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2D to 3D pedestrian detection algorithm based on deep learning 3

2D to 3D pedestrian detection algorithm based on deep learni...

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3rd international conference on Advanced Algorithms and signal Image processing, AASIP 2023

作者： Wang, Feng Ni, Weichuan Zou, Jiajun Wan, Zhiping Guangzhou Xinhua University Guangzhou China

ISBN: (纸本)9781510668522

Based on the actual demand of the public for 3D content generation technology and targeting the problems existing in traditional 2D to 3D image conversion methods, we propose a 2D to 3D pedestrian detection algorithm based on deep learning. The algorithm uses object segmentation to first extract the human body's 2D outline from the input image. It then combines shallow features of the image to use a fully connected layer to map latent vectors to low-dimensional mesh vertex feature vectors, ultimately generating a triangle mesh for the 3D model. By alternately using SpiralConv and upsampling operations, the 3D mesh of the pedestrian can be generated more accurately. Experimental results show that the algorithm can accurately construct a 3D model of a pedestrian and has real-Time feedback capability. The algorithm is suitable for real-Time monitoring scenarios such as action recognition. Compared to traditional 2D to 3D image conversion methods, the algorithm uses deep learning to automatically learn features, which has higher accuracy and transferability. Additionally, the algorithm extracts 2D contour information of the human body using object segmentation, which improves the accuracy of generating 3D meshes. We believe that this algorithm has an important role to play in the development of 3D content generation technology. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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IoT Based Air Quality and Noise Pollution Monitoring System

IoT Based Air Quality and Noise Pollution Monitoring System

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Amit Datta Md. Monjurul Islam Md. Sabbir Hassan Kuasha Bosu Aka Istiaque Ahamed Abir Ahmed Department of Electrical and Electronics Engineering (EEE) American International University - Bangladesh (AIUB) Dhaka Bangladesh

Air pollution is the presence of contaminants or poisonous substances that interfere with human health or welfare or create destructive natural impacts. With the fast improvement of communication innovations, remote sensing technology, and air pollution monitoring systems, it is possible to check the air concentration and take appropriate action. In this paper, a system is developed that can monitor different parameters, like O 3 , NO 2 , CO 2 , and temperature in real time. The control system converts all the data to human-readable values. With the development of a communication system, all data is stored in a cloud database. A decision-making calculation algorithm is developed using advanced technology like cloud computing. Further, a visual platform was created to allow the user to access the data remotely.

关键词： Temperature sensors Temperature measurement Industries Cloud computing Visualization Technological innovation signal processing algorithms

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3rd EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2022

3rd EAI International Conference on Cognitive Computing and ...

引用

3rd EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2022

ISBN: (纸本)9783031289743

The proceedings contain 22 papers. The special focus in this conference is on Cognitive Computing and Cyber Physical Systems. The topics include: Performance Evaluation of Fast DCP Algorithm for Single Image Dehazing;Non Destructive Analysis of Crack Using Image processing, Ultrasonic and IRT: A Critical Review and Analysis;ioT Enabled Driver Compatible Cost-Effective System for Drowsiness Detection with Optimized Response Time;Voice Based Objects Detection for Visually Challenged Using Active RFID Technology;depth Estimation and Navigation Route Planning for Mobile Robots Based on Stereo Camera;Water Level Forecasting in Reservoirs Using Time Series Analysis – Auto ARIMA Model;water Quality Monitoring Using Remote Control Boat;study of Smart City Compatible Monolithic Quantum Well Photodetector;frequency Reconfigurable Antenna for 5G Applications at n77 and n78 Bands;solar Energy Prediction using Machine Learning with Support Vector Regression Algorithm;analysis of Acoustic Channel Model Characteristics in Deep-Sea Water;A Comprehensive Review on Channel Estimation Methods for Millimeter Wave MIMO Systems;comparison of Acoustic Channel Characteristics in Shallow and Deep-Sea Water;price Estimation of Used Cars Using Machine Learning Algorithms;Machine Learning Framework for Identification of Abnormal EEG signal;alzheimer’s Disease Detection Using Ensemble of Classifiers;publishing data Objects in data Aware Networking;youTube Comment Analysis Using Lexicon Based Techniques;Information Theoretic Heuristics to Find the Minimal SOP Expression Considering Don’t Care Using Binary Decision Diagrams;single Image Dehazing Through Feed Forward Artificial Neural Network.

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Comparative Analysis of three outlier detection methods in univariate data sets 3

Comparative Analysis of three outlier detection methods in u...

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3rd international conference on Electronic Communication and Artificial Intelligence, IWECAI 2022

作者： Gong, Xunqiang Zhang, Fangze Lu, Tieding You, Wei East China University of Technology Faculty of Geomatics Nanchang China Southwest Jiaotong University Faculty of Geosciences And Environmental Engineering Chengdu China

ISBN: (纸本)9781665479974

In the field of geomatics engineering and signal processing, observational data can bring a lot of information. However, the abnormal data (i.e. outliers) may be acquired due to human carelessness or limiting condition, causing interference to subsequent research. To solve this problem, outlier detection methods have been proposed to detect and remove outliers. Three common outlier detection methods (i.e. Z-score method, boxplot method and median absolute deviation method) in signal processing are introduced in this paper. A comparison of the three methods is conducted through experimental evaluation with two sets of experiments. The results of experiments show that the number of outliers detected with median absolute deviation method significantly outperform those of Z-score and boxplot methods. It shows that the median absolute deviation method can more effectively detect and remove outliers so that the more reliable and accuracy results can be obtained. © 2022 IEEE.

关键词： Anomaly detection

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Deep Learning Approaches for Enhancing Content Retrieval 3

Deep Learning Approaches for Enhancing Content Retrieval

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Khurana, Saniya Singh, Shweta Ranganathaswamy, M.K. Renuka, G. Diwakar, Mandar P. Jain, Sandeep Kumar Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350319125

Deep learning strategies for content material retrieval have been explored and updated to cope with the want for accurate search effects. This machine up to date is used to date apprehend and examine text-up-to-date content material from sources, up-to-date webpages, or virtual up-todate motion pictures. As the quantity and complexity of online content will increase, so up-to-date have the want for higher up-to-date up to date those resources. Recent advancements in deep gaining knowledge have enabled better content retrieval structures that are up to date, updated correctly, and speedy to understand statistics from one-of-a-kind sources. Such structures include natural language processing (NLP), sentiment analysis, entity reputation, and up-to-date updated reputation. Those deep studying technologies have been used to decorate content material retrieval by enhancing search effects' accuracy, robotically updated extracting applicable statistics, categorizing information, and identifying patterns. Additionally, deep-getting, updated systems are up-to-date and quick to examine large volumes of data, generating and relevant consequences than up-to-date methods. In summary, deep up-to-date techniques can beautify the retrieval system of content material efficiently, enhancing the accuracy and pace of seek effects. © 2023 IEEE.

关键词： categorizing comprehend effectively learning techniques

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Investigation on the Height of Fracture Zone in Goaf of Steep Coal Seam Based on Microseismic Monitoring 3

Investigation on the Height of Fracture Zone in Goaf of Stee...

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2023 3rd international conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2023

作者： Yang, Shouguo Liu, Haoxin Huang, Rumin Li, Jialin School of Safety Science and Engineering Xi'an University of Science and Technology Xi'an710054 China Key Laboratory of Western Mine Mining and Disaster Prevention Ministry of Education Xi'an710054 China

The height of the fracture zone in the mined-out area is the basic parameter for controlling mine strata, preventing and controlling mine water, and arranging drilling holes for high-level gas extraction. Aiming at the development law of fracture zone in the steep seam and the limitation of traditional drilling inspection method, microseismic monitoring technology is used to investigate the height of fracture zone in goaf. Taking the 3103 south working face of a mine in Sichuan as an example, the scheme of microseismic monitoring and data collection of the working face is worked out. Through data analysis and processing, the range of the fracture zone in the goaf is 6.8 m- 34.2 m, and compared with the numerical simulation analysis results, the relevant results are basically consistent. It provides a new technical means for the investigation of "three zones"of overlying strata in the goaf of a steep seam. © Published under licence by IOP Publishing Ltd.

关键词： data handling

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Research on haze prediction method of Xianyang City based on STL decomposition and FEDformer 3

Research on haze prediction method of Xianyang City based on...

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3rd international conference on Algorithms, Microchips, and Network Applications, AMNA 2024

作者： Cao, Yanan Zhou, Qian Tang, Jinglei Liu, Zhenhong College of Information Engineering Northwest A&F University Yangling China

ISBN: (纸本)9781510680098

Due to the continuous impact of haze weather, Xianyang city's air quality has ranked in the bottom three of the province for three consecutive years. This has led to an urgent need to improve air quality. Haze pollution prediction is of great practical significance. By timely and accurate prediction of haze pollution, the government and relevant institutions can take necessary measures to improve air quality and protect the ecosystem. Although the traditional RNN and LSTM models can effectively capture the time sequence information in the haze data over the years for prediction, it is still difficult to achieve accurate prediction due to the complexity of haze prediction. In this study, 8769 pieces of heterogeneous data were successfully collected using multi-source big data acquisition technology. A series of pre-processing operations, including data conversion and dimensionality reduction, were performed on different data such as AQI, PM2.5, PM10, SO2, NO2, CO and O3. The method of big data fusion and deep learning is adopted to integrate haze data and discover hidden rules and trends in it. Finally, based on FEDformer model and STL time series decomposition method, the prediction model was established in this study, which achieved significant improvement in both short - and long-term time series prediction problems. © 2024 SPIE.

关键词： Big data

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Coherence Analysis of EEG signal in Happy and Sad Emotions During Visual Stimulation 3

Coherence Analysis of EEG Signal in Happy and Sad Emotions D...

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3rd IEEE international Biomedical Instrumentation and Technology conference, IBITeC 2023

作者： Pratasik, Stralen Wibawa, Adhi Dharma Wulandari, Diah Puspito Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Medical Technology Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350302424

Emotions play a crucial role in human life, influencing various aspects such as thoughts, behavior, and physical health. Understanding the neural mechanisms underlying emotions, particularly sadness, is essential. This study explores the relationship between brain activity associated with sad and happy emotions and the functioning of the left and right hemispheres during visual processing using coherence analysis of electroencephalogram (EEG) signals. When the coherence value is low, there is no correlation between the two compared conditions. EEG recordings were obtained from 25 participants using an OpenBCI device, and specific channels (Fp1-Fp2, F7-F8, and O1-O2) related to attention, emotion expression, and visual processing were selected. The recorded data underwent preprocessing to remove artifacts, and subsequent feature extraction was performed using coherence analysis in the gamma sub-band. The results of this study demonstrate alignment with the existing theory that each hemisphere has distinct functions when experiencing feelings of sadness and happiness, as evidenced by the observation of a weak coherence percentage in all hemisphere comparisons between Fp1-Fp2, F7-F8, and O1-O2 (all coherence value is © 2023 IEEE.

关键词： Electroencephalography

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Power dispatch control center log management platform based on multi-dimensional data mining

Power dispatch control center log management platform based ...

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signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Jiang Hongliang Du Fan Zhu Yuqi Jiang Di Electrical Dispatching and Control Center of Yunnan Power Grid Kunming China

In order to solve the problems of complex data application management process and low data processing efficiency in the power dispatch control center, a power dispatch control center log management platform based on multi-dimensional data mining is proposed. First, the process of generating multi-dimensional data for power dispatching is analyzed, and the main problems of the data are analyzed. The key technologies of data mining for the platform application proposed in this article are explained. The gray correlation analysis of data and the support vector machine model are discussed. The basic process of information data mining in the log management platform is given. The corresponding system architecture is proposed, the platform interface specifications are explained, and the platform security control measures are discussed. Scenario analysis was conducted on functions such as linkage between dispatch logs and other businesses, graphical display, comprehensive query, and shift handover.

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Multi-TransPathoNet: A transfer learning-based neural network for pathology image classification with multi-Task learning 3

Multi-TransPathoNet: A transfer learning-based neural networ...

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3rd international conference on Advanced Algorithms and signal Image processing, AASIP 2023

作者： Deng, Xin Li, Min Ren, Xiaojun Zheng, Xiuyan Liang, Fei He, Liang Zhu, Min School of Software Xinjiang University China School of Information Science and Engineering Xinjiang University China Xinjiang Key Laboratory of Clinical Genetic Testing and Biomedical Information China Xinjiang Clinical Research Center for Precision Medicine of Digestive System Tumor China Department of Pathology Karamay Central Hospital China Xinjiang Key Laboratory of Signal Detection and Processing China Dept. of Electron. Eng. and Beijing National Research Center for Information Science and Technology Tsinghua University China

ISBN: (纸本)9781510668522

Histopathological images are one of the common diagnostic tools used in the medical field. However, the complex cellular and tissue structures inside colorectal cancer histopathological images make it difficult for physicians to perform efficient cancer diagnosis and quantitative analysis. Therefore, this study proposed a multi-Task learning-based pathology image classification method Multi-TransPathoNet (A transfer learning-based neural network for pathology image classification with multi-Task learning, Multi-TransPathoNet) for detection and visualization output of colorectal cancer histopathology images, thus helping to improve medical efficiency. To verify the practical application value of this method, this study used the paracancerous, highly differentiated, and high-moderately differentiated histopathological images which are more difficult to distinguish in colorectal cancer as the dataset for classification test, and our method obtained 92% accuracy. Besides, we compared with the single-Task learning models Resnet50 and Vgg19 respectively, which also have good performance in image classification tasks, to comprehensively evaluate the proposed model's performance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computer aided diagnosis

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