Battle Royale Optimizer (BRO) is a recently proposed metaheuristic optimization algorithm used only in continuous problem spaces. The BinBRO is a binary version of BRO. The BinBRO algorithm employs a differential expr...
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Game is one of the entertainment media that is rapidly growing. On this day, playing games is one way to spare time for refreshing. One of the popular games this year is an action game such as First-Person Shooter (FP...
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Many different industries are currently making substantial use of the Internet of Things (IoT). IoT is the process through which electronic devices communicate with their surrounding virtual environment by continuousl...
Many different industries are currently making substantial use of the Internet of Things (IoT). IoT is the process through which electronic devices communicate with their surrounding virtual environment by continuously exchanging data via sensors. Due to increased IoT security concerns, digital forensic investigators now possess greater knowledge and abilities to investigate IoT devices. IoT-DigFor are required in terms of cybercrime due to the rapid growth in the number of electronic devices and the collection and consumption of enormous amounts of data. IoT systems with billions of devices have produced a significant amount of evidence, which presents a significant challenge to digital investigators and practitioners who must connect with IoT devices to conduct fast and thorough investigations into cybercrimes. This article presents detailed information about forensics in the IoT environment. The IoT forensic paradigm and the challenges that design-based security requirements and IoT system security offer for IoT-DigFor are covered in great detail at the beginning of the article.
The accuracy of a traffic prediction model used to predict the future state of a link (i.e. road segment) of interest in a traffic network, can be enhanced by integrating the traffic information of adjacent links. The...
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This paper presents a novel approach for tuning Proportional-Integral-Derivative (PID) controllers using deep learning techniques, specifically applied to the iTCLab, an advanced industrial control laboratory system. ...
This paper presents a novel approach for tuning Proportional-Integral-Derivative (PID) controllers using deep learning techniques, specifically applied to the iTCLab, an advanced industrial control laboratory system. PID controllers are widely used in industrial processes for their simplicity and effectiveness. However, their performance heavily relies on proper tuning, which can be a complex and time-consuming task. In this study, we leverage the capabilities of deep learning to automate and optimize the PID tuning process for the iTCLab setup. We propose a data-driven methodology that combines system identification, Deep Learning networks, and optimization algorithms to achieve superior PID controller tuning.
This study examines how blockchain technology and consensus mechanisms can safeguard and grow the metaverse ecosystem. Blockchain is transparent and decentralized. It is unchangeable, cryptographically verified, and r...
This study examines how blockchain technology and consensus mechanisms can safeguard and grow the metaverse ecosystem. Blockchain is transparent and decentralized. It is unchangeable, cryptographically verified, and runs without a central authority. Blockchain technology can secure the metaverse and users' data. This study compares blockchain-based consensus algorithms such as POW(PoW), PoS, and PBFT to ensure metaverse safety. These consensus mechanisms provide safe, decentralized transaction verification, waste reduction, and network consensus. Studying blockchain technology's metaverse scaling restrictions and scaling solutions, including sharding, sidechains, and off-chain protocols. These methods increase throughput, parallel computing, and transaction speed. They improve the metaverse's eco-system. Quantitative analysis, simulations, and experiments determine the security and scalability of blockchain-based metaverse consensus processes. Changes will be quantified by measuring transaction speed, network throughput, and resource consumption. This study enables safe, scalable metaverse environments. Blockchain-based consensus mechanisms allow the metaverse to retain user data safely, halt undesirable conduct, and allow users to join without trusting each other. Scalability solutions declutter networks, enabling the metaverse to handle more individuals and transactions. Blockchain-based consensus mechanisms could make the metaverse more secure, private, and scalable. Users will trust each other, create new apps, and be more open to this groundbreaking digital world.
Indexed modulation (IM) is an evolving technique that has become popular due to its ability of parallel data communication over distinct combinations of transmission entities. In this article, we first provide a compr...
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Smart services is an efficient concept to provide services to the citizen in an efficient manner. The online shopping and recommender system play an important role in this scenario that provides efficient item recomme...
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In millimeter-wave (mmWave) MIMO systems, when the number of radio frequency (RF) chains are limited, estimation of the beamspace channel can become compelling. Also, as the number of RF chains decreases, pilot overhe...
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ISBN:
(纸本)9781665459761
In millimeter-wave (mmWave) MIMO systems, when the number of radio frequency (RF) chains are limited, estimation of the beamspace channel can become compelling. Also, as the number of RF chains decreases, pilot overhead increases to make channel estimation reliable, eventually reducing the spectral efficiency. In this paper, we propose a channel estimation method which combines compressive sensing (CS) method of GM-LAMP that assumes beamspace channel elements follows the Gaussian mixture distribution a priori, with a novel denoising network based on sparse feature attention for the estimation. According to performance analysis and simulation results, the GM-LAMP combined with feature attention based denoising neural network outperforms state-of-the-art compressed sensing-based algorithms. Furthermore, the proposed method also outperforms previous LAMP-based neural networks with comparable processing time, albeit using less pilot transmission.
Contact tracing via digital tracking applications installed on mobile phones is an important tool for controlling epidemic spreading. Its effectivity can be quantified by modifying the standard methodology for analyzi...
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Contact tracing via digital tracking applications installed on mobile phones is an important tool for controlling epidemic spreading. Its effectivity can be quantified by modifying the standard methodology for analyzing percolation and connectivity of contact networks. We apply this framework to networks with varying degree distributions, numbers of application users, and probabilities of quarantine failures. Further, we study structured populations with homophily and heterophily and the possibility of degree-targeted application distribution. Our results are based on a combination of explicit simulations and mean-field analysis. They indicate that there can be major differences in the epidemic size and epidemic probabilities which are equivalent in the normal susceptible-infectious-recovered (SIR) processes. Further, degree heterogeneity is seen to be especially important for the epidemic threshold but not as much for the epidemic size. The probability that tracing leads to quarantines is not as important as the application adoption rate. Finally, both strong homophily and especially heterophily with regard to application adoption can be detrimental. Overall, epidemic dynamics are very sensitive to all of the parameter values we tested out, which makes the problem of estimating the effect of digital contact tracing an inherently multidimensional problem.
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