Rapid and accurate defect detection in printed circuit board (PCB) manufacturing plays a vital role in the quality control (QC) of consumer electronic products. Automated visual inspection is gaining increased popular...
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Parkinson's disease is an extremely debilitating condition where the brain is not producing enough dopamine to accurately coordinate movement. One symptom of Parkinson's disease, freezing of gait, prevents the...
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The current research in design space articulation tries to solve the problem that comes up because computer generative systems are becoming more complex, and there are more design options. This study makes a contribut...
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This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet's architecture is mathematical...
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The Industrial Internet of Things (IIoT) integrates smart sensors and actuators for the widespread digitization and enhancement of industrial and manufacturing processes. Smart equipment is used to improve the industr...
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The Industrial Internet of Things (IIoT) integrates smart sensors and actuators for the widespread digitization and enhancement of industrial and manufacturing processes. Smart equipment is used to improve the industrial intelligence and make industrial production more flexible, safer and more efficient. For complex equipment, product life-cycle management (PLM) including remaining useful life (RUL) is one of the essential issues for industrial intelligence. In this paper, a tensor-based remaining useful life prediction model is proposed to facilitate the life-cycle management, which combines features from time domain and frequency domain. For the characteristics of continuous generation of industrial data streaming, tensor singular value decomposition (t-SVD) is combined with long shortterm memory network (LSTM) method to predict the RUL of devices from high-order and high-noise time series data. Finally, experiments are carried out on three different data sets including the battery charge and discharge data set, the bearing acceleration life cycle data set, and the turbofan data set to measure the performance of the proposed model. IEEE
This paper proposes an Internet of Medical Things (IoMT) Seizure Detection Algorithm that uses smartphone acceleration sensors to detect early seizures. The propose algorithm used based on MATLAB Mobile, which seamles...
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The introduction of drone technology has transformed a variety of businesses, from surveillance and monitoring to delivery services. However, effective communication among drones is critical for guaranteeing smooth op...
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This work studies receding-horizon control of discrete-time switched linear systems subject to polytopic constraints for the continuous states and inputs. The objective is to approximate the optimal receding-horizon c...
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We study the problem of temporal database indexing, i.e., indexing versions of a database table in an evolving database. With the larger and cheaper memory chips nowadays, we can afford to keep track of all versions o...
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We study the problem of temporal database indexing, i.e., indexing versions of a database table in an evolving database. With the larger and cheaper memory chips nowadays, we can afford to keep track of all versions of an evolving table in memory. This raises the question of how to index such a table effectively. We depart from the classic indexing approach, where both current (i.e., live) and past (i.e., dead) data versions are indexed in the same data structure, and propose LIT, a hybrid index, which decouples the management of the current and past states of the indexed column. LIT includes optimized indexing modules for dead and live records, which support efficient queries and updates, and gracefully combines them. We experimentally show that LIT is orders of magnitude faster than the state-of-the-art temporal indices. Furthermore, we demonstrate that LIT uses linear space to the number of record indexed versions, making it suitable for main-memory temporal data management.
For determining the appropriate treatment for brain tumors, an accurate diagnosis is necessary. Many studies have focused on the deep learning-based classification of brain tumors. This study employed a comprehensive ...
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