Aldrin Claytus Vaz

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.

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.

Purpose: This paper aims to provide a mathematical modeling and design of H-infinity controller for an autonomous vertical take-off and landing (VTOL) Quad Tiltrotor hybrid unmanned aerial vehicles (UAVs). The variation in the aerodynamics and model dynamics of these aerial vehicles due to its tilting rotors are the key issues and challenges, which attracts the attention of many researchers. They carry parametric uncertainties (such as non-linear friction force, backlash, etc.), which drives the designed controller based on the nominal model to instability or performance degradation. The controller needs to take these factors into consideration and still give good stability and performance. Hence, a robust H-infinity controller is proposed that can handle these uncertainties.Design/methodology/approach: A unique VTOL Quad Tiltrotor hybrid UAV, which operates in three flight modes, is mathematically modeled using Newton–Euler equations of motion. The contribution of the model is its ability to combine high-speed level flight, VTOL and transition between these two phases. The transition involves the tilting of the proprotors from 90° to 0° and vice-versa in 15° intervals. A robust H-infinity control strategy is proposed, evaluated and analyzed through simulation to control the flight dynamics for different modes of operation.Findings: The main contribution of this research is the mathematical modeling of three flight modes (vertical takeoff–forward, transition–cruise-back, transition-vertical landing) of operation by controlling the revolutions per minute and tilt angles, which are independent of each other. An autonomous flight control system using a robust H-infinity controller to stabilize the mode of transition is designed for the Quad Tiltrotor UAV in the presence of uncertainties, noise and disturbances using MATLAB/SIMULINK. This paper focused on improving the disturbance rejection properties of the proposed UAV by designing a robust H-infinity controller for position and orientation trajectory regulation in the presence of uncertainty. The simulation results show that the Tiltrotor achieves transition successfully with disturbances, noise and uncertainties being present.Originality/value: A novel VTOL Quad Tiltrotor UAV mathematical model is developed with a special tilting rotor mechanism, which combines both aircraft and helicopter flight modes with the transition taking place in between phases using robust H-infinity controller for attitude, altitude and trajectory regulation in the presence of uncertainty.

Channel coding is a vital unit of any communications system. Future wireless systems are necessary to be equipped with high performance codes having low complexity encoder and decoder design. The requirements of these systems range from providing high throughput, operating in highly reliable scenarios, to working with short and long information messages, low and high code rates. To obtain maximum coding gain in wireless communication systems, Turbo codes and Polar Codes are used as error correcting codes. Satellite communication and 4G/5G services widely use these codes. These codes are found to approach Shannon capacity performance. Two recursive systematic convolution encoders separated by an interleaver are used to build a turbo encoder. Polar Codes are constructed on the concept of channel polarization. This work focusses on the Bit Error Rate evaluation and analysis of the codes for varying block lengths and code rates.

Solar Home Systems has the potential to offer many benefits to the user and to the society as a whole. The ease of using this clean energy makes it a viable choice. Solar Charge Controller (SCC) is a part of Solar Home System which is responsible for the energy conversion. The main aim of this work is to develop a high efficiency SCC at an affordable price without compromising on its performance. The proposed SCC works on the principle of Pulse Width Modulation (PWM) controlled DC-DC converter for charging the battery. PIC microcontroller is used in implementing the design. PWM pulses control the charging of the battery which are generated by the PIC microcontroller depending on various input variables. The designed SCC is tested to check whether it matches all the proposed technical specifications. The product is designed to support 120W of DC load.

With the increase in the connectivity among various electronic devices day-by-day, the technology has stepped up into a new era of Internet-of-Things. To ensure the accuracy, integrity and fault-tolerance in the transmitted data, Error Correcting Codes are used. Various techniques are available to decode the received data and correct the errors. In this paper, an approach based on Artificial Neural Networks (ANN) is been used to decode the received data because of their real-time operation, self-organization and adaptive learning. Back propagation Algorithm for feed forward ANN has been simulated using MATLAB for (7, 4) Hamming Code. The synaptic weights are updated during each training cycle. The designed ANN is trained for all possible combination of code words such that it can detect and correct 1-bit error. The Bit Error rate performance of the proposed ANN based method is compared with the syndrome decoding.