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2024 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Essakki, K. Swathi Anita, C.S. Sudharson, K. Alekhya, Badi Thanujaa, V.R. Varsha, S. R.M.D. Engineering College Dept. of Aiml Chennai India

ISBN: (纸本)9798350348460

Our research aims to magnify mobility support for visually impaired individuals by introducing an innovative EchoSight fortified with computer vision and deep learning algorithms. Central to our design ethos is facilitating heightened independence for those often dependent on external help due to visual limitations. The EchoSight leverages the power of YOLOv3, faster CNN, and R-CNN for precise obstacle detection coupled with ultrasonic sensors. In a comparative analysis, our model demonstrated an impressive 95% accuracy rate in detecting distant obstacles, outperforming traditional aids that identify at a rate of only 70%. Furthermore, EchoSight effectively alerts users of these hazards through a multi-modal system consisting of vibration feedback, buzzer notifications, and an integrated voice assistant. Such advancements represent not only a notable progression from conventional aids but also pave the way for enhanced mobility and independence for the visually impaired community. © 2024 IEEE.

关键词： computer Vision Deep Learning EchoSight Visually Impaired YOLOv3

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multi-scale Feature Fusion for Efficient Video Deraining Algorithm 5

Multi-scale Feature Fusion for Efficient Video Deraining Alg...

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5th international conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Yang, Donglei Du, Xuefeng Guo, Mengchao Wang, Yanhua Xu, Fengqiang Dalian Jiaotong University School of Software Dalian China Dalian Jiaotong University School of Mechanical Engineering Dalian China

ISBN: (纸本)9798350373820

Video deraining is a critical task in computer vision, as rain streaks degrade the image quality and affect the performance of outdoor vision tasks. Most existing methods are trained on synthetic data, making it difficult to simulate the diverse morphological features of rain in real scenes. For this reason, a video rain removal network with multi-scale feature fusion is proposed. Semi-supervised learning is used to combine synthetic and real data training, and the rain removal network is assisted by a rain streak generation network for rain removal. A multi-scale efficient channel attention mechanism is introduced into the rain removal network for feature fusion to capture spatial features and temporal correlations with less computational resources. Tested on the NTURain dataset, PSNR is improved by 1.73 dB and SSIM by 0.0060 compared to the state-of-the-art method. Experiments demonstrate that it shows good performance in both quantitative metrics and qualitative analysis. © 2024 IEEE.

关键词： Rain

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Optimizing SAR Target Detection Through the multi-Stage Pre-Training and Fine-Tuning Strategy 12

Optimizing SAR Target Detection Through the Multi-Stage Pre-...

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12th IEEE international conference on computer science and Network Technology, ICCSNT 2024

作者： Liu, Ziyuan Wang, Shaoping Gu, Yuantao Tsinghua University Dept. of Electronic Engineering Beijing100084 China Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen518055 China

ISBN: (纸本)9798350350791

Synthetic Aperture Radar (SAR) plays a crucial role in target detection due to its unique all-weather imaging capabilities. With the advancement of deep learning, numerous SAR target detection models have been proposed. However, the presence of speckle noise, low resolution, and the scarcity of SAR datasets have posed significant challenges to the training of these models. To address this issue, we propose a multi-stage pretraining-finetuning strategy. First, we pretrain the backbone on natural images, followed by fine-Tuning the entire model on simulated SAR images, and finally fine-Tuning it on real-world SAR data. Experimental results demonstrate that our approach effectively mitigates the gap between natural and SAR images, achieving superior results across various detection granularities. © 2024 IEEE.

关键词： Radar target recognition

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Design & Analysis of a Three-Stage Latched Comparator

Design & Analysis of a Three-Stage Latched Comparator

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2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Jois, Skanda V Roriech, P.A. Ravish Aradhya, H.V. R v College of Engineering Department of Ece Karnataka Bengaluru India

ISBN: (纸本)9798331529833

A latched comparator is an electronic component that compares two inputs and maintains its output state even if the inputs vary. This characteristic is especially useful for high-speed analog-to-digital converters (ADCs) as it ensures stable output during rapid signal changes. This paper explores various designs of multi-stage latched comparators, which are designed to enhance signal regeneration and reduce kickback noise, a common challenge in comparator circuits. The three- stage comparator presented shows a significant improvement, with a response time reduced to 306 picoseconds, accelerating signal regeneration. Furthermore, this design effectively minimizes kickback noise, which can disrupt circuit performance. These comparator circuits were carefully crafted at the schematic level using the Cadence Virtuoso tool. © 2025 IEEE.

关键词： Comparator circuits

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An Efficient Convolutional multi-Scale Vision Transformer for Image Classification 2

An Efficient Convolutional Multi-Scale Vision Transformer fo...

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2nd international conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Zhang, Ji Chen, Zhihao Ge, Yiyuan Yu, Mingxin Beijing Information Science and Technology University School of Instrument Science and Opto-Electronics Engineering Beijing China Beijing Information Science and Technology University Computer School Beijing China Jilin University Department of Software Engineering Changchun China

ISBN: (纸本)9798350331417

This paper introduces an innovative and efficient multi-scale Vision Transformer (ViT) for the task of image classification. The proposed model leverages the inherent power of transformer architecture and combines it with the concept of multi-scale processing generally used in convolutional neural networks (CNNs). The work aims to address the limitations of conventional ViTs which typically operate at a single scale, hence overlooking the hierarchical structure in visual data. The multi-scale ViT enhances classification performance by processing image features at different scales, effectively capturing both low-level and high-level semantic information. Extensive experimental results demonstrate the superior performance of the proposed model over standard ViTs and other state-of-the-art image classification methods, signifying the effectiveness of the multi-scale approach. This research opens new avenues for incorporating scale-variance in transformer-based models for improved performance in vision tasks. © 2023 IEEE.

关键词： Classification Convolutional neural networks multi-scale Vision Transformer

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Research on echo model of project organization capability system evolution based on computer simulation technology 6

Research on echo model of project organization capability sy...

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6th international conference on computer Information science and Application Technology, CISAT 2023

作者： Jiang, Zhuo-Yin Luo, Jia Oxbridge College Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781510668546

This paper take the sight of complex adaptive system theory to analyze the evolution mechanism of construction project organizational capacity, based on the description of seven interaction mechanisms and the introduction of matching speed and matching time, we established a three-layer echo model in order to describe the evolution of construction project organizational capability system for the first time. Using the method of multi-agent modeling and simulation, operating Swarm software to carry out computer simulation research on the optimization process of the main resources of engineering projects, furthermore taking this as an example to illustrate the evolution of engineering projects organization capability system. © 2023 SPIE.

关键词： Adaptive systems

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A multi-exposure image fusion method based on histogram matching and convolutional neural network

A multi-exposure image fusion method based on histogram matc...

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2023 international conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Zhu, Xinli Zhang, Yasheng Fang, Yuqiang Luo, Di Space Engineering University Beijing101416 China

ISBN: (纸本)9781510677685

For high dynamic range image reconstruction in dynamic scenes, this paper proposes a multi-exposure sequence image fusion method based on histogram matching and convolutional neural network. First, image alignment is performed on the reference image based on the histogram matching method. Then, a multi-exposure fusion model based on convolutional neural network is constructed for the aligned multi-exposure sequence images. Finally, the fusion model is trained based on the training data, and the multi-exposure sequence image fusion in dynamic scenes is realized. The experimental results show that the method proposed in this chapter effectively avoids the influence of dynamic scenes on the fusion of multi-exposure sequence images, and at the same time can generate high-quality fused images. © 2024 SPIE.

关键词： Image fusion

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Cutting-Edge Predictive Methods for Enhancing Short-Term Load Prediction

Cutting-Edge Predictive Methods for Enhancing Short-Term Loa...

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2024 international conference on Electrical and computer engineering Researches, ICECER 2024

作者： Ghazal, Taher M. Janjua, Jamshaid Iqbal Abushiba, Walid Ahmad, Munir Abbas, Tahir Ateeq, Karamath College of Arts & Science Applied Science University P.O.Box 5055 Manama Bahrain Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Selangor Malaysia Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul02841 Korea Republic of TIMES Institute Department of Computer Science Multan Pakistan School of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798331539733

Efficient management and cost-related factors in the power sector call for accurate short-term load forecasting as it enables better planning with the electric grid and achieving stability within it. This paper looks into the state-of-the-art forecasting strategies that enhance load forecasts made in shorter time frames. Such techniques include but are not limited to machine learning applications, effective data models, and multi-dimensional modeling leveraging historical, meteorological, and economic data. Their review is carried out in detail with their advantages and disadvantages and their application in practice. Furthermore, this study strives to understand the barriers that need to be overcome in the quest to increase the accuracy of the forecasting models especially concerning renewable energy sources. We illustrate how new trends relate to future developments of predictive models about supporting the decision-making in operating power systems and improving the reliability and sustainability of energy systems. © 2024 IEEE.

关键词： Prediction models

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Research on Combination Filling Method for multi-Dimensional Traffic Accident Missing Data 4

Research on Combination Filling Method for Multi-Dimensional...

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4th international conference on computer science and Blockchain, CCSB 2024

作者： Huang, Jiaoru Wen, Xiaoping Han, Junchi Zhang, Xiaoyan School of Electronic and Information Engineering Xi'an Technological University Xi'an China College of Environment and Life Science Weinan Normal University Weinan China School of Electronic and Information Engineering Xi'an University of Technological Xi'an China

ISBN: (纸本)9798350356502

At present, in the collection of traffic accident data, there are often missing data of different types and dimensions, which will lead to the accuracy of the analysis results decreased and the deviation increased. This paper proposes a combination filling method based on Maximum Likelihood Estimate (MLE), multi-layer perceptron (MLP), and Light Gradient Boosting Machine (LightGBM) is proposed to realize different processing of multidimensional data. Taking the example of traffic accidents in the UK in 2022 as the data source, the MLE-MLP-LightGBM combined filling method proposed in this paper is compared with MLP, LightGBM and KNN. The experimental results show that the combination filling method is more accurate than the other three filling methods, and provides a more universal method for data filling. © 2024 IEEE.

关键词： data filling LightGBM MLP Traffic accident

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MotionUVRNN: A Motion Capture Recurrent Neural Network for Wind Field Forecast Correction via U and V Components Modeling

IAENG International Journal of Computer Science

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IAENG international Journal of computer science 2024年第7期51卷 791-800页

作者： Gu, Zhan-Xin Wu, Jie Jin, Wei Han, Guo-Jing School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Anshan Meteorological Service Anshan114004 China

Wind field forecasting is crucial for human activities, but numerical weather prediction still has room to improve accuracy. In this paper, we formalize wind field forecast correction as a spatiotemporal sequence prediction task and propose MotionUVRNN, a recurrent neural network model suitable for wind field correction. The model consists of three elements: a feature decoupling module, an encoder, and a corrector. The feature decoupling module decouples the wind field into U-Wind (Wind component in x/longitude-direction) feature, V-Wind (Wind component in y/latitude direction) feature, and spatiotemporal feature. The encoder unifiedly models the motion trend and transient variation of the wind field. A novel long short-term memory dynamic capture unit (RS-LSTM) is proposed as the core part of the encoder to capture the different dynamic patterns of U-Wind and V-Wind. The corrector extracts and integrates multi-scale wind field information to capture regional features. By correcting the 3-12 hour wind field forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF;EC for short), the proposed model reduced the wind speed prediction error by 53.37% to 46.52%, and lowered the wind direction error by 21.99% to 13.26%. Moreover, this model surpasses the existing leading spatiotemporal sequence prediction models in wind field correction. © (2024) international Association of Engineers. All rights reserved.

关键词： Signal encoding

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