This paper presents a hybrid machine-learning framework for optimizing 3-Dimensional (3D) Unmanned Aerial Vehicles (UAV) node localization and resource distribution in UAV-assisted THz 6G networks to ensure efficient ...
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Phonocardiogram (PCG) signal is the digital sound recording of various heart sounds. To diagnose the different types of heart disorders, it is often necessary to analyse these PCG signals. However, PCG signal recordin...
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Photovoltaic (PV) system performance and reliability can be improved through the detection of defects in PV modules and the evaluation of their effects on system operation. In this paper, a novel system is proposed to...
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Photovoltaic (PV) system performance and reliability can be improved through the detection of defects in PV modules and the evaluation of their effects on system operation. In this paper, a novel system is proposed to detect and classify defects based on electroluminescence (EL) images. This system is called Fault Detection and Classification (FDC) and splits into four modules, which are (1) Image Preprocessing Module (IPM), (2) Feature Extraction Module (FEM), (3) Feature Selection Module (FSM), and (4) Classification Module (CM). In the first module (i.e., IPM), the EL images are preprocessed to enhance the quality of the images. Next, the two types of features in these images are extracted and fused together through FEM. Then, during FSM, the most important and informative features are extracted from these features using a new feature selection methodology, namely, Feature Selection-based Chaotic Map (FS-CM). FS-CM consists of two stages: filter stage using chi-square to initially select the most effective features and a modified selection stage using an enhanced version of Butterfly Optimization Algorithm (BOA). In fact, BOA is a popular swarm-based metaheuristic optimization algorithm that has only recently found success. While BOA has many benefits, it also has some drawbacks, including a smaller population and an increased likelihood of getting stuck in a local optimum. In this paper, a new methodology is proposed to improve the performance of BOA, called chaotic-based butterfly optimization algorithm. Finally, these selected features are used to feed the proposed classification model through CM. During CM, Hybrid Classification Model (HCM) is proposed. HCM consists of two stages, which are binary classification stage using Naïve Bayes (NB) and multi-class classification stage using enhanced multi-layer perceptron. According to the experimental results, the proposed system FDC outperforms the most recent methods. FDC introduced 98.2%, 89.23%, 87.2%, 87.9%, 87
Approximate computing and reversible logic have become vital concepts in designing low-power, area-efficient datapath units for portable image and signal processing applications. In this approach, a low-power, reversi...
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Approximate computing and reversible logic have become vital concepts in designing low-power, area-efficient datapath units for portable image and signal processing applications. In this approach, a low-power, reversible architecture targeting digital image watermarking is proposed. The reported low-power Reversible Watermark Architecture (LP-RWA) implements a novel high-speed embed unit to blend the watermark image onto the cover image. The proposed embed unit performs block-level parallel accumulation and incorporates hybrid computing with approximate parallel carry logic (APC) for n/2 least significant bits (LSBs) and the standard carry logic (SC) for n/2 most significant bits (MSBs) to achieve area efficiency and speed improvement. Note that the implementation of APC in the least n/2 bits introduces a maximal error of 2n/2;however, we add a suitable constant unit bias at an appropriate position to faithfully restrict the error within -2n/2–1 to + 2n/2–1. Furthermore, we use reversible logic for implementation to achieve low-power operation and perfect recovery of the watermark image from the blended image without much signal loss. An area-efficient version of the LP-RWA (AE-RWA) is also reported, eliminating the generation of least significant n/4 bits in the approximate part of the Reversible Faithful Embed (RFE) unit. This elimination operation increases the average error during recovery;however, the maximum error is maintained within ± 2n/2–1. Evaluations show that LP-RWA exhibits 38.8%, 25%, 27.7%, and 27.2% reductions, and AE-RWA exhibits 46%, 32.7%, 34.3%, and 58.6% reductions in Quantum Cost (QC), Gate Count (GC), Garbage Output (GO), and Logic Depth, respectively, when compared to the standard reversible watermarking architecture. Synthesis results of LP-RWA and AE-RWA designs with 90 nm Application Specific Integrated Circuit (ASIC) Process Design Kit (PDK) technology with a number of bits in input operand (n) = 16 and employing 4 bits in each block sho
The present research evaluates optical angular momentum's (OAM) performance in challenging atmospheric conditions and emphasizes its significance in free space optical (FSO) communication systems. It has been demo...
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It is difficult to improve both energy consumption and detection accuracy simultaneously, and even to obtain the trade-off between them, when detecting and tracking moving targets, especially for Underwater Wireless S...
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It is difficult to improve both energy consumption and detection accuracy simultaneously, and even to obtain the trade-off between them, when detecting and tracking moving targets, especially for Underwater Wireless Sensor Networks(UWSNs). To this end, this paper investigates the relationship between the Degree of Target Change(DoTC) and the detection period, as well as the impact of individual nodes. A Hierarchical Detection and Tracking Approach(HDTA) is proposed. Firstly, the network detection period is determined according to DoTC, which reflects the variation of target motion. Secondly, during the network detection period, each detection node calculates its own node detection period based on the detection mutual information. Taking DoTC as pheromone, an ant colony algorithm is proposed to adaptively adjust the network detection period. The simulation results show that the proposed HDTA with the optimizations of network level and node level significantly improves the detection accuracy by 25% and the network energy consumption by 10% simultaneously, compared to the traditional adaptive period detection schemes.
Random telegraph noise (RTN), primarily an gate dielectric-semiconductor interfacial phenomenon in field-effect transistors, is an important parameter of interest for downscaled devices. Existing methods proposed so f...
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The rapid industrial growth and increasing population have led to significant pollution and deterioration of the natural atmospheric *** atmospheric pollutants include NO_(2)and CO_(2).Hence,it is imperative to develo...
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The rapid industrial growth and increasing population have led to significant pollution and deterioration of the natural atmospheric *** atmospheric pollutants include NO_(2)and CO_(2).Hence,it is imperative to develop NO_(2)and CO_(2)sensors for ambient conditions,that can be used in indoor air quality monitoring,breath analysis,food spoilage detection,*** the present study,two thin film nanocomposite(nickel oxide-graphene and nickel oxide-silver nanowires)gas sensors are fabricated using direct ink *** nano-composites are investigated for their structural,optical,and electrical *** the nano-composite is deposited on the interdigitated electrode(IDE)pattern to form NO_(2)and CO_(2)*** deposited films are then exposed to NO_(2)and CO_(2)gases separately and their response and recovery times are determined using a custom-built gas sensing *** oxide-graphene provides a good response time and recovery time of 10 and 9 s,respectively for NO_(2),due to the higher electron affinity of graphene towards NO_(2).Nickel oxide-silver nanowire nano-composite is suited for CO_(2)gas because silver is an excellent electrocatalyst for CO_(2)by giving response and recovery times of 11 s *** is the first report showcasing NiO nano-composites for NO_(2)and CO_(2)sensing at room temperature.
Electronic Warfare is a type of military operation that uses electromagnetic radiation to identify, exploit, limit, or prohibit the use of the electromagnetic spectrum. The main objective of this paper is to improve t...
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Accurate crowd counting in natural images has become increasingly attractive owing to its numerous real-world applications, e.g., crowd analysis and video surveillance. Despite significant progress in crowd counting [...
Accurate crowd counting in natural images has become increasingly attractive owing to its numerous real-world applications, e.g., crowd analysis and video surveillance. Despite significant progress in crowd counting [1,2], challenges(such as scale variation and background clutter) *** fully utilize spatial information, existing crowd counting approaches [3, 4] mainly estimate a density map, where point annotations are smoothed via a Gaussian kernel to generate probabilities indicating the presence of a crowd.
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