1,721,035 research outputs found
Efficient Tradeoff between Throughput and Energy Efficiency of Massive-MIMO Technique for Satellite Communication applications
No full textThe rapidly increasing demand for mobile communication over satellite platforms and its applications necessitates a significant effort on the part of researchers to fulfill the prospective requirements of wireless network infrastructure. It is predicted that traffic will increase by multiples of hundreds soon. Therefore, the network's capacity has to multiply with high energy efficiency (EE), which can be achieved using massive multiple-input, multiple-output (M-MIMO). An adaptive scheme that maximizes energy efficiency is proposed in this paper at maximum spectral efficiency. Also, an efficient tradeoff between energy efficiency and throughput is mainly proposed. The analytical and simulation results prove that the proposed multi-cell minimum mean square error (M-MMSE) precoding scheme provides the maximum EE and efficient throughput of next-generation networks and satellite communication utilizing M- MIMO. Hence, it gives the optimum and most efficient tradeoff between EE and the throughput of the M-MIMO system
Design of a DC/DC Converter with a PID Controller and Backpropagation Neural Network for Electric Vehicles
No full textCurrently, global warming has become a major problem. The pollution caused because of conventional internal combustion engines are increasing dramatically. Electric Vehicles are good alternatives to conventional IC engine vehicles in promoting a green environment. Controllers, converters, and modulation schemes are needed to provide a safe and reliable power transmission from energy storage systems to the electric motor in electric cars. In this paper, a design of DC/DC boost converter based on a PID controller is proposed. Moreover, a Back Propagation Neural Network (BPNN) technique is applied to generate the optimal PID parameters before using the PID. The proposed DC/DC boost converter is simulated using MATLAB software. The simulation results proved that the proposed DC/DC converter with PID-BPNN achieved higher performance and a stable output voltage
Spline Global MPPT with PID Controller Based on Levy Invasive Weed Technique for Renewable Energy Resources of Smart Homes
No full textRenewable energy systems, particularly photovoltaic (PV) systems, have been played important role in the reducing carbon emissions. A primary concern in the field of photovoltaics (PV) based on the design of the maximum power point tracking (MPPT) is the capacity to accurately monitor power across many parameters in addition to ascertain the power production of solar cells or wind turbines and adjust the load to maximise power efficiency under varying weather conditions. On another hands, the hybrid smart system uses a wind catcher to reduce the amount of energy consumed by buildings from the grid, which is a historically significant architectural component for cross ventilation and passive cooling. This paper presents an updating of model that proposes improvements to the regulation of Spline MPPT with tuning PID controller by using Levy Invasive Weed optimization (LIWO)technique. A rapid, accurate, and straightforward approach for determining the (MPPT) of PV systems under consistent irradiation and partial shade, as well as wind turbines, is presented using the Spline- MPPT technique. The model of this paper employs LIWO in conjunction with a PID controller to improve the selection of PID gains. Moreover, is applied to generate the optimal values of duty cycle of the model. The comprehensive system design depicted has been simulated using MATLAB Simulink to verify the functionality of the system. The model has attained an accuracy of 93%. The outcomes of this model contribute greatly to our understanding of the suitability and efficacy of both AI and traditional MPPT controllers. This modeling will be beneficial to the renewable energy industry
Bee Colony-Reptile Search Optimization Technique for Blood Cell Cancer Detection
No full textThe biosystem is a crucial system grounded in classification and detection, utilizing Artificial Intelligence (AI) approaches or metaheuristic techniques. Currently, cancer of the blood cells is among the deadliest cancers in the world. Acute lymphoblastic leukemia (ALL) is a cancer of blood cells that causes excessive proliferation of lymphocytes. It is extremely time-consuming and expensive to conduct diagnostic calculations. The number of platelets in a patient's blood is computed by a platelet count. A lacking number of platelets can indicate cancer, infection, or other health problems. A patient with too many platelets is at risk for blood strokes. A single drop of blood includes tens of thousands of platelets. The main goal of this paper is how to detect the features of blood cells and classify with predicting cancer type based on platelets analysis by using Bee Colony followed by Reptile Search Optimization (BCRSO) technique. According to the results, BCRSO algorithm performed better in terms of classification efficacy and accuracy rate than other algorithms. Based on simulation results, the proposed method is more effective than previously published research for classification optimization
Drought Monitor Creation using SMAP L4 Soil Moisture Data
No full textThe socioeconomic and ecological consequences of drought are substantial. Vigilance in soil moisture surveillance is instrumental for effectual drought governance. The manuscript delineates a novel stratagem to devise a drought surveillance mechanism deploying granular SMAP L4 soil moisture datasets. This technique introduces the computation of a Standardized Soil Moisture Index (SSMI) that segregates drought into quintuple classifications. Periodic, hebdomadal modifications reflect the dynamism in pedosphere aridity. Comparative analyses corroborate the congruence with extant aridity indicators, endorsing its utility in strategic drought response endeavours. The method's adaptability is noteworthy, as it permits employment in regions bereft of com-prehensive terrestrial monitoring systems
Optimizing Solar-Powered Electrolysis Systems for Green Hydrogen Production Using Rat Swarm Optimization and Energy Storage Solutions
No full textThis study investigates the performance of a solar-powered electrolysis system for green hydrogen production, utilizing the Rat Swarm Optimization (RSO) algorithm to optimize Maximum Power Point Tracking (MPPT). Two cases are examined: one where the system operates solely on solar energy and another where energy storage is integrated to ensure continuous hydrogen production. MATLAB simulations were used to model both systems under varying environmental conditions. The results show that integrating energy storage significantly enhances system stability and efficiency, mitigating the intermittency of solar energy. In the solar-only scenario, hydrogen production fluctuated directly with solar irradiance, with reduced or no production during low sunlight periods or at night. In contrast, when energy storage was incorporated, the system demonstrated a steady hydrogen production rate by storing excess energy during peak sunlight hours and utilizing it during low solar power periods, ensuring continuous operation of the electrolyzer. Furthermore, the study highlights the advantages of using RSO for MPPT. The RSO algorithm demonstrated faster convergence, higher tracking accuracy, and improved stability compared to traditional algorithms like M&O. These improvements led to enhanced overall system performance, making solar-powered electrolysis systems for green hydrogen production more efficient, reliable, and suitable for practical, real-world applications
Cloud Detection and Removal from RGB Images using U-Net Semantic Segmentation and CloudGAN Models
No full textSatellite-based imagery provides an indispensable tool for many applications, ranging from environmental surveillance to urban development and managing natural disasters. Nevertheless, the presence of clouds can often impede the useful-ness of these images by veiling significant details. In the current study, we proposed an innovative strategy for identify-ing and eliminating clouds within RGB satellite images employing deep learning techniques. This involves using a Cloud Generative Adversarial Network (CloudGAN) to carry out image inpainting tasks and U-Net for semantic segmentation. The proposed methodology yields encouraging outcomes, showcasing its ability to discern and eradicate clouds effective-ly, thereby enhancing the clarity and practicality of satellite imagery. The proposed approach demonstrates superior cloud removal compared to traditional methods, achieving a remarkable overall accuracy of 95\% in both cloud detec-tion and removal. This underscores its effectiveness in enhancing image quality and utility. The qualitative assessment confirms the models' ability to produce high-quality, cloud-free images, preserving essential features and details faithfully. Additionally, the inpainted images closely resemble the ground truth, affirming the accuracy of the models in cloud removal
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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