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2021 International conference on Electronic communication, Computer Science and Technology

2021 International Conference on Electronic Communication, C...

引用

2021 International conference on Electronic communication, Computer Science and Technology, ECCST 2021

The proceedings contain 33 papers. The topics discussed include: robust visual place recognition method for robot facing drastic illumination changes;research on non-uniform interrupted sampling repeater jamming for phase coded radar;research on equalization technology of broadband satellite communication channel;measuring the orbital angular momentum mode of vortex beam based on phase matching sequence;design of intelligent anti-suffocation system in vehicle;application of information processing system based on DSP in electronic information engineering;analysis and research on the interference of civil aviation radio navigation equipment;research on application of artificial intelligence in communication network;dynamic power allocation in D2D communications using deep reinforcement learning;and design of multi-band compatible trigeminal monopole array antenna for unmanned aerial vehicle.

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An Effective Approach to Speech-based Email Assistance for visually Impaired People 8

An Effective Approach to Speech-based Email Assistance for V...

引用

8th International conference on Signal processing and communication, ICSC 2022

作者： Sharma, Aditi Ahmed, Visal Sharma, Shivika Jana, Bristi Rani, Kritika Computer Science Engineering Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9781665454308

In this paper, a new speech based mailing system is proposed for visually impaired. The user will be able to operate the functionalities of Gmail, the most prominent emailing system used around the world, using voice command only. The idea focuses on providing basic functionalities like compose, send, receive, delete and search E-mails in the user's pre-registered Gmail account. This web application accesses Gmail using text to speech and speech to text module to make emailing feasible for users with visual impairments. Unlike existing systems which usually focuses more on Graphical User Interface friendliness of normal user, this system focuses on providing accessibility to blind people. © 2022 IEEE.

关键词： Speech recognition

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DeepFakeGuard: Safeguarding Digital Platforms Against Fake Profiles Using AI 4

DeepFakeGuard: Safeguarding Digital Platforms Against Fake P...

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swarna Sudha, M. Manjula, S. Bharathi, Indira Krishnasamy, Valarmathi Vijayalakshmi, K. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India School of Computer Science and Engineering Vellore Institute of Technology Chennai India PSR Engineering College Department of Electronics and Communication Engineering Sivakasi India

ISBN: (纸本)9798331523923

This research introduces DeepFakeGuard, a hybrid deep learning framework designed to detect fake profiles on social media platforms, addressing the growing threat of fraudulent accounts online. DeepFakeGuard integrates multiple data modalities, including textual, visual, behavioral, and network features, to accurately classify profiles as real or fake. Leveraging advanced deep learning models such as BERT for textual analysis, ResNet for image processing, and LSTM for modeling behavioral patterns, DeepFakeGuard provides a comprehensive and robust solution for identifying fake profiles across diverse platforms. The framework processes a rich set of profile characteristics, including account information, posting behavior, post sentiment, and network relationships, ensuring a multi-dimensional analysis of social media accounts. The hybrid architecture of DeepFakeGuard merges outputs from individual sub-models, which are then processed through dense layers for final classification using a sigmoid activation function. This multi-faceted approach significantly enhances detection accuracy, precision, and recall compared to traditional methods. The framework is highly scalable and adaptable to various social media platforms, offering a versatile tool for strengthening online security. With strong performance metrics, including high classification accuracy, DeepFakeGuard demonstrates its potential for real-world applications in mitigating the risks associated with fake profiles. By fostering secure and trustworthy social media environments, this research contributes to the ongoing fight against fraudulent accounts, paving the way for safer online interactions. © 2025 IEEE.

关键词： Contrastive Learning

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A Fast Motion Deblurring Method in visual SLAM

A Fast Motion Deblurring Method in Visual SLAM

引用

International conference on information, communication and Networks (ICICN)

作者： Hong Zhu Hui Zhou Xinjing Song School of Computer and Software Dalian Neusoft University of Information Dalian China

ISBN: (数字)9798350355802

ISBN: (纸本)9798350355819

Motion blurring caused by camera shaking and scene movement often occurs, which is a key factor that reduces the accuracy of Simultaneous Localization and Mapping (SLAM). So a fast motion deblurring method in visual SLAM is proposed. Firstly, image quality is analyzed by sharpness evaluation module. Then motion deblurring network based on DeblurGANv2 is designed, which can deblur less sharpness images. Finally, the motion deblurring visual SLAM is acquired by combining above network with ORB-SLAM2. The experiments on the open dataset EuRoC show that the proposed algorithm has good deblurring effect and fast processing time. The total RMSE is 7.22cm. It can effectively eliminate the influence of blurred images on positioning accuracy.

关键词： Image quality Degradation visualization Simultaneous localization and mapping Accuracy Sensitivity Laplace equations Cameras Real-time systems

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DSP Implementation of Contrast Enhancement by Nonlinear Diffusion 23

DSP Implementation of Contrast Enhancement by Nonlinear Diff...

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15th International conference on Digital Image processing, ICDIP 2023

作者： Liu, Weijian Liang, Zhetong Yao, Ruohe Huang, Qiaojie School of Communication Engineering Guangzhou City University of Technology China School of Electronic and Information Engineering South China University of Technology China Guangdong Oppo Moblie Telecommucations Corp. Ltd China School of Intelligent Engineering Guangdong Agriculture Industry Business Polytechnic China

ISBN: (纸本)9798400708237

A real-time image enhancement algorithm is usually needed by an image processing system to improve the visual quality of the captured image sequence. Contrast enhancement by nonlinear diffusion (CEND) in the literature shows excellent performance in artifact removal and local contrast enhancement. However, CEND algorithm is too complex to be implemented on real-time systems. In this paper, in order to achieve real-time image enhancement algorithm based on CEND, we choose C674x DSP (Digital Signal processing) as targeted device and perform code simplification as well as in-depth code optimization, including compiler-based optimization, calculation simplification and on-chip memory utilization. These optimization measures do not involve access to peripherals, which makes the algorithm stable and convenient to be transplanted to other DSP platforms. We test the program of the CEND algorithm on TMS320DM8168 evaluation board and have verified that the implemented algorithm as a single thread on DSP subsystem is able to achieve above 25fps for 640×480 sized image sequence. © 2023 ACM.

关键词： Image enhancement

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RHLab RELIA: A Remote Integrated Environment forEmbedded Computing and RF communication Systems

RHLab RELIA: A Remote Integrated Environment forEmbedded Com...

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2023 ASEE Annual conference and Exposition - The Harbor of Engineering: Education for 130 Years, ASEE 2023

作者： Chap, Brian Moran, Marcos Jose Inonan Zhang, Zhiyun Orduna, Pablo Arabshahi, Payman Hussein, Rania University of Washington United States LabsLand United States

The development of technologies designed for the virtualization of signal and information processing experimentation has been persistently constrained by cost-associated hardware limitations, the inability to scale for larger audiences, and the lack of a flexible framework which supports user-specific interaction. Given recent advances in cloud computing, we introduce an adaptable, open-source system harnessing field-programmable gate array (FPGA) and software-defined radio (SDR) platforms to streamline the process of analyzing communication patterns between various individual transmitter-receiver pairs. A real-time user environment designed to complement the GNU Radio toolkit is demonstrated with minimal signal interference, enabling remotely-controlled, real information transfer. User-defined configuration files are processed through various Firejail-secured runner engines and dynamically visualized with adjustable feedback and interaction. By integrating novel images into the ADALM-PLUTO vanilla architecture, improved processed signal resolution was accomplished, with additional potential for ARM processor or FPGA reprogramming. The findings of this paper are targeted towards individuals of all communities, including those with insufficient access to requisite hardware. To remedy such issues, the integrated environment has been adopted and is currently accessible through LabsLand, a partner in this research, and its network of affiliated universities and institutions. Individual contributions to the constructed system are highly distributable by courtesy of the modular nature of the provided framework, encouraging collaboration and sharing of physical resources. Existing functionalities of LabsLand, including learning management systems, are anticipated to further contribute towards the fostering of a complete, visual environment for users, replicating the actual experience of end users in standard, on-site hardware experimentation without associated localized is

关键词： Open source software

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Towards Dynamic Neural communication and Speech Neuroprosthesis Based on Viseme Decoding

Towards Dynamic Neural Communication and Speech Neuroprosthe...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ji-Ha Park Seo-Hyun Lee Soowon Kim Seong-Whan Lee Dept. of Artificial Intelligence Korea University Seoul Republic of Korea Dept. of Brain and Cognitive Engineering Korea University Seoul Republic of Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Decoding text, speech, or images from human neural signals holds promising potential both as neuroprosthesis for patients and as innovative communication tools for general users. Although neural signals contain various information on speech intentions, movements, and phonetic details, generating informative outputs from them remains challenging, with mostly focusing on decoding short intentions or producing fragmented outputs. In this study, we developed a diffusion model-based framework to decode visual speech intentions from speech-related non-invasive brain signals, to facilitate face-to-face neural communication. We designed an experiment to consolidate various phonemes to train visemes of each phoneme, aiming to learn the representation of corresponding lip formations from neural signals. By decoding visemes from both isolated trials and continuous sentences, we successfully reconstructed coherent lip movements, effectively bridging the gap between brain signals and dynamic visual interfaces. The results highlight the potential of viseme decoding and talking face reconstruction from human neural signals, marking a significant step toward dynamic neural communication systems and speech neuroprosthesis for patients.

关键词： visualization communication systems Lips Neuroprostheses Signal processing Phonetics Brain modeling Decoding Speech processing Image reconstruction

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Identification of Building Damages using Satellite Images based on CNN- Recurrent Neural Network Approach 8

Identification of Building Damages using Satellite Images ba...

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8th International conference on communication and Electronics Systems, ICCES 2023

作者： Chavhan, Ganesh K. Singh, Sumeshwar Sahebrao Doke, Ankush Pilli, Daniel Kailasavalli, S. Vyankatesh Ghamande, Manasi K. J. Somaiya College of Arts Commerce and Science Department of Geography Ahmednagar Maharashtra Kopargaon India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Mahatma Phule Nutan Maha Vidyalaya Department of Geography Maharashtra Mirajgaon India Klbs Koneru Lakshmaiah Educational Foundation Mba Department Andhra Pradesh Vijayawada India Psna College of Engineering and Technology Mathematics Tamilnadu Dindigul India Vishwakarma Institute of Information Technology Engineering and Applied Science Maharashtra Pune India

ISBN: (纸本)9798350396638

Recently, numerous disaster-related studies have integrated Remote-Sensing (RS) technology alongside picture processing and conventional methods. One example is identifying earthquake damage to buildings in orthophotos. Common approaches to creating damage maps from RS pictures include automatic and visual methods. However, because of the need for manual sampling, the visual method is laborious. By extracting the defect features, the automatic technique can identify the damaged structure. Challenges to the widespread use of automatic methods are presented by the great variety of design approaches and the dynamic nature of real-world situations, such as variations in shadow and light. To better train neural networks with less interference from measurement noise, principal component analysis (PCA) is used. After PCA is applied to the DI values, an ensemble of neural networks is utilized to provide predictions about the location and severity of damage. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two widely used methods in machine learning, and the CRNN approach combines the two (RNNs). As compared to the CNN and RNN models, the proposed technique performs admirably. © 2023 IEEE.

关键词： Principal component analysis

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A Hybrid Transfer Learning Architecture Based Image Captioning Model for Assisting visually Impaired 3

A Hybrid Transfer Learning Architecture Based Image Captioni...

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3rd IEEE Applied Signal processing conference, ASPCON 2023

作者： Sarkar, Moumita Biswas, Shreya Ganguly, Biswarup Techno Main Saltlake Electronics & Communication Engineering Department Kolkata India National Institute of Technology Rourkela Clavictor Academy Private Limited Foundation for Technology & Business Incubation Odisha India National Institute of Technology Silchar Mieee Electrical Engineering Department Assam India

ISBN: (纸本)9798350325881

The task of automatically presenting the subject matter of a picture without human involvement is a highly intricate task. The issue at hand is commonly addressed through the utilization of computer vision and natural language processing techniques. Firstly, the image content must be analyzed and interpreted. Subsequently, this understanding needs to be transformed into grammatically accurate and contextually meaningful sentences. An issue that arises when employing deep learning (DL) based model that produces insufficient understanding of semantic context along with the encoder's inability to transmit essential visual information to the decoding unit in an efficient manner. To mitigate this issue, a hybrid transfer learning scheme has been proposed that is capable of generating image captions with a high degree of accuracy and minimal occurrence of errors. In this article, Inception module has been fused with residual network for extracting significant features from the images for encoding. Gated recurrent unit has been used as the decoder by producing appropriate captions for the images. The novelty lies in the inclusion of an attention layer that facilitates the acquisition of a linguistically relevant and syntactically coherent representation by the model. The attention interface is situated between the encoder and decoder components. Experimental study reveals the effectiveness of the proposed hybrid DL framework over existing DL methods. The proposed image captioning model can further be implemented for assisting the visually impaired. © 2023 IEEE.

关键词： Semantics

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Unified Image Mastery : An Integrated Approach to Image Enhancement, Object Detection and Classification

Unified Image Mastery : An Integrated Approach to Image Enha...

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Multimedia processing, communication & information Technology (MPCIT), International conference on

作者： Vandana N Vidyasagar K N Suguresh Vachan Arun Hanamanal Rahul R Pujari Electronics and Computer Engineering School of Electronics and Communication Engineering REVA University

ISBN: (数字)9798350375466

ISBN: (纸本)9798350375473

The proposed system integrates YOLOv5 [1] for object detection, CMFNet [2] for image enhancement, and ResNet [3] for image classification into a unified framework for comprehensive image analysis. This integrated approach overcomes limitations of standalone methods, providing a seamless platform for various image analysis tasks. The system demonstrates improved performance in visual quality enhancement, real-time object detection, and accurate image classification across diverse scenarios. The proposed design allows for flexibility and future extensibility, making it adaptable to evolving requirements in computer vision applications.

关键词： YOLO visualization Embedded systems Streaming media Real-time systems Extensibility information technology Image enhancement Optimization Image classification

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