Navya Thirumaleshwar Hegde

Navigating hazardous environments, such as areas with fire risks, wild animal activity, or inaccessible terrains, poses significant challenges, necessitating the development of bio-inspired robotic systems. This study focuses on the biomechanical design and kinematic analysis of a spider-mimicking robot, specifically examining quad and hexa-legged configurations to optimize movement efficiency and stability. The research employed 3D Computer-Aided Design (CAD) in Fusion 360 to model and simulate the robot's leg framework, analyzing deformation, tension, and strain. Fused Deposition Modelling (FDM) with Poly Lactic Acid (PLA) material was used for component fabrication, chosen for its balance of lightweight properties and structural integrity, validated through stress analysis. A single limb’s forward and reverse kinematics were studied, enabling the development of optimized gait patterns. SIMSCAPE Multibody in MATLAB was utilized for dynamic simulations, and Proportional Derivative (PD) and Proportional Integral Derivative (PID) controllers were tested to evaluate trajectory tracking accuracy and stability. Results show that the six-legged configuration exhibits superior stability with a 15% improvement in gait cycle efficiency and a 20% reduction in energy consumption per stride compared to the four-legged counterpart. The use of PID controllers further enhanced performance, achieving a 12% improvement in settling time and reducing oscillations in trajectory tracking tasks. The choice of PLA material ensured durability under operational loads, with minimal deformation over repeated stress cycles. Servomotor selection and configuration were tailored to optimize torque and speed, enabling precise leg control. This study highlights the potential of bio-inspired robots to advance robotic mobility through optimized kinematics and material choices.

Aircraft structures are frequently exposed to damage from environmental factors and operational wear, leading to the development of surface imperfections such as cracks. These imperfections can increase aerodynamic drag, a phenomenon known as excrescence drag. To mitigate these effects and quickly restore structural integrity, repair patches, often made from aluminium alloys and tailored to the damage, are applied following guidelines set out in the Aircraft Battle Damage Repair (ABDR) manuals. This paper investigates the aerodynamic performance impacts of applying such patches to two prominent airfoil models: the symmetric NACA 0012 and the cambered NACA 2412. Utilizing advanced computational tools including CATIA V5 for 3D modelling and CFD++ for fluid dynamics simulation, this study examines changes in aerodynamic characteristics such as lift, drag, and pressure distribution under various flow conditions. The research particularly focuses on quantifying the aerodynamic changes introduced by the patches through detailed analyses of lift curves, drag polars, and pressure coefficient (Cp) plots. Our findings aim to provide insights into the trade-offs involved in patch repairs, exploring how different patch configurations influence airfoil performance. This contributes to the development of optimized repair strategies that ensure minimal aerodynamic disruption while extending the operational lifespan of aircraft components.

In recent years, the escalating concern of space debris and its threats to space assets has driven the need for advanced analysis. This study’s primary objective is to analyze satellite data for collision prediction. Specialized algorithms have been developed to forecast orbital trajectories, assess proximity between objects, conduct conjunction analyses, visualize paths, and compare the conjunction analysis results with theoretical expectations. Extensive satellite data and the PYTHON coding platform were used, to predict orbital paths, distances between objects, and potential collisions within 24 hours. Key PYTHON libraries, including MATPLOTlib, SKYFIELD, PYTZ, Numpy, and Pandas, were utilized. The proposed algorithm accurately predicted orbital paths and distances, with a focus on x, y, and z coordinates. Notably, the algorithms can predict potential collisions within 24 hours. An extended version accommodates thousands of satellite input Two- Line Element (TLE) data, enhancing collision prediction. Rigorous validation compared the built-in to manual calculations based on the orbital elements. The study provides a visualization of predicted satellite collisions, emphasizing the importance of addressing space debris challenges for safer space missions.

A circular compact ultra‐wideband antenna for 5G microwave applications

This article presents a novel S/C/X band antenna specifically designed for 5G and beyond applications. The antenna's performance is thoroughly analyzed through comprehensive simulations using CST Microwave Studio on an FR-4 substrate, measuring 10λ × 14λ × 0.014λ (where λ is calculated at a lower resonance frequency). The proposed design displays remarkable characteristics, boasting an outstanding impedance bandwidth of 124% and an impressive return loss of −38 dBi. Its operational frequency ranges from 2.8 to 12 GHz, with a central frequency of 7.4 GHz. Throughout this range, the antenna maintains stable polar patterns, ensuring consistent and efficient performance with a wide gain of 3.98 dBi and an impressive efficiency of 84.2%. This antenna proves to be well-suited for diverse applications, including S-band: 3.1–4.2 GHz, C-band: 4.4–5.0 GHz, X-band: 8.0–12.0 GHz, and 5G and beyond communication. The innovative features of this S/C/X band antenna offer significant potential for advancing wireless communication technologies.

Stereo matching algorithms work with multiple images of a scene, taken from two viewpoints, to generate depth information. Authors usually use a single matching function to generate similarity between corresponding regions in the images. In the present research, the authors have considered a combination of multiple data costs for disparity generation. Disparity maps generated from stereo images tend to have noisy sections. The presented research work is related to a methodology to refine such disparity maps such that they can be further processed to detect obstacle regions. A novel entropy based selective refinement (ESR) technique is proposed to refine the initial disparity map. The information from both the left disparity and right disparity maps are used for this refinement technique. For every disparity map, block wise entropy is calculated. The average entropy values of the corresponding positions in the disparity maps are compared. If the variation between these entropy values exceeds a threshold, then the corresponding disparity value is replaced with the mean disparity of the block with lower entropy. The results of this refinement are compared with similar methods and was observed to be better. Furthermore, in this research work, the v-disparity values are used to highlight the road surface in the disparity map. The regions belonging to the sky are removed through HSV based segmentation. The remaining regions which are our ROIs, are refined through a u-disparity area-based technique. Based on this, the closest obstacles are detected through the use of k-means segmentation. The segmented regions are further refined through a u-disparity image information-based technique and used as masks to highlight obstacle regions in the disparity maps. This information is used in conjunction with a kalman filter based path planning algorithm to guide a mobile robot from a source location to a destination location while also avoiding any obstacle detected in its path. A stereo camera setup was built and the performance of the algorithm on local real-life images, captured through the cameras, was observed. The evaluation of the proposed methodologies was carried out using real life out door images obtained from KITTI dataset and images with radiometric variations from Middlebury stereo dataset.

Chest radiography needs timely diseases diagnosis and reporting of potential findings in the images, as it is an important diagnostic imaging test in medical practice. A crucial step in radiology workflow is the fast, automated, and reliable detection of diseases created on chest radiography. To overcome this issue, an artificial intelligence-based algorithm such as deep learning (DL) are promising methods for automatic and fast diagnosis due to their excellent performance analysis of a wide range of medical images and visual information. This paper surveys the DL methods for lung disease detection from chest X-ray images. The common five attributes surveyed in the articles are data augmentation, transfer learning, types of DL algorithms, types of lung diseases and features used for detection of abnormalities, and types of lung diseases. The presented methods may prove extremely useful for people to ideate their research contributions in this area.