Experimental investigation of a dual mode antenna using characteristic mode analysis with enhanced directivity for GSM/5G applications.

In this article, a dual-mode, dual-polarized antenna designed using characteristic mode analysis (CMA) is described. An elliptical-shaped patch radiator is chosen with double slits on its minor axis. This design is based on mode separation from the circular patch into the elliptical patch. The suggested antenna geometry has a footprint of 60 mm × 60 mm × 1.6 mm. To design and fabricate the antenna, an FR-4 substrate with a relative permittivity of 4.3 is used, along with copper sheets 0.035 mm thick for the ground plane and the radiating plane. The circular patch has the resonating mode at 1.8 GHz, whereas the elliptical radiator gives different resonant modes at 1.8 GHz and 3.5 GHz. An orthogonal mode is excited with a 50-Ω coaxial feed line at 3.5 GHz by applying a full-wave approach. The antenna gives a -10dB bandwidth of 51 MHz (1.77-1.82 GHz) centered at 1.8 GHz and a bandwidth of 210 MHz (3.37-3.58 GHz) centered at 3.5 GHz. The working principle is explained through modal analysis and characteristic angles. This dual-band antenna covers a 1.8 GHz GSM band with horizontal polarization and a 3.5 GHz 5G service with vertical polarization. Peak gain attained with these bands is 5.9 dBi and 7.1 dBi, respectively. A CST full-wave simulator is used for the simulations. As a result of the antenna, radiation is stable and enhanced. Compared to measured results, simulation results are close to reality. The characteristic mode analysis (CMA) provides an in-depth look into different operating modes on the antenna in contrast with the conventional method, which relies on the simulated current distribution to verify functionality.

Design of dual mode antenna using CMA and broadband dual-polarized antenna for 5G networks.

This article proposes a dual mode dual-polarized antenna configuration for IRNSS and fifth generation (5G) applications, operating at a frequency of 3.5 GHz based on characteristic mode analysis (CMA), and aims to provide broadband dual-polarized functionality. The original design of the antenna is a traditional patch antenna, and its dual-polarized features are determined using characteristic mode analysis. The full-wave method is used to stimulate both orthogonal modes using a 50 Ω coaxial input line at 3.5 GHz. In this design, the circular patch has been extended into an elliptical patch through a process of mode separation. The circular patch exhibits resonance at a frequency of 2.5 GHz, whereas the extended elliptical radiator demonstrates two resonance modes at 2.5 GHz and 3.5 GHz. The operational mechanism is elucidated by modal analysis and characteristic angle. This antenna operates on two different frequencies at the 2.5 GHz IRNSS band with horizontal polarization and the 3.5 GHz 5G service with vertical polarization. The maximum gain achieved with these frequency ranges is 5.31 dBi and 4.72 dBi, respectively. A ring resonator is chosen to improve the axial ratio and impedance bandwidth of the suggested prototype. The antenna's ground plane is shaped like a rectangle and features a V-shaped slot in the radiating patch. The antenna's physical footprint is 50 mm × 50 mm × 1.6 mm and an FR4 dielectric substrate serves as its foundation. Through its interaction with a PIN diode, the diode modifies the polarization of the antenna. The antenna functions as a right-handed circular polarization (RHCP), when the diode is operational. The bandwidth from 4.3 to 7.5 GHz is covered. On the other hand, it generates linear polarization (LP) between 4.2 and 5.3 GHz. The experimental antenna is evaluated and examined for its performance characteristics. The simulations are carried out utilizing the CST simulator. A prototype antenna has been manufactured and its performance has been validated against simulated findings.

Design and implementation of an innovative single-phase direct AC-AC bipolar voltage buck converter with enhanced control topology.

Direct AC-AC converters are strong candidates in the power converting system to regulate grid voltage against the perturbation in the line voltage and to acquire frequency regulation at discrete step levels in variable speed drivers for industrial systems. All such applications require the inverted and non-inverted form of the input voltage across the output with voltage-regulating capabilities. The required value of the output frequency is gained with the proper arrangement of the number of positive and negative pulses of the input voltage across the output terminals. The period of each such pulse for low-frequency operation is almost the same as the half period of the input grid or utility voltage. These output pulses are generated by converting the positive and negative input half cycles in noninverting and inverting forms as per requirement. There is no control complication to generate control signals used to adjust the load frequency as the operating period of the switching devices is normally greater than the period of the source voltage. However, high-frequency pulse width modulated (PWM) control signals are used to regulate the output voltage. The size of the inductor and capacitor is inversely related to the value of the switching frequency. Similarly, the ripple contents of voltage and currents in these filtering components are also inversely linked with PWM frequency. These constraints motivate the circuit designer to select high PWM frequency. However, the alignment of the high-frequency control input with the variation in the input source voltage is a big challenge for a design engineer as the switching period of a high-frequency signal normally lies in the microsecond. It is also required to operate some high-frequency devices for various half cycles of the source voltage, creating control complications as the polarities of the half cycles are continuously changing. This requires at least the generation of two high-frequency signals for different intervals. The interruption of the filtering inductor current is a big source of high voltage surges in circuits where the high-frequency transistors operate in a complementary way. This may be due to internal defects in the switching transistors or some unnecessary inherent delay in their control signals. In this research work, a simplified AC-AC converter is developed that does not need alignment of high-frequency control with the polarity of the source voltage. With this approach, high-frequency signals can be generated with the help of any analog or digital control system. By applying this technique, only one high-frequency control signal is generated and applied in AC circuits, as in a DC converter, without applying a highly sensitive polarity sensing circuit. So, controlling complications is drastically simplified. The circuit and configuration always avoid the current interruption problem of filtering the inductor. The proposed control and circuit topology are tested both in computer-based simulation and practically developed circuits. The results obtained from these platforms endorse the effectiveness and validation of the proposed work.

Mathematical modeling and machine learning-based optimization for enhancing biofiltration efficiency of volatile organic compounds.

Biofiltration is a method of pollution management that utilizes a bioreactor containing live material to absorb and destroy pollutants biologically. In this paper, we investigate mathematical models of biofiltration for mixing volatile organic compounds (VOCs) for instance hydrophilic (methanol) and hydrophobic ( α -pinene). The system of nonlinear diffusion equations describes the Michaelis-Menten kinetics of the enzymic chemical reaction. These models represent the chemical oxidation in the gas phase and mass transmission within the air-biofilm junction. Furthermore, for the numerical study of the saturation of α -pinene and methanol in the biofilm and gas state, we have developed an efficient supervised machine learning algorithm based on the architecture of Elman neural networks (ENN). Moreover, the Levenberg-Marquardt (LM) optimization paradigm is used to find the parameters/ neurons involved in the ENN architecture. The approximation to a solutions found by the ENN-LM technique for methanol saturation and α -pinene under variations in different physical parameters are allegorized with the numerical results computed by state-of-the-art techniques. The graphical and statistical illustration of indications of performance relative to the terms of absolute errors, mean absolute deviations, computational complexity, and mean square error validates that our results perfectly describe the real-life situation and can further be used for problems arising in chemical engineering.

Enhancing gait recognition by multimodal fusion of mobilenetv1 and xception features via PCA for OaA-SVM classification.

Gait recognition has become an increasingly promising area of research in the search for noninvasive and effective methods of person identification. Its potential applications in security systems and medical diagnosis make it an exciting field with wide-ranging implications. However, precisely recognizing and assessing gait patterns is difficult, particularly in changing situations or from multiple perspectives. In this study, we utilized the widely used CASIA-B dataset to observe the performance of our proposed gait recognition model, with the aim of addressing some of the existing limitations in this field. Fifty individuals are randomly selected from the dataset, and the resulting data are split evenly for training and testing purposes. We begin by excerpting features from gait photos using two well-known deep learning networks, MobileNetV1 and Xception. We then combined these features and reduced their dimensionality via principal component analysis (PCA) to improve the model's performance. We subsequently assessed the model using two distinct classifiers: a random forest and a one against all support vector machine (OaA-SVM). The findings indicate that the OaA-SVM classifier manifests superior performance compared to the others, with a mean accuracy of 98.77% over eleven different viewing angles. This study is conducive to the development of effective gait recognition algorithms that can be applied to heighten people's security and promote their well-being.

Comparative assessment of differently randomized accelerated particle swarm optimization and squirrel search algorithms for selective harmonics elimination problem.

A random initialization of the search particles is a strong argument in favor of the deployment of nature-inspired metaheuristic algorithms when the knowledge of a good initial guess is lacked. This article analyses the impact of the type of randomization on the working of algorithms and the acquired solutions. In this study, five different types of randomizations are applied to the Accelerated Particle Swarm Optimization (APSO) and Squirrel Search Algorithm (SSA) during the initializations and proceedings of the search particles for selective harmonics elimination (SHE). The types of randomizations include exponential, normal, Rayleigh, uniform, and Weibull characteristics. The statistical analysis shows that the type of randomization does impact the working of optimization algorithms and the fittest value of the objective function.

Prostate Segmentation in MRI Images using Transfer Learning based Mask RCNN.

INTRODUCTION: The second highest cause of death among males is Prostate Cancer (PCa) in America. Over the globe, it's the usual case in men, and the annual PCa ratio is very surprising. Identical to other prognosis and diagnostic medical systems, deep learning-based automated recognition and detection systems (i.e., Computer Aided Detection (CAD) systems) have gained enormous attention in PCA. METHODS: These paradigms have attained promising results with a high segmentation, detection, and classification accuracy ratio. Numerous researchers claimed efficient results from deep learning-based approaches compared to other ordinary systems that utilized pathological samples. RESULTS: This research is intended to perform prostate segmentation using transfer learning-based Mask R-CNN, which is consequently helpful in prostate cancer detection. CONCLUSION: Lastly, limitations in current work, research findings, and prospects have been discussed.

Pictorial depiction on controlling crowd in smart conurbations using Internet of Things with switching algorithms.

The proliferation of smart conurbations entails an efficient system design for managing all the crowds in public places. Multitude controlling procedures are carried out for controlling compact areas where more number of peoples is present at several groups. Therefore for controlling purpose the proposed method aims to design a pictorial representation using Internet of Things (IoT). The process is carried out by taking images and then organizing it using switching techniques in the presence of square boxes where entire populace is identified on real time experimentations. For processing and controlling the occurrence a separate architecture is designed with analytical equivalences where all data set is stored in cloud platform. Further the incorporation of system model is carried out using Switching Based Algorithm (SBA) which adds more number of columns even for high population cases. In order to verify the effectiveness of proposed model five scenarios are considered with performance evaluation metrics for SBA and all the test results provides best optimal results. Moreover the projected model is improved with an average percentage of 83 as compared to existing models.

Analysis of the performance of Faster R-CNN and YOLOv8 in detecting fishing vessels and fishes in real time.

This research conducts a comparative analysis of Faster R-CNN and YOLOv8 for real-time detection of fishing vessels and fish in maritime surveillance. The study underscores the significance of this investigation in advancing fisheries monitoring and object detection using deep learning. With a clear focus on comparing the performance of Faster R-CNN and YOLOv8, the research aims to elucidate their effectiveness in real-time detection, emphasizing the relevance of such capabilities in fisheries management. By conducting a thorough literature review, the study establishes the current state-of-the-art in object detection, particularly within the context of fisheries monitoring, while discussing existing methods, challenges, and limitations. The findings of this study not only shed light on the superiority of YOLOv8 in precise detection but also highlight its potential impact on maritime surveillance and the protection of marine resources.

A review on cultivating effective learning: synthesizing educational theories and virtual reality for enhanced educational experiences.

Immersive technology, especially virtual reality (VR), transforms education. It offers immersive and interactive learning experiences. This study presents a systematic review focusing on VR's integration with educational theories in higher education. The review evaluates the literature on VR applications combined with pedagogical frameworks. It aims to identify effective strategies for enhancing educational experiences through VR. The process involved analyzing studies about VR and educational theories, focusing on methodologies, outcomes, and effectiveness. Findings show that VR improves learning outcomes when aligned with theories such as constructivism, experiential learning, and collaborative learning. These integrations offer personalized, immersive, and interactive learning experiences. The study highlights the importance of incorporating educational principles into VR application development. It suggests a promising direction for future research and implementation in education. This approach aims to maximize VR's pedagogical value, enhancing learning outcomes across educational settings.

Consumer electronics based smart technologies for enhanced terahertz healthcare having an integration of split learning with medical imaging.

The proposed work contains three major contribution, such as smart data collection, optimized training algorithm and integrating Bayesian approach with split learning to make privacy of the patent data. By integrating consumer electronics device such as wearable devices, and the Internet of Things (IoT) taking THz image, perform EM algorithm as training, used newly proposed slit learning method the technology promises enhanced imaging depth and improved tissue contrast, thereby enabling early and accurate disease detection the breast cancer disease. In our hybrid algorithm, the breast cancer model achieves an accuracy of 97.5 percent over 100 epochs, surpassing the less accurate old models which required a higher number of epochs, such as 165.

Design of load-aware resource allocation for heterogeneous fog computing systems.

The execution of delay-aware applications can be effectively handled by various computing paradigms, including the fog computing, edge computing, and cloudlets. Cloud computing offers services in a centralized way through a cloud server. On the contrary, the fog computing paradigm offers services in a dispersed manner providing services and computational facilities near the end devices. Due to the distributed provision of resources by the fog paradigm, this architecture is suitable for large-scale implementation of applications. Furthermore, fog computing offers a reduction in delay and network load as compared to cloud architecture. Resource distribution and load balancing are always important tasks in deploying efficient systems. In this research, we have proposed heuristic-based approach that achieves a reduction in network consumption and delays by efficiently utilizing fog resources according to the load generated by the clusters of edge nodes. The proposed algorithm considers the magnitude of data produced at the edge clusters while allocating the fog resources. The results of the evaluations performed on different scales confirm the efficacy of the proposed approach in achieving optimal performance.

Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials.

This paper presents a study investigating the performance of functionally graded material (FGM) annular fins in heat transfer applications. An annular fin is a circular or annular structure used to improve heat transfer in various systems such as heat exchangers, electronic cooling systems, and power generation equipment. The main objective of this study is to analyze the efficiency of the ring fin in terms of heat transfer and temperature distribution. The fin surfaces are exposed to convection and radiation to dissipate heat. A supervised machine learning method was used to study the heat transfer characteristics and temperature distribution in the annular fin. In particular, a feedback architecture with the BFGS Quasi-Newton training algorithm (trainbfg) was used to analyze the solutions of the mathematical model governing the problem. This approach allows an in-depth study of the performance of fins, taking into account various physical parameters that affect its performance. To ensure the accuracy of the obtained solutions, a comparative analysis was performed using guided machine learning. The results were compared with those obtained by conventional methods such as the homotopy perturbation method, the finite difference method, and the Runge-Kutta method. In addition, a thorough statistical analysis was performed to confirm the reliability of the solutions. The results of this study provide valuable information on the behavior and performance of annular fins made from functionally graded materials. These findings contribute to the design and optimization of heat transfer systems, enabling better heat management and efficient use of available space.

Structural health monitoring of aircraft through prediction of delamination using machine learning.

Background: Structural health monitoring (SHM) is a regular procedure of monitoring and recognizing changes in the material and geometric qualities of aircraft structures, bridges, buildings, and so on. The structural health of an airplane is more important in aerospace manufacturing and design. Inadequate structural health monitoring causes catastrophic breakdowns, and the resulting damage is costly. There is a need for an automated SHM technique that monitors and reports structural health effectively. The dataset utilized in our suggested study achieved a 0.95 R2 score earlier. Methods: The suggested work employs support vector machine (SVM) + extra tree + gradient boost + AdaBoost + decision tree approaches in an effort to improve performance in the delamination prediction process in aircraft construction. Results: The stacking ensemble method outperformed all the technique with 0.975 R2 and 0.023 RMSE for old coupon and 0.928 R2 and 0.053 RMSE for new coupon. It shown the increase in R2 and decrease in root mean square error (RMSE).

An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions.

The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances.

Applications, challenges, and solutions of unmanned aerial vehicles in smart city using blockchain.

Real-time data gathering, analysis, and reaction are made possible by this information and communication technology system. Data storage is also made possible by it. This is a good move since it enhances the administration and operation services essential to any city's efficient operation. The idea behind "smart cities" is that information and communication technology (ICTs) need to be included in a city's routine activities in order to gather, analyze, and store enormous amounts of data in real-time. This is helpful since it makes managing and governing urban areas easier. The "drone" or "uncrewed aerial vehicle" (UAV), which can carry out activities that ordinarily call for a human driver, serves as an example of this. UAVs could be used to integrate geospatial data, manage traffic, keep an eye on objects, and help in an emergency as part of a smart urban fabric. This study looks at the benefits and drawbacks of deploying UAVs in the conception, development, and management of smart cities. This article describes the importance and advantages of deploying UAVs in designing, developing, and maintaining in smart cities. This article overviews UAV uses types, applications, and challenges. Furthermore, we presented blockchain approaches for addressing the given problems for UAVs in smart research topics and recommendations for improving the security and privacy of UAVs in smart cities. Furthermore, we presented Blockchain approaches for addressing the given problems for UAVs in smart cities. Researcher and graduate students are audience of our article.

Empowering coffee farming using counterfactual recommendation based RNN driven IoT integrated soil quality command system.

Soil health is essential for whirling stale soil into rich coffee-growing land. By keeping healthy soil, coffee producers may improve plant growth, leaf health, buds, cherry and bean quality, and yield. Traditional soil monitoring is tedious, time-consuming, and error-prone. Enhancing the monitoring system using AI-based IoT technologies for quick and precise changes. Integrated soil fertility control system to optimize soil health, maximize efficiency, promote sustainability, and prevent crop threads using real-time data analysis to turn infertile land into fertile land. The RNN-IoT approach uses IoT sensors in the coffee plantation to collect real-time data on soil temperature, moisture, pH, nutrient levels, weather, CO2 levels, EC, TDS, and historical data. Data transmission using a wireless cloud platform. Testing and training using recurrent neural networks (RNNs) and gated recurrent units gathered data for predicting soil conditions and crop hazards. Researchers are carrying out detailed qualitative testing to evaluate the proposed RNN-IoT approach. Utilize counterfactual recommendations for developing alternative strategies for irrigation, fertilization, fertilizer regulation, and crop management, taking into account the existing soil conditions, forecasts, and historical data. The accuracy is evaluated by comparing it to other deep learning algorithms. The utilization of the RNN-IoT methodology for soil health monitoring enhances both efficiency and accuracy in comparison to conventional soil monitoring methods. Minimized the ecological impact by minimizing water and fertilizer utilization. Enhanced farmer decision-making and data accessibility with a mobile application that provides real-time data, AI-generated suggestions, and the ability to detect possible crop hazards for swift action.

Polarization and incident angle independent multifunctional tunable terahertz metasurface based on graphene.

Motivated by the imperative demand for design integration and miniaturization within the terahertz (THz) spectrum, this study presents an innovative solution to the challenges associated with singular functionality, limited application scope, and intricate structures prevalent in conventional metasurfaces. The proposed multifunctional tunable metasurface leverages a hybridized grapheme-metal structure, addressing critical limitations in existing designs. Comprising three distinct layers, namely a graphene-gold resonance layer, a Topas dielectric layer, and a bottom gold film reflective layer, this terahertz metasurface exhibits multifunctionality that is both polarization and incident-angle independent. The metasurface demonstrates a broadband circular dichroism (CD) function when subjected to incident circularly polarized waves. In contrast, under linear incidence, the proposed design achieves functionalities encompassing linear dichroism (LD) and polarization conversion. Remarkably, graphene's chemical potential and the incident light's state can be manipulated to tune each functional aspect's intensity finely. The proposed tunable multifaceted metasurface showcases significant referential importance within the terahertz spectrum, mainly contributing to advancing CD metamirrors, chiral photodetectors, polarization digital imaging systems, and intelligent switches.

Multi-objective hybrid split-ring resonator and electromagnetic bandgap structure-based fractal antennas using hybrid metaheuristic framework for wireless applications.

Design closure and parameter optimisation are crucial in creating cutting-edge antennas. Antenna performance can be improved by fine-tuning preliminary designs created using theoretical considerations and rough dimension adjustment via supervised parameter sweeps. This paper introduces a frequency reconfigurable antenna design that can operate at 28/38 GHz frequencies to meet FCC and Ofcom standards for 5G applications and in the 18 GHz frequency band for K-band radar applications. A PIN diode is used in this design to configure multiple frequency bands. The antenna has a modified rectangular patch-like structure and two optimised plugins on either side. The study that is being presented focuses on maximising the parameters that are subject to optimisation, including length (Ls), width (Ws), strip line width (W1), and height (ht), where the antenna characteristic parameters such as directivity is tuned by a hybrid optimisation scheme called Elephant Clan Updated Grey Wolf Algorithm (ECU-GWA). Here, the performance of gain and directivity are optimally attained by considering parameters such as length, width, ground plane length, width, height, and feed offsets X and Y. The bandwidth of the proposed antenna at - 10 dB is 0.8 GHz, 1.94 GHz, and 7.92 GHz, respectively, at frequencies 18.5 GHz, 28.1 GHz, and 38.1 GHz. Also, according to the simulation results, in the 18 GHz, 28 GHz, and 38 GHz frequencies S11, the return loss is - 60.81 dB, - 56.31 dB, and - 14.19 dB, respectively. The proposed frequency reconfigurable antenna simulation results achieve gains of 4.41 dBi, 6.33 dBi, and 7.70 dBi at 18.5 GHz, 28.1 GHz, and 38.1 GHz, respectively. Also, a microstrip quarter-wave monopole antenna with an ellipsoidal-shaped complementary split-ring resonator-electromagnetic bandgap structure (ECSRR-EBG) structure has been designed based on a genetic algorithm having resonating at 2.9 GHz, 4.7 GHz, 6 GHz for WLAN applications. The gain of the suggested ECSRR metamaterial and EBG periodic structure, with and without the ECCSRR bow-tie antenna. This is done both in the lab and with numbers. The measured result shows that the ECSRR metamaterial boosts gain by 5.2 dBi at 5.9 GHz. At 5.57 GHz, the two-element MIMO antenna achieves its lowest ECC of 0.00081.