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A Vision-based Indoor Positioning Systems utilizing computer Aided Design Drawing 22

A Vision-based Indoor Positioning Systems utilizing Computer...

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28th Annual International conference on Mobile Computing and Networking (ACM MobiCom)

作者： Yoo, Dae-ha Shan, Gaoyang Roh, Byeong-hHee Ajou Univ Dept AI Convergence Network Suwon South Korea Ajou Univ Inst Informat & Commun Suwon South Korea

ISBN: (纸本)9781450391818

In recent years, with the increase in users' demand for location services, the research of Indoor Positioning Systems (IPS) has attracted much attention. Many researchers proposed schemes to estimate the user's location based on the Received Signal Strength Indicator (RSSI) values of wireless technologies. However, the RSSI value is affected by signal interference seriously. This causes the accuracy of the positioning to be greatly reduced. To solve this problem, we use computer Vision (CV) to replace traditional solutions in this paper. CV is known for its high performance and low complexity. The proposed scheme is capable of inferring the current location of users in the possible candidates from the interior structural features of buildings captured by cameras and computer-Aided Design (CAD) drawing.

关键词： Indoor Positioning System Artificial Intelligence Vision and Scene Understanding

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Dancer: Dynamic Compression and Quantization Architecture for Deep Graph Convolutional Network

Dancer: Dynamic Compression and Quantization Architecture fo...

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2025 Design, Automation and Test in Europe conference, DATE 2025

作者： Dong, Yunhao Zhong, Zhaoyu Wang, Yi Ma, Chenlin Wang, Tianyu College of Computer Science and Software Engineering Shenzhen University Shenzhen China State Key Laboratory of Radio Frequency Heterogeneous Integration China

ISBN: (纸本)9783982674100

Graph Convolutional Networks (GCNs) have been widely applied in fields such as social network analysis and recommendation systems. Recently, deep GCNs have emerged, enabling the exploration of deeper hidden information. Compared to traditional shallow GCNs, deep GCNs feature significantly more layers, leading to considerable computational and data movement challenges. Processing-In-Memory (PIM) offers a promising solution for efficiently handling GCNs by enabling near-data computation, thus reducing data transfer between processing units and memory. However, previous work mainly focused on shallow GCNs and has shown limited performance with deep GCNs. In this paper, we present Dancer, an innovative PIM-based GCN accelerator. Dancer optimizes data movement during the inference process, significantly improving efficiency and reducing energy consumption. Specifically, we introduce a novel compressed graph storage architecture and a dynamic quantization technique to minimize data transfers at each layer of the GCN. Additionally, through a detailed analysis of weight dynamics changes, we propose a sparsity propagation strategy to further alleviate the computational and data transfer burden between layers. Experimental results demonstrate that, compared to current state-of-the-art methods, Dancer achieves 3.7× speedup, 7.6× energy efficiency, and reduces of 9.6× DRAM access on average. © 2025 EDAA.

关键词： Recommender systems

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Channel Estimation for Massive MIMO Orthogonal Delay-Doppler Division Multiplexing Systems

Channel Estimation for Massive MIMO Orthogonal Delay-Doppler...

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2024 IEEE Global Communications conference, GLOBECOM 2024

作者： Wang, Dezhi Huang, Chongwen Liu, Lei Chen, Xiaoming Yang, Zhaohui He, Jiguang Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Technology Innovation Institute Abu Dhabi Masdar City9639 United Arab Emirates Nanyang Technological University School of Electrical and Electronics Engineering Singapore Khalifa University Department of Electrical Engineering and Computer Science The Ku 6G Center Abu Dhabi127788 United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France

ISBN: (纸本)9798350351255

Orthogonal delay-Doppler division multiplexing (ODDM) modulation has recently been considered a promising technology for enhancing communication system performance in high-mobility scenarios. Accurate and low-complexity channel estimation is one of the most significant challenges for massive multiple-input multiple-output (MIMO) ODDM systems, mainly due to the massive antenna arrays and high-mobility environments. In this paper, we focus on the downlink massive MIMO-ODDM communication systems, and propose a two-stage low-complexity channel estimation algorithm. Specifically, we first derive the effective channel model of the massive MIMO-ODDM systems, where the elements of the channel matrix do not follow a Bernoulli-Gaussian distribution, but their magnitudes do. Utilizing this characteristic, we employ the memory approximate message passing method to estimate the gains, delay, and Doppler of the multi-path channel, while the angles of the channel are estimated using the discrete Fourier transform method, achieving low-complexity Bayes-optimal results. Finally, numerical results demonstrate that the proposed algorithm can achieve improved estimation results, surpassing existing algorithms by approximately 2 dB. © 2024 IEEE.

关键词： Discrete Fourier transforms

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RefScale: Multi-temporal Assisted Image Rescaling in Repetitive Observation Scenarios 24

RefScale: Multi-temporal Assisted Image Rescaling in Repetit...

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32nd ACM International conference on Multimedia, MM 2024

作者： Zhang, Zhen Xiao, Jing Liao, Liang Wang, Mi School of Computer Science Wuhan University Wuhan China S-Lab for Advanced Intelligence Nanyang Technological University Singapore City Singapore State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China

ISBN: (纸本)9798400706868

With the continuous development of imaging technology and the gradual expansion of the amount of image data, how to achieve high compression efficiency of high-resolution images is a challenge problem for storage and transmission. Image rescaling aims to reduce the original data amount through downscaling to facilitate data transmission and storage before encoding, and reconstruct the quality through upscaling after decoding, which is a key technology to assist in high-ratio image compression. However, existing rescaling approaches are more focused on reconstruction quality rather than image compressibility. In repetitive observation scenarios, multi-temporal images brought by periodic observations provide an opportunity to alleviate the conflict between reconstruction quality and compressibility, that is, the historical images as reference indicates what information can be dropped at downscaling to reduce the information content in downscaled image and provides the dropped information to improve the image restoration quality at upscaling. Based on this consideration, we propose a novel multi-temporal assisted reference-based image rescaling framework (RefScale). Specifically, a referencing network is proposed to calculate the similarity map to provide the referencing condition, which is then injected into the conditional invertible neural network to guide the information drop at the downscaling stage and information fusion at the upscaling stage. Additionally, a low-resolution guidance loss is proposed to further constrain the data amount of the downscaled image. Experiments conducted on both satellite imaging and autonomous driving show the superior performance of our approach over the state-of-the-art methods. © 2024 ACM.

关键词： Image compression

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Evaluating Student Spatial Skills Learning in a Virtual Reality programming Environment

Evaluating Student Spatial Skills Learning in a Virtual Real...

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IEEE Frontiers in Education conference (FIE)

作者： Lu, Justin Seavey, Lauren Zuk, Samuel Dimino, James Martin, Fred Univ Massachusetts Lowell Kennedy Coll Sci Lowell MA 01854 USA

ISBN: (数字)9781665462440

ISBN: (纸本)9781665462440

In this research full paper, we examine students' improvement in spatial visualization skills when using MYR (short for "My Reality"), a browser-based, cloud-hosted programming environment for beginning through advanced programmers to create immersive, three-dimensional virtual reality scenes. The research literature suggests that there is correlation between students' spatial abilities and their success in programming. In this study, we conducted a three-week (six hour) virtual after-school program which introduced high school students with different programming backgrounds to coding in MYR. During the program, students learned the basics of MYR, introductory CS topics, and completed individual coding projects, creating an original MYR scene. A study examined changes in students' spatial reasoning as a result of this intervention. The students' performance in spatial skills was measured using the Revised Purdue Spatial Visualization Test: Visualization of Rotations (Revised PSVT:R) [1]. Students completed this instrument using a pre/post survey design. We analyzed the impact of the intervention using a paired samples T-test. We further developed a rubric for analyzing the sophistication of students' MYR code and applied it to evaluating the programming expertise of our study participants. The program was hosted twice with two different groups of high school students. Most showed interest in MYR programming and expressed their creativity and learned skills in their original project. With the first group of students, we found increases in their spatial visualization performance after the intervention with MYR, though statistical significance was not reached. The second group of students had higher baseline prior experience in computing and spatial visualization skills;these students did not further increase in their spatial visualization skill. The analysis showed that the MYR has a potential to improve spatial skills and engage students' interest in computing. We re

关键词： computer science education Spatial skills Virtual reality

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Edge Intelligence-Based UAV Human Target Recognition with Improved YOLOV5 Algorithm 23

Edge Intelligence-Based UAV Human Target Recognition with Im...

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23rd IEEE International conference on high performance Computing and Communications, 7th IEEE International conference on Data Science and Systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Cai, Zeyu Qu, Yuben Dong, Chao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9781665494571

Edge intelligent computing is often combined with the tasks of Unmanned Aerial Vehicle (UAV). Most applications of UAV require the location and identity information of human targets. Traditional human target detection and face recognition algorithms require a lot of time in edge intelligent computing. In addition, the final recognition result is not accurate enough. To this end, this paper proposes an improved YOLOV5-Cozy target detection algorithm to detect human targets and faces in a fast and accurate way. The algorithm uses MobileNetV3, depthwise separable convolution and shuffleconv to improve the original YOLOV5 (You Only Look Once version 5) algorithm. Compared with the original algorithm, the improved algorithm has an average detection accuracy drop of less than 5%, and its calculation time has been reduced by 20%. In terms of face recognition, we use the FaceNet algorithm with the addition of a face augmentation module. The module enhances the face recognition ability of the FaceNet algorithm in different environments, and the accuracy of face recognition has increased by 20%. We implement the proposed algorithm on the embedded computer Jetson Agx Xavier for UAV edge intelligent computing, and the average FPS achieves at about 23.6. © 2021 IEEE.

关键词： Target recognition Smart cities Convolution Face recognition Image edge detection Computational modeling Object detection

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Developing a Motor Learning System to Visualize the Force and Breath Control in Kung Fu: A Feasibility Study 11th

Developing a Motor Learning System to Visualize the Force an...

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11th International conference on Human Aspects of IT for the Aged Population, ITAP 2025, held as part of the 27th HCI International conference on Human-computer Interaction, HCII 2025

作者： Zhang, Hualin Ding, Yunlu Li, Qingchuan Wei, Xuetao Zhang, Jiaxin School of Automation and Intelligent Manufacturing Southern University of Science and Technology Guangdong Shenzhen 518055 China Future Design School Harbin Institute of Technology Shenzhen 518055 China Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen 518071 China

ISBN: (纸本)9783031927096

Kung Fu practice has been widely recognized for its health benefits, yet traditional learning methods face challenges such as lack of a scientific framework, high risk of injury, and environmental limitations. Current Kung Fu learning systems have improved these issues but fail to address the critical impact of force and breath, which are difficult to quantify and visualize. To address this gap, we designed an innovative Kung Fu learning system that visualizes force and breath and employs vibration to guide participants in exerting force at optimal moments. Experimental results demonstrate that our system with prompts significantly reduces usability and workload compared to the system without prompts. Future work should focus on enhancing movement accuracy and developing algorithms for monitoring force and breath, which will further improve the effectiveness and efficiency of Kung Fu learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Haptic feedback Motor Learning Movement Guidance performance Usability Virtual Reality Visual Effect Workload

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Solar Radio Spectrum Classification by Deep Conventional Neural Network Based on Transfer Learning

Solar Radio Spectrum Classification by Deep Conventional Neu...

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2023 International conference on New Trends in Computational Intelligence, NTCI 2023

作者： Zhao, Haiyan Wuyouteng Cheng, Jun College of Computer Science and Technology Inner Mongolia Minzu University Inner Mongolia China National Astronomical Observatories Cas Key Laboratory of Solar Activity Beijing China

ISBN: (纸本)9798350380859

The damage to communication facilities and navigation equipments may be caused by the violent solar radio explosion. With the emergence of high-performance observation equipments, solar data shows a tremendous growth trend. Deep learning, especially convolutional neural network, is used to classify solar radio spectrum images because it performs well in the processing of massive data, especially image data. In response to the lack of solar image datasets, a transfer learning method based on four pre-trained models with ImageNet image dataset is improved to solve the problem of classifying and recognizing three categories of solar spectral images. The experimental results show that MobileNet based on this model can achieve 98.95% accuracy, while the training speed of the model is improved also. © 2023 IEEE.

关键词： Convolutional neural networks

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MHSA: A Multi-scale Hypergraph Network for Mild Cognitive Impairment Detection via Synchronous and Attentive Fusion

MHSA: A Multi-scale Hypergraph Network for Mild Cognitive Im...

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2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yuan, Manman Jia, Weiming Luo, Xiong Ye, Jiazhen Zhu, Peican Li, Junlin Inner Mongolia University School of Computer Science Hohhot010000 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Northwestern Polytechnical University School of Artificial Intelligence Optics and Electronics Xi'an710072 China Inner Mongolia Autonomous Region People's Hospital Department of Imaging Medicine Hohhot010017 China

ISBN: (纸本)9798350386226

The precise detection of mild cognitive impairment (MCI) is of significant importance in preventing the deterioration of patients in a timely manner. Although hypergraphs have enhanced performance by learning and analyzing brain networks, they often only depend on vector distances between features at a single scale to infer interactions. In this paper, we deal with a more arduous challenge, hypergraph modelling with synchronization between brain regions, and design a novel framework, i.e., A Multi-scale Hypergraph Network for MCI Detection via Synchronous and Attentive Fusion (MHSA), to tackle this challenge. Specifically, our approach employs the Phase-Locking Value (PLV) to calculate the phase synchronization relationship in the spectrum domain of regions of interest (ROIs) and designs a multi-scale feature fusion mechanism to integrate dynamic connectivity features of functional magnetic resonance imaging (fMRI) from both the temporal and spectrum domains. To evaluate and op-timize the direct contribution of each ROI to phase synchronization in the temporal domain, we structure the PLV coefficients dynamically adjust strategy, and the dynamic hypergraph is modelled based on a comprehensive temporal-spectrum fusion matrix. Experiments on the real-world dataset indicate the effectiveness of our strategy. The code is available at https://***/Jia-Weiming/MHSA. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Animatable 3D Facial Detail Reconstruction from In-the-wild Images 24

Animatable 3D Facial Detail Reconstruction from In-the-wild ...

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7th International conference on Image and Graphics Processing, ICIGP 2024

作者： Liao, Zhouzhou Su, Zewei Dong, Lanfang School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9798400716720

With the introduction of the "metaverse"concept, digital human technology has garnered widespread attention, and how to quickly model a high-precision 3D human face has become a research focus in the field of digital humans. To address the issue of 3D Morphable Models (3DMM) being unable to reconstruct facial details, this paper proposes an animatable facial detail generation algorithm based on generative adversarial loss, which is used to restore more facial reliefs on the basic face obtained from three-dimensional facial reconstruction, achieving a highly realistic reconstruction of digital human faces. Firstly, this paper employs displacement mapping technology to recover facial detail information, constructing an autoencoder network to predict displacement maps from input images, controlling the vertices of the 3D model to express more relief details. Secondly, to effectively capture the mid-to-high frequency details of the face, generative adversarial loss is introduced to model the high-frequency attribute differences between images, jointly constrained with photometric losses and other content losses to ensure the accuracy of generated facial details. Lastly, to decouple static from dynamic facial details, this paper embeds 3DMM expression-related parameters into the generator model of displacement maps, and adopts an identity averaging strategy to constrain the encoder network to model only the static details of the face, whereas the dynamic details are provided by the 3DMM parameters, enabling the generation of dynamic facial details driven by expressions. The final experimental results show that the algorithm proposed in this paper demonstrates competitive performance in terms of model reconstruction quality and accuracy when compared to other state-of-the-art algorithms in facial detail generation. Additionally, the ability of the model to dynamically construct facial details was verified through expression transfer experiments, and the importance of gene

关键词： Image reconstruction

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