In recent days, the growing demand for automated language processing had brought Cross-Lingual Text Classification (CLTC) into focus as powerful approach for categorizing text across multiple languages which enabled u...
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In the last decade, huge development has been seen in the field of wireless communication. The performance depends on the shape and size of the antenna. The future aim of wireless communication is to provide data with...
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This research study investigates the use of the Internet of Things in the health sector in terms of minimizing hospital visit costs, optimizing human resources, and enhancing the quality of healthcare. It tackles a cr...
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Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by...
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Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human–machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.
Recently, due to the significant growth of RES(renewable energy source), the capacity of ESS (energy storage system) interconnected to RES is increasing rapidly. Especially, it is mandatory to install IMD (insulation ...
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This research uses deep learning to automatically colorize grayscale videos. Grayscale video frames are colored using the Generative Adversarial Network (GAN) architecture. A generator and a discriminator are both uti...
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Low-noise amplifiers (LNAs) are important in receivers due to the fact they are able to amplify signal without adding noise. The noise coefficient will limit the sensitivity of the receiver. Decreasing noise is critic...
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This paper presents the design, analysis and simulation of microstrip patch antenna in the field of wireless communication networks. This antenna is used for the design optimization of a microstrip patch antenna tuned...
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This paper proposes a Sierpinski fractal-based Microwave Metamaterial Absorber (MMA). Sierpinski fractal is a self-repetitive structure which can provide a dual-band operation. In the designed geometry, the sierpinski...
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MIMO-Multiple Input Multiple Output antenna system has attained significant attention in modern wireless communication due to its ability to enhance spectral efficiency, improve data rates, and mitigate the effects of...
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