In this paper, a non-orthogonal multiple access (NOMA) dual-hop free space optical (FSO)/radio frequency (RF) relaying communication system is presented where reconfigurable intelligent surface (RIS) with phase error ...
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ISBN:
(数字)9798331521165
ISBN:
(纸本)9798331521172
In this paper, a non-orthogonal multiple access (NOMA) dual-hop free space optical (FSO)/radio frequency (RF) relaying communication system is presented where reconfigurable intelligent surface (RIS) with phase error and hybrid automatic repeat request (H-ARQ) protocols are considered on the RF link. The FSO link is subjected to Gamma-Gamma distributions with pointing errors while the RF link follows Nakagamim distributions. To quantify the system performance, the exact outage probability expressions for the near and far users are obtained. Under the same conditions, the results reveal the superiority of NOMA over orthogonal multiple access (OMA) and the accuracy of the results are validated via the Monte-Carlo simulations.
Customer churn is a situation that receives extensive analysis using a variety of techniques from data mining or machine learning. Data mining techniques may be used to anticipate customer churn. A data mining algorit...
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The labor market is characterized by imbalances and dysfunctions that can be addressed through a study of the management and costing of workers’ payroll. Although there is an ongoing conflict of interest between empl...
The labor market is characterized by imbalances and dysfunctions that can be addressed through a study of the management and costing of workers’ payroll. Although there is an ongoing conflict of interest between employees and employers, it can be combated because the employment relationship is a mixture of conflicting and consensual elements (mixed-motive). This paper is about flexible forms of employment, which corporations can take advantage of in a constantly evolving environment. At the same time, reference is made to the institution of telecommuting that improves remote worker efficiency, minimizes labor costs, and opens the labor market frontiers. It is necessary to create new strategies aimed at improving the efficiency of the workforce, managing labor costs, and studying the sustainability of each activity of the business unit. Lastly, the constantly evolving technological environment and the constant development of the skills of the workforce require the incorporation of ongoing management and efficiency studies to achieve the maximum possible objectives at the lowest possible and most flexible cost and risk.
In this paper, we develop a six-dimensional movable antenna (6DMA) enhanced multi-access point (AP) coordination system for coverage enhancement and interference mitigation. First, we model the wireless channels betwe...
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ISBN:
(数字)9798350368369
ISBN:
(纸本)9798350368376
In this paper, we develop a six-dimensional movable antenna (6DMA) enhanced multi-access point (AP) coordination system for coverage enhancement and interference mitigation. First, we model the wireless channels between the APs and UTs to characterize their variation with respect to 6DMA movement, in terms of both the three-dimensional (3D) position and 3D orientation of each distributed AP's antenna. Then, an optimization problem is formulated to maximize the weighted sum rate of multiple UTs for their uplink transmissions by jointly optimizing the antenna position vector (APV), the antenna orientation matrix (AOM), and the receive combining matrix over all coordinated APs, subject to the constraints on local antenna movement regions. To solve this challenging non-convex optimization problem, we first transform it into a more tractable Lagrangian dual problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AOM, which are designed by applying the successive convex approximation (SCA) technique and Riemannian manifold optimization-based algorithm, respectively. Simulation results show that the proposed 6DMA-enhanced multi-AP coordination system can significantly enhance network capacity, and can attain considerable performance improvement compared to the conventional fixed antenna (FA)-based schemes.
Navigating cluttered indoor environments presents a significant challenge for aerial robots, requiring agility, speed, and a high level of reliability to avoid collisions. This project aims to address this challenge b...
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This paper formulates a three-clamping-level discontinuous pulsewidth modulation (DPWM) for three-phase cascaded H-bridge static compensators with star configuration. Analytical expressions of capacitor voltage and ze...
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Cognitive scientists believe adaptable intelligent agents like humans perform reasoning through learned causal mental simulations of agents and environments. The problem of learning such simulations is called predicti...
Cognitive scientists believe adaptable intelligent agents like humans perform reasoning through learned causal mental simulations of agents and environments. The problem of learning such simulations is called predictive world modeling. Recently, reinforcement learning (RL) agents leveraging world models have achieved SOTA performance in game environments. However, understanding how to apply the world modeling approach in complex real-world environments relevant to mobile robots remains an open question. In this paper, we present a framework for learning a probabilistic predictive world model for real-world road environments. We implement the model using a hierarchical VAE (HVAE) capable of predicting a diverse set of fully observed plausible worlds from accumulated sensor observations. While prior HVAE methods require complete states as ground truth for learning, we present a novel sequential training method to allow HVAEs to learn to predict complete states from partially observed states only. We experimentally demonstrate accurate spatial structure prediction of deterministic regions achieving 96.21 IoU, and close the gap to perfect prediction by 62 % for stochastic regions using the best prediction. By extending HVAEs to cases where complete ground truth states do not exist, we facilitate continual learning of spatial prediction as a step towards realizing explainable and comprehensive predictive world models for real-world mobile robotics applications. Code is available at https://***/robin-karlsson0/predictive-world-models.
The degradation and breakdown behaviors of top-gate self-aligned a-InGaZnO thin-film transistors (TFTs) under dynamic current stresses (DCSs) were systematically investigated. Both linear-and saturation-regime DCSs we...
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This paper presents a novel class of complex-valued sparse complementary pairs (SCPs), each consisting of a number of zero values and with additional zero-correlation zone (ZCZ) property for the aperiodic autocorrelat...
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In recent years,defending against adversarial examples has gained significant importance,leading to a growing body of research in this *** these studies,pre-processing defense approaches have emerged as a prominent re...
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In recent years,defending against adversarial examples has gained significant importance,leading to a growing body of research in this *** these studies,pre-processing defense approaches have emerged as a prominent research ***,existing adversarial example pre-processing techniques often employ a single pre-processing model to counter different types of adversarial *** a strategy may miss the nuances between different types of attacks,limiting the comprehensiveness and effectiveness of the defense *** address this issue,we propose a divide-and-conquer reconstruction pre-processing algorithm via multi-classification and multi-network training to more effectively defend against different types of mainstream adversarial *** premise and challenge of the divide-and-conquer reconstruction defense is to distinguish between multiple types of adversarial *** method designs an adversarial attack classification module that exploits the high-frequency information differences between different types of adversarial examples for their multi-classification,which can hardly be achieved by existing adversarial example detection *** addition,we construct a divide-and-conquer reconstruction module that utilizes different trained image reconstruction models for each type of adversarial attack,ensuring optimal defense *** experiments show that our proposed divide-and-conquer defense algorithm exhibits superior performance compared to state-of-the-art pre-processing methods.
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