This paper studies the problem of hybrid holographic beamforming for sum-rate maximization in a communication system assisted by a reconfigurable holographic surface. Existing methodologies predominantly rely on gradi...
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Semantic similarity detection mainly relies on the availability of laboriously curated ontologies, as well as of supervised and unsupervised neural embedding models. In this paper, we present two domain-specific sente...
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Speech is the primary medium for the exchange and transfer of messages, making recognition of speech an extensive area of study. The phonic and linguistic outline is probable for this, mostly for the English verbal. I...
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ISBN:
(数字)9798331504601
ISBN:
(纸本)9798331504618
Speech is the primary medium for the exchange and transfer of messages, making recognition of speech an extensive area of study. The phonic and linguistic outline is probable for this, mostly for the English verbal. In India, a considerable portion of the population faces challenges to communicate in English. Henceforward, English speech detection is not useful for such persons. The recognition method of Hindi words is an integral segment of the automatic speech detection technique that has been proposed in this paper. The primary objective of Automatic Speech Recognition (ASR) is to recognize and interpret spoken input from a device or microphone and convert it into a textual document to execute the appropriate action. The procedure of detecting speech solely relies on the abstraction of attributes and the detection of patterns within the content. This research paper employs the Hidden Markov Model (HMM) along with the Hidden Markov Toolkit (HMT) and Pratt software for attribute detection. In the proposed scheme, a set of data is apprehended having 10 Hindi words: from “Shunya” to “Nau” (Shunya, Ek, Do, Teen, Chaar, Paanch, Chae, Saat, Aath, Nau) from the 15 voices of different age group then applying training along with testing over the set of data. The projected structure computes and compares the Linear Predictive Coding (LPC), Perceptual Linear Prediction (PLP), and Mel Frequency Cepstral Coefficients (MFCC) algorithms. These algorithms are applied to talker-reliant ASR and talker-free ASR in sound-dependent and word-based environments to figure out the top feature mining technique which is MFCC in our proposed system.
Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devi...
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ISBN:
(数字)9798350383508
ISBN:
(纸本)9798350383515
Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devices while paying attention to their health. Thus, we study this significant problem in this paper, and propose a universal safety guaranteed power-delivered-to-load (PDL) maximization scheme (called SafeGuard). Technically, we first utilize the off-the-shelf electromagnetic simulator to perform the EMR distribution analysis to ensure the universality of the method. Then, we innovatively introduce the concept of multiple importance sampling for achieving efficient EMR safety constraint extraction. Finally, we treat the proposed optimization problem as an optimal boundary point search problem from the perspective of space geometry, and devise a brand-new grid-based multi-constraint parallel processing algorithm to efficiently solve it. We implement a system prototype for SafeGuard, and conduct extensive experiments to evaluate it. The results indicate that our SafeGuard can obviously improve the achieved PDL by up to 1.75× compared with the state-of-the-art baseline while guaranteeing EMR safety. Furthermore, SafeGuard can accelerate the solution process by 29.12× compared with the traditional numerical method to satisfy the fast optimization requirement of wireless charging systems.
Due to the convergence of healthcare and smart cities, information and technology are employed in health and medical procedures worldwide. Residents of smart cities now enjoy better lives and healthier bodies because ...
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Due to the convergence of healthcare and smart cities, information and technology are employed in health and medical procedures worldwide. Residents of smart cities now enjoy better lives and healthier bodies because to integration. The advent of smart watch technology and deep learning techniques has engendered a potential increase in performance and accuracy of such tasks. Our paper explores the application of recurrent neural networks particularly LSTM for action recognition task using smart watch sensor data, i.e., gyroscope and accelerometer data. We have taken into consideration some general activities like standing, walking, sitting, etc. and implemented a LSTM network for the action recognition job using the time sequences sensor data. This model can be useful for other health-based applications which can monitor the health conditions of a user by keeping record of his activities.
It is essential that all algorithms are exhaustively, somewhat, and intelligently evaluated. Nonetheless, evaluating the effectiveness of optimization algorithms equitably and fairly is not an easy process for various...
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Abstract: Over the past decades, interest has increased in studying different techniques to detect the importance of microbubbles in many industrial and medical applications such as mechanisms of hydraulic machinery c...
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The estimation of soil moisture content is required for agriculture, mainly to build the irrigation scheduling model. In this study, we present a smart watering system to deal with various factors derived from the sto...
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In this paper, we study the intricate realm of digital twin synchronization and deployment in multi-access edge computing (MEC) networks, with the aim of optimizing and balancing the two performance metrics Age of Inf...
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ISBN:
(数字)9798350351255
ISBN:
(纸本)9798350351262
In this paper, we study the intricate realm of digital twin synchronization and deployment in multi-access edge computing (MEC) networks, with the aim of optimizing and balancing the two performance metrics Age of Information (AoI) and energy efficiency. We jointly consider the problems of edge association, power allocation, and digital twin deployment. However, the inherent randomness of the problem presents a significant challenge in identifying an optimal solution. To address this, we first analyze the feasibility conditions of the optimization problem. We then examine a specific scenario involving a static channel and propose a cyclic scheduling scheme. This enables us to derive the sum AoI in closed form. As a result, the joint optimization problem of edge association and power control is solved optimally by finding a minimum weight perfect matching. Moreover, we examine the one-shot optimization problem in the contexts of both frequent digital twin migrations and fixed digital twin deployments, and propose an efficient online algorithm to address the general optimization problem. This algorithm effectively reduces system costs by balancing frequent migrations and fixed deployments. Numerical results demonstrate the effectiveness of our proposed scheme in terms of low cost and high efficiency.
The stable network has now become a prerequisite for every relationship. The security hazards are expanding and rendering wired/remote organizations and Internet providers fast, shaky and problematic. Today, safety ef...
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