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Antennas with higher gain and efficiency deliver superior performance across a wide frequency range. Achieving these characteristics at high frequencies while keeping a compact size necessitates sophisticated design approaches. This research presents a substrate-integrated waveguide (SIW) cavity-backed slotted patch antenna (SPA) tailored for the 28 GHz and 34 GHz frequency bands. Additionally, a linear tapered slot antenna is designed with a compact profile of 27.5 mm × 7.5 mm × 0.254 mm. The SIWs are implemented using vias on the outer profile of the antenna, and circular and rectangular slots are etched on the radiating surface. The goal of optimizing the antenna geometry is to enhance return loss within the desired frequency bandwidth, which means the Genetic Algorithm (GA) will determine the optimal antenna shape to achieve lower return loss than the original design within this bandwidth. The antenna exhibits dual resonance at 28 GHz and 38 GHz in the millimeter-wave range, providing an impedance bandwidth of 211 MHz (27.72 GHz-27.94 GHz) at 28 GHz and 127 MHz (37.88 GHz-37.98 GHz) centered at 38 GHz. The proposed antenna demonstrates gains of 8.04 dBi and 9.72 dBi at these operating bands. A prototype of the antenna is fabricated on RT/duroid 5880 and its characteristics are measured. The overall VSWR of the antenna ranges from 1 to 2, with a radiation efficiency of 94 %. The proposed antenna achieves dual-band performance with increased directivity and stable gain, exhibiting enhanced electric field distribution, radiation patterns, and reflection coefficient (S11), all of which contribute to a comprehensive understanding of the antenna's performance. This study compares the designed antenna's performance to that of the fabricated prototype. The proposed antenna is ideal for 5G applications due to its small size, broad spectral coverage, and excellent gain.

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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.

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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.

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This paper considers the main challenges for all components engaged in the driving task suggested by the automation of road vehicles or autonomous cars. Numerous autonomous vehicle developers often invest an important amount of time and effort in fine-tuning and measuring the route tracking to obtain reliable tracking performance over a wide range of autonomous vehicle speed and road curvature diversities. However, a number of automated vehicles were not considered for fault-tolerant trajectory tracking methods. Motivated by this, the current research study of the Differential Lyapunov Stochastic and Decision Defect Tree Learning (DLS-DFTL) method is proposed to handle fault detection and course tracking for autonomous vehicle problems. Initially, Differential Lyapunov Stochastic Optimal Control (SOC) with customizable Z-matrices is to precisely design the path tracking for a particular target vehicle while successfully managing the noise and fault issues that arise from the localization and path planning. With the autonomous vehicle's low ceilings, a recommendation trajectory generation model is created to support such a safety justification. Then, to detect an unexpected deviation caused by a fault, a fault detection technique known as Decision Fault Tree Learning (DFTL) is built. The DLS-DFTL method can be used to find and locate problems in expansive, intricate communication networks. We conducted various tests and showed the applicability of DFTL. By offering some analysis of the experimental outcomes, the suggested method produces significant accuracy. In addition to a thorough study that compares the results to state-of-the-art techniques, simulation was also used to quantify the rate and time of defect detection. The experimental result shows that the proposed DLS-DFTL enhances the fault detection rate (38%), reduces the loss rate (14%), and has a faster fault detection time (24%) than the state of art methods.

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The amount of road accidents caused by driver drowsiness is one of the world's major challenges. These accidents lead to numerous fatal and non-fatal injuries which impose substantial financial strain on individuals and governments every year. As a result, it is critical to prevent catastrophic accidents and reduce the financial burden on society caused by driver drowsiness. The research community has primarily focused on two approaches to identify driver drowsiness during the last decade: intrusive and non-intrusive. The intrusive approach includes physiological measures, and the non-intrusive approach includes vehicle-based and behavioral measures. In an intrusive approach, sensors are used to detect driver drowsiness by placing them on the driver's body, whereas in a non-intrusive approach, a camera is used for drowsiness detection by identifying yawning patterns, eyelid movement and head inclination. Noticeably, most research has been conducted in driver drowsiness detection methods using only single measures that failed to produce good outcomes. Furthermore, these measures were only functional in certain conditions. This paper proposes a model that combines the two approaches, non-intrusive and intrusive, to detect driver drowsiness. Behavioral measures as a non-intrusive approach and sensor-based physiological measures as an intrusive approach are combined to detect driver drowsiness. The proposed hybrid model uses AI-based Multi-Task Cascaded Convolutional Neural Networks (MTCNN) as a behavioral measure to recognize the driver's facial features, and the Galvanic Skin Response (GSR) sensor as a physiological measure to collect the skin conductance of the driver that helps to increase the overall accuracy. Furthermore, the model's efficacy has been computed in a simulated environment. The outcome shows that the proposed hybrid model is capable of identifying the transition from awake to a drowsy state in the driver in all conditions with the efficacy of 91%.

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Diabetes mellitus (DM), commonly known as diabetes, is a collection of metabolic illnesses characterized by persistently high blood sugar levels. The signs of elevated blood sugar include increased hunger, frequent urination, and increased thirst. If DM is not treated properly, it may lead to several complications. Diabetes is caused by either insufficient insulin production by the pancreas or an insufficient insulin response by the body's cells. Every year, 1.6 million individuals die from this disease. The objective of this research work is to use relevant features to construct a blended ensemble learning (EL)-based forecasting system and find the optimal classifier for comparing clinical outputs. The EL based on Bayesian networks and radial basis functions has been proposed in this article. The performances of five machine learning (ML) techniques, namely, logistic regression (LR), decision tree (DT) classifier, support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF), are compared with the proposed EL technique. Experiments reveal that the EL method performs better than the existing ML approaches in predicting diabetic illness, with the remarkable accuracy of 97.11%. The proposed ensemble learning could be useful in assisting specialists in accurately diagnosing diabetes and assisting patients in receiving the appropriate therapy.

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ABSTRACT: An expected outcome of economic reforms in India is enhanced pace of industrialization with manufacturing sector playing a crucial role by increasing its share in output via higher investments and increased productivity. This process of industrialization was also expected to usher in possibilities for the slow growing states to catch up with the fast growing ones. This paper assesses the extent of regional manufacturing performance in India by analyzing the trends in labour and total factor productivity for the organized manufacturing sector of 15 major Indian states. Data Envelopment Analysis is used to compute Malmquist total factor productivity index and its components. The results indicate that labour productivity diverges in the reform era and its growth and TFPG follow more or less a similar pattern. The study also finds that growth in productivity vary considerably across states and this variation in productivity growth can be explained, to a great extent, by differences in infrastructural development at the regional level. JEL CLASSIFICATION: D24, O47, R11.

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PURPOSE: To determine the effects of α-tocopherol supplementation to oocyte maturation media and embryo culture media on the yield of ovine embryos. METHODS: α-tocopherol, at concentrations of 0, 50, 100, 200, 400 and 500 µM was supplemented to ovine oocyte or embryo culture media and cultured at 5% or 20% O(2) levels. Percentages of cleavage, morula and blastocyst, total cell count and comet assay were taken as indicators of developmental competence of embryos. RESULTS: 200 µM α-tocopherol in embryo culture medium at 20% O(2) level significantly increased the rates of cleavage (P < 0.05), morulae (P < 0.05) and blastocyst (P < 0.01) formation and blastocyst total cell number (P < 0.01) when compared with control. The rates of blastocyst formation were also significantly higher in 100 µM (P < 0.01) and 400 µM (P < 0.05) supplemented groups than control. CONCLUSION: α-tocopherol supplementation may enhance the in vitro developmental competence of ovine embryos by protecting them from oxidative damage.

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