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检索条件"主题词=Signal Processing Algorithms"

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K-Means Clustering Algorithm Based on GEO Optimization

K-Means Clustering Algorithm Based on GEO Optimization

引用

IEEE International Conference on signal processing, Communications and Computing (ICSPCC)

作者： Xu Zhang Zesheng Dan Yangyang Liu Deyan Li Xiaoting Zhang Chengkai Tang School of Electronics and Information Northwestern Polytechnical University Xi'an China China United Network Communications Co. Ltd Hainan China Unicom (Hainan) Industrial Internet Co. Ltd. Hainan China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

This paper proposes a k-means algorithm enhanced with GEO (Golden Eagle Optimizer) optimization to address the challenges encountered by traditional k-means algorithm, such as being highly sensitive to initial values and prone to getting stuck in local optima during clustering. By incorporating GEO's attack and cruising vectors into the original loss function and iterative process of k-means, our algorithm enhances its exploratory capability while retaining its original clustering prowess. Theoretical analysis and simulation results demonstrate that our method can further minimize the loss function and exhibit superior clustering performance.

关键词： Simulation signal processing algorithms Clustering algorithms signal processing Vectors Iterative algorithms Optimization

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Construction of a signal-class Experimental Platform Based on Python

Construction of a Signal-class Experimental Platform Based o...

引用

Computer Technologies (ICCTech), International Conference on

作者： Ying Lu Niankun Ming Fei Dai Mingyang Wang School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

ISBN: (数字)9798350395419

ISBN: (纸本)9798350395426

There are many advanced concepts and abstractions involved in signal-class courses, which require complex mathematical formulas to be deduced and proven. The derivation process of these formulas is difficult to follow, and there is a challenge in correlating them to the experimental results, making it difficult for students to understand. In response to the non-open-source nature of Matlab, Python, as a free and open-source programming language, has powerful features and rich third-party library resources, supporting various signal processing methods and algorithms, showing unique advantages and effects. Currently, existing experimental simulation platforms for signal courses are often designed for a certain type of course, such as Digital signal processing, Communication Principle, signals and Systems, etc. These platforms only provide limited cases and experiments, and the course content they cover is relatively single and independent, lacking the connection and integration of knowledge points between different courses. To improve this situation, this article chose PyQt5 as the development tool library, with its excellent performance and rich resources, to design and develop an experimental simulation platform for signal-class courses. This platform closely follows the teaching syllabus and divides the platform into multiple modules according to the division of teaching content. Each module contains corresponding course knowledge points and experimental simulations. The platform also has various teaching service functions such as online self-study and teaching guidance, which can help teachers make teaching more vivid and interesting, and can also help students better understand abstract theories and concepts. The platform has been put into teaching and learning in our college and other brotherly universities, which receiving positive feedback and praise from teachers and students.

关键词： Simulation Instruments Education signal processing algorithms Digital signal processing Libraries Python

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Credit Card Fraud Detection Using Machine Learning & Deep Learning algorithms

Credit Card Fraud Detection Using Machine Learning & Deep Le...

引用

International Conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Tamilkodi R Pravallika Satti Maduri N Ratalu S Saritha K SanthaRaju N Dept of CSE (AIML&CS) Godavari Global University Rajahmundry Andhra Pradesh CSE Godavari Global University Rajahmundry Andhra Pradesh

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Credit card Abuse has increased lately, coinciding with a rise in credit card use. Both card holders & fiscal institutions suffer significant fiscal losses as a result about credit card theft. star ideal about this exploration study is towards relating these frauds. material literature presents a wide range about machine literacy- grounded credit card recognition algorithms, similar as XG Boost, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, & Extreme Learning Method. ultramodern deep literacy algorithms are still needed towards lower fraud losses due towards their low delicacy. exercising most recent developments in deep literacy algorithms has been primary ideal in order towards achieve this. towards achieve effective results, a relative analysis about machine literacy & deep literacy ways was conducted. Better results are set up when exploration is reviewed. These include an delicacy about 99.9, perfection, a f1- score about 85.71, & AUC angles with optimum values about 98, 93, & 88, independently. proposed model outperforms state- of- the- art deep literacy & machine literacy ways considering credit card identification tasks. towards lower false negative rate, we also experimented with data balancing & deep literacy styles. ways handed are salutary considering relating credit card fraud in factual deals.

关键词： Deep learning Support vector machines Machine learning algorithms signal processing algorithms Stars signal processing Credit cards Data models Fraud Classification algorithms

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An Improved Image Defogging Algorithm Based on Dehazenet Network

An Improved Image Defogging Algorithm Based on Dehazenet Net...

引用

signal processing and Intelligent Computing (SPIC), International Conference on

作者： Jingping Bai Shiqing Ren Zhao Fang School of Information Science and Engineering Shenyang Ligong University Shenyang China Guangzhou College of Technology and Business Guangzhou China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

The image dehaze technology is particularly important. This paper adopts the traditional physical model and deep learning network model approach, using the improved dehazenet network, using three different scales of convolution kernel for feature extraction of the original foggy image, and in the feature learning process using 3×3 tiny convolutional kernel to increase the model’s depth; in order to avoid the network overfitting as well as to enhance the convergence speed of the network, the batch after convolution was added Batch Normalization processing is added after the convolution to estimate the transmittance t , and the original bootstrap filter is improved to a weighted bootstrap filter so as to better strengthen the edge information, and the quadtree method is used to estimate the atmospheric light value A instead of the original method to ensure a more accurate estimation of the atmospheric light. Using RESIDE dataset, the outdoor image with sky, the building image and the indoor restaurant image are used for the defogging experiments and the three values of PSRN, MSE and SSIM are evaluated.

关键词： Convolution Image color analysis Atmospheric modeling Computational modeling signal processing algorithms Scattering Information filters Feature extraction Distortion Kernel

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Efficient and Stable Test Time Adaptation for Dynamic Reality Scene

Efficient and Stable Test Time Adaptation for Dynamic Realit...

引用

6th International Conference on Intelligent Computing and signal processing (ICSP)

作者： Juxin Liao School of Electronic Information Engineering Foshan University Foshan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Test-time adaptation (TTA) is designed to adapt machine learning models to test sets with dataset shifts during the testing phase, without revisiting the training data. However, deploying TTA methods in real-world applications often leads to significant performance degradation. Specifically, TTA may fail to improve or even harm model performance when the test data exhibits the following characteristics: 1) mixed distribution shift, 2)online imbalanced label distribution shift, which are common scenarios in practice, 3) small batch sizes. While existing TTA methods can handle these situations to some extent, they struggle to balance both performance and efficiency, two critical aspects in real-world applications. In this paper, we apply the proposed Efficient and Stable Test-Time Adaptation (ES-TTA) method to address these real-world scenarios, demonstrating superior performance and efficiency compared to current state-of-the-art TTA methods. ES-TTA efficiently and stably enhances TTA in three ways: 1) filtering out high-entropy corrupted samples, 2) encouraging model weights to converge towards flat minima on the loss surface, 3) utilizing a sharpness-sensitive data selection strategy to improve TTA efficiency. Extensive experiments on the large ImageNet-c dataset show that our proposed ES-TTA can be effectively and efficiently applied to complex real-world scenarios.

关键词： Adaptation models Filtering Training data signal processing algorithms Machine learning signal processing Filtering algorithms Minimization Data models Testing

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Projected Block Coordinate Descent for Sparse Spike Estimation

Projected Block Coordinate Descent for Sparse Spike Estimati...

引用

European signal processing Conference (EUSIPCO)

作者： Pierre-Jean Bénard Yann Traonmilin Jean–François Aujol Univ. Bordeaux Talence France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

We consider the problem of recovering off-the-grid spikes from linear measurements. The state of the art Over-Parametrized Continuous Orthogonal Matching Pursuit (OP-COMP) with Projected Gradient Descent (PGD) successfully recovers those signals. In most cases, the main computational cost lies in a unique global descent on all parameters (positions and amplitudes). In this paper, we propose to improve this algorithm by accelerating this descent step. We introduce a new algorithm, based on Block Coordinate Descent, that takes advantages of the sparse structure of the problem. Based on qualitative theoretical results, this algorithm shows improvement in calculation times in realistic synthetic microscopy experiments.

关键词： Microscopy signal processing algorithms Matching pursuit algorithms Europe Estimation Computational efficiency signal resolution

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R-YOLOV4: Enhancing Unmanned Aerial Vehicle Target Detection Through Rotational Object Detection and Feature Alignment

R-YOLOV4: Enhancing Unmanned Aerial Vehicle Target Detection...

引用

signal processing and Intelligent Computing (SPIC), International Conference on

作者： Xilin Jing Yutong Zhang Jiapeng Pu Hongwei Gao Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

In response to the challenges of target detection from the perspective of unmanned aerial vehicles (UAVs), we propose the R-YOLOV4 Rotational Object Detection Algorithm. Specifically, R-YOLOV4 replaces horizontal detection boxes with rotational detection boxes to improve target shape conformity and detection performance. Furthermore, R-YOLOV4 replaces the backbone network to enhance the capability of detecting small targets. It also incorporates feature alignment structures and orientation detection structures into the detection head to improve the alignment and classification-regression tasks for rotational objects and anchor boxes. Experiments conducted on the DOTAv1.0 dataset demonstrate that the R-YOLOV4 method exhibits excellent performance in terms of both detection efficiency and accuracy.

关键词： Head Accuracy Shape Fuses signal processing algorithms Object detection signal processing Feature extraction Autonomous aerial vehicles Classification algorithms

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Sidelobe Suppression in Multistatic Radar Imaging Using Cyclic Richardson-Lucy Deconvolutions

Sidelobe Suppression in Multistatic Radar Imaging Using Cycl...

引用

European Radar Conference (EURAD)

作者： Marius Brinkmann Matthias M. Saurer Gerhard F. Hamberger Thomas F. Eibert Rohde & Schwarz GmbH & Co. KG Munich Germany Technical University of Munich Munich Germany

ISBN: (数字)9782874870798

ISBN: (纸本)9798350385137

A fast deconvolution-based beamforming approach for spatially-resolved localization of scattering centers of distributed targets in the near field is presented. With significantly lower computational complexity compared to conventional imaging approaches such as the standard backprojection algorithm, the presented approach allows to evaluate the entire angular range for the unambiguous downrange by performing cyclic fast Fourier transform-based deconvolutions in the spherical coordinate system.

关键词： Location awareness Deconvolution Three-dimensional displays signal processing algorithms Scattering Radar imaging Radar signal processing Multistatic radar Computational complexity Standards

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SoundSignature: What Type of Music do you Like?

SoundSignature: What Type of Music do you Like?

引用

Internet of Sounds, International Symposium on the

作者： Brandon James Carone Pablo Ripollés Department of Psychology New York University Music and Audio Research Laboratory (MARL) NYU Center for Language Music and Emotion (CLaME) NYU New York New York

ISBN: (数字)9798350366525

ISBN: (纸本)9798350366532

SoundSignature is a music application that integrates a custom OpenAI Assistant to analyze users' favorite songs. The system incorporates state-of-the-art Music Information Retrieval (MIR) Python packages to combine extracted acoustic/musical features with the assistant's extensive knowledge of the artists and bands. Capitalizing on this combined knowledge, SoundSignature leverages semantic audio and principles from the emerging Internet of Sounds (IoS) ecosystem, integrating MIR with AI to provide users with personalized insights into the acoustic properties of their music, akin to a musical preference personality report. Users can then interact with the chatbot to explore deeper inquiries about the acoustic analyses performed and how they relate to their musical taste. This interactivity transforms the application, acting not only as an informative resource about familiar and/or favorite songs, but also as an educational platform that enables users to deepen their understanding of musical features, music theory, acoustic properties commonly used in signal processing, and the artists behind the music. Beyond general usability, the application also incorporates several well-established open-source musician-specific tools, such as a chord recognition algorithm (CREMA), a source separation algorithm (DEMUCS), and an audio-to-MIDI converter (basic-pitch). These features allow users without coding skills to access advanced, open-source music processing algorithms simply by interacting with the chatbot (e.g., can you give me the stems of this song?). In this paper, we highlight the application's innovative features and educational potential, and present findings from a pilot user study that evaluates its efficacy and usability.

关键词： Semantics Ecosystems Music signal processing algorithms Feature extraction Chatbots Acoustics Multiple signal classification Usability Music information retrieval

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Experimental Approach for IR-UWB Radar-Based People Detection in Industry 4.0 Environments

Experimental Approach for IR-UWB Radar-Based People Detectio...

引用

European signal processing Conference (EUSIPCO)

作者： Zaynab Baydoun Roua Youssef Emanuel Radoi Stéphane Azou Tina Yaacoub David Espes Univ Brest CNRS Lab-STICC Brest France ENIB CNRS Lab-STICC Plouzané France Saint Joseph University Beyrouth Lebanon

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

An experimental investigation of people detection in Industry 4.0 environments, using impulse radio ultra-wideband signals, is described in this paper. Given the specific charac-teristics of the considered indoor environment and signals of interest, we first address clutter reduction, and then propose a two-paths signal processing architecture for constant false alarm rate (CFAR) detection of moving and stationary people, using matched filtering and Doppler focusing, respectively. A statistical study of the residual clutter and noise is carried out, and optimal threshold values are derived for these two use cases. The performance of the two CFAR detectors is then assessed using simulated data for different signal-to-noise ratios (SNR) and false alarm probabilities. Finally, their detection capabilities are explored using real data, measured in a heavily cluttered Industry 4.0 indoor environment, for a specially designed scenario with a person alternating between stop and motion phases.

关键词： Prevention and mitigation Refining signal processing algorithms Probability Indoor environment Fourth Industrial Revolution Doppler effect Clutter signal to noise ratio Ultra wideband technology

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