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Optimal Channel Selection for EEG-Based Enhanced Brain Robot Interface for Autonomous Wheelchair 12

Optimal Channel Selection for EEG-Based Enhanced Brain Robot...

引用

12th International Conference on Awareness science and Technology, iCAST 2023

作者： Sibli, Sabbir Ahmed Kabir, Raihan Jannat, Nuray Watanobe, Yutaka Islam, Md Rashedul Dept. of Computer Science and Engineering Fareast International University Banani Dhaka1213 Bangladesh Dept. of Computer Science and Engineering University of Aizu Aizuwakamatsu Fukushima Japan Dept. of Computer Science and Engineering BRAC University Dhaka1212 Bangladesh Dept. of Computer Science and Engineering University of Asia Pacific Dhaka1216 Bangladesh

ISBN: (纸本)9798350324693

Creating an autonomous wheelchair system based on EEG (electroencephalogram) signals is a fascinating and challenging concept. In non-invasive Brain robot Interface (BRI), scalp EEG signals acquired from the multiple channels come with different artifact types, majorly as an electrooculogram (EoG). Also, all the channels don't carry equally important information regarding the particular motor activities. The primary contribution of this paper is to denoise the EEG signal and then find the optimal channels to achieve the most informative EEG signals for the motor activity recognition. Thus, this research proposes a model where firstly t he E oG a rtifacts h ave b een removed from EEG signals with the regression method and other noises with Discrete Wavelet Transform (DWT). Secondly, It proposes a model of selecting optimal channels using a hybrid feature selection process, which includes a filter a nd w rapper method using sequential floating forward search (SFFS). Maximum Class Separability (MCS) and Support Vector Machine (SVM), both have been used for evaluating the channels. Then again SVM has been applied for classifying the motor activities by the signals recorded from the optimal channels. A dataset with four classes of human motor activities has been used for evaluating the whole proposed model. This proposed model shows the highest accuracy of 84.15% for the optimal channel set with Standard Deviation (STD) feature among other optimal channel sets of features. © 2023 IEEE.

关键词： Support vector machines

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Image Enhancement for Iris Analysis Using Deep Learning Algorithms - A Brief Survey 2

Image Enhancement for Iris Analysis Using Deep Learning Algo...

引用

2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Poovayar Priya, M. Ezhilarasan, M. Pondicherry Engineering College Dept. of Computer Science and Engineering Puducherry India Pondicherry Engineering College Dept. of Information Technology Puducherry India

ISBN: (纸本)9798350328202

With applications including medical image analysis, picture sharpening and restoration, robot vision, pattern recognition, and video processing, among many others, image enhancement is the main topic in image processing and computer vision. There have been many algorithms for improving images established in the past. More extensive work on image-enhancing techniques has been done utilizing deep learning models in recent years, as these algorithms have achieved their peak in vision applications. In this study, we present a thorough analysis of machine learning, deep learning, and literature. The literature on image enhancement is then summarised, and future approaches for iris analysis are discussed. © 2024 IEEE.

关键词： Image enhancement

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Detecting Retinal Damage From OCT Images 15

Detecting Retinal Damage From OCT Images

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Vineetha, Kundrapu Ali, Md Jaffer Prathima, Narisetty Singh, Tripty Duraisamy, Prakash Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India University of Wisconsin-Green Bay Dept. of Computer Science and Engineering Wisconsin United States

ISBN: (纸本)9798350370249

Optical coherence tomography (OCT) is a crucial imaging modality in ophthalmology, offering detailed retinal images essential for diagnosing various retinal disorders. In this study, we investigate the application of convolutional neural networks (CNNs) and transfer learning to classify retinal OCT images. We compare a custom CNN model with two pretrained models, MobileNet and VGG16, to explore their effectiveness in categorizing retinal diseases. Our study focuses on improving classification accuracy through image enhancement techniques, training methodologies, and model evaluation using confusion matrices. The results highlight the VGG16-based CNN model as the most effective, demonstrating its potential for accurate classification of retinal disorders based on OCT images. Furthermore, we delve into strategies to further enhance the performance of these models, such as data augmentation, finetuning, and hyperparameter tuning. We discuss the importance of these techniques in optimizing model performance and reducing overfitting. Additionally, we emphasize the significance of evaluating models using confusion matrices to gain insights into their performance across different classes. Overall, our findings suggest that CNNs, particularly the VGG16based model, hold promise for improving the automated diagnosis of retinal diseases using OCT imaging, which could lead to better patient outcomes in ophthalmology. © 2024 IEEE.

关键词： Convolutional neural networks

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COVID-19 Image Retrieval Using Siamese Deep Neural Network and Hashing Technique 14

COVID-19 Image Retrieval Using Siamese Deep Neural Network a...

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14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Boroujeni, Farsad Zamani Afshari, Doryaneh Hossien Mahmoodi, Fatemeh Science and Research Branch Dept. of Computer Engineering Tehran Iran Branch Dept. of Computer Engineering Isfahan Iran

ISBN: (纸本)9798350349412

Medical images are crucial for early diagnosis and are increasingly important in modern medicine. This study presents a medical image retrieval system using a Siamese neural network with 13 layers. The system uses the Minimum Redundancy Maximum Relevance (mRMR) technique to extract deep features from the Siamese, and then uses binary hashing to retrieve similar images using Hamming distance. The experimental results on Covid-19 dataset show that the proposed method improves lung image retrieval by over 6% compared to previous methods, achieving an average accuracy of 93.83% and 92.73% in 5 and 10 retrieved images, respectively. © 2023 IEEE.

关键词： Image enhancement

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Stealth Encoding - Concealing data with the Least Significant Bit Method

Stealth Encoding - Concealing data with the Least Significan...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Kantapalli, Bhaskar Meruga, Siva Gnani Polagani, Prasanthi Pingali, Venu Babu Seelam, Surya Teja Seshadri Rao Gudlavalleru Engineering College Dept. of Computer Science and Engineering Gudlavalleru India Seshadri Rao Gudlavalleru Engineering College Dept. of Computer Science and Engineering Mandavalli India Seshadri Rao Gudlavalleru Engineering College Dept. of Computer Science and Engineering Vijayawada India Seshadri Rao Gudlavalleru Engineering College Dept. of Computer Science and Engineering Machilipatnam India

ISBN: (纸本)9798350396485

Data security is a major issue for computer users these days. So, we conceal the textual data using steganography, the art and science of concealing information. In this case, we conceal both the message's content and the fact that it is being sent. In order to make it challenging to Figure out that a hidden message is present in a specific film, this technique involves concealing secret data within a conventional, non-secret file. At its destination, the secret data is subsequently removed. Several uses of steganographic technology have distinct requirements. Here, we suggest a method for concealing data, including reverse data hiding for data extraction. The textual data will be secured in different frames of the video and audio using the Least Significant Bit (LSB) and related algorithms. We can conclude that the original data will never change the characteristics of video or audio even after applying steganography techniques. © 2023 IEEE.

关键词： Steganography

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Comparison of Faster RCNN and YOLO V3 for Video Anomaly Localization

Comparison of Faster RCNN and YOLO V3 for Video Anomaly Loca...

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2023 International Conference on Power, Instrumentation, control and Computing, PICC 2023

作者： Anoopa, S. Salim, A. Nadera Beevi, S. College of Engineering Trivandrum Dept. of Computer Science and Engineering Kerala India Government Engineering College Thrissur Dept. of Computer Science and Engineering Kerala India Tkm College of Engineering and Technology Dept. of Computer Applications Kerala India

ISBN: (纸本)9798350334463

Video Anomaly Localization techniques are interesting and emerging tasks in computer vision that is used to locate the position of an anomalous object with bounding boxes. Convolutional neural network-based object localization methods have been developed which detect and classify objects with high precision and accuracy. Many algorithms have been developed for object detection and localization with excellent outcomes. These algorithms are fast RCNN, faster RCNN, and YOLO. In this paper, a comprehensive analysis of faster RCNN and YOLO V3 algorithms for anomaly localization is performed based on the parameters FPS, mAP, Precision, Recall and F1 Score. © 2023 IEEE.

关键词： Convolutional neural networks

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Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems for Next-Generation Wireless Networks

Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems f...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Yadav, Satya Prakash Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350378092

This study explores the optimisation of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems through the use of phased arrays and advanced beamforming methods. With a focus on overcoming the challenges that come with 5G wireless communication, like high data rate needs, interference resistance, and efficient spectrum usage, this work presents a novel approach that combines dynamic beam steering and adaptive precoding to significantly enhance system performance. Through thorough modelling, we demonstrate considerable improvements in bit error rates (BER), throughput, and spectrum efficiency compared to traditional MIMO-OFDM configurations. The results demonstrate a 25% improvement in throughput and a 40% reduction in BER in densely populated metropolitan environments, underscoring the potential of phased arrays and beamforming to further improve 5G network performance. This work not only validates the efficacy of the proposed enhancements but also establishes a foundation for future advancements in wireless communication systems by highlighting the critical roles that phased arrays and beamforming will play in achieving the maximum potential of 5G technology. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Deep Analysis of Machine Learning Techniques for the Effective Retrieval of Images Based on Content 2

Deep Analysis of Machine Learning Techniques for the Effecti...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Jain, Ashish Varshney, Sudeep Set Sharda University Dept. of Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

Our research aims to investigate how machine learning techniques can be applied for efficient image retrieval based on content. It is a very difficult process to obtain accurate images from vast digital image collections. CBIR has been particularly interested in this area. It has been shown that content- or feature-based approaches are more effective than text-based query techniques. Research has shown that the performance of these methods varies depending on the type and size of the data set analyzed, with some algorithms performing well for specific small data sets and others for larger data sets. This article provides a comprehensive and systematic review of effective methods for obtaining the image. The paper's objective is to examine different ML classifiers that are applied via the researchers and compare their results based on accuracy and performance. These algorithms include SVM, KNN, random forest, BoVW, Naive Bayes, and LDA. This review paper can assist researchers in selecting the best classifier for their research, as well as in improving existing classifiers by combining them with other algorithms. © 2024 IEEE.

关键词： CBIR classifier feature extraction Random Forest SVM

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Reference-based Burst Super-resolution 24

Reference-based Burst Super-resolution

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32nd ACM International Conference on Multimedia, MM 2024

作者： Ko, Seonggwan Koh, Yeong Jun Cho, Donghyeon Dept. of Electronics Engineering Chungnam National University Daejeon Korea Republic of Dept. of Computer Science & Engineering Chungnam National University Daejeon Korea Republic of Dept. of Computer Science Hanyang University Seoul Korea Republic of

ISBN: (纸本)9798400706868

Burst super-resolution (BurstSR) utilizes signal information from multiple adjacent frames successively taken to restore rich textures. However, due to hand tremors and other image degradation factors, even recent BurstSR methods struggle to reconstruct finely textured images. On the other hand, reference-based super-resolution (RefSR) leverages the high-fidelity reference (Ref) image to recover detailed contents. Nevertheless, if there is no correspondence between the Ref and the low-resolution (LR) images, the degraded output is derived. To overcome the limitations of existing BurstSR and RefSR methods, we newly introduce a reference-based burst super-resolution (RefBSR) that utilizes burst frames and a high-resolution (HR) external Ref image. The RefBSR can restore the HR image by properly fusing the benefits of burst frames and a Ref image. To this end, we propose the first RefBSR framework that consists of Ref-burst feature matching and burst feature-aware Ref texture transfer (BRTT) modules. In addition, our method adaptively integrates features with better quality between Ref and burst features using Ref-burst adaptive feature fusion (RBAF). To train and evaluate our method, we provide a new dataset of Ref-burst pairs collected by commercial smartphones. The proposed method achieves state-of-the-art performance compared to both existing RefSR and BurstSR methods, and we demonstrate its effectiveness through comprehensive experiments. The source codes and the dataset are available at https://***/SeonggwanKo/RefBSR. © 2024 ACM.

关键词： Image reconstruction

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TSPvis: A Temperature Sensitive Paint Formulation Visual Design System 6

TSPvis: A Temperature Sensitive Paint Formulation Visual Des...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Liao, Jing Wu, Yadong Wang, Guijuan Wang, Fupan Chen, Huarong Long, Li Southwest University of Science and Technology Dept. Computer Science and Technology Mianyang China Sichuan University of Science and Engineering Dept. Computer Science and Engineering Yibin China

ISBN: (纸本)9798350325485

The composition and proportion parameters of temperature sensitive paint formulation are complex. The current methods have a long development cycle and low efficiency. We design a visual formulation design system for temperature sensitive paint, helps desingers use historical data to improve the efficiency of formulation design. TSPvis provides a visual formulation design function, implements the proportion decision of formulation based on decision tree, and recommends reference for ingredients proportion to the formulation designer. We use the DPC clustering algorithm to quickly obtain the radial basis center, and use the Adam algorithm to improve the weight calculation from the hidden layer to the output layer, to improve RBF neural network. TSPvis use this RBF as a model to predict performance of the formulation which designed before, and feeds back to the designer to improve the formulation designing of temperature sensitive paint. © 2023 IEEE.

关键词： Decision trees

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