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检索条件"任意字段=7th International Conference on Wavelet Analysis and Pattern Recognition"

共 3576 条记录，以下是1-10 订阅

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MTSA-SNN: A Multi-modal Time Series analysis Model Based on Spiking Neural Network 27th

MTSA-SNN: A Multi-modal Time Series Analysis Model Based on...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Liu, Chengzhi Luo, Zihong Tao, Zheng Liu, Chenghao Xu, Yitao Huang, Zile Xi’an Jiaotong-Liverpool University Suzhou China Shanghai Normal University Shanghai China

ISBN: (纸本)9783031783401

Time series analysis and modelling constitute a crucial research area. Traditional artificial neural networks struggle with complex, non-stationary time series data due to high computational complexity, limited ability to capture temporal information, and difficulty in handling event-driven data. To address these challenges, we propose a Multi-modal Time Series analysis Model Based on Spiking Neural Network (MTSA-SNN). the Pulse Encoder unifies the encoding of temporal images and sequential information in a common pulse-based representation. the Joint Learning Module employs a joint learning function and weight allocation mechanism to fuse information from multi-modal pulse signals complementary. Additionally, we incorporate wavelet transform operations to enhance the model’s ability to analyze and evaluate temporal information. Experimental results demonstrate that our method achieved superior performance on three complex time-series tasks. this work provides an effective event-driven approach to overcome the challenges associated with analyzing intricate temporal information. Access to the source code is available at https://***/Chenngzz/MTSA-SNN. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： wavelet transforms

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CHART-Info 2024: A Dataset for Chart analysis and recognition 27th

CHART-Info 2024: A Dataset for Chart Analysis and Recognit...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Davila, Kenny Lazarus, Rupak Xu, Fei Rodríguez Alcántara, Nicole Setlur, Srirangaraj Govindaraju, Venu Mondal, Ajoy Jawahar, C.V. School of Computing DePaul University ChicagoIL60604 United States International Institute of Information Technology Hyderabad India CSE University at Buffalo BuffaloNY14260 United States Facultad de Ingenieria Universidad Tecnológica Centroamericana Tegucigalpa Honduras

ISBN: (纸本)9783031784941

Charts are tools for data communication used in a wide range of documents. Recently, the pattern recognition community has shown interest in developing methods for automatically processing charts found in the wild. Following previous efforts on ICPR’s CHART-Infographics competitions, here we propose a newer, larger dataset and benchmark for analyzing and recognizing charts. Inspired by the steps required to make sense of a chart image, the benchmark is divided into 7 different tasks: chart image classification, chart text detection and recognition, text role classification, axis analysis, legend analysis, data extraction, and end-to-end data extraction. We also show the performance of different baselines for the first five tasks. We expect that the increased scale of the proposed dataset will enable the development of better chart recognition systems. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Charts Dataset Graphic recognition

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wavelet Transform and Random Forest Based Identification of Interfering Signals from Electromagnetic Radiation 24

Wavelet Transform and Random Forest Based Identification of ...

引用

Proceedings of the 7th international conference on Information Technologies and Electrical Engineering

作者： Yixuan Guo Junlin He China University of Petroleum-Beijing at Karamay Karamay Xinjiang China

ISBN: (纸本)9798400707087

In this study, we propose an electromagnetic radiation interference signal identification method based on wavelet transform and random forest. By systematically preprocessing the electromagnetic radiation and acoustic emission signal data, including missing value processing, outlier detection and duplicate value exclusion, we ensure the quality of the data. Macro features, frequency-domain features and time-domain features are used in the feature extraction stage, where macro features describe the overall characteristics of the signals through statistics such as mean and variance, frequency-domain features analyse the spectral distribution of the signals through the Fourier transform, and time-domain features reveal the distribution pattern of the signals in time. Based on these features, we performed a multi-scale analysis of the signal using the wavelet transform and applied the random forest classification algorithm to build an identification model of the interference signal. By training and evaluating the model, we obtained a classification accuracy of up to 98%, indicating the high effectiveness of the method in interference signal recognition. the prediction results show that the interference signals in electromagnetic radiation and acoustic emission signals have obvious temporal regularity, concentrating on specific dates and time periods. the method provides a reliable tool and reference for interference signal detection and control in practical applications.

关键词： Electromagnetic radiation

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Visualizing NASDAQ Composite Index Historical Data Using Tableau 6th

Visualizing NASDAQ Composite Index Historical Data Using Tab...

引用

6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Safuan, Halawati Abd Jalil Nyein, Chit Ko Ko Riskhan, Basheer Ashfaq, Farzeen School of Computing and Informatics Albukhary International University Alor Setar Malaysia School of Computer Science SCS Taylors University Subang Jaya Malaysia

ISBN: (纸本)9789819780891

the study explores the visualization and analysis of historical data for the NASDAQ Composite Index (^IXIC) using Tableau. It examines trends in daily closing prices and volume distribution and compares performance with the NYSE index. Findings reveal significant fluctuations in the NASDAQ index over 22 years, suggesting potential future price changes. Forecasted graphs offer insights into potential economic decline and recovery, highlighting the dynamic nature of the market. Volume distribution analysis identifies periods of heightened trading activity. Comparative performance analysis emphasizes different trends observed in the NASDAQ and NYSE indices. Due to market unpredictability and external influences, additional research is crucial for validation. this study showcases the value of data visualization and analysis in finance through Tableau. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inflation

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Accuracy Assessment and Calibration of a 6-DOF Robotic Arm through Simulation Data analysis 6th

Accuracy Assessment and Calibration of a 6-DOF Robotic Arm T...

引用

6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Kailash, M. Jones, Aida Babu, K. Dinesh Praveen, S. Department of Artificial Intelligence and Data Science KCG College of Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819780891

this study focuses on refining the real-time positional accuracy of a 6-degree-of-freedom (6-DOF) robotic arm’s end effector by comparing the actual X, Y, and Z axis positions with those calculated from joint angles using DH parameters. through the application of linear regression analysis, variations between these datasets are identified and quantified. the derived variations are then used to adjust the DH parameter-based calculations, resulting in significantly improved accuracy for the robotic arm’s end-effector positioning. this approach offers a systematic method for enhancing the precision of calculated positional data, thereby contributing to the advancement of robotic arm operations across diverse fields. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Degrees of freedom (mechanics)

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Stability Appraisal of the wavelet-and-Regression Method for Unsteady Network Traffic Trend Highlighting

Stability Appraisal of the Wavelet-and-Regression Method for...

引用

international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Yury S. Bekhtin Kirill S. Balanev Elena A. Dubrovskaya Anton V. Pavlovich National Research University “MPEI” Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

In this paper, we investigate the stability of the suggested method for trend highlighting from network traffic data based on a combination of wavelet transform and regression analysis. To obtain stability appraisal when a network traffic is unsteady, the initial trend highlighted from the real network traffic data was distorted by noise components using three models: the autoregressive (AR) model, the Markov modulated Poisson process (MMPP) model and the ON-OFF model. the results of re-highlighting the trend have shown that the proposed method allows preserving the features of network traffic while minimizing distortions caused by the noise components.

关键词： wavelet transforms Noise Time series analysis Nonlinear distortion Telecommunication traffic Market research wavelet analysis Stability analysis Data models Appraisal

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Combined Speckle Removal Based on the Wiener Filter and wavelet Transform using Texture analysis

Combined Speckle Removal Based on the Wiener Filter and Wave...

引用

international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Y. S. Bekhtin V. T. Trinh Department of Automatics and Information Technologies in Control Ryazan State Radio Engineering University named after V.F. Utkin Ryazan Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

the existing despeckling methods typically encounter difficulties in achieving an optimal equilibrium between the suppression of noise and the preservation of critical image features, particularly in scenarios involving images that encompass both homogeneous (smooth) areas and heterogeneous (textured or edge-dense) regions. this paper proposes a hybrid approach combining wavelet filtering and Wiener filtering, leveraging their complementary strengths. the results of computer modeling have shown that the wavelet filters are effective in homogeneous regions by exploiting multi-scale sparsity, while the Wiener filter adaptively preserves textures in heterogeneous areas. By segmenting the image based on regional homogeneity, we apply the corresponding filter, achieving superior noise reduction in the sense both objective and subjective criteria.

关键词： wavelet transforms Power engineering Image segmentation Wiener filters Filters Image edge detection Noise Speckle wavelet analysis Image texture analysis

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A Novel and Intelligent Neuroimage analysis for Tumor Classification with thermo-Color Segmentation Using VGG-16 6th

A Novel and Intelligent Neuroimage Analysis for Tumor Classi...

引用

6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Mohapatra, Krushna Chandra Mund, Sai Arpit Senapati, Aman Kumar Biswal, Shalini Barik, Ram Chandra Muduli, Debendra Department of Computer Science and Engineering C.V. Raman Global University Odisha Bhubaneswar India

ISBN: (纸本)9789819780891

this paper presents a comprehensive approach for brain tumor detection by leveraging convolutional neural networks (CNNs) in conjunction with thermal analysis. the integration of CNNs with thermal data provides a novel and complementary perspective on brain tumor detection, enhancing the accuracy and reliability of diagnosis. the thermal analysis involves the use of infrared (IR) thermography to capture the heat distribution within the brain. By quantifying the temperature variations, the study aims to detect abnormal thermal patterns associated with brain tumors. these thermal patterns can provide valuable insights into tumor physiology, potentially enhancing the diagnostic process. this innovative approach has the potential to significantly impact the field of neuroimaging and brain tumor diagnosis, leading to earlier detection and more effective treatment strategies. the integration of CNN-VGG-16 and thermo-colored analysis holds the promise for improvement of the overall understanding of brain tumors and their heterogeneity. the proposed model outperforms with 98.58% accuracy than existing models. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Religious Sentiment analysis and Detection on Social Media 6th

Religious Sentiment Analysis and Detection on Social Media

引用

6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Riskhan, Basheer Hossain, Md Shakil Ashfaq, Farzeen Albukhary International University Kedah Malaysia School of Computer Science SCS Taylors University Subang Jaya Malaysia

ISBN: (纸本)9789819780891

this research addresses the rising concern of religious hate speech on Facebook, employing a comprehensive methodology to create a dataset by annotating statements from diverse religious backgrounds. After preprocessing steps like noise removal and tokenization, various linguistic features, including character N-grams, TF-IDF-weighted N-grams, and text quality indicators, were utilized. Multiple supervised learning algorithms, such as logistic regression, linear SVC, random forest, Naive Bayes, decision tree, and a GRU-based neural network model, were examined for optimal performance. Evaluation metrics like accuracy, precision, recall, and F1 score were employed. the study illuminates the prevalence and characteristics of religiously motivated hate speech on social media, emphasizing the importance of algorithm selection based on desired performance metrics. this research offers a valuable tool for academics and platform management to effectively identify and mitigate the impact of religious hate speech on digital platforms, supporting ongoing efforts to combat online discrimination. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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Effect of Wind Interference on U Shaped Tall Building Surrounded by Square Buildings in T Shaped pattern 7th

Effect of Wind Interference on U Shaped Tall Building Surrou...

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7th international conference on Construction, Real Estate, Infrastructure, and Project Management, ICCRIP 2023

作者： Mandal, Shanku Das, Arghyadip Bhattacharya, Satadru Dalui, Sujit Kumar Bhattacharjya, Soumya Department of Civil Engineering Indian Institute of Engineering Science and Technology Howrah Shibpur India

ISBN: (纸本)9789819649013

Extremely tall asymmetrical buildings in places with strong winds are susceptible to devastating lateral loads. A comprehensive wind analysis of a U plan shaped building is described here, which involves four interfering buildings with square floor plans placed at a T shaped pattern. the 1:300 scaled U shape building with length, width and limb width of 150 mm, 250 mm and 50 mm is analysed in the Ansys CFX module considering Computational Fluid Dynamics (CFD) for isolated and interference conditions. the interference condition is formed by arranging the square buildings of 150 mm at 300 mm distance from the primary building in T shaped pattern. the height of both primary and interfering buildings is kept at 500 mm. the wind angle from 0° to 360° at 30° intervals is considered to assess the wind effect on the building. the validation and mesh refinement study is carried out to produce reliable information. A detailed comparison of flow streamlines, pressure and force coefficient of isolated and interference conditions has been made. the interference factor of pressure has been computed. A significant variation of flow streamlines has been observed since the interfering buildings restrict the wind flow. this causes a huge variation in force and pressure coefficients. the interfering buildings produce suction on the building faces where the positive pressure has been measured in isolated conditions. this variation indicates the careful design of clad elements. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computational fluid dynamics

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