Joint stiffness affects over 350 million people worldwide, creating demand for intelligent systems for detection and monitoring. Patients with joint stiffness need continuous rehab guided by experts, highlighting the need for tele-rehabilitation. This work presents a flexible, biodegradable, and economical strain sensor for joint stiffness monitoring, fabricated through a simple, cost-effective method. The sensor uses cotton fabric as a substrate and conductive paste from recycled dry-cell electronic waste for electrodes. A modified interdigitated capacitive (MIDC) structure is used, achieving high sensitivity with a GF value of 1006, response and recovery times of 0.38 sec. On-body testing on wrist, knee, and elbow joints yielded a minimum resolution of 5°. The proposed MIDC strain sensor is ideal for joint stiffness monitoring in tele-rehabilitation.

The use of Reconfigurable intelligent surface (RIS) technology has gained significant attention in wireless communication systems due to its ability to manipulate the impinging electromagnetic waves. However, the energy consumption of RIS deployment and operation has been a major concern. To address this issue, we aim to analyze RIS self-sustainability techniques including photovoltaic solar panels and wireless power transfer (WPT) for providing energy to the RIS, in the absence of grid power. We first analyze energy efficiency of RIS to determine most energy efficient way to operate RIS. Then we analyze the power consumption of RIS and its distribution over active components of RIS. Combined with energy efficient operation of RIS and reduction in power consumption from identified active components we propose energy-efficient self-sustained RIS through WPT. We also present measurement results for power consumption optimized 1-bit RIS design of size$\mathbf{27}\times \mathbf{28}$. We propose that if RIS power consumption is improved then near future WPT techniques can be readily utilized for self-sustainable RIS operation.

Microwave Imaging (MWI) has emerged as a potential candidate for brain stroke detection due to its low cost, time efficiency and accurate nature when compared to other screening techniques. TinyML is a revolutionary technique for utilizing AI in portable and low-powered devices. The need for more compact and concise systems grows by the day in order to provide smart services, particularly in the medical arena. This paper tries to fulfil these requirements by presenting the first-ever portable MWI-based TinyML brain stroke detection system with high accuracy. The head-imaging dataset, utilized here for the training of models, provides open-source data generated by our prototype head imaging system consisting of a low-cost vector network analyzer, single-board computer, rotating motor setup, and a Vivaldi antenna. The Tiny ML model is a compressed-size model of our proposed Deep Learning (DL) framework that obtains an accuracy of 93% on testing data with an F1-score of 0.929 deployed on the single-board computer. The compressed model obtained by pruning or quantization is not only small in size but also retains the above 90% accuracy of the DL model. This work reassures the possibility of successful deployment of Tiny ML- based solutions in microwave imaging systems for medical diagnostic applications in low-resource settings.

Breast Microwave Imaging (BMI) has emerged as a competitive and potentially disruptive alternative to conventional breast cancer screening techniques owing to its desirable features and improved detection rate. In this paper, we apply various artificial intelligence, and deep learning approaches for automatic breast tumor detection, classification and localization in an open-source experimental BreastCare dataset obtained using our pre-clinical, portable and cost-effective BMI system. We compare the effectiveness of various cutting-edge machine-learning detection algorithms to assess the usefulness of the obtained data-set. Also, we present a deep learning framework that outperforms state-of-the-art microwave imaging methods and ML algorithms for tumor detection, localization, and characterization. The proposed framework gives promising results using our BMI system's measured reflection coefficients ($S_{11}$). This work shows the potential advantages of applying cutting-edge deep learning algorithms in practical BMI systems.

The experience of using project-based learning (PBL) methods is talked about in the context of training STEM (science, technology, engineering, and math) students for the real-world scenarios in the industry. In this paper, a PBL strategy is proposed as a pedagogical tool targeting undergraduate engineering and CS+[X] students through the design of a biomedical imaging system for breast cancer detection. The primary goals of this interdisciplinary project are, firstly, to train students to implement the theoretical knowledge towards design of engineering product directly targeting one or more sustainable development goals; secondly, to motivate students to choose to learn more about biomedical imaging system development while appreciating the need of interdisciplinary approach in solving a complex engineering problem; and finally, to make students realize the need for technological innovations in the development of portable and cost-effective diagnostic products for developing countries. During the design and implementation of this project, students' competencies have improved significantly which reassures the usefulness of PBL as a pedagogical approach in student-centered learning.

We present the living lab methodology as a pedagogical approach to artificial intelligence (AI) based autonomous systems under the framework of place-based learning. Due to time, location, weather, traffic safety, and other issues, performing road testing on autonomous cars is challenging. Autonomous driving testing has been made easier by the virtual test platform, which can partly replace road testing. To improve the system-designed skills of the students and to validate autonomous driving ideas in real life settings to further refine solutions proposed, we proposed Mini-Living Lab system. The platform may also give a significant number of test scenarios for the driver during early verification of the autonomous driving control approach. We provide the detailed system design and implement an artificial intelligence based autonomous driving model on our proposed system. For the neural network model, we adopt PointNet++ and improve its design to process the lidar point cloud data, then further to perform the autonomous steering control tasks. The proposed project provides an opportunity for students to actively participate in co-creation of knowledge and innovation in real-life contexts, thus leading to an enhanced understanding of complex engineering problems and development of required skills for their innovative solutions.

Future communication systems are expected to employ Reconfigurable intelligent surfaces (RIS) for real-time manipulation of channel to achieve unprecedented efficiency and quality of service (QoS) improvement. Here, we have designed a practical RIS-enabled communication setup to demonstrate beam steering and multi-beam forming in near-to far-field trials. The design of RIS as a component of wireless communication system has many variables that dictate path loss compensation capability of RIS. We investigate RIS path loss compensation by varying RIS size with point source and plane wave source using a 1-bit RIS of size$27\times 28$unit-cells. It is observed that RIS size has less significance compared to phase quantization, in the near field while the effect of RIS size is dominant in the far field. The reported results provide useful insights for design of RIS-enabled wireless communication systems.

Reconfigurable Intelligent Surfaces (RIS), which comprise of large number of unit cells (meta-atoms), reflect the incoming wave according to desired radiation patterns. As the number of unit cells increase, complex radiation patterns can be generated. Similarly, a unit cell allowing more phase control and larger phase difference helps in the generation of complex radiation patterns at the surface level. However, the control circuit complexity and energy requirements also increase. In many practical applications, only a handful of radiation patterns may suffice. Additionally, the overall problem of finding the appropriate unit cell control state to generate any desired radiation patterns has combinatorial complexity. In this paper, we propose a benchmarking framework and suitable performance metrics that enable us to determine and compare the capabilities of RISs made from different unit cells. The proposed framework is numerically tested on three 40x40 RISs made from optimized and unoptimized unit cells of various resolutions reported in the literature. While the performance of RIS made from 1-bit unoptimized unit cell was overall poor, it was able to successfully generate some simple but useful benchmarking patterns. Thus, our framework provides a mechanism to identify the best candidate unit cell for a given set of requirements. The proposed framework has a potential to revolutionize future research and development on unit cell and RIS design.