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IEEE Workshop on Multimedia Signal Processing

作者： Diana-Alexandra Sas Leandro Di Bella Yangxintong Lyu Florin Oniga Adrian Munteanu Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Romania Vrije Universiteit Brussel ETRO Brussels Belgium Computer Science Department Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

Since the introduction of the self-attention mechanism and the adoption of the Transformer architecture for computer Vision tasks, the Vision Transformer-based architectures gained a lot of popularity in the field, being used for tasks such as image classification, object detection and image segmentation. However, efficiently leveraging the attention mechanism in vision transformers for the Monocular 3D Object Detection task remains an open question. In this paper, we present LAM3D, a framework that Leverages self-Attention mechanism for Monocular 3D object Detection. To do so, the proposed method is built upon a Pyramid Vision Transformer v2 (PVTv2) as feature extraction backbone and 2D/3D detection machinery. We evaluate the proposed method on the KITTI 3D Object Detection Benchmark, proving the applicability of the proposed solution in the autonomous driving domain and outperforming reference methods. Moreover, due to the usage of self-attention, LAM3D is able to systematically outperform the equivalent architecture that does not employ self-attention.

关键词： computer vision Image segmentation Three-dimensional displays Attention mechanisms Object detection computer architecture Signal processing Transformers Feature extraction Machinery

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RAC-Based One-Stop Professional Course Teaching Model Exploration and Practice - Taking the Course of "Microcomputer Principles and Interface Technology" as an Example 6th

RAC-Based One-Stop Professional Course Teaching Model Explor...

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6th International Conference on computer science and Educational Informatization, CSEI 2024

作者： Wan, Xiaorong Li, Yingna Luo, Chuan Li, Chuan You, Jinguo Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming650500 China

ISBN: (纸本)9789819637379

In the context of the country’s requirement for academic disciplines to be aligned and mutually reinforcing with industry chains, innovation chains, and talent chains, the integration of education and industry has become an important direction for the reform of higher education. In response to the iterative demand for edge intelligence in the Intelligent Internet of Things (AIOT), the course "Microcomputer Principles and Interface Technology" is deeply integrated with industry and education, establishing a hierarchical model of resource, capability, and commercialization (RAC) layers. It adopts a "one-stop" teaching mainline that starts with microcomputer theory knowledge, relies on hardware development boards, project orientation, competition innovation, and product commercialization training "five points and one line", connects knowledge product channels, integrates ideological and political education in courses, and constructs a multi-dimensional and comprehensive evaluation system to achieve the integration of industry and education from universities to enterprises, classrooms to markets, and from knowledge to products. The innovative teaching mode of this course has achieved significant results in innovative practice. This innovative teaching mode can effectively achieve the knowledge, ability, and quality goals of cultivating new engineering talents, and effectively improve the quality of talent cultivation and teaching. This teaching reform and innovation provide a reference and idea for further exploring the talent cultivation mode of schools and enterprises. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Teaching

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Relationship and contribution rate estimation analysis based on the EEMD for complicated mechanical signals

Relationship and contribution rate estimation analysis based...

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2023 International Workshop on Signal Processing and Machine Learning, WSPML 2023

作者： Zhang, Jian Yu, Gang Tang, Jian Liu, Pengbo School of Computer Science Nanjing University of Information Science and Technology Nanjing China State and Beijing Key Laboratory of Process Automation in Mining and Metallurgy Beijing China BGRIMM Technology Group Co. Ltd. Beijing China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781510671928

During the process of mineral grinding, mill load parameters (MLPs) determine the information of the mechanical signals. So, online MLPs detection is one of the key factors for improving the production efficiency of mineral processing plants. In this paper, the correlation between the multichannel mechanical signal and the different MLPs is explored by the power spectral density. Furthermore, the contribution rate of the multisource and multicomponent mechanical signals to the MLPs and mill load is measured on the basis of the correlation coefficient. Finally, a prediction model for MLPs can be constructed according to an adaptive decomposition strategy and the appropriate sub-signals. © 2023 SPIE. All rights reserved.

关键词： Empirical mode decomposition

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Estimation of the Impact of Dimensionality Reduction of the Feature Space on the Efficiency of Movements Classification Based on Surface Electromyography

Estimation of the Impact of Dimensionality Reduction of the ...

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Neural Networks and Neurotechnologies (NeuroNT), II International Conference on

作者： Ivan V. Kuznetsov Yulia V. Ponomarchuk Department of Computer Science and Computer Graphics Far Eastern State Transport University Khabarovsk Russia Faculty of Control Automation and Telecommunications Far Eastern State Transport University Khabarovsk Russia

ISBN: (数字)9798350363739

ISBN: (纸本)9798350363746

One of the actual tasks of movement classification based on electromyography is the choice of the most informative set of features characterizing the movements. Increasing the number of features may decrease the learning performance of classifiers and reduce the efficiency of gesture classification. This paper is devoted to study the possibilities of improving the movement's classification efficiency by using the feature space dimensionality reduction methods. Classifiers based on Support Vector Machines (SVM)., Decision Trees (DT)., Random Forest (RF) and K-nearest neighbors (KNN) were considered., and Principal Component Analysis method (PCA) was used to dimensionality reduction. 3% improvement in classification performance was achieved using PCA and KNN.

关键词： Dimensionality reduction Support vector machines Radio frequency Thumb Electromyography Sensors Task analysis

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A multi-view comparative learning method for spatial transcriptomics data clustering

A multi-view comparative learning method for spatial transcr...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Peng, Wei Zhang, Zhihao Dai, Wei Fu, Xiaodong Liu, Li Liu, Lijun Yu, Ning Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming China State University of New York The College at Brockport Department of Computing Sciences BrockportNY14422 United States

ISBN: (纸本)9798350337488

Clustering individual cells or spots based on their gene expression profiles in a spatial context is a powerful approach to uncovering the underlying biological diversity and relationships among cells. The intricate information within spatial transcriptomics data demands sophisticated algorithms that effectively integrate gene expression, cell position, and tissue image data for accurate cell or spot clustering. This work proposes a Multi-View Comparative Learning method for clustering Spatial Transcriptomics data (MVCLST). MVCLST first builds on two data views using gene expression profiles, cell space coordinates, and image features. Then it employs four different encoders to capture the common and private features of the two views. The model employs a contrastive learning loss to encourage effective interaction between the two views and ensure feature consistency. The shared and private features from both views are fused using corresponding decoders. Finally, the model employs the Leiden algorithm for downstream clustering of the learned features. We test the MVCLST method on a human dorsolateral prefrontal cortex dataset. The results show that MVCLST outperforms other state-of-the-art methods in most cases. Additionally, the clusters identified by MVCLST align closely with manual annotations and established neuroscience definitions. © 2023 IEEE.

关键词： contrast learning multiview learning spatial transcriptome data clustering

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Optimized Convolutional Neural Network Models for Skin Lesion Classification

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computers, Materials & Continua 2022年第2期70卷 2131-2148页

作者： Juan Pablo Villa-Pulgarin Anderson Alberto Ruales-Torres Daniel Arias-GarzónMario Alejandro Bravo-Ortiz Harold Brayan Arteaga-Arteaga Alejandro Mora-RubioJesus Alejandro Alzate-Grisales Esteban Mercado-Ruiz M.Hassaballah Simon Orozco-Arias Oscar Cardona-Morales Reinel Tabares-Soto Department of Electronics and Automation Universidad Autónoma deManizalesManizales170001Colombia SEDMATEC Corporación Universitaria Autónoma de Nari駉Pasto520002Colombia Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQena83523Egypt Department of Computer Science Universidad Autónoma de ManizalesManizales170001Colombia Department of Systems and Informatics Universidad de CaldasManizales170001Colombia

Skin cancer is one of themost severe diseases,andmedical imaging is among themain tools for cancer *** images provide information on the evolutionary stage,size,and location of tumor *** paper focuses on the classification of skin lesion images considering a framework of four experiments to analyze the classification performance of Convolutional Neural Networks(CNNs)in distinguishing different skin *** CNNs are based on transfer learning,taking advantage of ImageNet ***,in each experiment,different workflow stages are tested,including data augmentation and fine-tuning *** CNN models based on DenseNet-201,Inception-ResNet-V2,and Inception-V3 are proposed and compared using the HAM10000 *** results obtained by the three models demonstrate accuracies of 98%,97%,and 96%,***,the best model is tested on the ISIC 2019 dataset showing an accuracy of 93%.The proposed methodology using CNN represents a helpful tool to accurately diagnose skin cancer disease.

关键词： Deep learning skin lesion convolutional neural network data augmentation transfer learning

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LAM3D: Leveraging Attention for Monocular 3D Object Detection

arXiv

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arXiv 2024年

作者： Sas, Diana-Alexandra Di Bella, Leandro Lyu, Yangxintong Oniga, Florin Munteanu, Adrian Technical University of Cluj-Napoca Faculty of Automation and Computer Science Cluj-Napoca Romania Vrije Universiteit Brussel ETRO Brussels Belgium Technical University of Cluj-Napoca Computer Science Department Cluj-Napoca Romania

关键词： Image segmentation

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Efficient One-Stage Deep Learning for Text Detection in Scene Images

EEA - Electrotehnica, Electronica, Automatica

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EEA - Electrotehnica, Electronica, Automatica 2024年第4期72卷 65-71页

作者： Tibermacine, Ahmed Guettala, Walid Tibermacine, Imad Eddine Faculty of Exact Sciences Natural And Life Sciences University Mohamed Khider of Biskra Algeria Computer Science Department University Mohamed Khider of Biskra Algeria Department of Computer Automation and Management Engineering Sapienza University of Rome Italy

Text detection in natural images is a crucial task for extracting and recognizing valuable information, but it comes with significant challenges. Traditional image processing methods often rely on synthetic features, which may not be suitable for handling the diverse range of unstructured text scenarios encountered in real-world environments. To overcome this challenge, deep learning techniques have emerged as a powerful tool for adaptively learning and extracting features to digitize text. In this paper, we introduce a novel approach based on the YOLOv5 architecture for text detection. Our method leverages the strengths of YOLOv5, including its efficient and lightweight architecture, multi-scale prediction capability, and advanced data augmentation techniques. By employing these features, our proposed model can accurately identify regions in an image that are more likely to contain text. Through extensive experimentation on popular benchmark datasets, we have evaluated the performance of our proposed model. The results have demonstrated that our method outperforms state-of-the-art approaches in terms of both detection efficiency and accuracy. This indicates the effectiveness of our approach in handling the challenges posed by text detection in natural images. © Editura ELECTRA 2024. All rights reserved.

关键词： Deep Learning Natural scene image Text detection Yolov5

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A versatile framework for analyzing galaxy image data by incorporating Human-in-the-loop in a large vision model

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Chinese Physics C 2024年第9期48卷 176-187页

作者： Ming-Xiang Fu Yu Song Jia-Meng Lv Liang Cao Peng Jia Nan Li Xiang-Ru Li Ji-Feng Liu A-Li Luo Bo Qiu Shi-Yin Shen Liang-Ping Tu Li-Li Wang Shou-Lin Wei Hai-Feng Yang Zhen-Ping Yi Zhi-Qiang Zou National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China College of Electronic Information and Optical Engineering Taiyuan University of TechnologyTaiyuan 030024China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 101408China Key lab of Space Astronomy and Technology National Astronomical ObservatoriesBeijing 100101China School of Computer Science South China Normal UniversityGuangzhou 510631China CAS Key Laboratory of Optical Astronomy National Astronomical ObservatoriesBeijing 100101China University of Chinese Academy of Sciences NanjingNanjing 211135China University of Science and Technology Beijing Beijing 100083China Shanghai Astronomical Observatory Chinese Academy of SciencesShanghai 200030China School of Computer and Information Dezhou UniversityDezhou 253023China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 30024China School of Mechanical Electrical and Information EngineeringShandong UniversityWeihai 264209China Nanjing University of Posts&Telecommunications Nanjing 210023China School of Science University of Science and Technology LiaoningAnshan 114051China

The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the ***,effectively analyzing this vast amount of data poses a significant *** response,astronomers are turning to deep learning techniques,but these methods are limited by their specific training sets,leading to considerable duplicate *** overcome this issue,we built a framework for the general analysis of galaxy images based on a large vision model(LVM)plus downstream tasks(DST),including galaxy morphological classification,image restoration object detection,parameter extraction,and *** the low signal-to-noise ratios of galaxy images and the imbalanced distribution of galaxy categories,we designed our LVM to incorporate a Human-in-the-loop(HITL)module,which leverages human knowledge to enhance the reliability and interpretability of processing galaxy images *** proposed framework exhibits notable fewshot learning capabilities and versatile adaptability for all the abovementioned tasks on galaxy images in the DESI Legacy Imaging *** particular,for the object detection task,which was trained using 1000 data points,our DST in the LVM achieved an accuracy of 96.7%,while ResNet50 plus Mask R-CNN reached an accuracy of 93.1%.For morphological classification,to obtain an area under the curve(AUC)of~0.9,LVM plus DST and HITL only requested 1/50 of the training sets that ResNet18 *** addition,multimodal data can be integrated,which creates possibilities for conducting joint analyses with datasets spanning diverse domains in the era of multi-messenger astronomy.

关键词： artificial intelligence large vision model human-in-the-loop astronomy galaxies

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Fine-grained distributed averaging for large-scale radio interferometric measurement sets

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Research in Astronomy and Astrophysics 2021年第4期21卷 93-100页

作者： Shou-Lin Wei Kai-Da Luo Feng Wang Hui Deng Ying Mei Faculty of Information Engineering and Automation Computer Technology Application Key Lab of Yunnan ProvinceKunming University of Science and TechnologyKunming 650500China Center for Astrophysics Guangzhou UniversityGuangzhou 510006China

The Square Kilometre Array(SKA)would be the world’s largest radio telescope with eventually over a square kilometre of collecting ***,there are enormous challenges in its data *** use of modern distributed computing techniques to solve the problem of massive data processing in the SKA is one of the most important *** this study,basing on the Dask distribution computational framework,and taking the visibility function integral processing as an example,we adopt a multi-level parallelism method to implement distributed averaging over time and *** Array was used to implement super large matrix or arrays with supported *** maximize the usage of memory,we further exploit the data parallelism provided by Dask that intelligently distributes the computational load across a network of computer agents and has a built-in fault tolerance *** validity of the proposed pattern was also verified by using the Common Astronomy Software Application(CASA),wherein we analyze the smearing effects on images reconstructed from different resolution visibilities.

关键词： techniques:interferometric methods:data analysis methods:numerical instrumentation:interferometers

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