This paper introduces a novel methodology for designing secure hardware accelerator tailored for convolutional neural network (CNN) applications, leveraging security-aware high-level synthesis (HLS). The methodology o...
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This paper revisits the problem of bumpless transfer control(BTC) for discrete-time nondeterministic switched linear systems. The general case of asynchronous switching is considered for the first time in the field of...
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This paper revisits the problem of bumpless transfer control(BTC) for discrete-time nondeterministic switched linear systems. The general case of asynchronous switching is considered for the first time in the field of BTC for switched systems. A new approach called interpolated bumpless transfer control(IBTC) is proposed, where the bumpless transfer controllers are formulated with the combination of the two adjacent modedependent controller gains, and are interpolated for finite steps once the switching is detected. In contrast with the existing approaches, IBTC does not necessarily run through the full interval of subsystems, as well as possesses the time-varying controller gains(with more flexibility and less conservatism) achieved from a control synthesis allowing for the stability and other performance of the whole switched system. Sufficient conditions ensuring stability and H_(∞) performance of the underlying system by IBTC are developed, and numerical examples verify the theoretical findings.
For autonomous driving to operate in a safe and effective manner, efficient and precise object detection is essential. The efficacy of the network model is heavily challenged because of the high-speed movement of vehi...
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People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces....
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People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.
Cloud computing is an on-demand service resource that includes applications to data centres on a pay-per-use basis. While allocating resources, the node failure causes the cloud service failures. This reduces the qual...
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Software developers and maintainers frequently conduct software refactorings to improve software quality. Identifying the conducted software refactorings may significantly facilitate the comprehension of software evol...
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Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported w...
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Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.
Conversation between both the deaf and the general population is becoming exceedingly challenging, and there is no reputable translator accessible in society to aid. This program enables real-time voice and signal lan...
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In recent developments, the traditional binary class SVM has evolved into a multi-class classifier utilizing a ‘1-versus-1-versus-rest’ approach named K−SVCR. This innovative version efficiently categorizes multi-cl...
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This study offers a novel platform for mental health support that incorporates state-of-the-art technology to improve user experience. The platform analyzes user journals using BERT-based emotion analysis, and it gene...
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