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A Comparative Analysis of Deep Learning-Based Object Detectors for embedded systems 12

A Comparative Analysis of Deep Learning-Based Object Detecto...

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12th ieee international conference on Intelligent Data Acquisition and Advanced computing systems: Technology and applications, IDAACS 2023

作者： Boltov, Yehor Skarga-Bandurova, Inna Derkach, Maryna G.E. Pukhov Institute for Modelling in Energy Engineering Kyiv03164 Ukraine Oxford Brookes University OxfordOX33 1HX United Kingdom Ternopil Ivan Puluj National Technical University Ternopil46001 Ukraine

ISBN: (纸本)9798350358056

the paper presents a comparative analysis of popular deep learning-based object detectors, focusing on evaluating their real-time performance on embedded platforms. the objective was to identify the optimal algorithm that can efficiently operate within limited computational resources and memory constraints of embedded systems while minimizing energy consumption. We tested a set of popular detectors, such as Faster R-CNN, YOLO, and SSD MobileNet and evaluated their performance on the testing platform with CUDA cores. A faster variant of bounding box suppression, blazing fast Non-Maximum Suppression (NMS), was employed to enhance efficiency. Additionally, the generalized intersection over union (GIoU) metric was adopted as the optimal testing method, allowing for more accurate object localization and detection. Results demonstrate that the SSD MobileNet-V2 and Tiny YOLO detectors exhibit promising real-time performance on embedded platforms, although their accuracy is diminished compared to larger models. © 2023 ieee.

关键词： blazing fast NMS COCO CUDA embedded system Fast R-CNN GIoU object detector real-time SSD MobileNet YOLO

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Lightweight Security Authentication Scheme for Vehicle-Road Collaboration using Physical Unclonable Function 21

Lightweight Security Authentication Scheme for Vehicle-Road ...

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21st ieee international Symposium on Parallel and Distributed Processing with applications, 13th ieee international conference on Big Data and Cloud computing, 16th ieee international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xia, Zhuoqun Su, Chao Xu, Zisang Long, Kejun Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems Changsha China

ISBN: (纸本)9798350329223

the Internet of Vehicles (IoVs) is widely used to obtain information about vehicles and road conditions, which is transmitted in public channels. Hence, the most important requirement is the data security, which needs to keep in a strict delay. Authentication is a common method to solve it. Due to limited resources and delay sensitivity of IoVs, vehicles must complete authentication within appropriate resources cost and delay. However, existing schemes are prone to physical, forgery and collusion attacks, and moreover, they are computationally heavy. therefore, we propose a lightweight security identity authentication scheme for vehicle-road collaboration, which utilizes lightweight Physical Unclonable Function (PUF) as the trust root of entities to resist physical and collusion attacks;Besides, most of computation is offloaded to Road Side Units (RSUs) certified by Trusted Authority (TA) through the vehicle-road collaboration architecture. In addition, we utilize challenge-response pairs (CRPs) to protect privacy and expose malicious vehicular identities in identity tracking phase. Furthermore, there are formal and informal security analyses to prove our scheme is secure. Finally, the simulation experiment shows our scheme is more secure and efficient than other schemes in real scenarios. © 2023 ieee.

关键词： Roads and streets

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Semi-asynchronous Federated Learning Optimized for NON-IID Data Communication based on Tensor Decomposition 21

Semi-asynchronous Federated Learning Optimized for NON-IID D...

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作者： Wu, Qidong Fu, Xuemei Yang, Xiangli Zhao, Ruonan Wu, Chengqian Zhang, Tinghua Hainan University School of Computer Science and Technology Haikou China Hainan University School of Information and Communication Engineering Haikou China

ISBN: (纸本)9798350329223

Federated Learning (FL) is a new distributed machine learning framework that enables reliable collaborative training without collecting users' private data, it can successfully address the issue of data silos. However, FL has had trouble scaling to statistically varied data and large-scale models because of its frequent communication and average aggregation procedures. In this paper, we propose a Semi-asynchronous Stochastic Controlled Averaging based on Tensor Decomposition for Federated Learning (SATD-SCAFFOLD), in which we perform tensor decomposition on the client model to reduce communication costs. In addition, we design a semi-asynchronous aggregation approach for the server, which can prevent server from wasting too much time waiting for data from late clients in the case of an unstable network, and the overall model still has excellent performance. thorough experiments demonstrate that our proposed SATDSCAFFOLD algorithm can reduce both the communication cost and convergence time while maintaining good performance. © 2023 ieee.

关键词： Stochastic systems

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Discrete Geometric Coded Data Layout for Large-scale Object Storage systems 21

Discrete Geometric Coded Data Layout for Large-scale Object ...

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作者： Tian, Yi Xu, Guangping Yang, Hongzhang Ni, Yue Cao, JiaXin Yang, Lei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Novel Software Technology Tianjin Key Laboratory of Intelligence Computing Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Information Technology Co. Ltd. China

ISBN: (纸本)9798350329223

Regenerating codes are new network codes proposed to reduce the data required for fault repair, which can improve the recovery efficiency of faulty nodes in data storage systems. However, unlike Reed-Solomon code, which repairs at the granularity of bytes, regenerating codes require data stored in large chunks but leads to severe read amplification, which reads out excess data when degraded read objects and increases degraded read time. that reflects a mutual constraint between improving recovery efficiency and degraded read performance, as manifested in the amplification of data reads, a vital issue considered in this *** solve this problem, we propose a new type of data layout - discrete geometric, which splits the object into a series of geometric sequences of data blocks. they are placed discretely into corresponding containers on the disks at different nodes, with containers of the same size made into a strip for encoding. the discrete characteristic ensures lower repair costs for degraded reads. the geometric characteristic ensures the repair performance of regenerating codes by large blocks, and read amplification can be mitigated through small blocks. To reduce IOPS for discrete geometric, we propose Discrete Geometric-Locally Regenerating Codes (DG-LRCs), guaranteeing lower degraded read latency while improving recovery *** results show that the degraded read time of DG-LRCs compared to regenerating codes combined with geometric partitioning is 22.56% lower at 2Gbpsand 60.56% lower at 4Gbps, and the recovery performance is 7.04 times better than that of RS code. © 2023 ieee.

关键词： Efficiency

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Performance Acceleration of Secure Machine Learning Computations for Edge applications 28

Performance Acceleration of Secure Machine Learning Computat...

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28th ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Lin, Zi-Jie Wang, Chuan-Chi Tu, Chia-Heng Hung, Shih-Hao Natl Taiwan Univ Taipei Taiwan Natl Cheng Kung Univ Tainan Taiwan

ISBN: (数字)9781665453448

ISBN: (纸本)9781665453448

Edge appliances built with machine learning applications have been gradually adopted in a wide variety of application fields, such as intelligent transportation, the banking industry, and medical diagnosis. Privacy-preserving computation approaches can be used on smart appliances in order to secure the privacy of sensitive data, including application data and the parameters of machine learning models. Nevertheless, the data privacy is achieved at the cost of execution time. that is, the execution speed of a secure machine learning application is several orders of magnitude slower than that of the application in plaintext. Especially, the performance gap is enlarged for edge appliances. In this work, in order to improve the execution efficiency of secure applications, an open-source software framework CrypTen is targeted, which is widely used for building secure machine learning applications using the Secure Multi-Party Computation (SMPC) based privacy-preserving computation approach. We analyze the performance characteristics of the secure machine learning applications built with CrypTen, and the analysis reveals that the communication overhead hinders the execution of the secure applications. To tackle the issue, a communication library, OpenMPI, is added to the CrypTen framework as a new communication backend to boost the application performance by up to 50%. We further develop a hybrid communication scheme by combining the OpenMPI backend with the original communication backend with the CrypTen framework. the experimental results show that the enhanced CrypTen framework is able to provide better performance for the small-size data (LeNet5 on MNIST dataset by up to 50% of speedup) and maintain similar performance for large-size data (AlexNet on CIFAR-10), compared to the original CrypTen framework.

关键词： Edge appliance communication performance communication library OpenMPI library secure multi-party computation deep learning model inference machine learning applications CrypTen framework

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Tweeting from Left to Right: Tracing the Dynamics of Emotional Messaging and Public Engagement During the 2018 vs. 2022 US Campaigns 21

Tweeting from Left to Right: Tracing the Dynamics of Emotion...

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作者： Wang, Meng-Jie University of Canterbury School of Psychology Speech and Hearing Christchurch New Zealand

ISBN: (纸本)9798350329223

Despite numerous studies delving into social media politicking, there is, thus far, little understanding of how distinct emotions spread online. Much of the prior work has focused on positive vs. negative diffusion or measured emotional rhetoric without considering public engagement on such sites. Since elected officials increasingly turn to social media, even less is known about the impact of distinct emotional messaging on mobilizing users, attracting supporters, or disseminating political speeches. Moving beyond merely a valence-based approach, the study here seeks to address this gap by analyzing over 23,000 tweets posted by Senate candidates during the 2018 and 2022 US midterm elections through natural language processing. Specifically, leveraging the IBM Tone Analyzer, the present work implements a cross-sectional examination to explore: a) whether certain emotions - joy, confidence, anger, fear, and sadness - conveyed in candidates' tweets might be more likely to garner public attention;and b) if so, does this pattern vary across the political spectrum, illuminating the ways in which Democratic vs. Republican candidates adopt to expand their online reach over time? © 2023 ieee.

关键词： Natural language processing systems

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Execution of Resource Intensive Tasks on a Heterogeneous SoC for Low-Latency embedded Compute 24

Execution of Resource Intensive Tasks on a Heterogeneous SoC...

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24th international Carpathian Control conference, ICCC 2023

作者： Fekete, Gabor Kovacshazy, Tamas Budapest University of Technology and Economics Department of Measurement and Information Systems Budapest Hungary

ISBN: (纸本)9798350310221

Execution of resource-intensive tasks, such as artificial intelligence (AI), big-data algorithms, video processing, etc. is a common requirement in distributed embedded systems today. the typical solution is to execute such algorithms on some cloud computing service, as embedded systems do not have the computing resources for such tasks. However, this approach is not viable in real-time Cyber-Physical systems (RT-CPS) due to the high-latency, non-real-time nature of cloud computing. the latency requirements can be only satisfied with an edge compute solution in the form of an edge data center or by a heterogeneous System on Chip (SoC) based implementation, which has the required high-performance computing resources on the chip. In the paper, we investigate the viability of the heterogeneous SoC compute approach on the Texas Instruments AM5729 SoC, which was specifically designed for such applications. the paper introduces the hardware capabilities of the SoC, the software development, and the run-time (Linux-based) environment, and presents the results of a detailed performance evaluation on relevant computing tasks. © 2023 ieee.

关键词： System-on-chip

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SSE-YOLO: A Lightweight Model for Efficient Fire Detection in real-time applications 5

SSE-YOLO: A Lightweight Model for Efficient Fire Detection i...

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5th international conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Qiu, Zhike Qin, Yuhao Southwest University Business College Chongqing402460 China

ISBN: (纸本)9798331506612

the SSE-YOLO model, optimized for object detection, has exhibited exceptional effectiveness in fire detection tasks, particularly within environments where computational resources are limited, such as embedded devices and edge computing platforms. While the standard YOLOv8 model achieves commendable accuracy, its substantial computational load and memory requirements present obstacles for its adoption in embedded systems. In response to these limitations, this paper introduces the SSE-YOLO model, which integrates the SimAM module, a Slim-neck architecture, and a Shared Convolutional Detection head to enhance performance. the SimAM module is incorporated into the backbone network to improve feature extraction efficiency without increasing parameter counts. Additionally, the Slim-neck architecture is applied to streamline the model, reducing both computational burden and parameter complexity. Lastly, the Shared Convolutional Detection head is employed to further boost the model's computational efficiency, thereby lowering resource usage during inference. Experimental evaluations on a fire detection dataset reveal that SSE-YOLO reduces model complexity and parameters by 45% and 31%, respectively, compared to YOLOv8n, while achieving improvements of 1.8% in mAP@0.5 and 3.8% in mAP@0.5:0.95. these findings demonstrate that SSE-YOLO delivers significant reductions in computational requirements while sustaining high detection performance, making it an ideal candidate for real-time fire detection on mobile and embedded systems. © 2024 ieee.

关键词： Fire detectors

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An Integrated real-time and Security Scheduling Framework for CPS 29

An Integrated Real-Time and Security Scheduling Framework fo...

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29th ieee international conference on embedded and real-time computing systems and applications, RTCSA 2023

作者： Kansal, Kriti Chantem, thidapat Fisher, Nathan Baruah, Sanjoy Virginia Tech ECE Dept. ArlingtonVA United States Wayne State University CS Dept. DetroitMI United States Washington University in St. Louis CSE Dept. St. LouisMO United States

ISBN: (纸本)9798350337860

In the world of real-time systems (RTS), security has often been overlooked in the design process. However, with the emergence of the Internet of things and Cyber-Physical systems, RTS are now frequently used in interconnected applications where data is shared regularly. Unfortunately, this increased connectivity has also led to a larger attack surface. As a result, it is crucial to redesign RTS to not only meet real-time requirements but also to be resilient to threats. To address this issue, we propose a new real-time security co-design task model, and an accompanying scheduling framework, where schedulability can be used to indicate whether both real-time and security requirements are met. Our algorithm is designed to be flexible, allowing different security mechanisms to be used along with real-time tasks. Specifically, we augment the frame-based task model by introducing an n-dimensional security matrix, which serves as a powerful tool to enable our approach. this matrix clearly indicates which defense mechanisms are available for each task in the system by storing the worst-case execution times of tasks. then, we transform the problem of maximizing security, subject to schedulability, into a variant of the knapsack problem. To make this approach more practical, we implement a fully polynomial time approximation scheme (FPTAS) that reduces the time complexity of solving the knapsack problem from a pseudo-polynomial to a fully polynomial. We also experiment with a greedy-heuristic approach and compare the results of both algorithms. By using an FPTAS, we were able to significantly improve the efficiency of calculating the maximum security and produce near-optimal results against the optimal solution. Our experiments showed that an FPTAS can process a batch of 10,000 task sets 1.5 times faster than the traditional dynamic programming approach. © 2023 ieee.

关键词： real time systems

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Application of YOLO Object Detector for Horse Stress Monitoring During Transportation 5

Application of YOLO Object Detector for Horse Stress Monitor...

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5th ieee international conference on Cybernetics, Cognition and Machine Learning applications, ICCCMLA 2023

作者： Flatscher, Andreas Flatscher, Andreas Stojanovic, Branka Joanneum Research Forschungsgesellschaft MbH Austria Joanneum Research Austria

ISBN: (纸本)9798350338287

the emerging field of ubiquitous computing leads to new application and processing speed requirements in various every day aspects. When it comes to image processing tasks, which require many computations, the limited resources of embedded systems make those tasks rather challenging. this paper explores an implementation of an object detection task deployed on an embedded device NVIDIA Jetson Nano GPGPU: YOLO object detector detects the nostrils of horses using an infrared camera during transportation. Results of the proposed algorithm can directly be used for calculating the breathing rate and to find out if the horse is currently under stress. We compared YOLOv5 and YOLOv8 as object detection models. In the proposed implementation on the NVIDIA Jetson Nano GPGPU the inference time is around 40ms, which fulfills real-time processing requirements for the given environment. Both YOLO versions tested show high accuracy, while the additional robustness tests indicate a slight advantage of YOLOv5 comparing to YOLOv8. © 2023 ieee.

关键词： real time systems

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