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Enhancing RFID based human activity recognition through spatiotemporal feature construction 6

Enhancing RFID based human activity recognition through spat...

引用

6th international conference on Intelligent Control, Measurement and signal Processing, ICMSP 2024

作者： Zhao, Kun Lei, Junwen Chen, Yihong Huang, Lingbo China West Normal University Internet of Things Perception and Big Data Analysis Key Laboratory of Nanchong City Nanchong China Ganzi Meteorological Office Daocheng County Meteorological Bureau Tibetan Autonomous Prefecture of Garzê China

ISBN: (纸本)9798331516550

This article proposes a new method that significantly improves the accuracy of action recognition based on RFID systems by introducing advanced spatiotemporal features and utilizing spatiotemporal graph convolutional networks (ST-GCN). Traditional RFID motion recognition models often struggle to capture the subtle spatial and temporal relationships between joint movements, resulting in limited recognition accuracy in complex activity scenarios. To overcome this issue, we propose a new feature construction method that utilizes new signal features derived from RFID signal data (including RSSI, Doppler frequency, and phase), such as velocity, acceleration, phase change rate, and RSSI change rate. These features can finely capture the dynamic changes in time and space, thereby improving the performance of the model in distinguishing complex actions. The experimental results show that the ST-GCN model trained on enhanced feature sets significantly improves the accuracy of action recognition tasks, verifying the effectiveness of our method. In addition, feature construction methods have played a key role in improving the identification ability of RFID signal data, especially in distinguishing similar actions in complex environments, demonstrating their broad potential for application in behavior analysis. © 2024 ieee.

关键词： Deep learning

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Narrowband IoT Network Self Localization

Narrowband IoT Network Self Localization

引用

2023 ieee Jordan international Joint conference on Electrical Engineering and Information technology, JEEIT 2023

作者： Alashqar, Anas Khalifeh, Ala' Mesleh, Raed German Jordanian University Electrical Engineering Department Amman Jordan Princess Sumaya University for Technology Amman Jordan

ISBN: (纸本)9798350324051

This article proposes a self-localization method for narrowband internet of things (NB-IoT) networks. The proposed system uses the received signal strength indicator (RSSI) with a trilateration algorithm to determine the location of NB-IoT nodes within indoor environments. The adopted path loss model for the indoor environment is in accordance with the fifth-generation (5G) millimeter wave (mm-Wave) standard. The pro-posed method eliminates the need for additional infrastructure or external references, making it efficient and cost-effective. Simulation results are presented to corroborate the accuracy of the proposed technique, and to investigate the impact of different system and channel parameters on the overall performance. Reported results reveal the accuracy of the developed system where an average positioning error of less than 0.2 m is achieved. © 2023 ieee.

关键词： internet of things

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Technical Analysis of Coverage and Capacity Planning of Smart Metering based on Long Range Wide Area Network in State Electricity Company Bandung Region

Technical Analysis of Coverage and Capacity Planning of Smar...

引用

2023 ieee international conference on internet of Things and Intelligence systems, IoTaIS 2023

作者： Wardianto, Arief Syabakdan Murti, Muhammad Ary Adriansyah, Nachwan Mufti Telkom University Center for Regulation and Management of Magister Telecommunication Bandung Indonesia

ISBN: (纸本)9798350319040

Electricity with equipment in housing units can now be gathered more regularly thanks to the internet of Things. The Advanced Metering Infrastructure (AMI) is a smart metering system integrated by a communication system outfitted with a communication network and data management system that allows utilities and consumers to communicate in both directions which is expected to increase efficiency in monitoring kwhMeter activity detection. However, the meter on this electricity is still separate and placed in different locations both in the apartment and at home, the plan of this thesis proposes the design, development, and support of the development of advanced metering infrastructure. based on the planning result, LoRa WAnis Planning at least 22 gateways to roll out smart metering services in the Bandung metropolitan region with a Spreading Factor (SF) of 11, a Code Rate (CR) of 5/6/7/8, and a receiver sensitivity level of more than -160.53 dBm are required. The average received signal is -62.95 dBm, with a throughput distribution of 5.42 kbps, according to LoRaWancoverage projections. Meanwhile, the simulated SNR yielded a maximum SNR of 35 dB and a minimum SNR of -20 dB, providing an average SNR level of roughly 22.04 dB over the region. © 2023 ieee.

关键词： internet of things

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Detecting Spinal Alignment Through Depth image 16

Detecting Spinal Alignment Through Depth Image

引用

16th international conference on signal-image technology and internet-based systems (SITIS)

作者： Ishibashi, Reina Kameshimat, Hideto Nishio, Yuji Satot, Yukio Watanabe, Kota Aoki, Yoshimitsu Keio Univ Dept Elect Engn Fac Sci & Technol Yokohama Kanagawa Japan Space Vis Co Ltd Tokyo Japan Keio Univ Div Spine & Spinal Cord Dept Orthopaed Surg Sch Med Shinjuku Ku Tokyo Japan

ISBN: (纸本)9781665464956

Adolescent Idiopathic Scoliosis (AIS) is a common form of scoliosis found among adolescents. The abnormal spinal curvature along with twisting of the vertebrae requires early detection before the vertebrae alignment worsens. Furthermore, surgical treatment is crucial for those with severe conditions. In Japan, annual scoliosis screening is conducted during school health checkups. These screening procedures often involve an initial screening without the use of ionizing radiation (such as X-rays) and an X-ray screening later for those suspected with scoliosis. Currently, X-ray examinations produce the most accurate results for detecting the spinal position. However, non-invasive alternative methods, such as Moire imaging, provide satisfactory initial results. Unfortunately, the production of Moire cameras have halted in recent years. This paper proposes an alternative, non-invasive and non-ionizing radioactive method to detect spinal alignment. Depth images provide an extra dimension of information compared to RGB images. The proposed method utilizes this extra dimension of depth values to create different types of images that are then trained using Convolutional Neural Networks to predict the spinal alignment. Results indicate that Moire images reproduced from depth images produce the best spinal alignment. However, other types of images derived from the depth image also have higher accuracy when compared with the depth image itself.

关键词： Scoliosis keypoint detection depth images

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Design and Implementation of Battery Management and Wireless Charging in Electric Vehicles Using IoT 10

Design and Implementation of Battery Management and Wireless...

引用

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

作者： Manikandan, S. Shyam Sundar, S. Kumaran, K. Thirumeni, Mariammal Rajalakshmi Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350353068

The widespread adoption of electric vehicles (EVs) hinges on efficient battery management and convenient charging solutions. This paper presents the design and implementation of an IoT-based battery management system (BMS) integrated with wireless charging technology for EVs. The proposed system leverages sensor data acquisition, real-time monitoring, and cloud connectivity to ensure optimal battery health, extend lifespan, and enhance user experience. Wireless charging offers added convenience and eliminates cable handling, further improving charging experience. This paper details the hardware architecture, software development, and key features of the system, followed by an evaluation of its performance and discussion of potential applications. objects to be connected via the internet, bringing the world to the user's fingertips. Motivated by the aforementioned issues, this paper proposes the design and development of a wireless EV charging and battery monitoring system utilizing IoT technologies. © 2024 ieee.

关键词： internet of things

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Proceedings - 11th international conference on signal Processing and Integrated Networks, SPIN 2024

Proceedings - 11th International Conference on Signal Proces...

引用

11th ieee international conference on signal Processing and Integrated Networks, SPIN 2024

ISBN: (纸本)9798350308433

The proceedings contain 79 papers. The topics discussed include: comparative analysis of human and AI generated text;revitalizing ancient murals in the Shekhawati region through image inpainting techniques;analyzing muscle synergies for finger movement recognition using sEMG signals;modeling blockchain for different industries in developing economy;land use land cover classification using deep learning techniques: a comparative study;cyclic heap permutation based reconfiguration of solar PV array for optimal shade dispersion;optimizing stock market predictions: a comprehensive 3-stage model with residual modeling;FISmark: heritage image copyright protection with FIS based optimization;human speech extraction from composite audio signal in real-time using deep neural network;and an ultrathin compact orientation insensitive chipless RFID tag with high bit capacity.

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image Enhancement Using Deblur Generative Network and Deep Deblur Adversarial

Image Enhancement Using Deblur Generative Network and Deep D...

引用

2024 ieee international conference on Industry 4.0, Artificial Intelligence, and Communications technology, IAICT 2024

作者： Purbandini Fatichah, Chastine Amaliah, Bilqis Department of Informatics Institut Teknologi Sepuluh Nopember Department of Mathematics Airlangga University Surabaya Indonesia Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9798350353464

The increased use of digital cameras has given rise to new challenges such as reduced image quality resulting in poor clarity or distortion, which can result in loss or unclearness of recorded information. To overcome this problem, deep learning-based approaches such as DeblurGAN and DeepDeblur have been developed for the clarity of blurry images. This research aims to compare the performance of the two approaches in the context of improving the clarity of blurry images. The research steps include collecting data from the GoPro dataset, dividing the data into training data and test data, analyzing the data using the DeblurGAN and DeepDeblur methods, and evaluating the results using Structural Similarity Index Measure (SSIM), Mean Square Estimation (MSE), and Peak signal-to-Noise Ratio (PSNR). The evaluation results show that the DeblurGAN method achieves the best results with SSIM values of 0.958, MSE of 0.00057, and PSNR of 33.18. This shows that DeblurGAN has better evaluation performance compared to Deep Deblur in improving the clarity of blurry images. ©2024 ieee.

关键词： Generative adversarial networks

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signal Deinterleaving based on U-Net Networks 8

Signal Deinterleaving Based on U-Net Networks

引用

8th international conference on Computer and Communication systems, ICCCS 2023

作者： Kang, Zhi Zhong, Yi Wu, Yaoyun Cai, Yihong Institute of Electronic Equipment Southwest China Research Chengdu China

ISBN: (纸本)9781665456128

signal deinterleaving is a key part of electronic warfare radar signal processing. As signal environments become more complex, classical sorting methods are being put to the test. This paper draws on the idea of using fully convolutional network to achieve image segmentation, proposes a new signal deinterleaving method based on U-Net network, and introduces the algorithm principle and network training method in detail. Simulation results show that if the PDW sequence of a single target can be fully intercepted, signal sorting based on U-Net segmentation network can be well realized in a complex signal environment as long as the target signal parameters are separable in any dimension. © 2023 ieee.

关键词： Electronic warfare

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Invasive Ductal Carcinoma Detection using Convolutional Neural Network Architecture from Breast Histopathology images 2

Invasive Ductal Carcinoma Detection using Convolutional Neur...

引用

2nd international conference on Intelligent Cyber Physical systems and internet of Things (ICoICI)

作者： Kaur, Gurjot Sharma, Neha Chitkara Univ Inst Engn & Technol Rajpura Punjab India

ISBN: (纸本)9798331540661;9798331540678

The purpose of the work is to create a robust convolutional neural network-based classification model to distinguish images of invasive ductal carcinoma (IDC) breast disease. This collection includes histopathological images from breast tissue samples some with IDC-positive and others IDC-negative. First in data preparation are resizing images to 50x50 pixels and arranging them for training and testing. The sample consists of 3,747 IDC-positive images and 21,031 IDC-negative images for 24,678 images overall. The model's CNN architecture consisted of many layers: convolutional layers, max-pooling layers, batch normalization, and dropout layers aimed at reducing overfitting. Trained with binary cross-entropy loss function and an Adam optimizer running at 0.0001 learning rate. After 40 iterations on the training set, the model's accuracy on the testing set was 94.73%, and on the training, set was 99.12%. The performance of the model was assessed using several parameters including accuracy, precision, recall, and F1-score. The categorization report shows that both IDC-positive and IDC-negative classes have excellent recall and accuracy. Moreover, confirming the ability of the model to classify IDC-positive and IDC-negative samples is the confusion matrix. This unique CNN-based classification model of breast histomorphology images seems to do remarkably well in identifying IDC, showing both excellent accuracy and the capacity to distinguish between positive and negative IDC samples. The model could enable pathologists to identify breast cancer, therefore enhancing the accuracy and effectiveness of breast cancer screening campaigns. Future directions of this work could concentrate on improving the performance of the model with larger datasets and investigating alternative image augmentation techniques that will generalize its capabilities on diverse input datasets.

关键词： Invasive Ductal Carcinoma (IDC) Breast Histopathology images Convolutional Neural Network (CNN) Medical image Classification

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Deepfake image Detection Using Yolov8 7

Deepfake Image Detection Using Yolov8

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7th ieee Pune Section international conference, PuneCon 2024

作者： Sathe, Pranav Sabane, Varad Undale, Chaitanya Uttarkar, Atharva Chavhan, Vaibhav Sable, Nilesh P. Yenkikar, Anuradha Vishwakarma Institute of Information Technology Department of Cse - Artificial Intelligence Pune India

ISBN: (纸本)9798331527822

Due to growing number of fake media and the possible problems with misinformation and identity theft, deepfake image recognition has become a hot topic. In order to evaluate the efficacy of widely recognized deep learning models in detecting and classifying deepfake images, we compare and contrast YOLO (You Only Look Once) V8, CNN (Convolutional Neural Network), combination of LSTM (Long Short-Term Memory) with CNN and ResNet in this paper. The capacity to recognize photos that have been manipulated effectively is essential for maintaining trust regarding digital media and preventing the spread of deceptive data. The objective of our research is to explain each model's abilities, limitations and performance in the context of deepfake image recognition. We evaluated each model's accuracy, precision, and F1-scores using a dataset of 190402 images, half real and half fake that cover a wide spectrum of deep fake images. based on the evaluation metrics, each model was ranked in terms of its ability to separately describe and determine original versus generated photos with focus on detecting subtle changes eluding human senses. The comparison research shows each model's nuanced capabilities, offering light on the implications for applications in the real world like detecting and controlling the spread of deep fake information across numerous internet platforms. Our findings help towards building deep fake detection systems, further understanding the comparative performance of state of the art deep architectures. This study shall inform more effective deep fake detection systems that can guarantee trust and security in digital media environments. Accuracy achieved by model is 96%, precision score was 94%, recall value is 0.98, F1 score has a value of 0.95. © 2024 ieee.

关键词： Contrastive Learning

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