The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is p...
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The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri
Modernization of currently operational nuclear power plants is becoming increasingly important to maintain their performance and safety. Ensuring the safety of newer Instrumentation and Control (I&C) systems used ...
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To suppress the resonance in an LCL filter, the passive damping method is often favored over the active damping due to its simplicity and robustness. However, the passive damping suffers from decreasing LCL filter'...
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In-context learning for large language models (LLMs) is employed to design a novel hybrid optimization framework for multi-robot task allocation. Results show that this approach can improve existing approaches, such a...
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This paper proposes a novel adaptive synthetic inertia (SI) control scheme for the battery energy storage system (BESS) in the wind farm. The proposed approach dynamically adjusts the amplitude of the direct current (...
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Quantile regression (QR) is a powerful tool for estimating one or more conditional quantiles of a target variable Y given explanatory features X.A limitation of QR is that it is only defined for scalar target variable...
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Mitigating the adverse effect of high temperature on photovoltaic (PV) module's efficiency in hot environment by using a thermoelectric cooling (TEC) method with PV through a detailed analysis, is the pivot object...
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The International Solid-State Circuits Conference (ISSCC) is the flagship conference of the IEEE Solid-State Circuits Society. The theme for ISSCC 2022 is 'Intelligent Silicon for a Sustainable World.' This th...
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Recently,energy harvesting wireless sensor networks(EHWSN)have increased significant attention among research *** harvesting energy from the neighboring environment,the sensors in EHWSN resolve the energy constraint p...
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Recently,energy harvesting wireless sensor networks(EHWSN)have increased significant attention among research *** harvesting energy from the neighboring environment,the sensors in EHWSN resolve the energy constraint problem and offers lengthened network *** is one of the proficient ways for accomplishing even improved lifetime in *** clustering process intends to appropriately elect the cluster heads(CHs)and construct *** several models are available in the literature,it is still needed to accomplish energy efficiency and security in *** this view,this study develops a novel Chaotic Rider Optimization Based Clustering Protocol for Secure Energy Harvesting Wireless Sensor Networks(CROC-SEHWSN)*** presented CROC-SEHWSN model aims to accomplish energy efficiency by clustering the node in *** CROC-SEHWSN model is based on the integration of chaotic concepts with traditional rider optimization(RO)***,the CROC-SEHWSN model derives a fitness function(FF)involving seven distinct parameters connected to *** accomplish security,trust factor and link quality metrics are considered in the *** design of RO algorithm for secure clustering process shows the novelty of the *** order to demonstrate the enhanced performance of the CROC-SEHWSN approach,a wide range of simulations are carried out and the outcomes are inspected in distinct *** experimental outcome demonstrated the superior performance of the CROC-SEHWSN technique on the recent approaches with maximum network lifetime of 387.40 and 393.30 s under two scenarios.
One of the fundamental differences in the perception of electric (e-) vehicles is how their radiated noise is perceived with respect to classic internal combustion engines. Even though e-vehicles are usually quieter, ...
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