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检索条件"主题词=Signal Processing Algorithms"

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Efficient Approach for Epileptic Seizure Classification and Detection based on Genetic Algorithm with CNN-RNN Classifier

Efficient Approach for Epileptic Seizure Classification and ...

引用

Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： R. Priyanka M. Sathesh T Tamil Selvi P. Janardhan Saikumar K Venkatachalam M. Raja Department of Networking and Communications College of Engineering and Technology School of Computing SRM Institute of Science and Technology KTR Campus Chengalpattu India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Instrumentation Engineering Sri Sai Ram Engineering College Chennai Tamil Nadu India Department of ECE Audi Sankara College of Engineering & Technology Gudur India Department of Electronics and Communication Engineering Audi Sankara College of Engineering and Technology Gudur India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

One of the most physically and psychologically damaging neurological conditions that affect people of all ages is an epileptic seizure. The abnormality should be recognized early so that the proper treatment is timely. It is possible only with advanced signal processing techniques to distinguish and predict epileptic patterns in which substantial effort is invested. Therefore, efficient seizure detection and classification methods are proposed for machine learning and deep learning algorithms. The proposed method uses deep and machine-learning algorithms for seizure detection and classification. The objective is to analyze the performance and efficiency of deep and machine learning classifiers by comparing the various classifiers. This proposed work uses 11,500 EEG data samples from the UCI machine learning repository. To suggest an Improved Fitness Function Genetic Algorithm (IGA) technique for optimal feature selection to improve the detection rate and CNN-RNN algorithm used as the classifier. The analysis proved that the hybrid CNNRNN(LSTM with GRU) classifier with GA-based feGAbased-lection provides better densification accuracy results of 98% when compared with all other classifiers.

关键词： Machine learning algorithms Accuracy signal processing algorithms signal processing Prediction algorithms Brain modeling Classification algorithms

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Statistically Improving K-means Clustering Performance

Statistically Improving K-means Clustering Performance

引用

IEEE signal processing and Communications Applications (SIU)

作者： Abdullah İhsanoğlu Mounes Zaval Özyeğin University İstanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

The traditional K-means algorithm is a cornerstone in unsupervised learning, providing a simple yet effective method for data clustering. However, its reliance on random initialization often leads to sub-optimal clustering results. This paper introduces an enhanced version of K-means algorithm, aimed at improving the clustering results. Our proposed methodology depends on identifying clusters that need to be partitioned and clusters that need to be merged through a series of statistical operation and iteratively resolve the problem leading to better clusters. We compare our proposed approach to K-Means and K-means++ algorithms on S-2, California housing prices, and EMNIST datasets showing performance improvements.

关键词： signal processing algorithms Clustering algorithms Partitioning algorithms Unsupervised learning signal resolution

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Near-Field Attitude and Location Estimation Based on Partitioned Large-Scale Antenna Array

Near-Field Attitude and Location Estimation Based on Partiti...

引用

International Conference on Wireless Communications and signal processing (WCSP)

作者： Mingchen Zhang Boyu Teng Yuqing Zheng Xiaojun Yuan National Key Lab. of Sci. and Tech. on Commun. Univ. of Elec. Sci. and Tech. of China Chengdu China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

This paper studies a passive localization system, where a single base station (BS) is employed to estimate the position and attitude of a mobile station (MS). The BS and the MS are equipped with uniform rectangular arrays (URAs), and the MS is located in the near-field region of the BS array. To avoid the difficulty of tackling this problem directly based on the near-field signal model, we establish a subarray-wise far-field received signal model. In this model, the entire BS array is divided into multiple subarrays to ensure that the MS is in the far-field region of each BS subarray. By exploiting the angles of arrival (AoAs) of the MS antennas at different BS subarrays, we formulate the attitude and location estimation problem under the Bayesian inference framework. Based on the factor graph representation of the probabilistic problem model, a message passing algorithm named array partitioning based pose and location estimation (APPLE) is developed to solve this problem. Numerical results demonstrate that the proposed APPLE algorithm outperforms other baseline methods in the estimation accuracy.

关键词： Location awareness Accuracy Message passing Estimation signal processing algorithms Receiving antennas Inference algorithms Partitioning algorithms Bayes methods Antenna arrays

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Mean Estimation with User-Level Privacy for Spatio-Temporal IoT Datasets

Mean Estimation with User-Level Privacy for Spatio-Temporal ...

引用

International Conference on signal processing and Communications, SPCOM

作者： Prajjwal Gupta V. Arvind Rameshwar Anshoo Tandon Novoneel Chakraborty IUDX Program Unit SID IISc Bengaluru Northeastern University IUDX

ISBN: (数字)9798350350456

ISBN: (纸本)9798350350463

This paper considers the problem of the private release of sample means of speed values from traffic datasets. Our key contribution is the development of user-level differentially private algorithms that incorporate carefully chosen parameter values to ensure low estimation errors on real-world datasets, while ensuring privacy. We test our algorithms on ITMS (Intelligent Traffic Management System) data from an Indian city, where the speeds of different buses are drawn in a potentially non-i.i.d. manner from an unknown distribution. We then apply our algorithms to a synthetic dataset, generated based on the ITMS data, having either a large number of users or a large number of samples per user. Here, we provide recommendations for the choices of parameters and algorithm subroutines that result in low estimation errors, while guaranteeing user-level privacy.

关键词： Estimation error Data privacy Privacy Urban areas signal processing algorithms signal processing Synthetic data

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Line Segmentation in Persian Texts in Double Columns Using Hierarchical Clustering algorithms

Line Segmentation in Persian Texts in Double Columns Using H...

引用

International Conference of signal processing and Intelligent Systems (ICSPIS)

作者： Afsaneh Ajodani Mansoor Fateh Computer Engineering Department Shahrood University of Technology shahrood Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

This study addresses the line detection problem in digital images containing Persian text, which presents unique challenges due to the language's specific structural characteristics. The challenges include line deviations, overlaps, varying fonts, and the presence of unique symbols in Persian scripts. The proposed method utilizes a combination of preprocessing techniques, connected components analysis, and the DBSCAN clustering algorithm to effectively separate text lines from two-column images. By applying Gaussian filtering, morphological operations, and density-based clustering, the method successfully detects and isolates individual lines of text. The results demonstrate high accuracy, achieving 98.31 % in line detection across a dataset of 100 images. The proposed approach illustrates significant potential for improving document management and text recognition within Persian language processing.

关键词： Accuracy Machine learning algorithms Text recognition Noise signal processing algorithms Clustering algorithms Symbols Machine learning Optimization Morphological operations

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Simulation of the Active Filter Tuning Systems Based on Delay Times, Periods and Phases Comparison

Simulation of the Active Filter Tuning Systems Based on Dela...

引用

IEEE International Conference on Electrical Engineering and Photonics (EExPolytech)

作者： Alexander Gubin Evgenii Balashov Peter the Great St.Petersburg Polytechnic University Saint Petersburg Russia

ISBN: (数字)9798350388879

ISBN: (纸本)9798350388886

In this paper is described an auto tuning methods of active RC filters. The tuning range and the tuning accuracy are analyzed. To verify the method, analog and digital part of the tuning system are cosimulated together. The simulation results shows that the time constant tuning accuracy is ±3.8% for tuning circuit based on delay times comparison, 5.5% for circuit based on period comparison and 3.9% for tuning circuit based on phase comparison by 4-bit code sequence and time-constant variation ±28%.

关键词： Accuracy Simulation Software algorithms signal processing algorithms Active filters Delays System software Logic Tuning Photonics

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Denoising Diffusion Model-Based Channel Estimation in IRS-Assisted ISAC Systems

Denoising Diffusion Model-Based Channel Estimation in IRS-As...

引用

International Conference on Wireless Communications and signal processing (WCSP)

作者： Lan Yi Jiansong Miao Tao Zhang Zhenhui Yuan Jiawen Kang Dusit Niyato Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China School of Software Engineering Beijing Jiaotong University Beijing China School of Engineering University of Warwick UK School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In Integrated Sensing and Communications (ISAC) systems assisted by Intelligent Reflecting Surfaces (IRS), precise channel estimation is crucial for optimal system performance and maximizing the potential benefits. The estimation problem, however, becomes intricate due to complex channel coupling and specific environmental conditions. This paper addresses these challenges by formulating channel estimation as a denoising problem and proposing a novel diffusion-based framework. This approach mitigates the impact of ill-posedness, which is typically problematic for general neural network (NN)-based techniques. By modeling the joint distribution and sampling from the learned posterior distribution, we accurately recover the channel coefficients from noisy pilot-based observations. Simulation results demonstrate significant performance improvements of the proposed method compared to both least-squares and NN-based benchmarks. Moreover, thanks to the carefully designed architecture and residual prediction strategy, the number of sampling steps in the diffusion reverse process is greatly reduced, enabling real-time CSI acquisition.

关键词： Wireless communication System performance Noise reduction Channel estimation signal processing algorithms Estimation Artificial neural networks Integrated sensing and communication Prediction algorithms Diffusion models

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Pedestrian Re-recognition Method Based on Pose Estimation and Regional Feature Fusion

Pedestrian Re-recognition Method Based on Pose Estimation an...

引用

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

作者： Fan Yu Yingzi Wei Kanfeng Gu School of Information Science and Engineering Shenyang Ligong University Shenyang China Shenyang Institute of Automation of the Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Aiming at the local feature misalignment caused by the complexity of the pedestrian re-recognition scene and the difficulty of extracting the pedestrian features with invariance under the background clutter, we present a pedestrian re-recognition method based on the human body's gesture estimation and regional feature fusion. The pose estimation algorithm is introduced into the network to build aligned pedestrian features; The low-level global features are fused. Fine-grained pose features are constructed and aligned through two-branch structure in order that the model focuses more on key information about the target and the structural information of the human body. The correlation of global and local features is abstracted. The results of experiment on two mainstream datasets, Market-1501 and DukeMTMC-ReID, indicate that our method exceeds the level of the original baseline PCB algorithm. Compared with the baseline algorithm, the first hit accuracy Rank-1 is improved by 7.6% in the DukeMTMC-ReID dataset. The mean average precision mAP is improved by 12.8%.

关键词： Deep learning Pedestrians Correlation Accuracy Pose estimation signal processing algorithms signal processing Feature extraction Complexity theory Clutter

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Leveraging Standardization in Graph Learning

Leveraging Standardization in Graph Learning

引用

European signal processing Conference (EUSIPCO)

作者： Thu Ha Phi Alexandre Hippert-Ferrer Florent Bouchard Arnaud Breloy LEME Univ. Paris Nanterre LASTIG Univ. Gustave Eiffel L2S CNRS Univ. Paris Saclay CEDRIC CNAM

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Standardization of the variables is a staple scaling tool that can be beneficial in most statistical learning tasks. In the context of graph learning, we propose to take further advantage of this preprocessing, as it yields an additional implicit normalization structure for the covariance matrix. For Gaussian graphical models, we propose a new algorithm that aims at estimating a sparse precision matrix while its inverse is constrained to be a (possibly low-rank structured) correlation matrix. The corresponding optimization problem is addressed by using the framework of Riemannian optimization. Simulations over various graph structures illustrate the interest of the proposed approach.

关键词： Manifolds Correlation Image edge detection Statistical learning signal processing algorithms Standardization signal processing Sparse matrices Covariance matrices Optimization

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Anomaly Detection of Time Series Based on SSA-LASSO

Anomaly Detection of Time Series Based on SSA-LASSO

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

作者： Hongtao Sun Jing Zang Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

In order to improve the accuracy of time series anomaly detection and classification, aiming at the problem of large feature dimensions of high-dimensional data, on the basis of studying the traditional feature selection algorithm of Lasso (Least Absolute Shrinkage and Selection Operator), combining Sparrow Search Algorithm (SSA) to optimize the Lasso feature selection algorithm, by optimizing the regularization parameters of Lasso and combining the strength of maximizing the penalty weight, improves the stability of the traditional LASSO algorithm in feature selection and effectively improves the robustness of the model. When LASSO performs feature selection, it retains the feature items with high coefficient correlation. At the same time, it uses the SSA algorithm to optimize LASSO feature selection to avoid the overfitting problem caused by too many features, so as to achieve the optimization ability of feature selection and the accuracy of classification. The Python experimental results have verified that this method can effectively improve the accuracy of classification.

关键词： Accuracy Time series analysis Diversity reception signal processing algorithms Feature extraction Search problems Stability analysis Classification algorithms Anomaly detection Optimization

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