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A Stereovision EEG Channel Selection Method based on Cross Increment Entropy Maximization 14

A Stereovision EEG Channel Selection Method Based on Cross I...

引用

14th international conference on signal Processing systems, ICSPS 2022

作者： Zhou, Wei Zhang, Tingting Xia, Ling Liu, Xiaofeng College of Internet of Things Engineering Hohai University Changzhou213022 China

ISBN: (纸本)9798350336313

Electroencephalography (EEG) has been widely used in the research on stereo vision because it is a convenient neuroimaging technology. However, the multi-channel EEG signals make laboratory operations complicated, and the data analysis is also easily affected by redundant channels. Therefore, we proposed a method called Cross Increment Entropy Maximization (CIEM) for the selection of EEG channels. Results showed that the channels related to stereoscopic vision could be effectively preserved by CIEM. This method is significant for the removal of the interference of redundant channels and the facilitation of experimental process. © 2022 IEEE.

关键词： Entropy

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Personal image Classifier based Handy Pipe Defect Recognizer (HPD): Design and Test 7

Personal Image Classifier Based Handy Pipe Defect Recognizer...

引用

7th international conference on Intelligent Computing and signal Processing, ICSP 2022

作者： Moshayedi, Ata Jahangir Khan, Amir Sohail Yang, Shuxin Zanjani, S. Mohammadali Jiangxi University of Science and Technology School of Information Engineering Ganzhou China Islamic Azad University Smart Microgrid Research Center Najafabad Branch Najafabad Iran Islamic Azad University Najafabad Branch Department of Electrical Engineering Najafabad Iran

ISBN: (纸本)9781665478571

Pipelines are known as a traditional solution for transporting various media such as gas, oil, and water. But pipes are always combined with the defects. Some of these defects are caused by the manufactory process, while some defects occur after installation due to the type of medium and environmental condition. Currently, various methods are used to detect these defects from industry to the site, but vision-based systems due to huge amount of data that can capture machine learning development algorithms have more demand. In this research paper Personal image Classifier (PIC) is used as the machine learning method combine with the MIT APP inventor to make the handy system called Handy Pipe Defect assistant recognizer (HPD) to address the defect name and help the user to investigate and somehow correct their process of product in industry (as the production stage) or the possible change in usage place (as the maintenance stage). The HPD designed based on the hypothesis of having the handy, portable and available tool for the operator in industries as the quality control and site engineers. The HPD can classify the pipe defects, especially the welding type by taking the picture of the defect and give the user feedback. The HPD focuses on the welding defect with the manufactory source over 150 image number trained and tested with the real 28 sample image. The design process and training model for HPD described and result based on sample images from manufactory and real situation are shown the 100% defect name detection of the defect along with image quality affected by environment light and similarity between the defects. © 2022 IEEE.

关键词： Defects

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PERSON RE-IDENTIFICATION IN PANORAMIC VIEWS based ON BAYESIAN TRANSFORMERS 29

PERSON RE-IDENTIFICATION IN PANORAMIC VIEWS BASED ON BAYESIA...

引用

IEEE international conference on image Processing (ICIP)

作者： Song, Wenfeng Zhang, Xinyu Ye, Ying Gao, Yang Guo, Yifan Hao, Aimin Hou, Xia Beijing Informat Sci & Technol Univ Comp Sch Beijing Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

The panoramic view cameras offer more broad perspectives and continuous information for person re-identification (ReID). However, the panoramic-view videos suffer from objects distortion and bring more occlusion due to the fixed or moving capture points. This paper proposes a novel Bayesian Transformer Network (BTN) to adaptively capture the occlusion clues as Bayesian prior to guide the discriminative pedestrian-related feature extraction in the high-occlusion scenes. The Bayesian prior is built via a pre-trained CNN, which could recognize different occluded scenarios based on the severeness of noisy backgrounds. Moreover, to fully explore the occlusion prior, we propose to embed the semantic labels into a well-designed transformer network. By fostering the collaborative occlusion clues between the person and background, our method could achieve outstanding performance on both public benchmarks and panoramic view videos, which verifies the advantages of our BTN framework over existing methods.

关键词： Bayesian Prior Person Re-identification Panoramic View image

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A Framework for Mining and Applying Alarm Rules based on LLM

A Framework for Mining and Applying Alarm Rules Based on LLM

引用

international conference on Vision, image and signal Processing (ICVISP)

作者： Guoqing Li Qian Zhang Haijun Su Mengyuan Zhu Mengqi Liu Haokai Zhang Development Center China Information Technology Designing & Consulting Institute Co. Ltd Beijing China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Driven by 5G technology, network performance and management requirements are becoming increasingly sophisticated. However, massive and complex alarm information is also coming, posing severe challenges to network operation and maintenance. Accurately identifying alarm correlations and locating alarm root causes has become a core problem for operation and maintenance. In response to this challenge, this paper proposes a framework for mining and applying alarm rules based on large language models, which applies the text understanding capabilities of large language models to alarm processing and can mine uncommon alarm rules. Experimental results show that this framework significantly improves the accuracy and coverage of alarm rules, and improves the response speed and efficiency of network operation and maintenance.

关键词： Technological innovation Correlation Large language models Knowledge based systems signal processing algorithms signal processing Real-time systems Maintenance Communication networks Optimization

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Pedestrian Detection and Movement Direction Recognition with Convolutional Neural Network 1

引用

9th international conference on Pattern Recognition and Machine Intelligence (PReMI)

作者： Baheti, Bhakti Innani, Shubham Gajre, Suhas Talbar, Sanjay SGGSIET Ctr Excellence Signal & Image Proc Nanded Maharashtra India

ISBN: (数字)9783031127007

ISBN: (纸本)9783031126994;9783031127007

Pedestrian detection and movement intention recognition is one of the most challenging task and active research area in Advanced Driver Assistance systems (ADAS). It is important to detect the pedestrian and estimate future direction for safe integration of self driving cars on the existing roads. This paper proposes a system that would detect the pedestrians on the road or surroundings, and automatically recognize the direction in which they are moving viz. front, left and right by an end-to-end Convolutional Neural Network (CNN) based approach. We propose to tackle this problem as a two step approach. Firstly, pedestrians are detected by the advanced YOLOv3 architecture. The detection results are processed to recognize the movement direction. As real time performance is desirable for practical applications, we propose to use small CNN architectures like SqueezeNet, MobileNetV1, MobileNetV2 and ShuffleNet in the system. The proposed approach outperforms existing approaches in literature and provides a remarkable contribution to ensure safety in intelligent vehicles.

关键词： Convolutional Neural Network Object Detection YOLOv3 SqueezeNet MobileNet ShuffleNet

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Automated Detection of image-based Abusive Bengali Memes Using Ensemble and Deep Learning Models

Automated Detection of Image-Based Abusive Bengali Memes Usi...

引用

IEEE international conference on signal Processing, Information, Communication & systems (SPICSCON)

作者： Rukaiya Md Rabiol Amin Israt Jahan Syed Anayet Karim Department of CSE CCN University of Science and Technology Cumilla Bangladesh Department of CSE East West University Dhaka Bangladesh Department of Natural Science Port City International University Chattogram Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

In the realm of internet communication, memes have emerged as a powerful means of expression, swiftly disseminating emotions and ideas. The ability to classify memes, particularly in languages with regional nuances such as Bengali, poses unique challenges. The primary question of this study addresses how to efficiently classify Bengali memes, given their diverse and context-specific nature. The motivation for this research stems from the growing significance of memes as a form of internet communication and the need for effective methods to categorize and understand them. This work presents here a computational model of classifying Bengali memes using convolutional neural networks. Convolutional neural networks have the potential to learn, adapt, and rearrange themselves. Along-side employing traditional machine learning models-Support Vector Machine (SVM), Logistic Regression, and Random Forest for meme classification. The experiment illustrated a CNN model with Bi-LSTM layers is implemented, achieving a test accuracy of 69.49%. Additionally, Among the machine learning models, the Random Forest achieves the highest accuracy of 64%. Our study shows that this comparison between CNN-based and traditional models provides valuable insights for future research in meme analysis and content moderation.

关键词： Support vector machines Deep learning Adaptation models Logistic regression Accuracy Social networking (online) Computational modeling Vectors Convolutional neural networks Random forests

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Review of Computer Aided-Diagnosis (CAD) systems for MRI Gliomas brain tumors explorations based on Machine Learning and Deep learning 6

Review of Computer Aided-Diagnosis (CAD) Systems for MRI Gli...

引用

6th international conference on Advanced Technologies for signal and image Processing, ATSIP 2022

作者： Mzoughi, Hiba Njeh, Ines Slima, Mohamed Ben Hamida, Ahmed Ben Sfax University Atms Advanced Technologies for Medecine and Signal Enis Tunisia University of Sfax National Engineering School of Sfax Enis Tunisia University of Gabes Isimg Tunisia UR-ATISP Sfax University National School of Electronics and Telecommunications of Sfax Sfax Tunisia

ISBN: (纸本)9781665451161

During clinical routines, the manual Gliomas brain tumor exploration, via MRI anatomical modalities, is considered as critical task and time-consuming. Therefore, the Computer-aided diagnosis (CAD) system is highly recommended by Neuro-radiologists to ensure an accurate diagnosis and reduce the time required for *** to the revolution of Artificial Intelligence (AI) technology in the field of medical imaging analysis, several brain MRI CAD tools based on Deep Learning (DL) and Machine Learning (ML) have been presented in the literature. This work proposed a comprehensive review of recent trends of CAD tools for Gliomas brain tumors exploration with regards to DL and *** our study, we focused on three key steps involved generally in the implementation of CAD system mainly: the pre-processing, the segmentation, and the tumor grade classification. An objective evaluation via performance evaluation of state-of-the-art DL based-approaches s for MR images analysis is also addressed in this paper. We could confirm, based on the compared methods results, that a combination of a set of DL techniques will provide more accurate segmentation results rather than relying on a single specific technique. © 2022 IEEE.

关键词： Computer aided diagnosis

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Optimization of NB-IoT Uplink Resource Allocation via Double Deep Q-Learning 10th

Optimization of NB-IoT Uplink Resource Allocation via Double...

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10th international conference on Communications, signal Processing, and systems

作者： Zhong, Han Zhang, Runzhou Jin, Fan Ning, Lei Shenzhen Technol Univ Coll Big Data & Internet Shenzhen Peoples R China Shenzhen Univ Coll Appl Technol Shenzhen Peoples R China Shenzhen Winoble Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9789811903908;9789811903892

Recently, with the development of internet of Things (IoT) technology, the devices with the various features of traffic and mobility are increasing exponentially, and now the existing traditional resource allocation algorithms are becoming more and more difficult to meet the ever-increasing demand for terminal transmission. Aiming at the problem of radio resource fragment for complex access users of existing traditional algorithms, this paper proposes a dynamic scheduling algorithm based on Double Deep Q-learning Network(DDQN). At the same time, we design and simulate the NPUSCH transmission environment of the NB-IoT as the interactive environment of the agent. After training iterations, the resource utilization rate of the dynamic scheduling algorithm based on DDQN can be stabilized above 81%, which is better than traditional scheduling algorithms.

关键词： NB-IoT DDQN Reinforcement learning Resource allocation

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BTFCNN: Design of a brain tumor classification model using fused convolutional neural networks 10

BTFCNN: Design of a brain tumor classification model using f...

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10th international conference on Emerging Trends in Engineering and technology - signal and Information Processing, ICETET-SIP-22 2022

作者： Kushwaha, Vikas Maidamwar, Priya G H Raisoni College of Engineering Department of Computer Science & Engineering Nagpur India

ISBN: (纸本)9781665467414

Classification of brain tumour from magnetic resonance imaging (MRI) is a multidomain task that involves design of image denoising, segmentation, feature extraction, reduction of features, classification into different tumour types, and post-processing for application-specific use cases. Out of these tasks, feature extraction, feature reduction & classification are effectively performed via convolutional Neural Networks (CNN), but these models are highly generic, due to which their accuracy & precision performance is limited. To improve this performance, various pre-processing methods are combined with CNNs, which perform application-specific segmentation of images before their use for classification tasks. based on this observation, a segmentation model using Saliency maps is discussed in this text, which assists in application-independent segmentation of images based on their entropy. The segmented images are given to a novel fusion-based convolutional Neural Network (CNN) model, which combines VGGNet 16, AlexNet, Inception Net and Xception Net models. The VGGNet model is used for improving efficiency for Meningioma, because it requires largely varying features, AlexNet was observed to effectively classify Pituitary Adenoma due to its low variance-based classification capabilities, while Inception Net was observed to have better performance for Craniopharyngioma, because of its visible segmentation changes, and Xception Net was able to classify Schwannoma with better accuracy because of its high-capacity of feature extraction & selection under multiple image categories. It was observed that the proposed ensemble model had 97.4% accuracy for Meningioma, 97.9% accuracy for Pituitary Adenoma, 98.2% accuracy for Craniopharyngioma, and 97.6% accuracy for classification of Schwannoma & other disease types. Due to such a high performance, the proposed model is capable of being applicable for real-time use, and can be extended for other classification applications. The mo

关键词： image segmentation Magnetic resonance imaging Information processing Feature extraction Brain modeling Market research Real-time systems

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Application of internet of Thing (IoT) for Precision Farming

Application of Internet of Thing (IoT) for Precision Farming

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international IEEE conference on signal-image Technologies and internet-based System

作者： Apidat Songruk Somchai Arunrungrusmi Khanchai Tunlasakun Sirimonpak Suwannakhum Narong Mungkungline Wittawat Poonthong Toshifumi Yuji King Mongkut’s University of Technology Thonburi Bangkok Thailand Faculty of Industrial Education and Technology King Mongkut’s University of Technology Thonburi Bangkok Thailand Faculty of Education University of Miyazaki Miyazaki Japan

With the increasing popularity of the internet of Things (IOT) and devices getting smarter, this research has presented an idea to reform the weather information for precision farming. In this study, the information of weather forecast was prepared for air quality control systems using the sensor of network systems to increase the quality of organic vegetable in Thailand under the condition of Thailand weather The prototype model was described by the devices of embedded systems, which is gotten from the temperature and humidity sensor. Therefore, if there is any data from the input sensor, the users are allowed to access and granted to use this system, then, the log data are transmitted by the internet to the designation website by using a Wi-Fi module (ESP8266). As a result, it can be used the machine learning algorithms for implementing of monitor controller, which is applied for analyzing and collecting data to air quality control in green house

关键词： Temperature sensors Temperature measurement Weather forecasting Prototypes Predictive models Air quality Temperature control

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