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Research on Conversion between Analog signal and Digital signal of Single Chip Microcomputer Based on Improved Genetic Algorithm 2

Research on Conversion between Analog Signal and Digital Sig...

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2nd international conference on Artificial Intelligence and Autonomous robot Systems, AIARS 2023

作者： Wu, Wenbing Ji, Hongjie Weifang Engineering Vocational College Weifang262500 China

ISBN: (纸本)9798350324358

Digital signal amplifier circuit is also a typical amplifier circuit, which is commonly used in various electronic engineering designs. In the process of analog signal processing, there is a very direct relationship between sampling frequency and signal frequency. In this paper, an improved GA (Genetic Algorithm) based conversion method between analog signal and digital signal of single chip microcomputer is proposed. We use the improved GA of binary matrix coding method to obtain the appropriate analysis wavelet. The basic idea is to establish a cost function for the error between the signal and its approximation, and transform the above problem into an optimization cost function. The optimization process varies with the analysis wavelet. We use improved GA to solve this optimization problem, so as to obtain the best approximation of the signal. The research results show that compared with the traditional processing system, the designed system can effectively control the mean square error of filtering after effectively integrating GA with the integrated single chip microcomputer control signal processing system, which proves that the system can effectively process the control signal. © 2023 IEEE.

关键词： Genetic algorithms

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Integrating vision-Language Supervision for Uniform Appearance Tracking 31

Integrating Vision-Language Supervision for Uniform Appearan...

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2024 international conference on Image processing

作者： Alansari, Mohamad Abughali, Ahmed Habash, Obadah Alnuaimi, Khaled Javed, Sajid Werghi, Naoufel Khalifa Univ Dept Comp Sci & Phys Sci Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350349405;9798350349399

Integrating detailed Natural Language (NL) descriptions with modern tracking technologies represents a significant and emerging field within Uniform Appearance (UA) crowd-tracking research, demonstrating substantial potential for future developments. A prominent challenge in this area is the lack of NL descriptions tailored for UA crowd tracking datasets. Existing datasets for Drone-Person Tracking in Uniform Appearance Crowd (D-PTUAC) lack essential textual annotations. Our study aims to bridge this gap by innovatively introducing comprehensive natural language descriptions for the D-PTUAC dataset, specifically designed for Uniform Appearance crowd tracking using drones. This enhancement aims to provide a richer understanding of the dataset and facilitate more effective utilization in research and applications related to drone-based crowd tracking. These descriptions are meticulously designed to include extensive information about the target entities, thereby significantly augmenting the dataset's depth and applicability. Our evaluations utilizing the latest state-of-the-art (SOTA) NL-based tracking algorithms showed us a remarkable competitive performance in tracking when juxtaposed against SOTA visual trackers benchmarked on the D-PTUAC dataset. This outcome highlights the critical role and efficacy of integrated language descriptions in enhancing the methodologies employed in UA crowd tracking.

关键词： Drone-Person Tracking in Uniform Appearance Crowd Natural Language processing (NLP) Visual Object Tracking

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Smart Recycling robot: vision-Driven Autonomous Water Bottle Collection System using YOLOv5

Smart Recycling Robot: Vision-Driven Autonomous Water Bottle...

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2023 international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Ghadekar, Premanand Shaligram, Yash Shimpi, Vaishnavi Shingade, Omkar Singh, Shristi Thakur, Shiya Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune411037 India

ISBN: (纸本)9798350344387

The growing concern for environmental sustainability has led to an increased focus on waste recycling and management. Many initiatives have been taken to promote waste recycling and garbage management. In this research, an idea of an autonomous water bottle collector is presented that combines computer vision to detect, approach, and pick up water bottles for recycling. This smart recycling robot can move around in the surroundings, detect the bottles lying on the ground, autonomously moving towards the detected bottle, and pick it. The system utilizes the YOLOv5 for object detection. It efficiently detects the bottles. Jetson Nano is used for processing the data and ESP 32 is used as a controller for the robot's movement. Experimental results demonstrate the system's ability to autonomously detect, approach, and pick up bottles with high accuracy and efficiency. The proposed water bottle collector robot offers a promising solution for automated recycling processes, reducing manual labour and promoting efficient waste. © 2023 IEEE.

关键词： Computer vision Jetson Nano YOLOv5

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Real-Time robot Trajectory Correction through Computer vision: a Welding Application in the Automotive Industry 6

Real-Time Robot Trajectory Correction through Computer Visio...

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6th international conference on robot Systems and Applications (ICRSA)

作者： Souza, Bruno J. da Silva, Tiago M. Mayer, Fernando R. Cecchetti, Vitoria B. Szejka, Anderson L. Freire, Roberto Z. Pontif Catholic Univ Parana PUCPR Curitiba Parana Brazil Univ Tecnol Fed Parana UTFPR Curitiba Parana Brazil

ISBN: (纸本)9798400708039

To ensure the optimal performance of welding robots in industrial applications, the elements to be welded must be accurately positioned. Additionally, elements that do not meet the quality standards can negatively influence the welding process. In this sense, failures may result in incorrect welding, leading to various structural issues within the assembly, where manual rework may be necessary to correct the errors. This work consists of developing an embedded image processing system to collect the appropriate coordinates where welds should be made on the structure of a car backrest, to adapt the trajectory of a welding robot, improving the quality and reducing the need for reworking the parts. To accomplish this application, an image acquisition system was developed. The image was processed so that it could be sent to the robot controller, which modifies the coordinates of certain movement points to correct the welding trajectory by applying an offset along the required points. To assess the effectiveness of the system, 160 weld points were tested, and 159 were found to be within the required margin of error, with only one point exhibiting a variation of 0.07 mm greater than the average in one direction. Another test performed was considering three wrong positioning of the pieces, the intention was to verify if the system could absorb these errors, thus proving the project's operation.

关键词： Adaptive Trajectory Communication Protocols Computer vision Industrial Welding robotics

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Analysis of Binocular Visual Perception Technology of Underwater robot 3

Analysis of Binocular Visual Perception Technology of Underw...

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3rd international conference on Image processing and Intelligent Control, IPIC 2023

作者： Zhuang, Sheng Zhao, Qiang Wang, Gang Song, Yingjie Jilin Institute of Chemical Technology Jilin China Baicheng Normal University Baicheng China

ISBN: (纸本)9781510668140

vision technology plays an important role when AUVs (Autonomous Underwater Vehicles) operate underwater. In this paper, the three-dimensional mode of binocular stereo vision is constructed to complete the positioning of the target. This article then explains the problem of distorted underwater images and introduces a method to correct distorted images. Based on underwater physical imaging, the underwater image processing methods are divided into underwater image enhancement and underwater image restoration. The research status of the two ways is analyzed and reviewed. The advantages and disadvantages of the above methods are summarized and discussed, and the future development trend is predicted. © 2023 SPIE.

关键词： Image reconstruction

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The Interface Information Reading of Railway Computer Interlocking Station Data Based on Image Analysis 24

The Interface Information Reading of Railway Computer Interl...

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international conference on Computer and Multimedia Technology (ICCMT)

作者： Zhang, Bo Li, Qiang Lanzhou Jiaotong Univ Minist Educ Key Lab Optoelect Technol & Intelligent Control Lanzhou 730070 Gansu Peoples R China Lanzhou Jiaotong Univ Inst Automat Control Lanzhou 730070 Gansu Peoples R China Lanzhou Jiaotong Univ Res Inst Lanzhou Gansu Peoples R China

ISBN: (纸本)9798400718267

In response to the immense workload and error-prone nature of manually confirming the correct names of devices on the graphical user interface (GUI) of railway station layouts in large stations, which leads to inefficiency and long processing times, this paper proposes a method for reading interface information of railway computer interlocking station using OpenCV image processing. Validated station layout diagrams are employed as inputs to the image recognition system. By analyzing the graphical features of signal devices, a data model is generated, and a recognition process is designed utilizing OpenCV computer vision library and algorithms for image recognition. This process correctly associates the graphical representations of devices in the interlocking station GUI with their corresponding device names. This method can validate station data, thereby improving its reliability.

关键词： upper computer interface image recognition signal devices station data

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Infrared and Visible Image Fusion with Hierarchical Human Perception

Infrared and Visible Image Fusion with Hierarchical Human Pe...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Yang, Guang Li, Jie Liu, Xin Zhong, Zhusi Gao, Xinbo School of Electronic Engineering Xidian University Xi'an China Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350368741

Image fusion combines images from multiple domains into one image, containing complementary information from source domains. Existing methods take pixel intensity, texture and high-level vision task information as the standards to determine preservation of information, lacking enhancement for human perception. We introduce an image fusion method, Hierarchical Perception Fusion (HPFusion), which leverages Large vision-Language Model to incorporate hierarchical human semantic priors, preserving complementary information that satisfies human visual system. We propose multiple questions that humans focus on when viewing an image pair, and answers are generated via the Large vision-Language Model according to images. The texts of answers are encoded into the fusion network, and the optimization also aims to guide the human semantic distribution of the fused image more similarly to source images, exploring complementary information within the human perception domain. Extensive experiments demonstrate our HPFusoin can achieve high-quality fusion results both for information preservation and human visual enhancement. Our code is available at https://***/SSyangguang/HPFusion. © 2025 IEEE.

关键词： Human Perception Image Fusion Large vision-Language Model

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Enhancing Remote Sensing vision-Language Models for Zero-Shot Scene Classification

Enhancing Remote Sensing Vision-Language Models for Zero-Sho...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： El Khoury, Karim Zanella, Maxime Gérin, Benoît Godelaine, Tiffanie Macq, Benoît Mahmoudi, Saïd De Vleeschouwer, Christophe Ayed, Ismail Ben UCLouvain Belgium UMons Belgium ÉTS Montreal Canada

ISBN: (纸本)9798350368741

vision-Language Models for remote sensing have shown promising uses thanks to their extensive pretraining. However, their conventional usage in zero-shot scene classification methods still involves dividing large images into patches and making independent predictions, i.e., inductive inference, thereby limiting their effectiveness by ignoring valuable contextual information. Our approach tackles this issue by utilizing initial predictions based on text prompting and patch affinity relationships from the image encoder to enhance zero-shot capabilities through transductive inference, all without the need for supervision and at a minor computational cost. Experiments on 10 remote sensing datasets with state-of-the-art vision-Language Models demonstrate significant accuracy improvements over inductive zero-shot classification. Our source code is publicly available on Github: https://***/elkhouryk/RS-TransCLIP. © 2025 IEEE.

关键词： remote sensing scene classification transductive inference vision-language models zero-shot

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5th international conference on 3D Imaging Technologies-Multidimensional signal processing and Deep Learning, 3DIT-MSP and DL 2023

5th International Conference on 3D Imaging Technologies-Mult...

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5th international conference on 3D Imaging Technologies-Multidimensional signal processing and Deep Learning, 3DIT-MSP and DL 2023

ISBN: (纸本)9789819751839

The proceedings contain 29 papers. The special focus in this conference is on 3D Imaging Technologies-Multidimensional signal processing and Deep Learning. The topics include: The Role and Effect of Deep Learning in Landscape Design Innovation;application of 3D Image Technology and Deep Learning in Landscape Design;The Influence of University Yoga Course Based on VR Technology on University Students’ Physical Fitness and Special Sports Skills;enhancing Mobile robot Path Planning Through Advanced Deep Reinforcement Learning;Innovative Application of VR Technology in Ceramic Art Design Exhibition;deep Learning-Based Species Classification Using Joint ResNet and Data Augmentation;simulation and Application of Digital Display Model Based on Computer vision and Virtual Reality;chinese Image Description Generation Model Based on Recurrent Fusion Encoding;rapid 3D Reconstruction of E-commerce Product Scenes Based on Neural Radiance Fields;research on Video Reuse and Structured Spatio-Temporal Big Data Technology Based on 3D Remote Sensing;evaluation of Slicing Scheme Based on Virtual 3D Printing;based on the Deep Study of 3D Printing Defect Detection Technology Research;three-Dimensional Hexagonal Braiding Machine Chassis Parametric Modeling;from Titles to Genres: An Exploration of Machine Learning Techniques in Movie Classification;construction and Optimization Strategy of Financial Shared Service Centers Based on Blockchain Technology in the Digitization Background;three-Dimensional Model Resume Transfer Technology for the Entire Lifecycle of Power Grid Equipment;application of Virtual Reality Technology in Physical Education Teaching Resources and Student Experience;deep Learning-Based Research on the Comprehensive Evaluation System for College Faculty Competence.

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CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer for Downstream vision Tasks Under Unknown Degradations

CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer fo...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Yuwei Wu, Yan Liu, Yanming Peng, Xinyue College of Electronic and Information Engineering Tongji University Shanghai China School of Software Technology Zhejiang University Hangzhou China College of Electronic and Information Engineering Southeast University Nanjing China

ISBN: (纸本)9798350368741

Extracting valuable visual cues for downstream vision tasks poses a particular challenge under unknown degradations. A straightforward solution is to preprocess images using image restoration methods, but their high computational complexity renders them unsuitable for real-time tasks. Recent efforts have aimed to enhance image representations optimized by downstream task losses. However, these approaches are confined to known single degradation scenarios, constraining their practical applicability in unpredictable environments. Therefore, we propose a Chain-of-thought Prompted Adaptive Enhancer, CPA-Enhancer, for enhancing important features crucial for downstream vision tasks under unknown degradations. Specifically, CPA-Enhancer progressively adapts its enhancement strategies under the step-by-step guidance of CoT prompts that encode degradation-related information. Overall, CPA-Enhancer is a plug-and-play lightweight enhancement model that can be integrated into any vision pipeline and trained with task-related losses, without any prior knowledge of the degradation type. Extensive experiments demonstrate that CPA-Enhancer significantly improves performance across various vision tasks under unknown degradations. The codes is available at https://***/zyw-stu/CPA-Enhancer. © 2025 IEEE.

关键词： adaptive enhancer chain-of-thought prompting unknown degradations

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