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检索条件"主题词=Signal Processing Algorithms"

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Research on personalized learning path recommendation model of artificial intelligence in new business

Research on personalized learning path recommendation model ...

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signal processing, Communications and Engineering Management Conference (ISPCEM), International

作者： Kun Liang Jia You Guangxi Vocational Normal University Guangxi China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Due to the flourishing expansion of online education, students' appetite for individualized learning experiences has significantly intensified. Consequently, we introduce a model that utilizes machine learning to recommend personalized learning paths. The model incorporates several algorithms such as collaborative filtering, content recommendation and reinforcement learning. First, we use collaborative filtering algorithms to analyze students' learning behavior data to identify users with similar interests and academic backgrounds. Then, through the content recommendation algorithm, we dynamically adjust the recommendation results according to the course characteristics and students' learning preferences. Finally, the reinforcement learning algorithm plays a role in optimizing the long-term learning path in the model, and constantly improves the recommendation strategy through real-time feedback. In the experiment, we used multiple real learning platform data sets for validation. The results show that the model has excellent performance in improving students' learning effect and satisfaction, and can effectively recommend learning paths that meet individual needs. Our research provides new ideas and methods for personalized learning of new business education, and promotes the deep integration of education and artificial intelligence.

关键词： Machine learning algorithms Heuristic algorithms Collaborative filtering Education signal processing algorithms Learning (artificial intelligence) Reinforcement learning signal processing Real-time systems Business

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Joint Decomposition of Multichannel signals with Multimarginal Optimal Transport

Joint Decomposition of Multichannel Signals with Multimargin...

引用

European signal processing Conference (EUSIPCO)

作者： Jean Malléjac Jérôme Idier Charles Soussen Xiangyi Li Université Paris-Saclay CNRS Centralesupélec L2S Gif-sur-Yvette France Nantes Université Ecole Centrale Nantes CNRS LS2N UMR 6004 Nantes France Aix Marseille Université CNRS Centrale Marseille Institut Fresnel Marseille France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

The joint decomposition of a sequence of signals is addressed, where each signal is coded within a predefined dictionary. We propose a convex variational formulation involving a regularization term based on optimal transport. The latter is designed so as to promote proximity between neighboring channels. Two kinds of regularization criteria are designed, corresponding to distinct cross-channel information on the transport of dictionary atoms from one channel to another. The resulting optimization problems are reformulated as quadratic programs and solved using a proximal algorithm. The proposed approach is analyzed for a multichannel deconvolution problem.

关键词： Measurement Dictionaries Deconvolution signal processing algorithms Europe Mean square error methods Atoms Trajectory Optimization signal resolution

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A High-Speed FPGA Implementation of the Multidimensional Reconciliation for Continuous Variables Quantum Key Distribution

A High-Speed FPGA Implementation of the Multidimensional Rec...

引用

Communication, Image and signal processing (CCISP), International Conference on

作者： Jialu Sun Hao Cheng Zeyuan Jin Yong Chen Genlong Chen Lihong Ren Xueqin Jiang School of Information Science and Technology Donghua University Shanghai China

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

For continuous variable quantum key distribution (CV-QKD) protocol, information reconciliation (IR) is a key step that can significantly affect the performance of CV-QKD. Because of multidimensional reconciliation suitable for low signal-to-noise ratio (SNR) and long transmission distance scenario, a parallel architecture for the multidimensional reconciliation in CV-QKD implemented on field programmable gate array (FPGA) is proposed in this paper. Using a binarized d-dimensional spherical vector c and a parallel computation of the coefficient a and rotation matrix M, the parallel architecture achieves 45.635 Mbps processing throughput at 250 MHz in the system clock, with frame error rate (FER) less than 0.001 when the SNR exceeds 0.71 dB.

关键词： Protocols signal processing algorithms Throughput Vectors Real-time systems Quantum key distribution Parallel architectures Field programmable gate arrays signal to noise ratio Pipeline processing

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Deep Learning based Real-Time Noise Removal in Retinal Fundus Images

Deep Learning based Real-Time Noise Removal in Retinal Fundu...

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signal processing and Advance Research in Computing (SPARC), International Conference on

作者： Gaurav Sharma K.M. Soni Basant Kumar Maninder Singh Amity Institute of Information Technology Amity University Noida INDIA Electronics and Communication Engineering Department MNNIT Allahabad Prayagraj INDIA

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Real-time noise removal in fundus images, which are images of the back of the eye (retina), is a critical task in various medical applications, particularly in ophthalmology. This study presents a deep learning approach for denoising retinal fundus images affected by gaussian and speckle noise. A diverse dataset comprising various retinal image acquisition conditions was utilized, with synthetic speckle and gaussian noise introduced at different levels to simulate in real-world scenarios. In the proposed approach, the noise is introduced by considering the variance of 0.5 and 1. Moreover, pre-processing steps were employed to mitigate noise impact, followed by feeding the images into a proposed CNN-based denoising architecture. The architecture’s performance was rigorously evaluated using quantitative metrics and expert clinician assessments. Results include an average Peak signal-to-Noise Ratio (PSNR) of 47.5 dB, an average Structural Similarity Index Measure (SSIM) of 0.90, and an average $R 2$ score of $71 \pm 0.7 \%$. Additionally, the BRISQUE score averaged 59.41. These results demonstrate the efficacy of the approach in enhancing image quality and preserving essential features, highlighting its potential for clinical applications in retinal imaging.

关键词： PSNR Noise reduction Noise signal processing algorithms Medical services Speckle Retina Real-time systems Ophthalmology Indexes

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A 7×4 Silicon Photonic Reconfigurable Optical Analog Processor with Algorithmic Calibration

A 7×4 Silicon Photonic Reconfigurable Optical Analog Proces...

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Optical Fiber Communication (OFC) Conference

作者： Md Jubayer Shawon Vishal Saxena Department of Electrical and Computer Engineering University of Delaware Newark DE

ISBN: (纸本)9798350377583

This work presents a large-scale 7x4 square optical mesh-based programmable analog optical processor, or optical FPGA, fabricated in a standard CMOS-compatible foundry. The processor employs an electronic backend with optimized on-chip monitors and microheaters for algorithmic calibration for the automatic configuration of optical circuits.

关键词： Optical fibers Integrated optics Scalability Optical device fabrication signal processing algorithms Silicon photonics Calibration

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Scanning Radar Super-Resolution Based on Fast Iterative Shrinkage Thresholding Network

Scanning Radar Super-Resolution Based on Fast Iterative Shri...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ding Yuan Juezhu Lai Jifang Pei Wenjing Wang Yin Zhang Yulin Huang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu P.R. China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Airborne scanning radar imaging is widely used both in military and civilian fields. However, the azimuth resolution of the imaging system is constrained by the size of the antenna. Iterative optimization super-resolution algorithms based on regularization can be used to overcome this limitation. But these methods demand manual tuning of parameters, which can be laborious. Deep unfolding network is a method of unfolding iterative optimization algorithms to deep learning networks, which combines the interpretability of iterative algorithms and the advantages of deep learning. Considering the excellent performance of the deep unfolding network in other signal processing tasks, this paper introduces the deep unfolding network based on fast iterative shrinkage-thresholding algorithm (FISTA) called FISTA-Net into the field of scanning radar imaging. For the task of scanning radar azimuth superresolution, we improve the model to fully learn the characteristics of radar azimuth data. Simulation results verify the effectiveness of the proposed method.

关键词： Azimuth Superresolution Neural networks signal processing algorithms Radar Radar imaging Iterative algorithms

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Crack Image Classification: Performance Comparison Among Different Machine Learning algorithms

Crack Image Classification: Performance Comparison Among Dif...

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International Conference on Image and signal processing and their Applications (ISPA)

作者： Benelmir Okba Dhiabi Fathi Electrical Engeneering Departement Faculty of Sciences and Technology University Mohamed Khider Biskra Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Classification of surface crack images is an ever-evolving area of research that aims to develop machine learning models capable of identifying and categorizing cracks in materials. This study concentrates on evaluating and comparing the effectiveness of various machine learning algorithms when employed in the classification of crack images. The objective is to identify the most suitable classification technique for this purpose. To conduct this comparative study, several machine learning algorithms are selected, such as k-nearest neighbors (KNN), decision trees, support vector machines (SVM), random forests and neural networks (NN). Each of these algorithms is trained and evaluated on a dataset of previously annotated crack images.

关键词： Support vector machines Machine learning algorithms signal processing algorithms Prediction algorithms Vectors Classification algorithms Data mining

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FPGA Implementation of Short Message Authentication Based on the SM4 Algorithm

FPGA Implementation of Short Message Authentication Based on...

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IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Kuangjie Jiang Ziming Wang Guangzu Liu Qian Sun School of Electronic Engineering an Optical Engineering Nanjing University of Science and Technology Nanjing Jiangsu School of Electronic Engineering and Optical Engineering Nanjing University of Science and Technology Nanjing Jiangsu Digital Signal Processing Research and Development Center Jiangsu YEE-Space Technology GO. LTD. Nanjing Jiangsu

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

This paper presents a FPGA implementation of SM4 Encryption and Decryption which has been used in the short message service (SMS) to solve the security risks of information leakage and illegal tampering in the short message service by converting the plaintext into ciphertext for transmission within short message communication system. This paper expands the key bit width based on the traditional SM4 so that the algorithm supports 256-bit keys. The extension of Key Size significantly complicates cryptanalysis efforts, making it exceedingly difficult for analysts to deduce algorithm characteristics from ciphertext, thereby substantially enhancing the security of the algorithm, but throughput (blocks processed per second) is unaffected. Additionally, this paper also introduces enhancements to the operational mode of the encryption algorithm, allowing the authentication module to encrypt and decrypt data of any length. This paper implements an authentication module based on FPGA for encryption and decryption, providing effective security for the transmission of short message data.

关键词： signal processing algorithms Authentication signal processing Message services Throughput Information leakage Encryption Security Field programmable gate arrays Message authentication

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Evaluating the Impact of Point Cloud Colorization on Semantic Segmentation Accuracy

Evaluating the Impact of Point Cloud Colorization on Semanti...

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International Conference on Vision, Image and signal processing (ICVISP)

作者： Qinfeng Zhu Jiaze Cao Yuanzhi Cai Lei Fan Department of Civil Engineering Xi'an Jiaotong-Liverpool University Suzhou China CSIRO Mineral Resources CSIRO Kensington Australia

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Point cloud semantic segmentation, the process of classifying each point into predefined categories, is essential for 3D scene understanding. While image-based segmentation is widely adopted due to its maturity, methods relying solely on RGB information often suffer from degraded performance due to color inaccuracies. Recent advancements have incorporated additional features such as intensity and geometric information, yet RGB channels continue to negatively impact segmentation accuracy when errors in colorization occur. Despite this, previous studies have not rigorously quantified the effects of erroneous colorization on segmentation performance. In this paper, we propose a novel statistical approach to evaluate the impact of inaccurate RGB information on image-based point cloud segmentation. We categorize RGB inaccuracies into two types: incorrect color information and similar color information. Our results demonstrate that both types of color inaccuracies significantly degrade segmentation accuracy, with similar color errors particularly affecting the extraction of geometric features. These findings highlight the critical need to reassess the role of RGB information in point cloud segmentation and its implications for future algorithm design.

关键词： Point cloud compression Accuracy Three-dimensional displays Image color analysis Semantic segmentation Image edge detection signal processing algorithms signal processing Feature extraction

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Low Latency Single-Cycle EOG Classification Using Cascaded ANN & CNN

Low Latency Single-Cycle EOG Classification Using Cascaded A...

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International Conference on Computer and Information Technology (ICCIT)

作者： Wakim Sajjad Sakib Abdullah Bin Shams Md. Rifatuddin Romel Raisa Tasrin Ridi Mohammad Liton Hossain Dept. of Electronics and Communication Engineering Institute Of Science and Technology Dhaka Bangladesh Dept. of Electrical & Computer Engineering University of Toronto Toronto Canada Dept. of Aeronautical Engineering Military Institute of Science and Technology Dhaka Bangladesh Dept. of Computer Science and Engineering American International University - Bangladesh Dhaka Bangladesh Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Electrooculography (EOG) is a noninvasive method to record the motion of the eye from the electrical potential difference between the cornea and retina. This technique, paired with AI, is extensively utilized predominantly in assistive technologies. The complexity of AI-driven EOG analysis scales with the number and type of expected eye movements. This challenge intensifies when the detection of eye movements relies solely on a single EOG cycle. An essential metric for real-time applications is the latency of an AI algorithm, which refers to the delay in making predictions. The latency should be smaller than the average human visual reaction time for any AI-driven system to promptly respond. The majority of EOG classification studies tend to overlook latency. In this experimental study, we overcome this multi-facet problem by proposing cascaded Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) architectures that have latency smaller than average human reaction time. Also, we developed a generalized & robust signal processing method to correctly identify single EOG cycle and deliver high prediction performances with either ANN or CNN. To demonstrate, we successfully classified nine distinct eye movements based only on single cycle EOG signal, with the performance metrics reaching close to 100% for both ANN & CNN, and latency well below the reaction time.

关键词： Measurement Electrooculography Wheelchairs signal processing algorithms Computer architecture Prediction algorithms Real-time systems Classification algorithms Convolutional neural networks Artificial intelligence

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