9,837 research outputs found
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Load Balancing in Distributed Cloud Computing: A Reinforcement Learning Algorithms in Heterogeneous Environment
Balancing load in cloud based is an important aspect that plays a vital role in order to achieve sharing of load between different types of resources such as virtual machines that lay on servers, storage in the form of hard drives and servers. Reinforcement learning approaches can be adopted with cloud computing to achieve quality of service factors such as minimized cost and response time, increased throughput, fault tolerance and utilization of all available resources in the network, thus increasing system performance. Reinforcement Learning based approaches result in making effective resource utilization by selecting the best suitable processor for task execution with minimum makespan. Since in the earlier related work done on sharing of load, there are limited reinforcement learning based approaches. However this paper, focuses on the importance of RL based approaches for achieving balanced load in the area of distributed cloud computing. A Reinforcement Learning framework is proposed and implemented for execution of tasks in heterogeneous environments, particularly, Least Load Balancing (LLB) and Booster Reinforcement Controller (BRC) Load Balancing. With the help of reinforcement learning approaches an optimal result is achieved for load sharing and task allocation. In this RL based framework processor workload is taken as an input. In this paper, the results of proposed RL based approaches have been evaluated for cost and makespan and are compared with existing load balancing techniques for task execution and resource utilization.
International Academic Symposium of Social Science 2022
This conference proceedings gathers work and research presented at the International Academic Symposium of Social Science 2022 (IASSC2022) held on July 3, 2022, in Kota Bharu, Kelantan, Malaysia. The conference was jointly organized by the Faculty of Information Management of Universiti Teknologi MARA Kelantan Branch, Malaysia; University of Malaya, Malaysia; Universitas Pembangunan Nasional Veteran Jakarta, Indonesia; Universitas Ngudi Waluyo, Indonesia; Camarines Sur Polytechnic Colleges, Philippines; and UCSI University, Malaysia. Featuring experienced keynote speakers from Malaysia, Australia, and England, this proceeding provides an opportunity for researchers, postgraduate students, and industry practitioners to gain knowledge and understanding of advanced topics concerning digital transformations in the perspective of the social sciences and information systems, focusing on issues, challenges, impacts, and theoretical foundations. This conference proceedings will assist in shaping the future of the academy and industry by compiling state-of-the-art works and future trends in the digital transformation of the social sciences and the field of information systems. It is also considered an interactive platform that enables academicians, practitioners and students from various institutions and industries to collaborate
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Effects of Particle Swarm Optimisation on a Hybrid Load Balancing Approach for Resource Optimisation in Internet of Things
This article belongs to the Special Issue Emerging Machine Learning Techniques in Industrial Internet of ThingsCopyright © 2023 by the authors. The internet of things, a collection of diversified distributed nodes, implies a varying choice of activities ranging from sleep monitoring and tracking of activities, to more complex activities such as data analytics and management. With an increase in scale comes even greater complexities, leading to significant challenges such as excess energy dissipation, which can lead to a decrease in IoT devices’ lifespan. Internet of things’ (IoT) multiple variable activities and ample data management greatly influence devices’ lifespan, making resource optimisation a necessity. Existing methods with respect to aspects of resource management and optimisation are limited in their concern of devices energy dissipation. This paper therefore proposes a decentralised approach, which contains an amalgamation of efficient clustering techniques, edge computing paradigms, and a hybrid algorithm, targeted at curbing resource optimisation problems and life span issues associated with IoT devices. The decentralised topology aimed at the resource optimisation of IoT places equal importance on resource allocation and resource scheduling, as opposed to existing methods, by incorporating aspects of the static (round robin), dynamic (resource-based), and clustering (particle swarm optimisation) algorithms, to provide a solid foundation for an optimised and secure IoT. The simulation constructs five test-case scenarios and uses performance indicators to evaluate the effects the proposed model has on resource optimisation in IoT. The simulation results indicate the superiority of the PSOR2B to the ant colony, the current centralised optimisation approach, LEACH, and C-LBCA.This research received no external funding
Multilink and AUV-Assisted Energy-Efficient Underwater Emergency Communications
Recent development in wireless communications has provided many reliable
solutions to emergency response issues, especially in scenarios with
dysfunctional or congested base stations. Prior studies on underwater emergency
communications, however, remain under-studied, which poses a need for combining
the merits of different underwater communication links (UCLs) and the
manipulability of unmanned vehicles. To realize energy-efficient underwater
emergency communications, we develop a novel underwater emergency communication
network (UECN) assisted by multiple links, including underwater light,
acoustic, and radio frequency links, and autonomous underwater vehicles (AUVs)
for collecting and transmitting underwater emergency data. First, we determine
the optimal emergency response mode for an underwater sensor node (USN) using
greedy search and reinforcement learning (RL), so that isolated USNs (I-USNs)
can be identified. Second, according to the distribution of I-USNs, we dispatch
AUVs to assist I-USNs in data transmission, i.e., jointly optimizing the
locations and controls of AUVs to minimize the time for data collection and
underwater movement. Finally, an adaptive clustering-based multi-objective
evolutionary algorithm is proposed to jointly optimize the number of AUVs and
the transmit power of I-USNs, subject to a given set of constraints on transmit
power, signal-to-interference-plus-noise ratios (SINRs), outage probabilities,
and energy, which achieves the best tradeoff between the maximum emergency
response time (ERT) and the total energy consumption (EC). Simulation results
indicate that our proposed approach outperforms benchmark schemes in terms of
energy efficiency (EE), contributing to underwater emergency communications.Comment: 15 page
Intelligent Reflecting Surface Aided Multi-Tier Hybrid Computing
The Digital twin edge network (DITEN) aims to integrate mobile edge computing
(MEC) and digital twin (DT) to provide real-time system configuration and
flexible resource allocation for the sixth-generation network. This paper
investigates an intelligent reflecting surface (IRS)-aided multi-tier hybrid
computing system that can achieve mutual benefits for DT and MEC in the DITEN.
For the first time, this paper presents the opportunity to realize the
network-wide convergence of DT and MEC. In the considered system, specifically,
over-the-air computation (AirComp) is employed to monitor the status of the DT
system, while MEC is performed with the assistance of DT to provide low-latency
computing services. Besides, the IRS is utilized to enhance signal transmission
and mitigate interference among heterogeneous nodes. We propose a framework for
designing the hybrid computing system, aiming to maximize the sum computation
rate under communication and computation resources constraints. To tackle the
non-convex optimization problem, alternative optimization and successive convex
approximation techniques are leveraged to decouple variables and then transform
the problem into a more tractable form. Simulation results verify the
effectiveness of the proposed algorithm and demonstrate the IRS can
significantly improve the system performance with appropriate phase shift
configurations. Moreover, the results indicate that the DT assisted MEC system
can precisely achieve the balance between local computing and task offloading
since real-time system status can be obtained with the help of DT. This paper
proposes the network-wide integration of DT and MEC, then demonstrates the
necessity of DT for achieving an optimal performance in DITEN systems through
analysis and numerical results
2023-2024 Boise State University Undergraduate Catalog
This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State
Design and Advanced Model Predictive Control of Wide Bandgap Based Power Converters
The field of power electronics (PE) is experiencing a revolution by harnessing the superior technical characteristics of wide-band gap (WBG) materials, namely Silicone Carbide (SiC) and Gallium Nitride (GaN). Semiconductor devices devised using WBG materials enable high temperature operation at reduced footprint, offer higher blocking voltages, and operate at much higher switching frequencies compared to conventional Silicon (Si) based counterpart. These characteristics are highly desirable as they allow converter designs for challenging applications such as more-electric-aircraft (MEA), electric vehicle (EV) power train, and the like. This dissertation presents designs of a WBG based power converters for a 1 MW, 1 MHz ultra-fast offboard EV charger, and 250 kW integrated modular motor drive (IMMD) for a MEA application. The goal of these designs is to demonstrate the superior power density and efficiency that are achievable by leveraging the power of SiC and GaN semiconductors. Ultra-fast EV charging is expected to alleviate the challenge of range anxiety , which is currently hindering the mass adoption of EVs in automotive market. The power converter design presented in the dissertation utilizes SiC MOSFETs embedded in a topology that is a modification of the conventional three-level (3L) active neutral-point clamped (ANPC) converter. A novel phase-shifted modulation scheme presented alongside the design allows converter operation at switching frequency of 1 MHz, thereby miniaturizing the grid-side filter to enhance the power density. IMMDs combine the power electronic drive and the electric machine into a single unit, and thus is an efficient solution to realize the electrification of aircraft. The IMMD design presented in the dissertation uses GaN devices embedded in a stacked modular full-bridge converter topology to individually drive each of the motor coils. Various issues and solutions, pertaining to paralleling of GaN devices to meet the high current requirements are also addressed in the thesis. Experimental prototypes of the SiC ultra-fast EV charger and GaN IMMD were built, and the results confirm the efficacy of the proposed designs. Model predictive control (MPC) is a nonlinear control technique that has been widely investigated for various power electronic applications in the past decade. MPC exploits the discrete nature of power converters to make control decisions using a cost function. The controller offers various advantages over, e.g., linear PI controllers in terms of fast dynamic response, identical performance at a reduced switching frequency, and ease of applicability to MIMO applications. This dissertation also investigates MPC for key power electronic applications, such as, grid-tied VSC with an LCL filter and multilevel VSI with an LC filter. By implementing high performance MPC controllers on WBG based power converters, it is possible to formulate designs capable of fast dynamic tracking, high power operation at reduced THD, and increased power density
Emerging Power Electronics Technologies for Sustainable Energy Conversion
This Special Issue summarizes, in a single reference, timely emerging topics related to power electronics for sustainable energy conversion. Furthermore, at the same time, it provides the reader with valuable information related to open research opportunity niches
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