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Indoor Accidents Detection for Elderly based on Features Fusion and LSTM 21

Indoor Accidents Detection for Elderly Based on Features Fus...

引用

21st ieee international conference on Computer Applications, ICCA 2024

作者： San, Win Pa Pa Zaw, Sai Maung Maung University of Computer Studies Mandalay Image and Signal Processing Lab Mandalay Myanmar University of Computer Studies Mandalay Faculty of Computer Systems and Technology Mandalay Myanmar

ISBN: (纸本)9789998601673

Nowadays, with aging of the human society, the 'ratio of family care givers and elderly' is not equivalent and cannot give enough caring to the elderly in some countries. Therefore, automatic health monitoring system for elderly draws the interests of many researchers and become as special issue. In the implementation of elderly people monitoring system, the detection of fall events plays as an important role because the fall is the common cause which is very risky and threaten the health of elderly. The aim of this research is to implement the vision-based fall detection system which is important for health monitoring of elderly by applying the computer vision and deep learning technology. Most of the traditional vision-based fall detection systems which based on the height, width of human posture, fail to detect the fall events from different camera viewpoints. In this research, to accurately detect the fall from different camera viewpoint, the features fusion approach is applied over the deep learning model. In the implementation of the system, firstly, the human silhouette image is created by using the background subtraction. After that, the human silhouette image which have been extracted from two consecutive frames are fused as a single image called Silhouette History image (SHI). Then, the SHI results are used as shape feature. On the other hand, the motion features called Dense Optical Flow (DOF) is extracted over the two consecutive video frames and fuse SHI and DOF as a single input image. Then, the results of features fusion are feed as input for pretrained Convolution Neural Network (CNN) for extracting the deep CNN features. Finally, the Recurrent Neural Network (RNN) called Long-Short-Term-Memory (LSTM) is trained over those CNN features for recognizing the sequence of fall event. The experiments are performed using the publicly available dataset of UP-fall detection dataset for evaluating and confirming the effectiveness of the proposed method. © 2024

关键词： Fall detection

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Research on Industrial System Operation Pattern Recognition based on Deep Generative Model 14

Research on Industrial System Operation Pattern Recognition ...

引用

14th ieee international conference on Electronics Information and Emergency Communication, ICEIEC 2024

作者： Wang, Zhong Ma, Siming Wang, Tingting Tao, Wei Institute of System Research National Industrial Information Security Development Research Center Beijing China

ISBN: (纸本)9798350361889

As a new digital infrastructure, industrial internet provides massive data for intelligent operation and maintenance of industrial equipment. In order to solve the operation pattern recognition of complex industrial systems, the architecture of digital twins was designed, and the variational self-encoder model considering the influence of operating conditions was *** correlation among the multivariate variables of industrial system was identified by the reconstruction probability.A gas turbine in service was selected for model validation, and the result shows that the proposed model has a higher prediction accuracy and is more sensitive to changes in operating conditions. © 2024 ieee.

关键词： signal encoding

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FedDistillSC: A Distributed Semantic Communication System based on Federated Distillation 23

FedDistillSC: A Distributed Semantic Communication System Ba...

引用

23rd ieee international conference on Communication technology, ICCT 2023

作者： Xing, Huanlai Ma, Huaming Xiao, Zhiwen Zhao, Bowen Peng, Jincheng Luo, Shouxi Feng, Li Xu, Lexi School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Research Institute China United Network Communications Corporation Beijing China

ISBN: (纸本)9798350325959

In this paper, we propose a federated-distillation-based distributed semantic communication system for image classification tasks, called FedDistillSC, which considers multiple transmitters with varying channel conditions. After local training with public and private datasets, the local models of each transmitter are knowledge distilled by weighted aggregation based on the classification probabilities generated at the receiver to achieve knowledge sharing without exposure of private data, improving the semantic extraction capability of each transmitter. Moreover, our method does not upload/download model parameters, thus saving bandwidth resources and avoiding data security problems caused by model parameter leakage. Simulation results show that the proposed FedDistillSC exhibits excellent Top-1 accuracy performance under three channels. Especially for those transmitters with low signal-to-noise ratio, knowledge distillation could signiflcantly improve their semantic extraction capability. © 2023 ieee.

关键词： Federated Learning image Classification Knowledge Distillation Semantic Communication

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BD-YOLO: Optimising Insect Imbalance Data Detection Models

BD-YOLO: Optimising Insect Imbalance Data Detection Models

引用

2024 ieee international conference on systems, Man, and Cybernetics, SMC 2024

作者： Wang, Jian-Yi Jia, Zhen-Hong Zhou, Gang Wang, Jia-Jia School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Uygur Autonomous Region Signal Detection and Processing Key Laboratory Xinjiang University Urumqi830046 China

ISBN: (纸本)9781665410205

Vision-based insect detection is an important task in smart agriculture. Field-collected insect image datasets usually show severe imbalances in categories. Previous models have insufficient accuracy as well as low real-time performance for detecting unbalanced data of insects. To address the issues described above, an improved framework Balance Detect YOLO(BD-YOLO) based on YOLOv8 is proposed in this paper. We innovatively designed Focaler-CPIoU loss to increase the weight of a few classes of samples to achieve the optimisation of pest imbalance data detection. We introduced lightweight convolutional MBConv to improve the recognition accuracy while reducing the number of model parameters. In order to verify the effectiveness of the method in this paper, we established an insect dataset containing six types of insects, in which the most numerous thrips is 431.5 times more than the least numerous chrysopaperla. The experimental results show that compared with YOLOv8, the mAP50 reaches 61.7% with a 73.1% reduction in the number of model parameters, an improvement of 5.4%, and an improvement of 769 in FPS. A feasible approach to optimise the detection of unbalanced number of insects is provided in this paper along with the ability to be deployed on lightweight hardware devices. © 2024 ieee.

关键词： Invertebrates

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Harnessing image Analysis for Early Leukemia Detection in Bone Marrow Samples

Harnessing Image Analysis for Early Leukemia Detection in Bo...

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2024 ieee international conference on signal Processing, Informatics, Communication and Energy systems, SPICES 2024

作者： Rajalakshmi, R. Sivakumar, P. Krishnakumari, Ms. Subhashini, G. Ramco Institute of Technology Department of ECE. Tamil Nadu Rajapalayam India Kalasalingam Academy of Research and Education Department of ECE Tamil Nadu Krishnankoil India

ISBN: (纸本)9798350376135

White blood cells, or leukocytes, are important in determining a person's health since they can alter the immune system, leading to infections, anemia, leukemia, and other issues. Blood cancer is caused by these white blood cells proliferating beyond control. In this article, we develop a model for identifying and classifying cancer white blood cells using convolutional neural networks (CNNs). We train and test with the Bone Marrow Microscopic images of Blood Samples dataset. CNN initially processes the submitted photographs. The max pooling and convolution layers then collect features, with the convolution layer handling classification and the fully connected layer handling feature extraction. The contour technique is utilized when the output indicates that the cells are cancerous. The contour approach aids in the segmentation process by allowing for the localization of the afflicted cell. The photographs in the collection are organized into four categories: neutrophils, eosinophils, monocytes, and lymphocytes. The output image shows the type of damage, as well as the mapping of the afflicted cell. © 2024 ieee.

关键词： image segmentation

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Application of Artificial Intelligence Algorithms in Wearable Device signal Processing systems

Application of Artificial Intelligence Algorithms in Wearabl...

引用

2023 international conference on Data Science and Network Security, ICDSNS 2023

作者： Qi, Xiaojing Xiong, Ying School of Communication Engineering and Internet of Things Chongqing Telecommunication Polytechnic College Chongqing China

ISBN: (纸本)9798350301595

Wearable devices refer to electronic devices that can be worn on the body, aiming to provide users with more information and internet services. This study proposed a wearable device signal processing system based on artificial intelligence algorithms. By investigating the basic principles and technical routes of wearable device signal processing systems, suitable artificial intelligence algorithms were selected and improved. A prototype of wearable device signal processing system based on artificial intelligence algorithms was developed, tested and validated. The experimental results showed that the accuracy of the artificial intelligence algorithm in predicting health status reached 95%, with a time consumption of 2.3 seconds. The wearable device signal processing system based on artificial intelligence algorithms could not only effectively process the raw signals collected by wearable devices, but also achieve automated analysis and prediction, which had good application prospects. © 2023 ieee.

关键词： signal processing

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Research and Analysis on image Reconstruction Algorithm based on Compressed Sensing 4

Research and Analysis on Image Reconstruction Algorithm Base...

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4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Gao, Wenhuan Ren, Lifeng Jilin Agricultural Science and Technology University School of Electrical and Information Engineering Jilin China

ISBN: (纸本)9798331534622

Compressed Sensing (CS) is an efficient signal processing technique that can accurately acquire and reconstruct signals at lower sampling rates exploiting the sparsity of the signals. In the field of image processing, compressed sensing image reconstruction algorithms utilize the sparse representation of images to recover the original image from small number of observations. The core of this algorithm lies in the sparse representation of the signal, the construction of the observation matrix, and the selection of the reconstruction. First, it is assumed that the original image can be sparsely represented in a certain sparse basis, such as wavelet transform or sparse dictionary. Then the sparse coefficients are randomly sampled to obtain the observations. Finally, the image is reconstructed using a compressed sensing algorithm based on the sampling data and the observation matrix. performance of the reconstruction algorithm is affected by factors such as the sparsity of the signal, the design of the observation matrix, and the selection of the reconstruction algorithm To improve the reconstruction performance, various improved algorithms have been proposed in recent years, such as the iterative hard thresholding algorithm with variable step size and the deep learning image compressed sensing algorithm. © 2024 ieee.

关键词： image reconstruction

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Optimizing image Steganography: A Comparative Study of Pre-Processing Techniques and the Hybrid U-Encoder Network 2

Optimizing Image Steganography: A Comparative Study of Pre-P...

引用

2nd ieee international conference on Integrated Intelligence and Communication systems, ICIICS 2024

作者： Karan, Akula Sai Abhinav, Bazuru Sai Durga Ashutosh, Kande Venkata Nadukooru, Vijaya Suhaas Department of Information Technology Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331504960

image steganography requires balancing high fidelity, security, and imperceptibility when embedding secret information into cover images. Effective pre-processing and advanced deep learning models are essential for achieving these goals. In this study, we propose an enhanced image steganography framework that combines optimal preprocessing techniques with advanced deep learning-based models to achieve secure, high-quality image embedding. We first evaluate five distinct pre-processing methods-CLAHE, Bilateral Filtering, Gaussian Filtering, Median Filtering, and Laplacian Filtering-on cover images to determine their impact on performance metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Cross-Correlation (NCC). Among these techniques, Bilateral Filtering demonstrated superior results with the lowest MSE (58.78), highest PSNR (30.44 dB), and near-perfect SSIM (0.948) and NCC (0.988), making it the optimal pre-processing method for steganography. Following pre-processing, two steganography models were implemented and compared: a simple concatenation-based model and a novel Hybrid U-Encoder Network inspired by U-Net architecture. The Hybrid U-Encoder Network was designed to enhance the embedding and extraction process of secret images. Comparative analysis using MSE, PSNR, and SSIM showed that the Hybrid U-Encoder Network outperformed the concatenation-based model, achieving a lower MSE of 71.55, PSNR of 58.62 dB, and SSIM of 0.982. This research highlights the importance of effective pre-processing in improving the steganography process and demonstrates the advantage of using a Hybrid U-Encoder Network for embedding secret information. By combining Bilateral Filtering with the Hybrid U-Encoder Network, the proposed approach provides a robust solution for secure and imperceptible image steganography, significantly improving the trade-off between embedding cap

关键词： Median filters

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Post refocusing algorithm for high resolution spaceborne SAR image processing in large rolling terrain 8

Post refocusing algorithm for high resolution spaceborne SAR...

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8th international conference on Intelligent Computing and signal Processing, ICSP 2023

作者： Gao, Yang Xiang, Tianshun Yang, Juanjuan Dang, Hongxing Tan, Xiaomin China Academy of Space Technology Xi'an China

In spaceborne SAR image processing, focusing algorithms are applied with a uniform altitude throughout the observation area. The uniform altitude assumption shows good performance in most spaceborne SAR systems. Howev... 详细信息

ISBN: (纸本)9798350302455

关键词： Synthetic aperture radar

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Traffic signal Automation by Sensing and Detecting Traffic Intensity Through IR Sensors

Traffic Signal Automation by Sensing and Detecting Traffic I...

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2023 international conference on Sustainable Emerging Innovations in Engineering and technology, ICSEIET 2023

作者： Shahana, Shaik Revathi, Rajamoni Sowmya, Pothuraju Dhanalakshmi, D. Mandala, Sowmya Hariharan, Shanmugasundaram Vardhaman College of Engineering Department of Cse Hyderabad India

ISBN: (纸本)9798350329186

The rapid urbanization and increasing traffic congestion pose significant challenges for traffic management systems. To alleviate these issues, we proposed an innovative solution for traffic signal automation using internet of Things (IoT) technology. The objective is to develop a smart traffic management system that can dynamically adapt traffic signal timing based on real-time traffic intensity. This research paper presents a novel approach to traffic signal automation using IoT technology and IR sensors. The proposed system provides realtime traffic monitoring, adaptive signal control, and efficient decision-making, leading to enhanced traffic management and improved overall urban mobility. Traffic congestion is a persistent problem in urban areas, leading to increased travel times, fuel consumption, and environmental pollution. To address these challenges, this study proposes an internet of Things (IoT)-based traffic signal automation system that utilizes infrared (IR) sensors to sense and detect traffic intensity at intersections. The objective of this research is to develop an efficient and reliable traffic signal control mechanism that dynamically adapts to varying traffic conditions. The proposed system employs IR sensors strategically placed at different locations to monitor the presence and movement of vehicles. These sensors detect infrared radiation emitted by vehicles, enabling the system to estimate traffic density and occupancy. © 2023 ieee.

关键词： Microcontrollers

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