Motivated by the early works on bidirectional interaction and the breakthrough to estimate seismic response to bidirectional shaking via unidirectional analysis,it is essential to answer the question:When is the inter...
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Motivated by the early works on bidirectional interaction and the breakthrough to estimate seismic response to bidirectional shaking via unidirectional analysis,it is essential to answer the question:When is the interaction effect significant?Early works concluded that the effect of interaction is pronounced for stiff systems;consequently,the straightforward method for estimating seismic response to bidirectional excitation by using unidirectional analyses is verified primarily for short period ***,it is essential to identify the domain of significance for bidirectional interaction before adopting this simple methodology in *** parametrically defined systems with elastoplastic and degrading hysteresis models are studied under near-fault motions,assuming strength-independent and strength-dependent *** force-based and displacement-based analyses,conducted in parallel,reveal that the interaction effect is considerable for stiff systems,especially with degrading characteristics in a relatively low inelasticity ***,the bidirectional effect may be significant even for highly flexible systems,especially for residual deformation,which in earlier works was *** range of significance depends on the hysteresis model,system parameters,and response *** analysis is carried out with the results of the case studies,and the derived regression models may be used for a preliminary assessment of the impact of interaction in advance.
The convolution layer in a convolutional neural network (CNN) is highly computationally intensive. It is crucial to design reusable low-cost hardware IP for convolutional layer for enabling hardware-based feature extr...
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In an Unsupervised Domain Adaptation (UDA) task, extracted features from the entire image lead to a negative transfer of irrelevant knowledge. An attention mechanism may highlight the suitable transferable region of a...
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Eye gestures are widely used in many applications, including device control, biometrics, visual analytics, and health-care, like Alzheimer's, accessibility, etc. The conventional method for eye gesture detection n...
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Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions i...
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Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd datas
Unmanned Combat Aerial Vehicles (UCAVs) are becoming a critical part of the military to automate complex missions with minimum risk and increased efficiency. Path planning is a necessary routine for UCAVs to guide the...
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Efficient navigation of emergency response vehicles (ERVs) through urban congestion is crucial to life-saving efforts, yet traditional traffic systems often slow down their swift passage. In this work, we introduce Dy...
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This paper surveys some of the most important recent works related to micro-expression analysis. It includes discussions on algorithms for spotting and recognizing micro-expressions, their performances, databases, and...
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Natural language processing (NLP) is an area of research and study that makes it possible for computers to comprehend human language by utilising software engineering concepts from computerscience and artificial inte...
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Internet of Vehicles (IoV) integrates with various heterogeneous nodes, such as connected vehicles, roadside units, etc., which establishes a distributed network. Vehicles are managed nodes providing all the services ...
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