deep learning techniques like Convolutional Neural Networks for recognizing Telugu characters. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optical character recognition

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Goggle Wear Detection Algorithm based on Improved Faster R-CNN 24

Goggle Wear Detection Algorithm based on Improved Faster R-C...

引用

7th international conference on Image and Graphics processing, ICIGP 2024

作者： Nie, Zhuangzhuang Wang, Jun School of Computer and Information Anhui Polytechnic University China

ISBN: (纸本)9798400716720

Aiming to address the issues of insufficient model training, low detection accuracy, and poor generalization capabilities caused by the low quality of the current goggles-wearing detection dataset, insufficient samples, and imbalanced sources, a goggles-wearing detection dataset that includes a variety of real and complex scenarios was constructed. An improved object detection algorithm based on Faster R-CNN was proposed to detect the wearing of protective goggles. this algorithm improves upon the Faster R-CNN by refining a more reasonable loss function, solving the problem of ineffective calculation of loss under special circumstances. At the same time, PAFPN is used to replace the original FPN, allowing the detection model to have a bottom-to-up secondary fusion, effectively enhancing feature extraction capabilities. Experimental results on the goggles-wearing detection dataset indicate that the improved Faster R-CNN model has an average precision of 82.7%. Compared to the Faster R-CNN model, the average precision has increased by 3.8%, enabling the detection of goggles wearing in complex environments. © 2024 ACM.

关键词： Object detection

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Novel Approaches in Machine Learning for Enhanced Facial Recognition Systems 7

Novel Approaches in Machine Learning for Enhanced Facial Rec...

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7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Maddel, Manisha Kakarla, Mohanthi Sindhu, D. Geetha Rani, K. Amol Ubale, Swapnaja Mahesh Kumar, A.S. Indira School of Business Studies Department of Mba Maharashtra Pune411033 India Sri Sairam College of Engineering Department of Electronics And Communication Engineering Karnataka Bengaluru562106 India Bgs Institute of Technology Department of Information Science And Engineering Karnataka BG Nagara571448 India Jain University Department of Computer Science of Engineering Karnataka Bangalore562112 India Marathwada Mitra Mandal College of Engineering Department of Information Technology Maharashtra Pune411052 India Pes College of Engineering Department of Electronics And Communication Engineering Karnataka Mandya571 401 India

ISBN: (纸本)9798350359299

Face recognition (FR) is the latest method of authentication and authorization, and it has advanced significantly in the previous decade, with such systems becoming widespread in industries such as security, commerce, and so on. Because of this, FR has become one of the most investigated fields, numerous algorithms having been developed over the years by academic and commercial researchers. Since most existing methods perform poorly in unrestricted environments, this area of study continues to expand rapidly. In this research, a new method for boosting FR accuracy called Gray Wolf Optimization-based AdaBoost (GWO-AdaBoost) is provided. Images of people's faces have been chosen from the OLR Database. the acquired images are then sent to the processing phase, where they are transformed from their raw form into the required form. Gabor Filter (GF) is used to retrieve features from the processed images, and recognition is done using the suggested Machine Learning (ML) model, GWO-AdaBoost. the results of the proposed ML model are compared to those obtained using the AdaBoost method. the results demonstrate that the proposed model outperforms AdaBoost in terms of recognition rate. the impact of input sample count on execution time was also examined. the proposed method takes more time than AdaBoost to identify a person's face. © 2024 IEEE.

关键词： Face recognition

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A Survey of DNS Tunnel Detection 7

A Survey of DNS Tunnel Detection

引用

7th international conference on Signal and Image processing, ICSIP 2022

作者： Hou, Jiangang Li, Xin Zhao, Kun Liang, Wei Li, Yanmiao Jiang, Tongqing Liu, Zhi Beijing Institute of Technology School of Computer Science and Technology Beijing China Shandong University School of Information Science and Engineering Qingdao China Inspur Electronic Information Industry Co. Ltd Jinan China Qilu University of Technology School of Information and Automation Jinan China Beijing University of Posts and Telecommunications Center of Information Security Beijing China Shandong Gentle Tecchnology Co. Ltd Jinan China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

While the network brings convenience to people's lives, it also causes security issues. In recent years, hackers have exploited the vulnerability of DNS protocol design and architecture to build DNS tunnels for DNS protocol-based information interaction through DNS tunneling tools. However, the DNS protocol is the infrastructure of network communication and can naturally pass through the firewall, which greatly increases the threat of DNS tunnel-based attacks. So researchers began to study how to detect DNS tunnels, and have gotten good results. this paper provides an overview of the detection methods for DNS tunnels in recent years. Firstly, we briefly discuss DNS protocol and vulnerability, then introduce the principles of DNS tunnel implementation and commonly applied DNS tunnel tools, secondly, we compare and analyze the methods for detecting DNS tunnels, and finally, we point out the future directions of DNS tunnel detection that are worthy of research. © 2022 IEEE.

关键词： Personal computing

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AI-driven Dynamic Workload Balancing for Real-time Applications on Cloud Infrastructure 7

AI-driven Dynamic Workload Balancing for Real-time Applicati...

引用

7th international conference on Contemporary Computing and Informatics, IC3I 2024

作者： Sreeramulu, Madhusudhan Dasari Mohammed, Abdul Sajid Kalla, Dinesh Boddapati, Nagesh Natarajan, Yuvaraj American Express Department of Cyber Security United States University of the Cumberlands Department of School of Computer and Information Sciences KY United States Colorado Technical University Department of Computer Science CharlotteNC United States Microsoft Department of Computer Science CharlotteNC United States Sri Shakthi Institute of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350350067

the specification provides a new, resource-efficient method of Dynamic Workload Balancing in AI-driven Real-time Applications over Cloud Infrastructure. the real-time application keeps processing data at high speeds, and it is too difficult to get or make this type of arrangement using our traditional cloud setups as the system employs artificial intelligence methods responsively reallocate resources between virtual machines in accordion with demanded quality of services slabs in behalf-to-capacity ratio. It is scheduling jobs to resources efficiently and delivering results in a timely fashion, which is necessary for real-time applications like video processing or stock trading. It uses machine learning models based on historical data to predict the resources required for upcoming tasks. these predictions are subsequently utilized to smartly assign resources across VMs, factoring in network latencies and inter-dependence. It allows the system to modify queues in real-time according to changes in workload patterns, performing a dynamic load balancing. © 2024 IEEE.

关键词： Adversarial machine learning

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A Hybrid Cooperative Guided Local Search for Solving the Traveling Salesman Problem 7

A Hybrid Cooperative Guided Local Search for Solving the Tra...

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7th IEEE international conference for Convergence in technology, I2CT 2022

作者： Alalmaee, Zainab Tairan, Nasser King Khalid University College of Computer Science Abha Saudi Arabia

ISBN: (纸本)9781665421683

In this paper, a hybrid cooperative Guided Local Search (GLS) algorithm for solving the Travelling Salesman Problem (TSP) is proposed. the idea of the algorithm is to run a number of agents of GLS and after a number of local search calls, the agents exchange information about their search utilizing the crossover operator to make their further search rational. the partially mapped crossover (PMX) is applied based on the penalty's values of features to preserve the good components of the best solutions found by the agents during the search. the preliminary experimental results demonstrate that the proposed algorithm is promising and outperforms parallel GLS without collaboration. © 2022 IEEE.

关键词： Genetic algorithms

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