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In recent decades, image processing and computer vision models have played a vital role in moving object detection on the synthetic aperture radar (SAR) images. Capturing of moving objects in the SAR images is a difficult task. In this study, a new automated model for detecting moving objects is proposed using SAR images. The proposed model has four main steps, namely, preprocessing, segmentation, feature extraction, and classification. Initially, the input SAR image is pre-processed using a histogram equalization technique. Then, the weighted Otsu-based segmentation algorithm is applied for segmenting the object regions from the pre-processed images. When using the weighted Otsu, the segmented grayscale images are not only clear but also retain the detailed features of grayscale images. Next, feature extraction is carried out by gray-level co-occurrence matrix (GLCM), median binary patterns (MBPs), and additive harmonic mean estimated local Gabor binary pattern (AHME-LGBP). The final step is classification using deep ensemble models, where the objects are classified by employing the ensemble deep learning technique, combining the models like the bidirectional long short-term memory (Bi-LSTM), recurrent neural network (RNN), and improved deep belief network (IDBN), which is trained with the features extracted previously. The combined models increase the accuracy of the results significantly. Furthermore, ensemble modeling reduces the variance and modeling method bias, which decreases the chances of overfitting. Compared to a single contributing model, ensemble models perform better and make better predictions. Additionally, an ensemble lessens the spread or dispersion of the model performance and prediction accuracy. Finally, the performance of the proposed model is related to the conventional models with respect to different measures. In the mean-case scenario, the proposed ensemble model has a minimum error value of 0.032, which is better related to other models. In both median- and best-case scenario studies, the ensemble model has a lower error value of 0.029 and 0.015. [ABSTRACT FROM AUTHOR]

The study aims to contribute valuable insights into the potential applications of the photocatalyst, particularly in the realms of sustainable energy and environmental remediation. Here, Zn-doped NiO nanoparticles with different mole percentages of zinc ingredients are produced and analyzed. Synthesized Zn-doped NiO nanoparticles were evaluated structurally, optically, morphologically, elementally, and photocatalytically. According to X-ray diffraction analysis, cubic NiO and hexagonal Zn-doped cubic NiO nanoparticles were formed, and Fourier transform infrared spectroscopy revealed metal dopants and metal-oxygen stretching, as well as Zn substitution and stabilization. A UV analysis revealed that zinc dopants reduced visible light absorption and bandgap. A decrease in bandgap indicates the importance of zinc incorporation and its interface with NiO. Electron scanning microscopy and transmission electron microscopy confirmed that the nanoparticles exhibited quasi-spherical morphologies and contained Ni, Zn, and O elements. Photocatalytic activity of the synthesized Zn-doped NiO nanoparticles increased with increasing Zn content, achieving a maximum at 8% Zn doping into NiO lattices of 92%. Through XPS analysis, the valencies of Zn, Ni, and O elements are demonstrated, as well as electron movements and bonding between the atoms. The zinc dopants on the metal oxide surface led to charge separation and radical reactions, resulting in enhanced degradation of phorate, salbutamol, and rhoda mine B activities. Hence, Zn-doped NiO nanoparticles are proposed as effective photocatalysts for environmental remediation. The findings are expected to have implications for advancing the field of photocatalysis and addressing challenges related to pollution and energy sustainability. [ABSTRACT FROM AUTHOR]

The increasing demand for energy and electricity and the depletion of fossil fuels are global problems. In recent years, dye-sensitized solar cell (DSSC) technologies have gained notoriety for their application in solar energy. DSSCs are considered a promising alternative renewable energy source to both inorganic and organic photovoltaic (PV) cells. Many types of dyes are being investigated to enhance the light-harvesting properties of DSSCs, but the actual realization of these absorbers in cell structure requires optimum parameters. The main aim of this study was to simulate proposed zinc phthalocyanine (ZnPC)-based structures to validate their design, assess their performance for commercial implementation, and optimize the cell parameters for optimum efficiency. To that end, Scaps-1D was employed to evaluate the performance of DSSCs to determine their optimum parameters. We found that ZnPC and isopropoxy ZnPC molecules outperform others molecules because of better optoelectronic properties. Several other parametric effects, such as photoactive layer thicknesses, doping densities, trap densities, and charge carrier mobilities, were also evaluated to observe their impact on device performance. The results show that moderate thickness, low defect density, moderate doping, and charge carrier mobility are favorable for better device performance due to low recombination losses, electrical losses, and better transport of charge carriers. The utmost power conversion efficiency values found for ZnPC- and ZnPC: PC70BM-based DSSCs after optimization were 9.50% and 9.81%. This paper also suggests a practical method for efficiently using DSSC cells by modifying factors that are significantly reliant on DSSC performance and output. [ABSTRACT FROM AUTHOR]

Recently, the preparation of smart multifunctional hybrid nanoparticles has captured significant interest in versatile areas, including medicine, environment, and food, due to their enhanced physicochemical properties. The present study focuses on the synthesis of Fe-doped NiO nanoparticles by the coprecipitation method using the sources of nickel (II) acetate tetrahydrate and iron (III) nitrate nonahydrate. The prepared Fe-doped NiO nanoparticles are characterized by X-ray diffraction, Fourier transform infrared spectroscopy, UV–visible spectroscopy, field-emission scanning electron microscopy, transmission electron microscopy, and X-ray photon spectroscopic analysis. The XRD results clearly confirm the face-centered cubic structure and polycrystalline nature of the synthesized Fe-NiO nanoparticles. The Tauc plot analysis revealed that the bandgap energy of the Fe-doped NiO nanoparticles decreased with the increasing concentration of the Fe dopant from 2% to 8%. The XPS analysis of the samples exhibited the existence of elements, including Fe, Ni, and O, with the absence of any surplus compounds. The FE-SEM and TEM analyses proved the formation of nanostructured Fe-NiO with few spherical and mostly unevenly shaped particles. Further, the photocatalytic efficiency of the prepared Fe-doped NiO nanoparticles were identified by using the cationic dye rhodamine B (Rh-B). The photocatalytic results proved the 8% of Fe doped with NiO nanoparticles achieved 99% of Rh-B degradation within 40 min of visible-light irradiation. Hence, the results of the present study exemplified the Fe-doped NiO nanoparticles have acted as a noticeable photocatalyst to degrade the Rh-B dye. [ABSTRACT FROM AUTHOR]

The paper presents the development of a GaN DC–DC power converter with a pre-driver module and power device based on a monolithic integrated GaN E/D-mode metal–insulator–semiconductor high electron mobility transistors (MIS-HEMTs) platform. The pre-driver module with direct coupled field effect transistor logic circuit structure has been monolithically integrated with a GaN power transistor on a Si-based AlGaN/GaN commercial epitaxial wafer. The MIS-HEMTs structure adopts an insulated gate dielectric layer to suppress the leakage current and increase gate voltage robustness. The GaN-based floating buck converter employs a 1 mH inductor and a 9 μ F capacitor to achieve 35 V–5 V power conversion. As a result, the pre-driver module is capable of delivering a 10 V driving voltage. GaN monolithic integration of the pre-driver module and power device can reduce the system area in the DC–DC application, which allows for an integrated chip size of 1.8 mm2. [ABSTRACT FROM AUTHOR]

The present work shows the obtainment of biosynthesized SiO2 with the aid of Jasminum grandiflorum plant extract and the study of its photocatalytic ability in dye degradation and antibacterial activity. The obtained biosynthesized SiO2 nanoparticles were characterized using X-ray diffractometer analysis, Fourier transform infrared spectroscopy analysis, ultraviolet–visible diffuse reflectance spectroscopy, field-emission scanning electron microscope with energy-dispersive X-ray analysis, transmission electron microscopy and X-ray photoelectron spectroscopy. The UV-light irradiated photocatalytic activity of the biosynthesized SiO2 nanoparticles was examined using methylene blue dye solution. Its reusability efficiency was determined over 20 cycles and compared with the commercial P-25 titanium dioxide. The bacterial resistivity of the biosynthesized SiO2 nanoparticles was examined using S. aureus and E. coli. The biosynthesized SiO2 nanoparticles showed a high level of crystallinity with no impurities, and they had an optimum crystallite size of 23 nm, a bandgap of 4 eV, no Si-OH groups and quasi-spherical shapes with Si-2p at 104 eV and O-1s at 533 eV. Their photocatalytic activity on methylene blue dye solution could reach 90% degradation after 40 min of UV light exposure, and their reusability efficiency was only 4% less than that of commercial P-25 titanium dioxide. At the concentration of 100 μg/mL, the biosynthesized SiO2 nanoparticles could allow the resistivity of E. coli to become borderline to the resistant range of an antibiotic called Amikacin. [ABSTRACT FROM AUTHOR]

Liver cancer ranks as the sixth most prevalent cancer among all cancers globally. Computed tomography (CT) scanning is a non-invasive analytic imaging sensory system that provides greater insight into human structures than traditional X-rays, which are typically used to make the diagnosis. Often, the final product of a CT scan is a three-dimensional image constructed from a series of interlaced two-dimensional slices. Remember that not all slices deliver useful information for tumor detection. Recently, CT scan images of the liver and its tumors have been segmented using deep learning techniques. The primary goal of this study is to develop a deep learning-based system for automatically segmenting the liver and its tumors from CT scan pictures, and also reduce the amount of time and labor required by speeding up the process of diagnosing liver cancer. At its core, an Encoder–Decoder Network (En–DeNet) uses a deep neural network built on UNet to serve as an encoder, and a pre-trained EfficientNet to serve as a decoder. In order to improve liver segmentation, we developed specialized preprocessing techniques, such as the production of multichannel pictures, de-noising, contrast enhancement, ensemble, and the union of model predictions. Then, we proposed the Gradational modular network (GraMNet), which is a unique and estimated efficient deep learning technique. In GraMNet, smaller networks called SubNets are used to construct larger and more robust networks using a variety of alternative configurations. Only one new SubNet modules is updated for learning at each level. This helps in the optimization of the network and minimizes the amount of computational resources needed for training. The segmentation and classification performance of this study is compared to the Liver Tumor Segmentation Benchmark (LiTS) and 3D Image Rebuilding for Comparison of Algorithms Database (3DIRCADb01). By breaking down the components of deep learning, a state-of-the-art level of performance can be attained in the scenarios used in the evaluation. In comparison to more conventional deep learning architectures, the GraMNets generated here have a low computational difficulty. When associated with the benchmark study methods, the straight forward GraMNet is trained faster, consumes less memory, and processes images more rapidly. [ABSTRACT FROM AUTHOR]

Ecosystems worldwide face a serious and life-threatening water crisis due to water contamination. Nanotechnology offers a promising solution to this issue by providing methods for removing pollutants from aquatic sources. In this study, we utilized a green and simple approach to biosynthesize NiSe NPs using Hibiscus rosa-sinensis extract as the bio-source. The plant extract acts as a reducing, stabilizing, and capping agent in the synthesis process. A simple hydrothermal method was employed to blend the NiSe NPs photocatalysts. UV-Visible DRS spectroscopy was utilized to confirm the reduction in and stabilization of Ni2+ and Se2− ions. The resulting NiSe NPs have a bandgap of 1.74 eV, which facilitates electron and hole production on their surfaces. To characterize the functional groups on the NiSe NPs and their surface interactions with bio-compounds, FTIR spectroscopy was utilized. XRD analysis revealed the crystallite size of the NiSe NPs to be 24 nm, while FE-SEM and TEM imaging showed their spherical shape and material distribution. EDX spectroscopy confirmed the integrity of the NiSe NPs' material. XPS analysis provided information on the chemical composition, nickel and selenium valency, and their interface. The efficacy of the NiSe NPs as a blended photocatalyst in photodegrading Methylene Blue (MB) dye was tested under visible light, resulting in 92% degradation. Furthermore, the NiSe NPs exhibited bactericidal activity against Escherichia coli and Staphylococcus aureus bacteria due to their advanced oxidation and reduction in charge particles, which increased the degradation efficiency and suppressed cell proliferation. Based on the obtained findings, the NiSe NPs show promise as a powerful agent for water remediation and microbial resistance. [ABSTRACT FROM AUTHOR]

The expansion of the Internet of Things is expected to lead to the emergence of the Internet of Medical Things (IoMT), which will revolutionize the health-care industry (IoT). The Internet of Things (IoT) revolution is outpacing current human services thanks to its bright mechanical, economical, and social future. Security is essential because most patient information is housed on a cloud platform in the hospital. The security of medical images in the Internet of Things was investigated in this research using a new cryptographic model and optimization approaches. For the effective storage and safe transfer of patient data along with medical images, a separate framework is required. The key management and optimization will be chosen utilizing the Rivest–Shamir–Adleman-based Arnold map (RSA-AM), hostile orchestration (HO), and obstruction bloom breeding optimization (OBBO) to increase the encryption and decryption processes' level of security. The effectiveness of the suggested strategy is measured using peak signal-to-noise ratio (PSNR), entropy, mean square error (MSE), bit error rate (BER), structural similarity index (SSI), and correlation coefficient (CC). The investigation shows that the recommended approach provides greater security than other current systems. [ABSTRACT FROM AUTHOR]

Driving safely is crucial to avoid death, injuries, or financial losses that can be sustained in an accident. Thus, a driver's physical state should be monitored to prevent accidents, rather than vehicle-based or behavioral measurements, and provide reliable information in this regard. Electrocardiography (ECG), electroencephalography (EEG), electrooculography (EOG), and surface electromyography (sEMG) signals are used to monitor a driver's physical state during a drive. The purpose of this study was to detect driver hypovigilance (drowsiness, fatigue, as well as visual and cognitive inattention) using signals collected from 10 drivers while they were driving. EOG signals from the driver were preprocessed to remove noise, and 17 features were extracted. ANOVA (analysis of variance) was used to select statistically significant features that were then loaded into a machine learning algorithm. We then reduced the features by using principal component analysis (PCA) and trained three classifiers: support vector machine (SVM), k-nearest neighbor (KNN), and ensemble. A maximum accuracy of 98.7% was obtained for the classification of normal and cognitive classes under the category of two-class detection. Upon considering hypovigilance states as five-class, a maximum accuracy of 90.9% was achieved. In this case, the number of detection classes increased, resulting in a reduction in the accuracy of detecting more driver states. However, with the possibility of incorrect identification and the presence of issues, the ensemble classifier's performance produced an enhanced accuracy when compared to others. [ABSTRACT FROM AUTHOR]

The green synthesis of metal oxide nanoparticles is an efficient, simple, and chemical-free method of producing nanoparticles. The present work reports the synthesis of Murraya koenigii-mediated ZrO2 nanoparticles (ZrO2 NPs) and their applications as a photocatalyst and antibacterial agent. Capping and stabilization of metal oxide nanoparticles were achieved by using Murraya koenigii leaf extract. The optical, structural, and morphological valance of the ZrO2 NPs were characterized using UV-DRS, FTIR, XRD, and FESEM with EDX, TEM, and XPS. An XRD analysis determined that ZrO2 NPs have a monoclinic structure and a crystallite size of 24 nm. TEM and FESEM morphological images confirm the spherical nature of ZrO2 NPs, and their distributions on surfaces show lower agglomerations. ZrO2 NPs showed high optical absorbance in the UV region and a wide bandgap indicating surface oxygen vacancies and charge carriers. The presence of Zr and O elements and their O=Zr=O bonds was categorized using EDX and FTIR spectroscopy. The plant molecules' interface, bonding, binding energy, and their existence on the surface of ZrO2 NPs were established from XPS analysis. The photocatalytic degradation of methylene blue using ZrO2 NPs was examined under visible light irradiation. The 94% degradation of toxic MB dye was achieved within 20 min. The antibacterial inhibition of ZrO2 NPs was tested against S. aureus and E. coli pathogens. Applications of bio-synthesized ZrO2 NPs including organic substance removal, pathogenic inhibitor development, catalysis, optical, and biomedical development were explored. [ABSTRACT FROM AUTHOR]

Water pollution is a serious concern for developing and undeveloped countries. Photocatalytic degradation of organic pollutants is an effective degradation method to restrain the green ecosystem. This research article presents a green, low-cost, and benevolent eco-friendly biosynthesis of cobalt oxide (Co3O4) nanoparticles using Curcuma longa plant extract. The UV and visible region absorbance of Co3O4 nanoparticles estimated the Co2+ and Co3+ transitions on the lattice oxygen, and their bandgap of 2.2 eV was confirmed from the UV-DRS spectroscopy. The cubic structure and spherical shape of Co3O4 nanoparticles were estimated by using XRD and TEM characterizations. Plant molecules aggregation and their agglomerations on the nanoparticles were established from FTIR and EDX spectroscopy. Multiple cobalt valences on the oxygen surfaces and their reaction, bonding, and binding energies were analyzed from XPS measurements. The biogenic Co3O4 nanoparticles were executed against gram-positive (Staphylococcus aureus—S. aureus) and gram-negative (Escherichia coli—E. coli) bacteria. A gram-positive bacterial strain exhibited great resistivity on Co3O4 nanoparticles. Degradation of organic dye pollutants on the Co3O4 nanoparticles was performed against methylene blue (MB) dye under the conditions of visible light irradiation. Dye degradation efficiency pseudo-first-order kinetics on the pseudo-first-order kinetics denotes the rate of degradation over the MB dye. This research work achieved enhanced degradation potency against toxic organic dye and their radicals are excited from visible light irradiations. Absorption light and charged particle recombinations are reformed and provoked by the plant extract bio-molecules. In this process, there is no inferior yield development, and electrons are robustly stimulated. Furthermore, the biosynthesized Co3O4 nanoparticles determined the potency of bacterial susceptibility and catalytic efficacy over the industrial dye pollutants. [ABSTRACT FROM AUTHOR]

Systems for medical analytics and decision making that make use of multimodal intelligence are of critical importance in the field of healthcare. Liver cancer is one of the most frequent types of cancer and early identification of it is crucial for effective therapy. Liver tumours share the same brightness and contrast characteristics as their surrounding tissues. Likewise, irregular tumour shapes are a serious concern that varies with cancer stage and tumour kind. There are two main phases of tumour segmentation in the liver: identifying the liver, and then segmenting the tumour itself. Conventional interactive segmentation approaches, however, necessitate a high number of intensity levels, whereas recently projected CNN-based interactive segmentation approaches are constrained by low presentation on liver tumour images. This research provides a unique deep Learning based Segmentation with Coot Extreme Learning Model approach that shows high efficiency in results and also detects tumours from the publicly available data of liver images. Specifically, the study processes the initial segmentation with a small number of additional users clicks to generate an improved segmentation by incorporating inner boundary points through the proposed geodesic distance encoding method. Finally, classification is carried out using an Extreme Learning Model, with the classifier's parameters having been ideally chosen by means of the Coot Optimization algorithm (COA). On the 3D-IRCADb1 dataset, the research evaluates the segmentation quality metrics DICE and accuracy, finding improvements over approaches in together liver-coloured and tumour separation. [ABSTRACT FROM AUTHOR]

In recent years, lung cancer prediction is an essential topic for reducing the death rate of humans. In the literature section, some papers are reviewed that reduce the accuracy level during the prediction stage. Hence, in this paper, we develop a Multi-Process Remora Optimized Hyperparameters of Convolutional Neural Network (MPROH-CNN) aimed at lung cancer prediction. The proposed technique can be utilized to detect the CT images of the human lung. The proposed technique proceeds with four phases, including pre-processing, feature extraction and classification. Initially, the databases are collected from the open-source system. After that, the collected CT images contain unwanted noise, which affects classification efficiency. So, the pre-processing techniques can be considered to remove unwanted noise from the input images, such as filtering and contrast enhancement. Following that, the essential features are extracted with the assistance of feature extraction techniques such as histogram, texture and wavelet. The extracted features are utilized to classification stage. The proposed classifier is a combination of the Remora Optimization Algorithm (ROA) and Convolutional Neural Network (CNN). In the CNN, the ROA is utilized for multi process optimization such as structure optimization and hyperparameter optimization. The proposed methodology is implemented in MATLAB and performances are evaluated by utilized performance matrices such as accuracy, precision, recall, specificity, sensitivity and F_Measure. To validate the projected approach, it is compared with the traditional techniques CNN, CNN-Particle Swarm Optimization (PSO) and CNN-Firefly Algorithm (FA), respectively. From the analysis, the proposed method achieved a 0.98 accuracy level in the lung cancer prediction. [ABSTRACT FROM AUTHOR]

In recent years, the high improvement in communication, Internet of Things (IoT) and cloud computing have begun complex questioning in security. Based on the development, cyberattacks can be increased since the present security techniques do not give optimal solutions. As a result, the authors of this paper created filter-based ensemble feature selection (FEFS) and employed a deep learning model (DLM) for cloud computing intrusion detection. Initially, the intrusion data were collected from the global datasets of KDDCup-99 and NSL-KDD. The data were utilized for validation of the proposed methodology. The collected database was utilized for feature selection to empower the intrusion prediction. The FEFS is a combination of three feature extraction processes: filter, wrapper and embedded algorithms. Based on the above feature extraction process, the essential features were selected for enabling the training process in the DLM. Finally, the classifier received the chosen features. The DLM is a combination of a recurrent neural network (RNN) and Tasmanian devil optimization (TDO). In the RNN, the optimal weighting parameter is selected with the assistance of the TDO. The proposed technique was implemented in MATLAB, and its effectiveness was assessed using performance metrics including sensitivity, F measure, precision, sensitivity, recall and accuracy. The proposed method was compared with the conventional techniques such as an RNN and deep neural network (DNN) and RNN–genetic algorithm (RNN-GA), respectively. [ABSTRACT FROM AUTHOR]

Simple Summary: The classification is performed later by an interactively learning Swin Transformer block, the core unit for feature representation and long-range semantic information. In particular, the proposed strategy improved significantly and was very resilient while dealing with small liver pieces, discontinuous liver regions, and fuzzy liver boundaries. The experimental results confirm that the proposed APESTNet is more effective in classifying liver tumours than the current state-of-the-art models. Without compromising accuracy, the proposed method conserved resources. However, the proposed method is prone to slight over-segmentation or under-segmentation errors when dealing with lesions or tumours at the liver boundary. Therefore, our future work will concentrate on completely utilizing the z-axis information in 3D to reduce errors. Diagnosis and treatment of hepatocellular carcinoma or metastases rely heavily on accurate segmentation and classification of liver tumours. However, due to the liver tumor's hazy borders and wide range of possible shapes, sizes, and positions, accurate and automatic tumour segmentation and classification remains a difficult challenge. With the advancement of computing, new models in artificial intelligence have evolved. Following its success in Natural language processing (NLP), the transformer paradigm has been adopted by the computer vision (CV) community of the NLP. While there are already accepted approaches to classifying the liver, especially in clinical settings, there is room for advancement in terms of their precision. This paper makes an effort to apply a novel model for segmenting and classifying liver tumours built on deep learning. In order to accomplish this, the created model follows a three-stage procedure consisting of (a) pre-processing, (b) liver segmentation, and (c) classification. In the first phase, the collected Computed Tomography (CT) images undergo three stages of pre-processing, including contrast improvement via histogram equalization and noise reduction via the median filter. Next, an enhanced mask region-based convolutional neural networks (Mask R-CNN) model is used to separate the liver from the CT abdominal image. To prevent overfitting, the segmented picture is fed onto an Enhanced Swin Transformer Network with Adversarial Propagation (APESTNet). The experimental results prove the superior performance of the proposed perfect on a wide variety of CT images, as well as its efficiency and low sensitivity to noise. [ABSTRACT FROM AUTHOR]

A MANET consists of a group of mobile nodes. In a MANET, scalability and mobility have a greater influence on routing performance. The clustering technique plays a vital role in enhancing the routing mechanism and improving the network lifetime of a large-scale network like a MANET. The clustering process will degrade network performance if the malicious node is chosen as the Cluster Leader (CL). Thus, the secure clustering process in a MANET is a very challenging task. To overcome this problem, the following key factors like Trust Value (TV), Residual Energy Level (REL), and Mobility (M) of the node are used as decision-making parameters to elect a Cluster Leader (CL). In this work, we have proposed a soft computing-based neuro-fuzzy model, ANFIS-based Energy-Efficient Secure Clustering Model (ANFIS-EESC), with a primary objective of forming energy-aware stable trust-based clustering in a MANET. Moreover, we have proposed two working novel algorithms: Weight-Based Trust Estimation (WBTE) algorithm and the Fuzzy-Based Clustering (FBC) algorithm. The primary objective of the WBTE algorithm is to measure the trustworthiness of the nodes and to mitigate the malicious nodes. Fuzzy-Based Clustering (FBC) algorithm is a fuzzy logic-based cluster formation algorithm. In our proposed work, each non-CL in the system applies the cluster density of CL and mobility for each CL node using the Mamdani Fuzzy Inference system, and makes the decision to join as a member with a CL that holds maximum value. Simulation results show that the proposed work enhances the network performance by electing a more stable trust-aware and energy-aware node as Cluster leader (CL). We compare the performance parameters of the proposed work, such as packet delivery rate, energy consumption, detection rate, and reaffiliation, with the existing work, Weighted Clustering Algorithm (WCA). The network lifetime is 39% greater in the proposed ANFIS-EESC model than in the other existing work, WCA. Moreover, ANFIS-EESC shows an enhancement of 22% to 32% in packet delivery ratio and 32% and 39% in throughput. From the above analysis, it has been proved that the proposed work gives a better performance in terms of reliability and stability when compared to the existing work, WCA. [ABSTRACT FROM AUTHOR]

An environmentally safe, efficient, and economical microwave-assisted technique was selected for the production of silver nanoparticles (AgNPs). To prepare uniformly disseminated AgNPs, xanthan gum (XG) was utilized as both a reducing and capping agent. UV–Vis spectroscopy was used to characterize the formed XG-AgNPs, with the absorption band regulated at 414 nm under optimized parameters. Atomic force microscopy was used to reveal the size and shape of XG-AgNPs. The interactions between the XG capping agent and AgNPs observed using Fourier transform infrared spectroscopy. The XG-AgNPs were placed in between glassy carbon electrode and Nafion® surfaces and then deployed as sensors for voltammetric evaluation of mercury ions (Hg2+) using square-wave voltammetry as an analytical mode. Required Nafion® quantities, electrode behavior, electrolyte characteristics, pH, initial potentials, accumulation potentials, and accumulation durations were all comprehensively investigated. In addition, an electrochemical mechanism for the oxidation of Hg2+ was postulated. With an exceptional limit of detection of 0.18 ppb and an R2 value of 0.981, the sensors' measured linear response range was 0.0007–0.002 µM Hg2+. Hg2+ evaluations were ultimately unaffected by the presence of many coexisting metal ions (Cd2+, Pb2+, Cr2O4, Co2+,Cu2+, CuSO4). Spiked water samples were tested using the described approach, with Hg2+ recoveries ranging from 97% to 100%. [ABSTRACT FROM AUTHOR]

In recent decades, big data analysis has become the most important research topic. Hence, big data security offers Cloud application security and monitoring to host highly sensitive data to support Cloud platforms. However, the privacy and security of big data has become an emerging issue that restricts the organization to utilize Cloud services. The existing privacy preserving approaches showed several drawbacks such as a lack of data privacy and accurate data analysis, a lack of efficiency of performance, and completely rely on third party. In order to overcome such an issue, the Triple Data Encryption Standard (TDES) methodology is proposed to provide security for big data in the Cloud environment. The proposed TDES methodology provides a relatively simpler technique by increasing the sizes of keys in Data Encryption Standard (DES) to protect against attacks and defend the privacy of data. The experimental results showed that the proposed TDES method is effective in providing security and privacy to big healthcare data in the Cloud environment. The proposed TDES methodology showed less encryption and decryption time compared to the existing Intelligent Framework for Healthcare Data Security (IFHDS) method. [ABSTRACT FROM AUTHOR]

The notion of predictive maintenance is perceived as a breakthrough in the manufacturing and other industrial sectors. The recent developments in this field can be attributed to the amalgamation of Artificial Intelligence- and Machine Learning (ML)-based solutions in predicting the health state of the machines. Most of the existing machine learning models are a hybridization of common ML algorithms that require extensive feature engineering. However, the real time deployment of these models demands a lower computational effort with higher accuracy. The proposed Multi-labeled Context-based Multilayered Bayesian Inferential (M-CMBI) predictive analytic classification framework is a novel approach that uses a cognitive approach by mimicking the brain's activity, termed MisMatch Negativity (MMN), to classify the faults. This adaptive model aims to classify the faults into multiple classes based on the estimated fault magnitude. This model is tested for efficacy on the Pump dataset which contains 52 items of raw sensor data to predict the class into normal, broken and recovering. Not all sensor data will contribute to the quality of prediction. Hence, the nature of the sensor data is analyzed using Exploratory Data Analysis (EDA) to prioritize the significance of the sensors and the faults are classified based on their fault magnitude. The results of the classification are validated on metrics such as accuracy, F1-Score, Precision and Recall against state of art techniques. Thus, the proposed model can yield promising results without time-consuming feature engineering and complex signal processing tasks, making it highly favorable to be deployed in real-time applications. [ABSTRACT FROM AUTHOR]

Recently, Plug-in Hybrid Electric Vehicles (PHEVs) have gathered a lot of attention by integrating an electric motor with an Internal Combustion Engine (ICE) to minimize fuel consumption and greenhouse gas emissions. The On-Board Chargers (OBCs) are selected in this research because they are limited by dimensions and mass, and also consume low amounts of power. The Equivalent Series Resistance (ESR) of a filter capacitor is minor, so the zero produced by the ESR is positioned at a high frequency. In this state, the system magnitude gradually drops, causing a ripple in the circuit that generates a harmful impact on the battery's stability. To improve the stability of the system, a Neural Network with an Improved Particle Swarm Optimization (NN–IPSO) control algorithm was developed. This study establishes an isolated converter topology for PHEVs to preserve battery-charging functions through a lesser number of power electronic devices over the existing topology. This isolated converter topology is controlled by NN–IPSO for the PHEV, which interfaces with the battery. The simulation results were validated in MATLAB, indicating that the proposed NN–IPSO-based isolated converter topology minimizes the Total Harmonic Distortion (THD) to 3.69% and the power losses to 0.047 KW, and increases the efficiency to 99.823%, which is much better than that of the existing Switched Reluctance Motor (SRM) power train topology. [ABSTRACT FROM AUTHOR]

Generally, battery power is a valuable resource for mobile devices in a Mobile Ad Hoc Network (MANET). Therefore, energy efficiency and network lifetime should be taken into account when developing control strategies. However, designing an energy-efficient routing mechanism necessitates consideration of many nodes from many layers, such as remaining energy, overall traffic load, and channel assumptions. The traditional layered strategy is unsuccessful in dealing with power-related issues that might affect all layers of the stack. In this paper, the Energy Centric Tunicate Swarm Algorithm (ECTSA) is proposed to perform the cross-layer routing over MANET. The fitness metrics considered in the ECTSA to improve the cross-layer routing are residual energy, communication cost, Data Success Rate (DSR), and mobility. Additionally, an Adaptive Competition Window (ACW) adjustment is used for minimizing the energy consumption caused by the contentions. The performance of the proposed ECTSA is analyzed by means of energy consumption, Packet Delivery Ratio (PDR), End-to-End Delay (EED), and routing overhead. Next, the existing techniques, namely, CEELBRP and EECRP-PSO, are used to evaluate the efficiency of the ECTSA method. The energy consumption of the ECTSA is 7.1 joules and prolongs the network lifetime up to 1603 s for 50 nodes, which is better when compared to the existing CEELBRP and EECRP-PSO techniques. [ABSTRACT FROM AUTHOR]

Multi-stakeholder and organizational involvement is an integral part of the medicine supply chain. Keeping track of the activities associated with medical products is difficult when the system is complex. Their complexity limits transparency and data provenance. Deficiencies within existing supply chains result in the counterfeiting of drugs, illegal imports, and inefficient operations. Due to these limitations, product integrity is compromised, resulting in product wastage. Visibility of the entire product supply chain is crucial for the pharmaceutical industry in terms of product safety and reduction of manufacturing costs. The Cloud-based Blockchain-powered architecture of the system provides a platform for addressing the need of pharma-material traceability, data storage, privacy of data, and quality assurance. This framework comprises of the identification of activities through tagging, information sharing in a secure environment; cloud-based storage using an off-chain Interplanetary File System (IPFS) and an on-chain couch DB; and access to this information that is controlled by the system's regulator. Electronic drug records will be accessed via a smart contract in Hyperledger Blockchain. The system assists in identifying false and cross-border products through the manufacturer and country of origin. A scan will identify counterfeit medications, showing that they are unauthorized products which may pose a risk to patients. Our experiments demonstrated the efficiency and usability of the design platform. Finally, we benchmarked the system using Hyperledger Caliper. [ABSTRACT FROM AUTHOR]

Biogenic CoFe2O4 nanoparticles were prepared by co-precipitation and Hibiscus rosa sinensis plant leaf was used as a bio-reductant of the nanoparticle productions. The biosynthesized CoFe2O4 nanoparticles were characterized by XRD, FTIR, UV, VSM, and SEM via EDX analysis. The cubic phase of biosynthesized CoFe2O4 nanoparticles and their crystallite size was determined by XRD. The Co-Fe-O bonding and cation displacement was confirmed by FTIR spectroscopy. The presence of spherically-shaped biosynthesized CoFe2O4 nanoparticles and their material were confirmed by SEM and TEM via EDX. The super-paramagnetic behaviour of the biosynthesized CoFe2O4 nanoparticles and magnetic pulse was established by VSM analysis. Organic and bacterial pollutants were eradicated using the biosynthesized CoFe2O4 nanoparticles. The spinel ferrite biosynthesized CoFe2O4 nanoparticles generate radical and superoxide ions, which degrade toxic organic and bacterial pollutants in the environment. [ABSTRACT FROM AUTHOR]

Medical records generated in hospitals are treasures for academic research and future references. Medical Image Retrieval (MIR) Systems contribute significantly to locating the relevant records required for a particular diagnosis, analysis, and treatment. An efficient classifier and effective indexing technique are required for the storage and retrieval of medical images. In this paper, a retrieval framework is formulated by adopting a modified Local Binary Pattern feature (AvN-LBP) for indexing and an optimized Fuzzy Art Map (FAM) for classifying and searching medical images. The proposed indexing method extracts LBP considering information from neighborhood pixels and is robust to background noise. The FAM network is optimized using the Differential Evaluation (DE) algorithm (DEFAMNet) with a modified mutation operation to minimize the size of the network without compromising the classification accuracy. The performance of the proposed DEFAMNet is compared with that of other classifiers and descriptors; the classification accuracy of the proposed AvN-LBP operator with DEFAMNet is higher. The experimental results on three benchmark medical image datasets provide evidence that the proposed framework classifies the medical images faster and more efficiently with lesser computational cost. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is a network approach achieved by decoupling of the control and data planes. The control plane is logically centralized and the data plane is distributed across the network elements. The real-time network is in need of the incorporation of distributed controllers to maintain distributed state information of the traffic flows. Software-based solutions aid distributed SDN controllers to handle fluctuating network traffic and the controller's configurations are dynamically programmed in real time. In this study, SDN controllers were programmed with a stateful firewall application to provide firewall functionalities without the support of committed hardware. A stateful firewall filtered traffic based on the complete context of incoming packets; it continuously evaluated the entire context of traffic flows, looking for network entry rather than specific traffic flows. In addition, a flow-based scheduling module was implemented in the distributed controllers to improve network scalability. A network cluster was configured with three distributed controllers and we experimented with three independent network topologies. The performance of the proposed network model was evaluated by measuring and analyzing metrics such as network throughput (kbps), delay (ms) and network overhead (pkt/ms) for various combinations of controllers and topologies. The results of the analysis were determined using the mininet emulator. The findings of the performance evaluation indicate that the distributed SDN controllers performs better than a centralized controller. When comparing distributed SDN with two controllers and distributed SDN with three controllers the overall network throughput is increased by 64%, the delay is decreased by 43% and network overhead is reduced by 39%. [ABSTRACT FROM AUTHOR]

Activated carbon doping TiO2 nanoparticles were synthesised by zapota leaf extract using the co-precipitation method. The bio-constituents of plant compounds were used in the reactions of stabilization and reductions. The carbon loading on the TiO2 nanoparticles was characterised by XRD, FTIR, UV-DRS, SEM with EDX, and TEM analysis. The loading of activated carbon onto the TiO2 nanoparticles decreased the crystallite size and optical bandgap, and their doping improved the surface structure of AC/TiO2 nanoparticles. Mesoporous/microporous instability was remodified from the activated carbon, which was visualised using SEM and TEM analysis, respectively. The photocatalytic dye degradation of Rh-B dye was degraded in TiO2 and AC/TiO2 nanoparticles under visible light irradiation. The degradation efficiencies of TiO2 and AC/TiO2 nanoparticles were 73% and 91%, respectively. The bacterial abilities of TiO2 and AC/TiO2 nanoparticles were examined by E. coli and S. aureus. The water reclamation efficiency and bactericidal effect of TiO2 and AC/TiO2 nanoparticles were examined via catalytic dye degradation and bacterial efficiency of activated carbon-doped titanium dioxide nanoparticles. [ABSTRACT FROM AUTHOR]

Iron oxide nanoparticles are produced using simple auto combustion methods with honey as a metal-stabilizing and -reducing agent. Herein, α-Fe2O3 nanoparticles are produced using an iron nitrate precursor. These prepared samples are analyzed by an X-ray diffractometer (XRD), FTIR spectroscopy, UV-DRS, and a field-emission scanning electron microscope (FESEM) combined with energy-dispersive spectroscopy and a vibrating sample magnetometer (VSM). The XRD results confirm a rhombohedral structure with an R3 c ¯ space group single-phase formation of α-Fe2O3 in all samples. FESEM images reveal the different morphologies for the entire three samples. TEM analysis exhibits spherical shapes and their distribution on the surfaces. XPS spectroscopy confirms the Fe-2p and O-1s state and their valency. The VSM study shows strong ferromagnetic behavior. The prepared α-Fe2O3 nanoparticles exhibit exceptional charge carriers and radical production. The prepared sample retains excellent photocatalytic, antifungal and antibacterial activity. [ABSTRACT FROM AUTHOR]

In the recent times, there has been a lot of speculation related to advanced driver-assistance system (ADAS) which provides best driving experience for the drivers. ADAS technology helps to detect the unhealthy driving conditions which lead to road accidents today. Road accidents are basically caused due to distracted driving, over speeding, drink and drive, foggy weather, no proper headlights, or due to some object which suddenly trespasses the vehicle. Today the major advancements in ADAS include parking assistance, road traffic detection, object detection on highways, and lane detection. But the major risk limitation in ADAS system is the speed and time at which the object is detected and tracked. Several algorithms such as R-CNN, Fast R-CNN, and YOLO were used for effective object detection and tracking earlier, but sometimes, the system do fail to detect due the speed factor. Hence, the proposed work presents a novel approach called "A Real-Time Object Detection Framework for Advanced Driver Assistant Systems" by implementing the state-of-the-art object detection algorithm YOLOv5 which improves the speed in detection of object over real-time. This paper provides a comparison between other state-of-the-art object detectors such as YOLOv3 and YOLOv4. Comparison is done based on mean average precision (mAP) and frames per second (FPS) on three benchmark datasets collected as a part of research findings. YOLOv5 proves to be faster and 95% accurate than the other object detection algorithms in the comparison. This framework is used to build a mobile application called "ObjectDetect" which helps users make better decisions on the road. "ObjectDetect" consists of a simple user interface that displays alerts and warnings. [ABSTRACT FROM AUTHOR]

In the field of intelligent transportation systems (ITS), video surveillance is a hot research topic; this surveillance is used in a variety of applications, such as detecting the cause of an accident, tracking down a specific vehicle, and discovering routes between major locations. Object detection and shadow elimination are the main tasks in this area. Object detection in computer vision is a critical and vital part of object and scene recognition, and its applications are vast in the fields of surveillance and artificial intelligence. Additionally, other challenges arise in regard to video surveillance, including the recognition of text. Based on shadow elevation, we present in this work an inner-outer outline profile (IOOPL) algorithm for detecting the three levels of object boundaries. A system of video surveillance monitoring of traffic can be incorporated into this method. It is essential to identify the type of detected objects in intelligent transportation systems (ITS) to track safely and estimate traffic parameters correctly. This work addresses the problem of not recognizing object shadows as part of the object itself in-vehicle image segmentation. This paper proposes an approach for detecting and segmenting vehicles by eliminating their shadow counterparts using the delta learning algorithm (Widrow-Hoff learning rule), where the system is trained with various types of vehicles according to their appearance, colors, and build types. An essential aspect of the intelligent transportation system is recognizing the type of the detected object so that it can be tracked reliably and the traffic parameters can be estimated correctly. Furthermore, we propose to classify vehicles using a machine learning algorithm consisting of artificial neural networks trained using the delta learning algorithm, a high-performance machine learning algorithm, to obtain information regarding their travels. The paper also presents a method for recognizing the number plate using text correlation and edge dilation techniques. In regard to video text recognition, number plate recognition is a challenging task. [ABSTRACT FROM AUTHOR]

Nowadays, the Long-Term Evolution-Advanced system is widely used to provide 5G communication due to its improved network capacity and less delay during communication. The main issues in the 5G network are insufficient user resources and burst errors, because it creates losses in data transmission. In order to overcome this, an effective Radio Resource Management (RRM) is required to be developed in the 5G network. In this paper, the Long Short-Term Memory (LSTM) network is proposed to develop the radio resource management in the 5G network. The proposed LSTM-RRM is used for assigning an adequate power and bandwidth to the desired user equipment of the network. Moreover, the Grid Search Optimization (GSO) is used for identifying the optimal hyperparameter values for LSTM. In radio resource management, a request queue is used to avoid the unwanted resource allocation in the network. Moreover, the losses during transmission are minimized by using frequency interleaving and guard level insertion. The performance of the LSTM-RRM method has been analyzed in terms of throughput, outage percentage, dual connectivity, User Sum Rate (USR), Threshold Sum Rate (TSR), Outdoor Sum Rate (OSR), threshold guaranteed rate, indoor guaranteed rate, and outdoor guaranteed rate. The indoor guaranteed rate of LSTM-RRM for 1400 m of building distance improved up to 75.38% compared to the existing QOC-RRM. [ABSTRACT FROM AUTHOR]

In recent trends, renewable energies are infinite, safe, and are becoming a reliable source for electricity requirements. However, they have certain variations in their results because of climate change, which is its major issue. To solve this challenge, a hybrid renewable energy system was created by combining various energy sources. Energy management strategies must be employed to determine the best possible performance of renewable energy-based hybrid systems, as well as to fulfil demand and improve system efficiency. This work describes an Energy Management System (EMS) for a Hybrid Renewable Energy System (HRES) called Improved Mayfly Optimization-based Modified Perturb and Observe (IMO-MP&O). The developed EMS is based on basic conceptual constraints and has the goal of meeting the energy demand of connected load, ensuring energy flow stabilization, and optimizing battery utilization. In addition, the suggested IMO-MP&O can identify the condition and operating state of every HRES sub-system and assure the network stability of frequency and voltage changes. Numerical simulations in the MATLAB/Simulink environment were used to evaluate the proposed EMS. The simulated results show that the proposed IMO-MP&O achieves the harmonic error of 0.77%, which is less than the existing Maximum Power Point Tracking (MPPT) control and Artificial Neural Network (ANN)-based Z-Source Converter methods. [ABSTRACT FROM AUTHOR]

TiO2 electrode material is a poor choice for supercapacitor electrodes because it has low conductivity, poor cyclic stability, and a low capacitance value. It is inevitable to enhance electrode materials of this kind by increasing the surface area and combining high electronic conductivity materials. In the current research work, it was proposed to combine reduced graphene oxide (rGO) as it might provide a large surface area for intercalation and deintercalation, and also, it could establish the shorter paths to ion transfer, leading to a reduction in ionic resistance. The size, surface morphology, and crystalline structure of as-prepared rGO/TiO2 nanocomposites were studied using HRTEM, FESEM, and XRD, respectively. Using an electrochemical workstation, the capacitive behaviors of the rGO/TiO2 electrode materials were assessed with respect to scan rate and current density. The capacitances obtained through cyclic voltammetry and galvanostatic charge-discharge techniques were found to be higher when compared to TiO2 alone. Furthermore, the as-synthesized nanocomposites were able to achieve a higher energy density and better cycle stability. [ABSTRACT FROM AUTHOR]

Visible light-driven photocatalyst BiFeO3 (BFO) nanoparticles were synthesised by the auto-combustion method. The honey was used to fuel the auto combustion method to synthesise the BFO nanoparticles. The structural, optical and morphological activities of the bismuth loaded BFO nanoparticles were characterised by X-ray diffraction (XRD), FTIR, UV, photoluminescence (PL) and SEM analysis, respectively. The bismuth content modifies the lattice parameters of XRD and reduces the bandgap energy. The observed crystallite size varies from 19 to 27 nm and the bandgap region is 2.07 to 2.21 eV. The photo-charge carriers increased upon the BFO nanoparticles and their emission at 587 nm in the visible region of the PL spectrum. The 2% bismuth loaded BFO nanoparticles showed better morphology than 0% and 5% bismuth loaded BFO nanoparticles. The oxidation state of BFO nanoparticles and their binding energies were characterised by X-ray Photoelectron Spectroscopy (XPS) analysis. The methylene blue dye (MB) degradation against 2% BFO nanoparticles showed enhanced catalytic activity (81%) than the remaining samples of BFO nanoparticles. The bacterial activity of BFO nanoparticles was assessed against Gram-positive and Gram-negative bacteria, including S. aureus and E. coli. 2% Excess bismuth BFO nanoparticles exhibit better antibacterial activity. Comparatively, 2% Excess bismuth BFO nanoparticles derived an outstanding crystallinity, charge separation, and reduced bandgap activities. Based on these findings, BFO nanoparticles may be applicable in drug delivery and water remediation applications. [ABSTRACT FROM AUTHOR]

Hadoop is a framework for storing and processing huge amounts of data. With HDFS, large data sets can be managed on commodity hardware. MapReduce is a programming model for processing vast amounts of data in parallel. Mapping and reducing can be performed by using the MapReduce programming framework. A very large amount of data is transferred from Mapper to Reducer without any filtering or recursion, resulting in overdrawn bandwidth. In this paper, we introduce an algorithm called Inner MAPping Combiner (IMapC) for the map phase. This algorithm in the Mapper combines the values of recurring keys. In order to test the efficiency of the algorithm, different approaches were tested. According to the test, MapReduce programs that are implemented with the Default Combiner (DC) of IMapC will be 70% more efficient than those that are implemented without one. To make computations significantly faster, this work can be combined with MapReduce. [ABSTRACT FROM AUTHOR]

5.2-GHz all-digital frequency synthesizer implemented proposed reference spur reducing with the tsmc 0.18 µm CMOS technology is proposed. It can be used for radar equipped applications and radar-communication control. It provides one ration frequency ranged from 4.68 GHz to 5.36 GHz for the local oscillator in RF frontend circuits. Adopting a phase detector that only delivers phase error raw data when phase error is investigated and reduces the updating frequency for DCO handling code achieves a decreased reference spur. Since an all-digital phase-locked loop is designed, the prototype not only optimized the chip dimensions, but also precludes the influence of process shrinks and has the advantage of noise immunity. The elements of novelties of this article are low phase noise and low power consumption. With 1.8 V supply voltage and locking at 5.22 GHz, measured results find that the output signal power is −8.03 dBm, the phase noise is −110.74 dBc/Hz at 1 MHz offset frequency and the power dissipation is 16.2 mW, while the die dimensions are 0.901 × 0.935 mm2. [ABSTRACT FROM AUTHOR]

This article designs and analyzes LC injection-locked frequency dividers (ILFDs) using dual-resonance LC resonator. The ILFD consists of two single-resonance LC-tank capacitive cross-coupled sub-ILFDs operating at 3.7 and 5.1 GHz, respectively, and the two sub-ILFDs are coupled by the electric field through a pair of metal–insulator–metal (MIM) capacitors. The die area in the Taiwan Semiconductor Manufacturing Corporation (TSMC) 0.18- $\mu \text{m}$ CMOS is $0.65\times1.023$ mm2. By controlling the gate voltages of the switching transistors, the ILFD has three different operational modes—high-band dominant mode, low-band dominant mode, and concurrent oscillation mode. Overlapped locking range is demonstrated for the first time in the designed electric-field coupling resonator ILFD. Non-overlapped locking range is found for the ILFD manifesting an effect of the dual-resonance resonator. [ABSTRACT FROM AUTHOR]

This article presents and analyzes a wide-locking range divide-by-5 injection-locked frequency divider (ILFD) manufactured in the TSMC 0.18- $\mu \text{m}$ processes. The ILFD uses the balanced injection method and uses the pure harmonic mixing approach at low injection power, and it is designed with an RLC dual-resonance resonator. The harmonic mixer relies on the self-generated second harmonic, which increases with injection power. Because of no narrowband filter as used in the linear mixer counterpart, the divide-by-5 ILFD uses a wideband design method. In addition, the ILFD enhances the divide-by-5 locking range via the overlapped dual-band locking ranges as verified by both experiment and simulation. At the drain–source bias ${V} _{\text{DD}}$ of 0.8 V and at the incident power of 0 dBm, the locking range of the divide-by-5 ILFD is 3.6 GHz, from the incident frequency 13.0–16.6 GHz with the percentage 24.3%. The core power consumption is 4.09 mW, and the die area is $1.02\times0.93$ mm2. [ABSTRACT FROM AUTHOR]

This article designs and analyzes wide locking range (LR) high even-modulus LC-tank injection-locked frequency dividers (ILFDs) with current-reused topologies. The current-reused LC ILFD uses two stacked LC sub-ILFDs sharing the same dc current. The current-reused ILFD becomes a divide-by-4 ($\div 4$) ILFD, when both sub-ILFDs are used $\div 2$ ILFDs, and it is used as a divide-by-8 ($\div 8$) ILFD when one sub-ILFD is used a $\div 4$ ILFD. Both sub-ILFDs use nMOSFETs as linear injection mixers for high conversion gain. For the $\div 8$ LC ILFD designed in the TSMC 0.18- $\mu \text{m}$ CMOS process, the circuit uses one high-frequency $\div 4$ sub-ILFD and one low-frequency $\div 2$ sub-ILFD, at the supply of 1.6 V, and at the incident power of 0 dBm, the LR is 4 GHz (38.835%), from the incident frequency 8.3 to 12.3 GHz. The $\div 8$ ILFD core power consumption is 13.98 mW, and the die size is $1.2 \times 1.2$ mm2. Both the $\div 8$ LC ILFD and the $\div 4$ sub-ILFD have nonoverlapped and overlapped LRs, which are due to a dual-resonance resonator used in the varactor-free n-core sub-ILFD. The LC dual-resonance resonator is due to parasitic capacitors in active FETs and on-chip spiral inductors and inductive elements, and it is used to get wide overlapped LR. Two $\div 4$ LC ILFDs inherent in the designed circuit are also studied. [ABSTRACT FROM AUTHOR]

Distributed LC resonator and resistively distributed resonator belong to the same technique used to extend the locking range of injection-locked frequency divider (ILFD). ILFD using the former resonator often has one locking range, and extension of locking range is attributed to oscillation frequency increment. This paper measures and analyzes the input sensitivity of a CMOS ILFD with a resistively distributed resonator and with the divide-by-3 and divide-by-2 functions, and the input sensitivity shows a wide single-band locking range. The fabricated 0.18- $\mu \text{m}$ CMOS ILFD is made of a pair of cross-coupled n-type transistors, two direct-injection MOSFETs, and a resistively dual-resonance resonator. The wide single-band locking range of the designed ILFD is owing to the overlapped locking ranges, and it is verified by the smooth tuning range without forbidden region and nonoverlapped locking ranges measured on the same chip with the unbalanced injection structure. The frequency tuning is obtained with large tank resistance, which also reduces the frequency tuning hysteresis effect. [ABSTRACT FROM AUTHOR]

A new compact design of CPW-fed wideband (WB) spiral-shaped slot antenna is proposed. The proposed antenna has a compact size with overall dimensions 37-33 mm and is fabricated on FR4 substrate with dielectric constant ɛr = 4.4 and thickness h = 1.6 mm. With the different length of the spiral-shaped slots, simulated and experimental results of the antenna are suitable for WB operations. The-10 dB bandwidth of the WB antenna from measurement is approximately 115.2% (2.36-8.53 GHz). The proposed antenna provides nearly omni-directional radiation characteristics. The new antenna configuration operates in several different bands: 2.4, 3.5, 5.2, 5.5, and 5.8 GHz covering 2.4/5.2/5.8 GHz WLAN bands and 2.5/3.5/5.5 GHz WiMAX bands. The results for S11, far-field H- and E-plane radiation patterns and gain of the proposed antennas are presented and discussed. The agreement between measured results and full-wave simulation validates the feasible configuration of the proposed antennas. [ABSTRACT FROM AUTHOR]

A varactorless divide-by-4 injection-locked frequency divider (ILFD) with LC resonator is designed. The ILFD uses the parasitic capacitors and the inductor architecture to construct a dual-resonance resonator. The ILFD has two locking ranges associated with the dual-resonance resonator. The locking range of the ÷4 ILFD is wider than conventional single-band ÷4 LC-tank ILFD because of overlapped locking ranges. The designed single-stage ÷4 ILFD was fabricated in the TSMC 0.18 μm 1P6M CMOS process. At the incident power of 0 dBm, the locking range is 6.6 GHz from 7.3 to 13.9 GHz at the power consumption of 5.59 mW. [ABSTRACT FROM AUTHOR]

A wide locking range nMOS divide-by-2 RLC injection-locked frequency divider (ILFD) was designed and implemented in the TSMC 0.18-µm BiCMOS process. The ILFD is based on a cross-coupled oscillator with one direct injection MOSFET and a RLC resonator. The RLC resonator is used to extend the locking range so that dual-band locking ranges can be merged in one locking range at both low and high injection powers. At the drain-source bias of 0.9 V for switching transistors, and at the incident power of 0 dBm the locking range of the divide-by-2 ILFD is 7.24 GHz, from the incident frequency 2.65 to 9.89 GHz, the locking range percentage is 115.47%. The power consumption of ILFD core is 8.685 mW. The die area is 0.726 × 0.930 mm2. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]