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Robot Arm Design and Application Case Analysis Combining Artificial Intelligence and Bionics

Robot Arm Design and Application Case Analysis Combining Art...

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robotics and Technologies for Industrial Automation (ROBOTHIA), ieee international conference on

作者： Nuo Pang Mustaffa Halabi Azahari School of design Dalian University of Science and Technology Dalian China City Graduate School City University Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

With the application of artificial intelligence (AI) technology in medical diagnosis, treatment, and rehabilitation, combining AI with bionics to design more intelligent and adaptable robotic arms can improve the effectiveness of rehabilitation training. However, there are still many challenges in accurately identifying and simulating the movement patterns of the human arm. In this article, we investigated the application of bionics in the design of artificial joints and motion-assistive devices, as well as the role of machine learning and deep learning in motion pattern recognition and electromyography signal processing. In addition, combining AI with bionic technology, we designed and developed a bionic rehabilitation arm with highly intelligent and personalized features. The arm excels in motion pattern recognition and electromyography signal processing and can effectively enhance the effectiveness of rehabilitation training.

关键词： Training Service robots Biomimetics Arms signal processing Manipulators Solids Electromyography Pattern recognition Artificial intelligence

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Research on Residual Learning of Deep CNN for Image Denoising 7

Research on Residual Learning of Deep CNN for Image Denoisin...

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7th international conference on intelligent Computing and signal processing, ICSP 2022

作者： Gu, Feida Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

ISBN: (纸本)9781665478571

Image denoising is a classical but still popular research topic. Removing noise from corrupted images is an indispensable step for many practical applications. Deep Learning for image denoising has shown favorable performance. Residual Learning of Deep CNN (DnCNN) is proposed for image denoising, and shows desired performance. In the paper, based on DnCNN, some hyperparameters are adjusted for better performance. In addition, a validation step is added during the training process, which allows us to observe the training process to avoid overfitting. With the validation step during the training process, a novel method of learning rate adjustment is introduced to help train the best model for the network. The results show the adjusted network has a better performance compared to the baseline of DnCNN. © 2022 ieee.

关键词： Image denoising

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Voice Encryption using Chaotic signal 7

Voice Encryption using Chaotic Signal

引用

7th international conference on intelligent Computing and Control systems, ICICCS 2023

作者： Prabhakar, Abhinav Shetty, Anish S Tabassum, Kadiri Zafira Deb, Susmita Raghumanth, A. PES University EEE Department Bangalore India

ISBN: (纸本)9798350397253

The history of voice encryption dates back to the world war II when secure communication was used. During that period, noise was added to the voice signal in order to prevent data breach. Due to advancements in the field of internet, the risk of data breach is ever increasing. This in turn increases the need for secure data transmission. In recent decades, cyber security has been used to shield Internet data from adversaries. This has hence made randomness in data processing an essential part of the security. For a number of years, researchers and scientists have been interested in the field of randomness and chaos. The randomness of the data can be implemented using chaos theory. For voice encryption and decryption, this report uses chaotic circuits. Bhalekar-Gejji system has been used here as the master for encryption and Chen system has been used as the slave. The slave follows the master when the controller is turned on and hence the decrypted voice signal is obtained back. This model ensures secure voice transmission. © 2023 ieee.

关键词： Cryptography

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Multi-Functional RIS-Aided Anti-Jamming Communication in Semantic Integrated Satellite-Ground Networks 16

Multi-Functional RIS-Aided Anti-Jamming Communication in Sem...

引用

16th international conference on Wireless Communications and signal processing, WCSP 2024

作者： Sun, Yifu An, Kang He, Yuanzhi Cao, Haotong Zhong, Xudong Guo, Kefeng Zhu, Hongbo Sixty-third Research Institute National University of Defense Technology Nanjing China Institute of Systems Engineering Academy of Military Sciences Beijing China College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China School of Space Information Space Engineering University Beijing China

ISBN: (纸本)9798350390643

Integrated satellite-ground network (ISGN) emerges as a promising key component of the sixth-generation (6G) wireless networks due to its potential capabilities in providing ubiquitous connectivity for global coverage and reliable services. However, the inevitable existences of high-volume data, uncontrollable propagation environment, and malicious jamming attacks pose three significant bottlenecks for enabling efficient ISGN. With these focuses, we propose a novel framework of multi-functional reconfigurable intelligent surface (MF-RIS) aided semantic anti-jamming communication in ISGN. Under this framework, a semantic transceiver exhibits inherent robustness and data compression capability, and MF-RIS can customize the full-space wireless environment by leveraging its signal reflection, refraction, amplification, and energy harvesting functions, thereby achieving substantial global coverage, reliable connectivity, and high-rate transmission. Based on our proposed framework, we formulate a total semantic rate maximization problem considering the impacts of jammer's channel state information (CSI) imperfection, while maintaining the semantic similarity requirement, semantic rate target, and MF-RIS's self-sustainability. Then, by transforming the imperfect CSI into a worst-case one by exploiting a discretization method, we propose a fast-converging monotonic optimization algorithm that is combined with decoupling second-order cone programming to obtain a globally optimal solution with fewer feasibility evaluations. Numerical simulations demonstrate the superiority of our proposed framework and algorithms compared to various benchmarks. © 2024 ieee.

关键词： Satellite communication systems

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SPRGAN: Streamlined Progressive Refinement for Adversarial Point Cloud Video Upsampling

SPRGAN: Streamlined Progressive Refinement for Adversarial P...

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

作者： Zhang, Zhiyong Liu, Ruyu Wang, Chaochao Zhang, Xianchao Zhang, Jianhua Liu, Xiufeng School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Information Science and Technology Hangzhou Normal University Hangzhou China Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Zhejiang China Department of Technology Management and Economics Technical University of Denmark Lyngby Denmark

ISBN: (纸本)9798350368741

Getting dense, uniform, time-series point cloud data is critical for effective rendering. However, due to the limited computational power of edge devices, existing methods cannot achieve real-time results, which affects the visual quality of the consumer experience. To effectively address this issue, this paper presents a self-supervised adversarial upsampling method for point cloud video streams called SPR-GAN. In the generator, we design the Temporal Iterative Graph module to learn local features for each frame and captures long-range spatial information using three iterations of graph convolution operations. Then the Contextual Temporal Fusion module is developed to merge information between different frames, synthesizing temporal information and enriching the dynamic feature representation of the point cloud. Meanwhile, in the discriminator, we introduce the Efficient Shape module. Through dynamic graph convolution operations and stacked learning, it significantly improves the resolution efficiency of global shape information in point clouds. The final experiments show that the proposed method exhibits high practicality and superiority. The model achieves a good result on both the D-FAUST and DeformingThing4D-Animals datasets. © 2025 ieee.

关键词： point cloud sequence Point cloud upsampling self-supervised temporal aggregation

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KAN-Face: Efficient Resource Usage and Precision Lip-Sync in Talking Head Generation

KAN-Face: Efficient Resource Usage and Precision Lip-Sync in...

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

作者： Feng, Guanwen Jin, Siyu Qian, Zhihao Li, Yunan Miao, Qiguang School of Computer Science and Technology Xidian University Shaanxi Xi'an710071 China Xi'an Key Laboratory of Big Data and Intelligent Vision Xidian University Shaanxi Xi'an710071 China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi'an710071 China

ISBN: (纸本)9798350368741

Despite significant progress in NeRF-based talking head generation, problems like poor lip synchronization and inefficient resource usage remain. To solve these, we propose KAN-Face, a lightweight framework. In preprocessing, we introduce a Lip-Sync Enhancement Module that uses Wav2Lip to extract high-resolution audio features and map them to an explicit intermediate representation, ensuring precise lip movement alignment with the speaker's identity. These predicted lip-sync features are combined with fundamental audio-extracted lip features and injected into the rendering module to improve synchronization. For rendering, we introduce FastKAN to map spatial points to color values. As a variant of KAN, FastKAN's sensitivity to 3D scenes and efficient structure enable precise, fast color prediction. Our framework reduces resource consumption while enhancing lip-sync accuracy and facial reconstruction, making it ideal for talking head generation tasks in resource-limited settings. Project: https://***/KAN-Face/. © 2025 ieee.

关键词： Face Animation KAN Network Low Resource Neural Radiance Fields Talking Head Generation

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Using Machine Learning Regression Model to Predict the Optimum Election Algorithm for Parallel and Distributed Computing systems 3

Using Machine Learning Regression Model to Predict the Optim...

引用

3rd international conference on Smart Technologies, Communication and robotics, STCR 2023

作者： Dhariwal, Naman Vellore Institute of Technology Department of Software Systems Vellore India

ISBN: (纸本)9798350370867

Parallel and distributed computing has become a necessity when it comes to high-performance computing, artificial intelligence, big data analytics, machine learning, deep learning, signal processing and bioengineering. System engineers connect distributed systems, with large number of nodes, that then compute the solution for a problem concurrently. The distributed system has a leader that manages the overall coordination of these connected nodes. This research provides a novel approach to predict the most efficient and time-feasible election algorithm, that must be ran on a distributed system, using linear regression machine learning approach. This research implements the prediction process for three election algorithms and successfully predicts the most efficient and least time-consuming algorithm with 94.98% accuracy. Initially, the dataset for the regression model training was created by executing the three election algorithms parallelly using MPI running 16 parallel processors (cores). The research also presents three regression line equations, trained and modelled on this dataset, that can be used to predict the execution time for the three algorithms for a system with any number of nodes. The results are duly visualized, and regression lines are plotted for comparison. © 2023 ieee.

关键词： Forecasting

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Neurocognitive assessment under various human-robot teaming environments 46

Neurocognitive assessment under various human-robot teaming ...

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46th Annual international conference of the ieee Engineering in Medicine and Biology Society, EMBC 2024

作者： Packy, Anna L. Jayahankar, Jayesh Teymourlouei, Arya Stone, Joshua Oh, Hyuk Katz, Garrett E. Reggia, James A. Gentili, Rodolphe J. University of Maryland Neuroscience and Cognitive Science Program College Park United States University of Maryland Department of Computer Science College Park United States University of Maryland Department of Electrical & Computer Engineering College Park United States University of Maryland Department of Kinesiology College Park United States Syracuse University Department of Electrical Engineering & Computer Science Syracuse United States

ISBN: (纸本)9798350371499

Human-robot teaming has become increasingly important with the advent of intelligent machines. Prior efforts suggest that performance, mental workload, and trust are critical elements of human-robot dynamics that can be altered by the robot's behavior. Most prior human-robot teaming studies used behavioral analyses, but a limited number used neural markers, without the use of physical robots and complex tasks. Here we combine behavioral and EEG cortical dynamics to examine cognitive-motor processes when individuals complete a complex task under various team environments with a robot. The results revealed that altering the robot quality affected both behavioral and EEG dynamics. Task completion with an experienced robot led to greater team performance and human trust along with lower mental workload compared to an inexperienced teammate or when individuals performed alone. EEG changes suggest that different attentional processes were engaged when humans worked with the robot and performed alone, and that visual processing was more prominent when teaming with an inexperienced teammate. This work can inform human cognitive-motor processes and the design of robotic controllers in human-robot teams. © 2024 ieee.

关键词： Man machine systems

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On Some Applications of the Orthogonal systems of Functions 3

On Some Applications of the Orthogonal Systems of Functions

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3rd ieee international conference on System Analysis and intelligent Computing, SAIC 2022

作者： Voloshyna, Tetiana Karakhanov, Denys Voloshyn, Mychaylo Lesya Ukrainka Volyn National University Faculty of Information Technology and Mathematics Lutsk Ukraine Lesya Ukrainka Volyn National University Faculty of Geography Lutsk Ukraine

ISBN: (数字)9798350396744

ISBN: (纸本)9798350396744

When developing automatic signal processing systems, it is important to determine a set of parameters in order to optimize them and increase their efficiency. In the paper the modified Fourier transformation methods for digital signal processing have been used. A new approach to the construction of special polynomials has been offered by means of which the summing function is realized. The matrix methods of summation with rectangular matrices and their efficiency in comparison with the methods based on the use of triangular matrices have been investigated. Necessary and sufficient conditions for uniform convergence of the constructed trigonometric series for any continuous function on a segment in terms of a system of numerical coefficients have been established. The Lebesgue convergence of the constructed series for any summable periodic function has been studied. The obtained results can be used in digital signal processing systems based on the use of Fourier transformations. © 2022 ieee.

关键词： Digital signal processing

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Learnable Flow Model Conditioned on Graph Representation Memory for Anomaly Detection 48

Learnable Flow Model Conditioned on Graph Representation Mem...

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48th ieee international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Zhu, Ziyu Liu, Wenlei Deng, Zhidong Tsinghua University Department of Computer Science and Technology Beijing100084 China State Key Laboratory of Intelligent Technology and Systems China

ISBN: (纸本)9781728163277

Anomaly detection could be applied in a wide range of fields from industrial scene to medical imaging analysis. Although invertible flow models are developed to accomplish unsupervised anomaly detection, they are usually hard to train and have limited capabilities of accurately modeling the distribution of normal samples. To address this problem, we propose a novel enhanced flow model conditioned on graph representation memory (FlowGRM) for visual surface defect detection. In our FlowGRM, a graph neural network is trained to query graph embedding features from memory bank and incorporate them into a learnable flow model as conditional information. Such memorized conditional information, together with the invertible flow model, is jointly optimized to give rise to a probability density score. Experimental results obtained on the industrial visual anomaly detection datasets including MVTec and MTD show that the proposed model has state-of-the-art performance, which could even reach up to 99.6% and 99.2% AUROC scores on the above two bench-marks, respectively. Meanwhile, it requires fewer parameters and saves over 80% training time compared to existing flow models. © 2023 ieee.

关键词： Anomaly detection

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