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  • 1.
    Wu, Yirui
    et al.
    Hohai University, Jiangsu Province, Nanjing City, China.
    Zhang, Lilai
    Hohai University, Jiangsu Province, Nanjing City, China.
    Gu, Zonghua
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Lu, Hu
    Jiangsu University, Jiangsu Province, Zhenjiang City, China.
    Wan, Shaohua
    University of Electronic Science and Technology of China, ,Guangdong Province, Shenzhen City, China.
    Edge-AI-driven framework with efficient mobile network design for facial expression recognition2023Ingår i: ACM Transactions on Embedded Computing Systems, ISSN 1539-9087, E-ISSN 1558-3465, Vol. 22, nr 3, artikel-id 57Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Facial Expression Recognition (FER) in the wild poses significant challenges due to realistic occlusions, illumination, scale, and head pose variations of the facial images. In this article, we propose an Edge-AI-driven framework for FER. On the algorithms aspect, we propose two attention modules, Arbitrary-oriented Spatial Pooling (ASP) and Scalable Frequency Pooling (SFP), for effective feature extraction to improve classification accuracy. On the systems aspect, we propose an edge-cloud joint inference architecture for FER to achieve low-latency inference, consisting of a lightweight backbone network running on the edge device, and two optional attention modules partially offloaded to the cloud. Performance evaluation demonstrates that our approach achieves a good balance between classification accuracy and inference latency.

  • 2.
    Zhao, Qingling
    et al.
    The PCA Lab, School of Computer Science and Engineering, Nanjing University of Science and Technology, Systems for High-Dimensional Information of Ministry of Education, Jiangsu Key Lab of Image and Video Understanding for Social Security, Jiangsu, Nanjing, China.
    Chen, Mingqiang
    The PCA Lab, School of Computer Science and Engineering, Nanjing University of Science and Technology, Systems for High-Dimensional Information of Ministry of Education, Jiangsu Key Lab of Image and Video Understanding for Social Security, Jiangsu, Nanjing, China.
    Gu, Zonghua
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Luan, Siyu
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Zeng, Haibo
    Department of Electrical and Computer Engineering, Virginia Tech, VA, Blacksburg, United States.
    Chakrabory, Samarjit
    Department of Computer Science, University of North Carolina, NC, Chapel Hill, United States.
    CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detection2022Ingår i: ACM Transactions on Embedded Computing Systems, ISSN 1539-9087, E-ISSN 1558-3465, Vol. 21, nr 4, artikel-id 45Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The Controller Area Network (CAN) is a ubiquitous bus protocol present in the Electrical/Electronic (E/E) systems of almost all vehicles. It is vulnerable to a range of attacks once the attacker gains access to the bus through the vehicle's attack surface. We address the problem of Intrusion Detection on the CAN bus and present a series of methods based on two classifiers trained with Auxiliary Classifier Generative Adversarial Network (ACGAN) to detect and assign fine-grained labels to Known Attacks and also detect the Unknown Attack class in a dataset containing a mixture of (Normal + Known Attacks + Unknown Attack) messages. The most effective method is a cascaded two-stage classification architecture, with the multi-class Auxiliary Classifier in the first stage for classification of Normal and Known Attacks, passing Out-of-Distribution (OOD) samples to the binary Real-Fake Classifier in the second stage for detection of the Unknown Attack class. Performance evaluation demonstrates that our method achieves both high classification accuracy and low runtime overhead, making it suitable for deployment in the resource-constrained in-vehicle environment.

  • 3.
    Zhao, Qingling
    et al.
    Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
    Qu, Mengfei
    Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
    Gu, Zonghua
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Zeng, Haibo
    Virginia Tech, Blacksburg, United States.
    Minimizing Stack Memory for Partitioned Mixed-criticality Scheduling on Multiprocessor Platforms2022Ingår i: ACM Transactions on Embedded Computing Systems, ISSN 1539-9087, E-ISSN 1558-3465, Vol. 21, nr 2, artikel-id 20Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A Mixed-Criticality System (MCS) features the integration of multiple subsystems that are subject to different levels of safety certification on a shared hardware platform. In cost-sensitive application domains such as automotive E/E systems, it is important to reduce application memory footprint, since such a reduction may enable the adoption of a cheaper microprocessor in the family. Preemption Threshold Scheduling (PTS) is a well-known technique for reducing system stack usage. We consider partitioned multiprocessor scheduling, with Preemption Threshold Adaptive Mixed-Criticality (PT-AMC) as the task scheduling algorithm on each processor and address the optimization problem of finding a feasible task-To-processor mapping with minimum total system stack usage on a resource-constrained multi-processor. We present the Extended Maximal Preemption Threshold Assignment Algorithm (EMPTAA), with dual purposes of improving the taskset's schedulability if it is not already schedulable, and minimizing system stack usage of the schedulable taskset. We present efficient heuristic algorithms for finding sub-optimal yet high-quality solutions, including Maximum Utilization Difference based Partitioning (MUDP) and MUDP with Backtrack Mapping (MUDP-BM), as well as a Branch-And-Bound (BnB) algorithm for finding the optimal solution. Performance evaluation with synthetic task sets demonstrates the effectiveness and efficiency of the proposed algorithms.

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