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Improved YOLO-based algorithm for urban traffic object detection 4

Improved YOLO-based algorithm for urban traffic object detec...

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4th international conference on internet of Things and Smart City, IoTSC 2024

作者： Zhang, Liguo Yan, Xu Jin, Mei College of Electrical Engineering Yanshan University Hebei Province Qinghuangdao066004 China

ISBN: (纸本)9781510681767

Urban traffic vehicle detection is a key component of smart city transportation systems, aimed at improving traffic management and safety through modern technologies and information methods. In response to the characteristics and challenges of vehicle detection in smart cities, this paper proposes a vehicle detection method based on drone aerial images, employing an object detection algorithm based on YOLOv4, namely the Adaptive Cropping YOLO algorithm. Through training and optimization on a large-scale dataset, this method can accurately detect and identify different types of urban vehicles. Experimental results show that this algorithm can effectively detect large-sized image targets that traditional YOLO algorithms may miss, providing reliable technical support for traffic safety monitoring and management. © 2024 SPIE.

关键词： Vehicle detection

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Retinal image segmentation algorithm based on hybrid pooling and multi-dimensional attention

Retinal image segmentation algorithm based on hybrid pooling...

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2024 international conference on image, signal Processing, and Pattern Recognition, ISPP 2024

作者： Zhao, Dequn Li, Chunsheng Ma, Hongwei Zhang, Suming Faculty of Information Technology Beijing University of Technology Beijing China Beijing Institute of Aerospace Systems Engineering Beijing China

ISBN: (纸本)9781510680425

This paper propose a retinal image segmentation model according to hybrid pooling and multi-scale attention mechanism. Due to the crucial importance of retinal vessels for the segmentation of the retina and their intricate structure, which serves as vital diagnostic information for diseases, we focus on retinal images in medical imaging. We introduce a novel medical image segmentation model, MDA-Unet, built upon the Unet architecture. Extensive experiments are conducted on the DRIVE dataset. Addressing challenges such as limited data, varying image quality, and the presence of small structures in segmented target areas causing reduced segmentation accuracy, the proposed enhanced model MDA-Unet aims to overcome these issues. We investigate the impact of incorporating modules such as Exponential Moving Average (EMA), Multi-Path Module (MPM), and serpentine convolutional kernels on the model. The research results indicate that the addition of these modules enhances the ability of the model to be segmented. This improved model is tested with the DRIVE dataset, yielding segmentation results that surpass those of previously proposed segmentation models. Compared with the previous model segmentation results, the method proposed in this paper has achieved good results in fundus vascular segmentation. © 2024 SPIE.

关键词： image segmentation

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The concept of the internet of Things in the development of information and analytical systems based on the method of constructing a scalar assessment of the results of research activities of scientists 14

The concept of the Internet of Things in the development of ...

引用

14th international conference on Emerging Ubiquitous systems and Pervasive Networks / 13th international conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2023

作者： Biloshchytskyi, Andrii Omirbayev, Serik Mukhatayev, Aidos Biloshchytska, Svitlana Toxanov, Sapar Faizullin, Adil Astana IT University Astana010000 Kazakhstan Information Technologies Kyiv National University of Construction and Architecture Kiev01001 Ukraine

The article describes the creation of tools for building intelligent systems in the field of scientometrics as the concept of the internet of Things. The concept is presented as an information system where scientometric databases such as Scopus, WoS, as well as sites with databases of scientific projects and other databases act as sensors that generate data. systems of data accumulation and processing are systems created using microservices technology. At the output, we get a control signal in the form of a proposal to correct the trajectory of the development of the scientific potential as an organization or a scientist. The system will also suggest how to strengthen the research team by selecting scientific partners from the global scientific community. The article describes the structural model of the concept of the internet of Things in the form of a multi-level structure using microservice technologies. Also, the article suggests methods of constructing a scalar evaluation of the results of scientific research activities of scientists based on the analysis of citations of their publications. © 2024 Elsevier B.V.. All rights reserved.

关键词： internet of things

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Deep Learning Gesture Recognition System based on 3D Convolution 9

Deep Learning Gesture Recognition System Based on 3D Convolu...

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9th international conference on Intelligent Computing and signal Processing, ICSP 2024

作者： Long, Ken Li, Zhiyuan Xie, Binghong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9798350376548

The rapid development of the internet of Things has promoted the progress of human-computer interaction technology, in which gesture recognition, as a key component, provides diversified applications for smart homes, elderly care, and other fields. In recent years, with the wide application of Wi-Fi, gesture recognition based on channel state information (CSI) has attracted much attention due to its advantages of flexibility, convenience, and cost-effectiveness. In this paper, we propose a gesture recognition system that includes an image conversion module and a 3D convolutional network recognition module. In the image conversion module, we treat the CSI subcarrier information from multiple receivers as a one-dimensional time series and convert them into images separately. Then by aggregating all the image data on the new depth dimension, we finally input the processed data into the 3D Convolutional network recognition module for gesture recognition. This module focuses on capturing changes in CSI information over time and space. We evaluated our model on a Widar3.0 open data set, and the experimental results show that the proposed method has a good effect in realizing cross-domain gesture recognition. © 2024 IEEE.

关键词： Wi-Fi

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An IoT based Electromyography signal Transmission from sEMG Electrodes to Client's Server with ICT Infrastructure 10

An IoT Based Electromyography Signal Transmission from sEMG ...

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10th international conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Pavithra, P.M. Balakrishnan, M. Anandaraj, A. Ramya, M. Sudharsan, R. Raja Sri Shanmugha College of Engineering and Technology Department of Artificial intelligence and Data Science Salem India Dr. Mahalingam College of Engineering and Technology Department of Artificial intelligence and Data Science Pollachi India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Sri Shanmugha College of Engineering and Technology Department of Information Technology Salem India Sri Shanmugha College of Engineering and Technology Department of Biomedical Engineering Salem India

ISBN: (纸本)9798350384369

Bio-electric signal like Electromyography (EMG) can measure signal data from nerves and muscles separately in the particular region of affected nerve or muscles from a Human Being. For incorporating these measurements, can use a non-invasive technique. The Surface electrode can be used for measuring these problems in non-invasive fashion. Here, the idea of internet of Things (IoT) can bring included advantage to applications with bio-electric signals to communicate to the doctors and patients such as human-PC cooperation by incorporating different advances for example, sensors, remote correspondence and information science. In this research, we worked on a remote bio-electric signals to another remote observing handling framework. A model with the contextual investigation of nerve and muscle signal acknowledgment utilizing 4-channel sEMG signals is executed. A Gaussian filter is prepared from one individual's sEMG signals with 4-nerve regions of human body such as ulnar, radial, median and radial. The introduced IoT application framework is actualized based on an 11-terminal bio-electric signal measuring device, with Arduino based Controller, Gateway, ESP8266 (Wifi) module, Server being the major mediums of remote transmission of signal data. The signal transmits at 35KB/s. In the framework, the ongoing sEMG signal from surface electrode is filtered from noise and other distortions using is given to the controller for performing the operation. © 2024 IEEE.

关键词： Gateways (computer networks)

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Attending to Transforms: A Survey on Transformer-based image Captioning 2

Attending to Transforms: A Survey on Transformer-based Image...

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2nd international conference on Paradigm Shifts in Communications Embedded systems, Machine Learning and signal Processing, PCEMS 2023

作者： Ambilduke, Kshitij Jayakumar, Thanmay Farooqui, Luqman Padole, Himanshu Singh, Anamika Visvesvaraya National Institute of Technology Nagpur India Indian Institute of Technology Bhubaneswar India

ISBN: (纸本)9798350310719

image captioning is a challenging task that lies at the intersection of Computer Vision and Natural Language Processing. There exists a legion of works that generate meaningful and realistic descriptions of images. Recently, with the advent of attention mechanisms and transformers, there has been a drastic shift in modelling both language and vision tasks. However, there are very few extensive studies that review these approaches based on their progression, advantages and disadvantages. This paper presents a detailed summary of transformer-based models employed for tackling image captioning. In addition to this, we provide an overview of various pre-training tasks, datasets and metrics used for image captioning. Finally, the performance of all the reviewed approaches are compared on the COCO Captions dataset. © 2023 IEEE.

关键词： Computer vision

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Anomalous IoT Behavior Detection by LSTM-based Power Waveform Prediction 10

Anomalous IoT Behavior Detection by LSTM-Based Power Wavefor...

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10th international conference on internet of Things, Big Data and Security, IoTBDS 2025

作者： Eda, Ryusei Togawa, Nozomu Department of Computer Science and Communications Engineering Waseda University Japan

ISBN: (纸本)9789897587504

internet of Things (IoT) devices have very rapidly spread out in recent years. In IoT devices where applications run on operating system (OS), the power consumption of the OS and the power consumption of the applications overlap, resulting in complex power waveform. Previous methods need to explicitly extract the application power waveform from the multiple signal sources in the measured power waveform, which often fail to detect anomalous behaviors. In this paper, we propose a method to detect anomalous behaviors by using LSTM (Long Short Term Memory). The proposed method learns power waveform containing multiple signal sources and compares the predicted waveform and the actual one. Then, we can successfully detect anomalous behaviors, even though the measured power waveform is composed of multiple signal sources. Experimental results show that anomalous behavior can be successfully detected from an IoT device built with Raspberry Pi4. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Malware

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Automated Waste Classification using Convolutional Neural Network 14

Automated Waste Classification using Convolutional Neural Ne...

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14th international conference on Software technology and Engineering, ICSTE 2024

作者： Thinakaran, Rajermani Somasekar, J. Neerugatti, Vikram Guru Saran, P. INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia Department of Computer Science and Engineering Karnataka Banglore India

ISBN: (纸本)9798350378955

Efficient waste management relies on precise waste classification, which facilitates recycling, composting, and appropriate disposal. Conventional waste classification methods often involve manual sorting or simple rule-based systems, which are labor intensive and prone to errors. This study investigates the application of Convolutional Neural Networks (CNNs) for automated waste classification using image data. CNNs have shown significant success in various image classification tasks due to their ability to learn hierarchical features directly from pixel values. The methodology outlined covers data collection, preprocessing, model architecture design, and training procedures for the CNN model. Experimental results demonstrate the effectiveness of the CNN approach in accurately categorizing various waste types. Additionally, we discuss the practical implications of CNN-based waste classification systems for real-world waste management applications. By leveraging CNNs, waste management processes can be optimized, leading to better resource utilization and environmental sustainability. The authors also highlight potential avenues for future research, including exploring advanced CNN architectures and integrating emerging technologies like internet of Things (IoT) sensors for improved waste management solutions. Overall, this study emphasizes the potential of CNNs to transform waste classification practices and enhance the efficiency of waste management systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on high accuracy classification of temperature signals based on OFDR technology and EfficientNet 4

Research on high accuracy classification of temperature sign...

引用

4th international conference on Advanced Manufacturing technology and Electronic Information, AMTEI 2024

作者： Shang, Chuanyin Li, Peng Tong, Zhengrong Wang, Hao Li, Sibo Tianjin Key Laboratory of Thin Film Electronics and Communication Devices Engineering Research Center for Optoelectronic Devices and Communication Technology the Ministry of Education School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

The development of machine learning is driving revolutionary changes and unprecedented opportunities across various industries. This paper introduces a new temperature monitoring method that combines the efficient deep learning model EfficientNet with Optical Frequency Domain Reflectometry (OFDR). We utilize distributed fiber-optic sensing technology based on OFDR to collect one-dimensional temperature signals under different temperature conditions, which we then transform into a two-dimensional image format suitable for machine learning processing. The dataset is subsequently trained using the EfficientNet deep learning model. The results show that the trained model achieves a high accuracy rate of 97.5% on the test dataset. This research not only demonstrates the feasibility of efficiently and accurately monitoring temperature changes but also provides new solutions and technological pathways for areas such as environmental monitoring, industrial automation, and safety systems. © 2025 SPIE.

关键词： Education and training Data modeling Performance modeling Fiber optics sensors Machine learning Matrices Fiber optics image processing Reliability signal processing

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An IoT-based Deep Belief Network in Intrusion Detection System using Butterfly Optimization 10

An IoT-based Deep Belief Network in Intrusion Detection Syst...

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10th international conference on Communication and signal Processing, ICCSP 2024

作者： Cherian, Aswathy K. Vaidhehi, M. Arshey, M. Simpson, Serin V. Briskilal, J. Singh, Astitva Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India Scms School of Engineering and Technology Department of Ai & Ds Kerala Ernakulam India Madanapalle Institute of Technology & Science Department of Computer Science and Technology Chittoor Andhra Pradesh Madanapalle India

ISBN: (纸本)9798350353068

Rapid technological advancements in the communication and internet industries have led to a huge increase in network scale and data volume. Network security is having a hard time identifying attacks as new threats are being created. Additionally, you can't overlook the possibility of unauthorized users trying to attack the network in various methods. Numerous technologies, such as an intrusion detection system, can be utilized to protect the network from potential threats (IDS). The collecting and dissemination of data by internet-connected devices is referred to as the 'internet of Things' (IoT). There are now more privacy and security problems due to the proliferation of internet connections and the introduction of new technologies like the internet of Things (IoT). IoT incursions are becoming increasingly complex and numerous as a result of the proliferation of connected devices. Companies are boosting their investment in research and development in order to better detect these threats. In this research, Deep Belief Network (DBN) and Butterfly Optimization Algorithm (BOA) Intrusion Detection systems (IDS) are proposed for detecting Io T network assaults. In order to minimize information leakage on the test data, the UNSW-NB15 dataset was normalized using the Absolute Maximum Scaling (AMS) approach in the first stage of this study process. This dataset contains a mixture of current assaults and typical network traffic, categorized into nine different attack types. The next step was to use Principal Component Analysis to reduce the dimensionality (PCA). Last but not least, results showed that DBN-BOA was superior to other IoT intrusion detection systems in terms of accuracy and speed. © 2024 IEEE.

关键词： Principal component analysis

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