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A three-dimensional DV-Hop algorithm based on the improved grey wolf algorithm for wireless sensor networks 3

A three-dimensional DV-Hop algorithm based on the improved g...

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3rd international conference on Advanced Algorithms and signal image Processing, AASIP 2023

作者： Ye, Shuai Yu, Xiuwu Liu, Yong School of Electrical Engineering University of South China Hengyang China

ISBN: (纸本)9781510668522

Location technology has become one of the core technologies of the internet of Things, especially for wireless sensor networks. The DV-Hop algorithm is used in various situations as one of the representative algorithms. Aiming at the large positioning error of the DV-Hop algorithm, this paper proposes a three-dimensional DV-Hop algorithm based on the improved Grey Wolf algorithm (TDIGW). First of all, the algorithm refines the number of hops through a multicommunication radius strategy, which reduces errors caused by the number of node hops. Then, mean square error is introduced to optimize the average hop distance of the beacon nodes. Finally, the grey wolf algorithm with dynamic weight factor optimization is used instead of least squares to calculate the coordinate positions of the unknown nodes. The simulation results show that compared with the traditional 3D-DVHop algorithm and other *** average localization error of this algorithm is reduced by 20.13%, 12.62% and 8.98% respectively. The algorithm proposed in this paper reduces the localization error effectively © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Sensor nodes

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Fish Detection and Quantity Estimation based on Sonar images 1st

Fish Detection and Quantity Estimation Based on Sonar Images

引用

1st international conference on Extended Reality, ICXR 2024

作者： Du, Wenxiang Yan, Shuai Qi, Yue State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Qingdao Research Institute of Beihang University Qingdao266100 China

ISBN: (纸本)9789819636785

Due to the complex underwater environment and dense targets, it is difficult to quickly and accurately calculate the location and quantity of fish targets in marine ranching. We have developed an underwater target detection and counting algorithm based on sonar images, which includes four parts: image preprocessing, background subtraction, contour detection, and target counting. Firstly, we design a sliding-window-based gain algorithm based on the uneven grayscale value of sonar images, which amplifies the effective signal while smoothing the grayscale of the image. Secondly, background subtraction is used to separate the foreground and background of the sonar image, which can remove background noise and filter the target using a filtering algorithm to generate a binary image with clear targets. Then, target detection is combined with morphological processing techniques and contour detection algorithms. Finally, we use image erosion technology to separate overlapping targets, calculate the contour position of the targets, and count the quantity. A large number of results indicate that our algorithm can quickly and accurately detect the position of fish in sonar images while also counting the number of fishes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Binary images

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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...

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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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SuperTA-Net: A Supervised Transmission-Augmented Network for image Compressive Sensing Reconstruction 6

SuperTA-Net: A Supervised Transmission-Augmented Network for...

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6th international conference on Data-Driven Optimization of Complex systems (DOCS)

作者： Zhang, Zhijie Bai, Huang Stankovic, Ljubisa Sun, Junmei Li, Xiumei Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou Peoples R China Univ Montenegro Fac Elect Engn Podgorica Montenegro

ISBN: (纸本)9798350377859;9798350377842

Compressive sensing (CS) has seen extensive use in signal processing, particularly in tasks related to image reconstruction. CS simplifies the sampling and compression procedures, but leaves the difficulty to the nolinear reconstruction. Traditional CS reconstruction algorithms are usually iterative, having a complete theoretical foundation. However, these iterative algorithms are constrained by significant computational complexity. While modern deep network-based methods can achieve high-precision reconstruction in compressed sensing (CS) with satisfactory speed, they often lack theoretical analysis and interpretability. To leverage the strengths of both types of CS methods, the deep unfolding networks (DUNs) have been developed. In this paper, a novel DUN named supervised transmission-augmented network (SuperTA-Net) is proposed. based on the framework of our previous work PIPO-Net, the multi-channel transmission strategy is put forward to reduce the influence of critical information loss between modules and improve the reliability of data. Besides, in order to avoid the issues such as high information redundancy and high computational burden when too many channels are set, the attention based supervision scheme is presented to dynamically adjust the weight of each channel and remove the redundant information. Through experiments focused on reconstructing CS images, the proposed neural network architectures are shown to be highly effective.

关键词： Compressive sensing deep unfolding network multi-channel transmission attention based supervision alternating optimization

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IOT based Smart Wheelchair for Elderly Healthcare Monitoring

IOT Based Smart Wheelchair for Elderly Healthcare Monitoring

引用

2023 international conference on Power, Instrumentation, Energy and Control, PIECON 2023

作者： Dar, Reyaz Ahmad Khatoon, Shahida Saleem, Basharat Khan, Huma Jamia Millia Islamia Department of Electrical Engineering New Delhi India Uttrakhand Technical University Department of Computer Science Dehradun India

ISBN: (纸本)9798350399769

With every field of technology witnessing advancement and innovation, application of such innovations with refined expertise has eased human welfare. In present times also, internet of Things (IOT) is proving to be panacea of all hiccups for handicapped patients. Patients enduring physical disabilities cannot take care of themselves sans help of other individuals, and therefore need artificial tools to move around like wheelchair. Under a promising technology, smart/capable wheelchairs are supposed to be developed which are laced with pro-active sensory system. The technology incorporated is internet of Things (IOT). The disabled patients using these rare wheelchairs, no longer need to depend on others rather sensors fitted with the wheelchairs, monitor the vital bio indices as that of blood, heart, lungs and neural system. In case of any irregularity, signal transaction using embedded systems, onto cloud takes place. This results in flagging a signal which can be accessed by doctors available in online mode. Hence the redressel of medical problems would become much more effective, easier and in less time. IOT is certainly a promising technology for medical care also. © 2023 IEEE.

关键词： internet of things

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Design of XSRP-based automatic identification system for Analog modulated signals 3

Design of XSRP-based automatic identification system for Ana...

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3rd international conference on Green Communication, Network, and internet of Things, CNIoT 2023

作者： Xu, Jiayue Ni, Ming Zhu, Danjiang Ding, Xiyao East China Institute of Computer Technology Shanghai201800 China College of Intelligence Science and Technology National University of Defense Technology Changsha410000 China

ISBN: (纸本)9781510668867

The automatic identification of the modulation mode of the communication signal is the main task of the intelligent receiver, which is now widely used in a variety of fields, including civil and military. In this paper, an XSRP-based analog modulated signal automatic identification system is designed, and the overall program block diagram is drawn using LabVIEW software. The design of the automatic identification system is divided into seven modules, focusing on the following two modules: the first is the extraction of each feature parameter when the modulated signals of AM, DSB, SSB and FM are subdivided layer by layer, and the second is the simulation of modulation mode prediction using the library function of MATLAB BP neural network algorithm. Simulations were carried out in conjunction with the XSRP software radio platform, with suitable parameters and sample sizes selected for validation, and improvements made to feature parameters to improve system performance. Computer simulations show that the XSRP-based analogue modulated signals are eventually identified correctly at no less than 95% and can be expected to be used for the detection and fast identification of signals in practical non-collaborative communication systems. © 2023 SPIE.

关键词： MATLAB

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SIW-based Network Antenna Design and Analysis 8

SIW-based Network Antenna Design and Analysis

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8th IEEE international conference on image and signal Processing and their Applications, ISPA 2024

作者： Abed, Ahcene Amrouche, Aissa Kraimia, Hind Bouchekhlal, Ahmed Bendoumia, Redha Herizi, Abdelghafour PO Box166 Ichebilia M'sila28000 Algeria CRSTDLA Scientific and Technical Research Center Fir the Development of Arabic Language Algiers Algeria University of Blida 1 DIC Laboratory Department of Electronics Algeria Telecommunications Department Po Box 11 Tipaza Kolea42070 Algeria

ISBN: (纸本)9798350309249

This work is concerned with the design of an antenna array using SIW technology, where the basic element is a rectangular patch antenna. The main goal is to improve the radiation pattern. This technology allows this type of antenna to be produced at a lower cost and easily integrated into communications systems. The antenna was designed in the HFSS simulator on a FR-4 Epoxy substrate with a dielectric constant of ϵr=4.4 and a thickness of 1.6mm. Three structures have been proposed: the array antenna (AR2), the array antenna (AR4), and the SIW array antenna (SAR4). This paper proposes to insert metal vias between the four elements of the AR4 antenna to direct the antenna radiation towards the main lobe. The antenna resonates at two different frequencies: 6.1 GHz with a matching coefficient S11=17.77 dB and 6.85 GHz with a matching coefficient S11=16.63dB. © 2024 IEEE.

关键词： Microstrip antennas

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Efficient Employee Attendance System Integrating RFID and Android-based Face Recognition with Liveness Detection

Efficient Employee Attendance System Integrating RFID and An...

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2024 international conference on Electrical and Information technology, IEIT 2024

作者： Malabi, Yudas Haniah, Mamluatul Noprianto Wijayaningrum, Vivi Nur Al Hadid Firdaus, Vipkas Himawan, Afrizal Politeknik Negeri Malang Department of Information Technology Malang Indonesia Pt Hummatech Digital Indonesia Malang Indonesia

ISBN: (纸本)9798331516888

Attendance systems are crucial in corporate management, as accurate attendance recording allows companies to manage human resources more efficiently, identify attendance patterns, and improve employee productivity. With the advancement of information technology, attendance systems have utilized various technologies, such as RFID cards, biometrics like fingerprints or facial recognition, and mobile-based attendance systems. RFID-based attendance systems are widely used due to their ease of quickly and accurately recording attendance. However, these systems have limitations, such as the possibility of RFID cards being lost or damaged and being lent or misused by others, thus not always ensuring the actual physical presence of employees. Therefore, integrating attendance systems with RFID and human biometric features is necessary to ensure employee presence. This study proposes an Android-based attendance system using face recognition and liveness detection technology with CNN (Convolutional Neural Network) integrated with RFID. This technology ensures attendance is recorded only if the employee is on-site and recognized as a genuine cardholder. The MiniFASNet model, which used Fourier transforms to transform the face image into a frequency wave signal as a liveness detection feature, was combined with the FaceNet model for facial feature extraction. Cosine similarity is then used to determine the similarity between real-time captured facial features and the stored dataset features. The experiment results showed a liveness detection confidence score of 0.9 and an average cosine similarity threshold of 0.65 for face recognition. The best recognition accuracy achieved was 98.46% at a distance 50 cm with a front-facing pose and bright lighting conditions. © 2024 IEEE.

关键词： Human resource management

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Cross-modal image text retrieval based on Cross-optimization and Joint strategy

Cross-modal image text retrieval based on Cross-optimization...

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

作者： Zheng, Wei Han, Na Hu, Yan Kang, Peipei Zhang, Yue Yang, Aqing Zhang, Lei School of Computer Science Guangdong Polytechnic Normal University China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data China School of Computer Science and Technology Guangdong University of Technology China School of Information Engineering East University of Heilongjiang China

ISBN: (纸本)9781510680425

In the fields of computer vision and natural language processing, cross-modal retrieval is of great importance that cannot be ignored. In existing multi-granularity alignment methods, significant progress has been made by globally aligning images and sentences or locally aligning regions and words. However, most of these methods rely on attention mechanisms, which may be affected by noise and cannot balance the differences between modalities, resulting in suboptimal image-text alignment. In addition, training with attention mechanisms consumes a lot of computational resources, and the retrieval process is time-consuming. To address these challenges, we develop a novel multigranularity image-text alignment model, which we call DFVLM. First, we independently train the intra-modal encoder and cross-modal encoder to reduce interference between different modal encoders and better learn intra-modal and inter-modal information. Then, we propose a joint strategy to combine the intra-modal and inter-modal information to better capture the information between image and text. In addition, we introduce a hard negative pair-based method to train the performance of the intra-modal multi-granularity encoder without using attention mechanisms. Unlike existing methods, We treat image and text retrieval as a two-way process and delve into the intrinsic connections between the two matrices. We propose a cross-validation method to optimize the retrieval result based on common image-to-text alignment scenarios in daily life, i.e., during the image-to-text retrieval process, a text-to-image reverse verification is performed on the retrieved corresponding text. Through extensive qualitative experiments and analysis, our approach performs well on both Flickr30K and MSCOCO datasets and also significantly reduces the time required for testing. © 2024 SPIE.

关键词： Natural language processing systems

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MobileNet-V3-based sea-land segmentation for maritime search radars 15

MobileNet-V3-based sea-land segmentation for maritime search...

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15th international conference on signal Processing systems, ICSPS 2023

作者： Xue, Jian Ma, Manshan School of Communications and Information Engineering Xi’an University of Posts and Telecommunications China

ISBN: (纸本)9781510675056

The effective perception of marine detection scenarios is essential for maritime search pulse radar to detect and track maritime targets. However, the dynamically changing and complex marine environment makes it challenging for maritime search pulse radar to accurately perceive the sea and land regions. To improve the accuracy of sea-land region perception, and reduce the computation and complexity of algorithm of the network, this paper proposes a MobileNetV3-based maritime search pulse radar sea-land segmentation method. Firstly, a radar sea-land segmentation dataset based on PPI (Plan Position Indicator) is constructed using real measured data from different scenarios and sea conditions. Subsequently, the MobileNetV3 network is trained on this dataset to achieve sea-land segmentation for maritime search pulse radar. Experimental results demonstrate that the segmentation accuracy of the MobileNetV3-Lbased radar sea-land segmentation method surpasses its competitors. © 2024 SPIE.

关键词： image segmentation

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