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2024 International Conference on Brain Computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Veerasingam, Sridevi Boby, Kiran Paulose, Amal P Varsha, V. Raju, Dhole Shubham Anisha, S. Rahul, R.D. Joseph Ponniah, R. NIT Tiruchirappalli Department of Instrumentation and Control Engineering Tiruchirappalli India RIT Kottayam Department of Electronics and Communication Engineering Kottayam India SSN College of Engineering Department of Biomedical Engineering Chennai India PSG College of Arts and Science Department of English and Social Sciences Coimbatore India Indian Institute of Technology Department of Humanities and Social Sciences Dhanbad India NIT Tiruchirappalli Department of Humanities and Social Sciences Tiruchirappalli India

ISBN: (纸本)9798331540067

Reading comprehension is a complex cognitive process that involves semantic decoding, and schema integration influenced by reading practice. This study investigates the neural correlates of reading comprehension and explores how brain activation patterns differ between frequent and infrequent readers. Using Electroencephalography (EEG), the brain activation patterns were recorded from participants while performing the reading comprehension tasks. The EEG signal is recorded with a sampling rate of 1024 Hz to capture the dynamic changes in brain activation during reading tasks, revealing how frequent and infrequent readers differ in their cognitive processing and neural efficiency. The 21 channels of EEG signal acquired from 9 participants were pre-processed to obtain a 512 Hz band-limited signal. The pre-processed EEG signals were segmented before being fed into a twenty-four-layered convolutional neural network (CNN) model. The convolution stage extracts the temporal and spatial features, and the most significant features are extracted at the max-pooling stage. Frequent and infrequent readers are classified using the fully connected layer based on their EEG signals. The proposed model obtained a classification accuracy of 93.94%. © 2024 IEEE.

关键词： Convolutional neural networks

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Study on Fluctuation Characteristics of Acoustic Signals under Density Layered Fluid Disturbance

Study on Fluctuation Characteristics of Acoustic Signals und...

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IEEE/OES China Ocean Acoustics (COA)

作者： Jintao Yong Yunfei Chen Zhenshan Wang Bing Jia Rui Wang Shiqi Mo Science and Technology on Underwater Test and Control Laboratory Dalian China College of Underwater Acoustic Engineering Harbin Engineering University Harbin China College of Information and Communication Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798350340297

ISBN: (纸本)9798350340303

This study investigates the fluctuation characteristics of acoustic signals in the presence of flow field perturbations within density-stratified fluids through laboratory simulation experiments. A density stratification tank was used to replicate the ocean's stratified environment. The SUBOFF scaled model was employed to mimic the underwater vehicle's navigation process in towing mode. controlled density stratification simulation experiments were conducted to explore the acoustic signal fluctuation characteristics within the wake flow field of the submerged model. Data on the undulation properties of acoustic signals that propagated through the wake flow field of the underwater vehicle were collected. The undulation characteristics of the acoustic signal at two towing speeds and various spatial positions were analyzed. In alignment with the typical marine environment characteristics, this study designed the stratification conditions, the scale of the scaled model, and the towing speed based on the Froude number (Fri) similarity criterion and the geometric similarity law. The analysis of the experimental results indicates that the fluctuating frequency of the acoustic signal amplitude diminishes over time as the wake dissipates post-target passage. Concurrently, the intensity of the high-frequency fluctuation component of the signal also decreases with the dissipation of the wake.

关键词： Time-frequency analysis Fluctuations Fluids Navigation Perturbation methods Oceans Attenuation Acoustics Underwater vehicles

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Performance analysis of Polar Codes in a Visible Light communication System 4

Performance analysis of Polar Codes in a Visible Light Commu...

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4th National Conference on communication Systems, NCOCS 2022

作者： Vaz, Aldrin Claytus Hegde, Navya Thirumaleshwar Shetty, Akhil Appu Department of Electronics and Communication Engineering St Joseph Engineering College Vamanjoor Karnataka Mangaluru575028 India Department of Aeronautical and Automobile Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

One of the vital entity of any communication systems is Channel coding, which leads to the design of high performance codes for future wireless systems having low complexity encoder and decoder design. These systems will have a requirement of operating in highly reliable conditions, maximum throughput, and low and high code rates and to work with short and long information messages. Polar codes are one of the promising error correcting channel codes that can be used in these situations to obtain maximum throughput and coding gain in a communication systems because of their capacity approaching performance and finds interests in Satellite communication and 4G/5G services. These codes uses the concept of channel polarization to be constructed. The work proposed in this paper focusses on the Bit Error Rate evaluation and analysis of the Polar codes using traditional approach and Deep learning approach. The feedforward deep learning networks using different activation functions were used for the Deep learning approach. The Successive Cancellation algorithm and its variant using List decoding were used as traditional decoding methodology. The results obtained using Deep learning approach were satisfactory and was matching as per the traditional decoding. © Published under licence by IOP Publishing Ltd.

关键词： Decoding

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Enhanced Security Through Honeywords and Video CAPTCHA: An Innovative Authentication and Tracking Mechanism 5

Enhanced Security Through Honeywords and Video CAPTCHA: An I...

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5th IEEE International Conference on Advances in Electrical, Computing, communication and Sustainable Technologies, ICAECT 2025

作者： Gurale, Madhuri Borate, Komal Sangitrao, Rachana R. Tiwari, Garima D. Y. Patil College of Engineering Information Technology Department Akurdi Pune412101 India D. Y. Patil College of Engineering Artifical Intelligency and Data Science Department Akurdi Pune412101 India D. Y. Patil College of Engineering Department of Instrumentation and Control Akurdi Pune412101 India D. Y. Patil College of Engineering Department of Electronics & Communication Akurdi Pune412101 India

ISBN: (纸本)9798350356236

In recent years, activities across countries have increasingly been carried out digitally, with information being shared over networks to boost speed and efficiency. However, this digital transformation poses security threats. Cybercrime has consistently evolved, necessitating improved network security measures to combat digital scams. To maintain security, users are assigned unique identification passwords, which they are instructed to keep secret. Despite this, attackers often manage to steal passwords. To counter these threats, we propose using honeywords, which are decoy passwords created from the existing user password. If an attacker inputs a honeyword, an alert is activated for the administrator. Moreover, we implement IP and location monitoring for users and introduce a approach known as the video click-based CAPTCHA system. This scheme authenticates users by distinguishing between humans and bots, addressing issues in traditional graphic password CAPTCHA schemes. This architecture significantly enhances protection and security of data and applications over online networks. © 2025 IEEE.

关键词： Malware

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Multi Modal EEG-Based Classification of Alzheimer's Disease and Mild Cognitive Impairment Using Hilbert Huang Transform

Multi Modal EEG-Based Classification of Alzheimer's Disease ...

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Advances in Modern Age Technologies for Health and engineering Science (AMATHE), International Conference on

作者： G. Gopu S. Sathish T. S. Shanmathan G. Suthakar N. Sivakumaran M S Geetha Devasena Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Instrumentation and Control Engineering National Institute of Technology Tiruchirappalli India Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

This study investigates whether high-resolution brain-functional connectivity, which is measured by using Electroencephalography (EEG) combined with Hilbert Huang Transform (HHT) can be useful in Alzheimer Disease Diagnosis. The major issue with Alzheimer's disease (AD) is that it leads to cognitive decline and affects millions of people worldwide. Early prediction of the disease is mandatory and crucial for timely intervention and effective care. EEG signals, a non-invasive and cost-effective method to assess brain activity, have shown potential in predicting this disease. In this study, we propose an approach that utilizes EEG data combined with Hilbert-Huang Transform (HHT) and machine learning algorithms for classification which is a novelty of the study that enables early prediction of Alzheimer's disease. Our methodology was tested on a dataset comprising EEG signals recorded using 19 electrodes from both healthy individuals and patients with Alzheimer's and the Mild Cognitive Impairment (MCI). The results were promising, demonstrating high accuracy, sensitivity, and specificity. Machine learning techniques can be employed for categorizing EEG signals, but it essential to validate their effectiveness using large and diverse datasets. This research highlights the potential of HHT and EEG data in the prediction of this disease. Principal Component Analysis (PCA) is used for dimensional reduction of the dataset. Notably EEG signals combined with Hilbert Huang Transform and Principal Component Analysis gives better accuracy of 98.6% for KNN. This research highlights the potential of EEG analysis combined with advanced techniques for accurate classification of dementia-related disorders.

关键词： Accuracy Machine learning algorithms Empirical mode decomposition Transforms Machine learning Sensitivity and specificity Electroencephalography

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A Review of Machine Tool Motorized Spindle Variable Pressure Preload technology Research

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Recent Patents on engineering 2025年第5期19卷

作者： Dai, Ye Deng, Hao Pan, Gaofeng Chen, Xinda Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education School of Mechanical and Power Engineering Harbin University of Science and Technology Harbin150080 China School of College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China

High-precision spindles are a vital study topic because of the growing demand for high-speed, high-precision, reliable, and long-lasting machine tools. Bearing preload is the primary means of improving the accuracy of the spindle rotor system. This patent study aims to summarize the research results, systematically analyze and evaluate the machine tool motorized spindle variable pressure preload technology, and provide a scientific basis for the practical application of the motor spindle variable pressure preload technology. This study is an in-depth analysis of the basic principles of the electric spindle preloading technology, the study of its influence mechanism on the thermal deformation, stiffness, slewing error, and life of the spindle system, and a discussion of the current shortcomings in the field of spindle preloading to provide a reference for the subsequent optimization. In the spindle system, the performance of rolling bearings and the design of bearing preload have an important impact on the spindle's accuracy and stability. The traditional preload method has yet to meet the requirements of modern high-speed machine tools. Real-time controllable preload has become the new direction of development, but the current preload technology still has certain limitations and needs to be further improved. In the future, the spindle bearing variable pressure preload technology will be more efficient and intelligent. © 2025 Bentham Science Publishers.

关键词： Machine tools

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Optimizing Performance in Automatic Powder Filling Systems

Optimizing Performance in Automatic Powder Filling Systems

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International Conference on Power, Energy, control and Transmission Systems (ICPECTS)

作者： Mohanraj K Ramesh A K.S. Vairavel Mohammed Thoufik S Muthuraja R Mohamed Arshad M Department of Instrumentation & Control Engineering Sri Sairam Engineering College Chennai India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Electronics and Instrumentation Engineering Bannari Amman Institute of Technology

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

There have been crucial changes in the packaging industry recently, especially with regard to Form Fill and Seal (FFS) machines. These machines can be found in different sizes making them available for use by small businesses that want to lower their plant costs using inexpensive automated packaging solutions. This paper describes a low-cost weighing and pouring mechanism for improving accuracy of pouch filling machine. The process flow is carefully designed so as to achieve optimum production rates. Manipulators under the control of our mechatronics system are regulated through feedback from sensors managed by microcontroller.

关键词： Industries Mechatronics Costs Automation Seals Packaging Filling Sensor systems Sensors Safety

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Frequency Domain Separation of DAS Multi-Source Aliased Signals

Frequency Domain Separation of DAS Multi-Source Aliased Sign...

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2023 Asia communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Gu, Huaxin Liu, Shuaiqi Yu, Feihong Xu, Deyu Chen, Xingwei Shao, Liyang Wu, Yu Zhang, Haifeng College of Innovation and Entrepreneurship Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Electrical and Electronic Engineering Shenzhen China School of Information and Communication Engineering University of Electronic Science and Technology Chengdu China Research Institute of Tsinghua University Advanced Measurement and Control and Equipment R&d Center Chengdu China

ISBN: (纸本)9798350312614

In this paper, fast independent vector analysis (FastIVA) based on convolutional aliasing model is proposed to separate the aliased signals collected by distributed acoustic sensing (DAS) system, and the time-frequency entropy is used to judge the separation performance. The results show that the time-frequency entropy interval of the separated signals and the source signals correspond to each other, which means that the aliased signals are separated. This method is used to improve the accuracy of DAS system signal detection and recognition in complex real environment, and reduce the number of false alarm events. © 2023 IEEE.

关键词： convolutional aliasing model DAS FastIVA time-frequency entropy

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Remotely controllable Data Self-Destruction for Privacy-Preserving Sensitive Data Services 25

Remotely Controllable Data Self-Destruction for Privacy-Pres...

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25th IEEE International Conferences on High Performance Computing and communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhou, Chengxin Xue, Xianbin Riad, Khaled Zhu, Yan Chu, William Cheng-Chung School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China North Navigation Control Technology Co. Ltd. Beijing China College of Computer Science and Information Technology King Faisal University Saudi Arabia Tunghai University Department of Computer Science Taichung Taiwan

ISBN: (纸本)9798350330014

This paper address a comprehensive solution to address data privacy and security challenges in data science, providing secure self-destruction of sensitive data, confidentiality through encryption, and flexible control over message destruction. Based on outsourcing cloud data services, we introduce a cloud retrograde storage (CRS) system for privacy-preserving self-destruction of sensitive data. The CRS is based on a frequently colliding hash table and recycle pool, and supports remote time, count, and range control. Hereby, we provide a remotely controllable mechanism for the prompt destruction of inactive messages against digital forensics without reducing the availability of services. The method used in the CRS involves storing messages in a recycle pool, where they are automatically erased at the end of their validity period or after being viewed by all recipients. The system also incorporates encryption and decryption processes to ensure confidentiality. The result of performance evaluation shows that the CRS effectively handles the destruction of messages and provides secure data removal from storage pools. The experiments demonstrate the efficiency and practicality of the CRS for large-scale data services. © 2023 IEEE.

关键词： Sensitive data

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An Image-to-image Target Reconstruction Network for Capacitively Coupled Electrical Resistance Tomography Based on Transfer Learning

An Image-to-image Target Reconstruction Network for Capaciti...

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Computer, Vision and Intelligent technology (ICCVIT), International Conference on

作者： Chunfen Luo Yandan Jiang Vivo Mobile Communication Co. Ltd Hangzhou China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

This work develops an image-to-image target reconstruction network for capacitively coupled electrical resistance tomography. The novel target reconstruction network is constructed by two Unets to extract and fuse the multi-frequency features of the coarse images reconstructed by traditional image reconstruction algorithms, and incorporated with transfer learning to improve the generalization ability of the network. Experimental results show that the developed target reconstruction network is effective. The Unet-based image-to-image framework improves the target reconstruction quality with quantitatively higher image score. The introduced transfer learning strategy fills the gap between simulation data and experimental data, and further improves the performance of the network.

关键词： Resistance Computer vision Fluids Fuses Transfer learning Reconstruction algorithms Feature extraction Image reconstruction Image fusion

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