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An Edge Computing System for Fast Image Recognition Based on Convolutional Neural Network and Petri Net Model

Multimedia Tools and Applications 2024年第5期83卷 12849-12873页

作者： Yang, Cheng-Ying Lin, Yi-Nan Wang, Sheng-Kuan Shen, Victor R.L. Tung, Yi-Chih Lin, Jia-Fu Department of Computer Science University of Taipei 1 Ai-Kao W. Rd. Taipei 100 Taipei Taiwan Department of Electronic Engineering Ming Chi University of Technology 84 Gongzhuan Rd. Taishan District New Taipei City 243 New Taipei Taiwan Department of Electrical Engineering Ming Chi University of Technology 84 Gongzhuan Rd. Taishan District New Taipei City 243 New Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University 151 University Rd. Sanxia District New Taipei City 237 New Taipei Taiwan Department of Information Management Chaoyang University of Technology Taichung Taiwan

As a computer system can precisely detect some target objects, many application scenarios will be developed. In this study, the customized object recognition model was trained by using a software tool, You Only Look Once (YOLO), and burned into one hardware component, Maix Bit, for an image edge computing system, so as to fast recognize humans or objects in the images. In the beginning of system development, a Petri net (PN) model was established to detect all possible abnormal processes and to verify the feasibility and completeness of an edge computing system by using Petri net software tool, Workflow Petri net Designer (WoPeD). Compared with many other development boards, Maix Bit is smaller, more flexible, more economical, and more adaptable to those needs of the edge side scenarios. When equipped with an Artificial Intelligence (AI) chip, Kendryte K210, it is suitable for object recognition due to its superior power consumption, film frames, and processors. YOLO, mainly characterized by its fast speed in using the convolutional neural network (CNN) to recognize objects, can be used to predict the positions and types of multiple objects concurrently. Also, CNN can be used to detect and recognize targets from end to end promptly, when configured to train an image recognition model. Finally, the experimental results have shown that the promising mean Average Precision (mAP), 78.6%, was obtained, which outperforms other existing systems. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Object detection

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Conversion of a MATLAB Code to Python: Increasing Accessibility to Surface Plasmon Resonance Biosensing Studies

Conversion of a MATLAB Code to Python: Increasing Accessibil...

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International Conference on Advances in Signal Processing and Communication engineering, ICASPACE 2023

作者： Phillips, Jonathan Gollapalli, Ravi Terwilliger, Mark Heinrich, Sidney Department of Engineering and Industrial Professions University of North Alabama 1 Harrison Plaza FlorenceAL35632 United States Department of Computer Science and Information Systems University of North Alabama 1 Harrison Plaza FlorenceAL35632 United States

ISBN: (纸本)9789819705610

In this paper, a Python coded simulation of a surface plasmon resonance (SPR) biosensor that considers the application of electrical bias across the sensor surface is compared to the same simulation written in MATLAB. MATLAB is a high-level, expensive, and hardware demanding programming language that is commonly used in the data science domain due to its efficiency and convenience in calculation-heavy programming. However, Python is a high-level, free-to-use, popular, and extremely accessible programming language that is comparatively less demanding of hardware. The goal of this comparison was aimed at investigating the process of converting the MATLAB program to Python and determining the advantages and disadvantages of the Python simulation to determine the feasibility of performing the simulation on a more accessible computing device such as a Raspberry Pi. The conversion process included manual translation and translation performed by artificial intelligence. Upon completion of the conversion, the MATLAB simulation dramatically outperformed the Python counterpart by completing the calculations in times between 30 and 50 min;compared to Python’s completion times between 90 and 120 min. Ultimately, it was determined that while MATLAB has many disadvantages involving costs, with the use of dynamic translation it is able to execute the simulation program much more efficiently than the Python program. Therefore, economic and performance factors must be balanced for the subject utilizing the program. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Python

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Occupancy estimation in smart buildings using predictive modeling in imbalanced domains

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 10917-10929页

作者： Guo, Jiaxun Amayri, Manar Najar, Fatma Fan, Wentao Bouguila, Nizar Concordia Institute for Information Systems Engineering Concordia University MontrealQCH3G1T7 Canada Grenoble Institute of Technology Grenoble France The Department of Computer Science and Technology Huaqiao University Xiamen China

This paper introduces a novel approach for occupancy estimation in smart buildings. In particular, we focus on the challenging yet common situation where the amount of training data is small and imbalanced (i.e. the classes are not approximately equally represented). Our model is based on two parts namely predictive modelling, performed via the inverted Dirichlet mixture (IDMM), and an over-sampling approach that we propose. The first part, in which the main goal is to tackle the small training data problem, concerns the calculation of the predictive distribution of the IDMM by marginalizing over its parameters, with their posterior distributions, which are estimated by a Bayesian variational inference approach that we develop. Based on over-sampling, the second part can be viewed as a complement to tackling the imbalanced domains problem. Extensive experiments and simulations involving synthetic data as well as real data extracted from smart buildings sensors show the merits of our statistical framework. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Intelligent buildings

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Investigation of the separation zone for split-domain light field visualization 28

Investigation of the separation zone for split-domain light ...

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2024 28th International Conference on Information Technology, IT 2024

作者： Javidi, Kamran Martini, Maria G. Kara, Peter A. School of Computer Science and Mathematics Faculty of Engineering Computing and the Environment Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science Penrhyn Road Campus Kingston upon Thames LondonKT1 2EE United Kingdom Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary

ISBN: (纸本)9798350369618

Light field visualization commonly provides a single content over the entire field of view. However, the angularly-selective nature of the technology enables the simultaneous visualization of different contents at different viewing angles. Yet segmenting the valid viewing area comes with content interference as well in forms of separation zones, resulting invalid viewing domains. In this paper, we introduce a study on the separation zone of split-domain light field visualization. We created a static-content-based scenario in which we split the valid viewing area into two distinct domains, with a separation zone in between them. This was achieved by merging two light field contents in the middle with an instantaneous switch. The resulting visualization on a light field display was captured by a DSLR camera from two viewing distances, and it was compared to the crosstalk effect caused by insufficient angular resolution. © 2024 IEEE.

关键词： Visualization

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Joint Multiple Access Based on RSMA for Integrated Satellite-Terrestrial Network

Joint Multiple Access Based on RSMA for Integrated Satellite...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Han, Shuai Li, Zhiqiang Li, Cheng Benslimane, Abderrahim Harbin Institute of Technology School of Electronics and Information Engineering Harbin150001 China Simon Fraser University School of Engineering Science BurnabyBCV5A 1S6 Canada University of Avignon Department of Computer Science and Engineering Avignon84029 France

ISBN: (纸本)9798350351255

The integrated satellite-terrestrial network (ISTN) has attracted much interest due to its global information serviceability. Recently, rate-splitting multiple access (RSMA) has been widely investigated to achieve highly efficient access. Motivated by this, we design the joint RSMA scheme based on spectrum sharing for the downlink ISTN, where part data of terminals are shared by satellite and base station. Furthermore, the max-min rate (MMR) maximization problem is formulated, and an alternating optimization algorithm based on weighted minimum mean square error is introduced to solve the non-convex problem. Simulations show that the joint RSMA scheme has a higher MMR than baseline schemes. © 2024 IEEE.

关键词： Satellite communication systems

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Leveraging Wavelets and Deep CNN for Sleep Pattern Recognition in Road Safety: An EEG Study 1

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6th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023

作者： Arif, Saad Munawar, Saba Marie, Rashiq Rafiq Shah, Syed Aziz Department of Mechanical Engineering HITEC University Taxila47080 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Wah Campus Wah Cantt47040 Pakistan College of Computer Science and Engineering Taibah University Medina41477 Saudi Arabia Research Centre for Intelligent Healthcare Coventry University CoventryCV1 5FB United Kingdom

ISBN: (数字)9783031530821

ISBN: (纸本)9783031530814

A major cause of fatal traffic accidents and injuries worldwide is drowsy driving. The fatal aftereffects can be considerably reduced, and road safety can rise, with earlier and more efficient diagnosis. In this study, electroencephalography (EEG) biosignals-based brain-computer interface (BCI) is established for early identification of human inattentiveness during driving activities. EEG signals are recorded from six EEG electrode positions viz Fp1, Fp2, F7, F8, O1, and O2. Spectral information of the alpha and beta frequency bands is extracted in terms of continuous wavelet transform (CWT) with a temporal correlation for complete experiments. CWT components are processed as scalograms which are color images used for visual inspection of brain states. The scalogram images of 10 s epochs are stored as the image dataset. The SqueezeNet deep convolutional neural network (CNN) is fine-tuned with transfer learning for effective drowsiness detection from CWT scalograms. Transfer-learned CNN classification model achieved the best results with 89.8% accuracy, 88.0% precision, 96.1% recall, 89.9% F1 -score, 87.7% specificity, and 79.7% Matthew’s correlation coefficient. The statistical significance of the achieved results is validated with a p-value of less than 0.05. Using wavelet information of EEG biosignals, the suggested BCI methodology offers a viable method for quicker and more accurate driving drowsiness detection. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electroencephalography

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Robust Hybrid Transceiver Designs for Linear Decentralized Estimation in mmWave MIMO IoT Networks in the Face of Imperfect CSI

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IEEE Internet of Things Journal 2023年第20期10卷 18125-18139页

作者： Maity, Priyanka Rajput, Kunwar Pritiraj Srivastava, Suraj Venkategowda, Naveen K. D. Jagannatham, Aditya K. Hanzo, Lajos Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur208016 India Kirchberg1855 Luxembourg Linköping University Department of Science and Technology Norrköping60174 Sweden University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

Hybrid transceivers are designed for linear decentralized estimation (LDE) in a mmWave multiple-input-multiple-output (MIMO) IoT network (IoTNe). For a noiseless fusion center (FC), it is demonstrated that the mean squared error (MSE) performance is determined by the number of RF chains used at each IoT node (IoTNo). Next, the minimum-MSE RF transmit precoders (TPCs) and receiver combiner (RC) matrices are designed for this setup using the dominant array response vectors, and subsequently, a closed-form expression is obtained for the baseband (BB) TPC at each IoTNo using Cauchy's interlacing theorem. For a realistic noisy FC, it is shown that the resultant MSE minimization problem is nonconvex. To address this challenge, a block-coordinate descent-based iterative scheme is proposed to obtain the fully digital TPC and RC matrices followed by the simultaneous orthogonal matching pursuit (SOMP) technique for decomposing the fully digital transceiver into its corresponding RF and BB components. A theoretical proof of the convergence is also presented for the proposed iterative design procedure. Furthermore, robust hybrid transceiver designs are also derived for a practical scenario in the face of channel state information (CSI) uncertainty. The centralized MMSE lower bound has also been derived that benchmarks the performance of the proposed LDE schemes. Finally, our numerical results characterize the performance of the proposed transceivers as well as corroborate our various analytical propositions. © 2014 IEEE.

关键词： Wireless sensor networks

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Non-Orthogonal Broadcast and Unicast Transmission Based on Novel Centralized Frequency Reuse for Multibeam Satellite System

Non-Orthogonal Broadcast and Unicast Transmission Based on N...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Zhiqiang Han, Shuai Li, Cheng Benslimane, Abderrahim Harbin Institute of Technology School of Electronics and Information Engineering Harbin150001 China Simon Fraser University School of Engineering Science BurnabyBCV5A 1S6 Canada University of Avignon Department of Computer Science and Engineering Avignon84029 France

ISBN: (纸本)9798350351255

The multibeam satellite system is crucial for the next generation communication, providing seamless and various information services, such as broadcast and unicast messages. However, catering to the burgeoning number of users within limited spectrum resources presents formidable challenges. In response, rate-splitting multiple access (RSMA) has emerged, leveraging non-orthogonal transmission and precoding strategies concurrently. Therefore, we devise the non-orthogonal broadcast and unicast (NOBU) joint transmission framework using RSMA. Furthermore, amalgamating traditional precoding with frequency reuse techniques, we propose a novel centralized frequency reuse strategy, exhibiting commendable performance alongside reduced computational complexity. Furthermore, we maximize the weighted sum rate (WSR) and introduce an improved alternating optimization algorithm, adept at converting intricate non-convex problem into tractable convex counterpart. Simulation outcomes demonstrate that our proposed schemes have significant improvements in WSR performance and are promising for various practical applications. © 2024 IEEE.

关键词： Convex optimization

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Sensorimotor System Design of Socially Intelligent Robots 26th

Sensorimotor System Design of Socially Intelligent Robots

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26th International Conference on Pattern Recognition, ICPR 2022

作者： Trajcevski, Aleksander Filho, Helio Perroni Javed, Nizwa Naheyan, Tasneem Bhargava, Kartikeya Elder, James H. Department of Electrical Engineering and Computer Science Centre for AI & Society York University TorontoONM3J 1P3 Canada

We describe and demonstrate a sensorimotor architecture for social robots, highlighting in particular the value of attentive sensing for acquisition of more detailed information about people in the far field, and how ... 详细信息

ISBN: (纸本)9783031377440

关键词： Microrobots

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Efficient convolutional neural networks on Raspberry Pi for image classification

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Journal of Real-Time Image Processing 2023年第2期20卷 21页

作者： Ju, Rui-Yang Lin, Ting-Yu Jian, Jia-Hao Chiang, Jen-Shiun Department of Electrical and Computer Engineering Tamkang University No. 151 Yingzhuan Rd. Tamsui Dist. New Taipei City251301 Taiwan Department of Engineering Science National Cheng Kung University No. 1 University Rd. East Dist. Tainan City70101 Taiwan

With the good performance of deep learning in the field of computer vision (CV), the convolutional neural network (CNN) architectures have become main backbones of image recognition tasks. With the widespread use of mobile devices, neural network models based on platforms with low computing power are gradually being paid attention. However, due to the limitation of computing power, deep learning algorithms are usually not available on mobile devices. This paper proposes a lightweight convolutional neural network TripleNet, which can operate easily on Raspberry Pi. Adopted from the concept of block connections in ThreshNet, the newly proposed network model compresses and accelerates the network model, reduces the amount of parameters of the network, and shortens the inference time of each image while ensuring the accuracy. Our proposed TripleNet and other State-of-the-Art (SOTA) neural networks perform image classification experiments with the CIFAR-10 and SVHN datasets on Raspberry Pi. The experimental results show that, compared with GhostNet, MobileNet, ThreshNet, EfficientNet, and HarDNet, the inference time of TripleNet per image is shortened by 15%, 16%, 17%, 24%, and 30%, respectively. The detail codes of this work are available at https://***/RuiyangJu/TripleNet. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Image classification

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