The deployment of large flexible antennas is becoming critical for space applications today. Such antenna systems can be reconfigured in space for variable antenna footprint, and hence can be utilized for signal transmission to different geographic locations. Due to quasi-static shape change requirements, coupled with the demand of large deflection, shape memory alloy (SMA) based actuators are uniquely suitable for this system. In this paper, we discuss the design and development of a reconfigurable parabolic antenna structure. The reflector skin of the antenna is vacuum formed using a metalized polycarbonate shell. Two different strategies are chosen for the antenna actuation. Initially, an SMA wire based offset network is formed on the back side of the reflector. A computational model is developed using equivalent coefficient of thermal expansion (ECTE) for the SMA wire. Subsequently, the interaction between the antenna and SMA wire is modeled as a constrained recovery system, using a 1D modified Brinson model. Joule effect based SMA phase transformation is considered for the relationship between input voltage and temperature at the SMA wire. The antenna is modeled using ABAQUS based finite element methodology. The deflection found through the computational model is compared with that measured in experiment. Subsequently, a point-wise actuation system is developed for higher deflection. For power-minimization, an auto-locking device is developed. The performance of the new configuration is compared with the offset-network configuration. It is envisaged that the study will provide a comprehensive procedure for the design of intelligent flexible structures especially suitable for space applications.

The paper demonstrates a low-cost rice quality assessment system based on image processing and machine learning (ML) algorithms. A Raspberry-Pi based image acquisition module was developed to extract the structural and geometric features from 3081 images of eight different varieties of rice grains. Based on features such as perimeter, area, solidity, roundness, compactness, and shape factor, an automatic identification system is developed to segment the grains based on their types and classify them by using seven machine learning algorithms. These ML models are trained using the images and are compared using different ML models. ROC curves are plotted for each model for quantitative analysis to assess the model’s performance. It is concluded that the random forest classifier presents an accuracy of 77 percent and is the best-performing model for the classification of rice varieties. Furthermore, the same algorithm is efficiently employed to determine the price of adulterated rice samples based upon the market price of individual rice.

Active control of antenna radiation by mechanical reconfiguration has large potential applications in space as well as in ground segment. Such reconfigurable antenna primarily has two functionalities: beam steering and beam shaping. While previous research has shown individual control of these functions; we have developed a suitable antenna actuation system which takes care of both the functionalities. To demonstrate our concept, we have fabricated a 1-m diameterC-band flexible reflector of poly-ether-ether-ketone using precisely machined aluminum mold. A periphery feed structure is connected to the center of the reflector by a goose-neck-shaped supporting frame. Next, we have designed and developed a smart control-disk containing two types of shape memory alloy actuator-based self-locking devices. A group of actuators are used for locking/unlocking the rotational degree of freedom of the antenna; while the other group generates deformation of antenna at the desired direction. An 8-bit absolute encoder is installed with each locking device to sense and feedback the displacement of a control point. We have achieved a controllable deflection up to 25 mm with a precision of 0.3 mm with this system. A directional precision of $0.2\circ$ is also achieved simultaneously. Presently, we have developed eight such actuation points on the disk to control the shape of the antenna. For numerical simulation of the structural deformation and study of the radio frequency pattern, finite element analysis is carried out using ABAQUS, and the far-field radiation pattern corresponding to the deformed shape is obtained. The electromagnetic patterns are analyzed using geometric optics technique. Finally, a methodology of achieving surface coordinates for desired radiation pattern is presented. The study provides useful insights into the design of flexible and reconfigurable antenna system.

This study introduces a method for diagnosing multiple brain tumors using an array of eight compact ultrawideband (UWB) antennas. Employing radar technology, UWB monopole antennas encircle a brain phantom to detect inhomogeneities. Designed and characterized via a commercial electromagnetic solver, the antennas operate within a 1–6 GHz frequency range. A 3-D brain and tumor phantoms are fabricated, and eight antennas array based microwave imaging system is implemented. The system reconstructed radar-based images by analyzing back-scattered signals, using the delay-and-sum algorithm for tumor detection. Results show the system effectively identifies single or multiple brain tumors, with high correlation between measured and simulated data.