With the rapid development of emerging services such as cellular vehicle-to-everything and immersive video service, network connections have further evolved from tangible physical connections to intangible virtual con...
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Speech emotion recognition is a difficult task that is gaining attention in a variety of domains, including psychology, human–computer interaction, and speech processing. To recognize speech emotions, machine learnin...
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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 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
Business modelling often involves extensive data collection and analysis, raising concerns about privacy infringement. Integrating Privacy Information Retrieval (PIR) mechanisms within business models is crucial to ad...
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The aim of this paper is to analyze the implementation of intelligent lighting within the concept of smart energy based on the possibility of saving and efficient use of energy, which is largely based on non-renewable...
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This article presents a new optical integrated sensing and communication (O-ISAC) framework tailored for cost-effective light-emitting diode (LED) for enhanced Internet of Things (IoT) applications. Unlike prior resea...
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Future wireless networks could benefit from the energy-efficient, low-latency, and scalable deployments that Reconfigurable Intelligent Surfaces (RISs) offer. However, the creation of an effective low overhead channel...
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The Internet of Things (IoT) stands as a revolutionary leap in digital connectivity, envisioning a future network connecting billions of devices, seamlessly. Amidst the myriad benefits, there arises an intricate web o...
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The Internet of Things (IoT) stands as a revolutionary leap in digital connectivity, envisioning a future network connecting billions of devices, seamlessly. Amidst the myriad benefits, there arises an intricate web of challenges, prominently centered around potential threats and data security implications. Recent cryptography techniques, such as DNA-based cryptography, 3D chaos-based cryptography, and optical cryptography, face challenges including large encryption times, high energy consumption, and suboptimal rather than optimal performance. Particularly, the burden of long encryption cycles strains the energy resources of typical low-power and compact IoT devices. These challenges render the devices vulnerable to unauthorized breaches, despite large storage capacities. The hallmark of the IoT ecosystem, characterized by its low-power compact devices, is the burgeoning volume of data they generate. This escalating data influx, while necessitating expansive storage, remains vulnerable to unauthorized access and breaches. Historically, encryption algorithms, with their multifaceted architectures, have been the bulwark against such intrusions. However, their inherently-complex nature, entailing multiple encryption cycles, strains the limited energy reserves of typical IoT devices. In response to this intricate dilemma, we present a hybrid lightweight encryption strategy. Our algorithm innovatively leverages both one-dimensional (1D) and two-dimensional (2D) chaotic key generators. Furthermore, it amalgamates a classical encryption philosophy, harmonizing the strengths of Feistel and substitution-permutation networks. The centerpiece of our strategy is achieving effective encryption in merely three rounds, tailored expressly for compressed Three-Dimensional Video (3DV) frames, ensuring their unwavering integrity. Our workflow commences with the H.264/MVC compression algorithm, setting the stage for the subsequent encryption phase. Through rigorous MATLAB simulations,
Due to the nature of monetary and spatial constrictions, larger systems on small satellites are getting replaced by smaller but more inaccurate sensors. To improve the satellite orientation estimate, multiple differen...
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Iron ore sintering is a critical step in the blast furnace ironmaking process and tumbler strength is a key physical parameter for evaluating sintered ore quality. Accurate prediction of tumbler strength aids in adjus...
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