Breast cancer, as one of the main causes of cancer death in the world, requires early detection to increase rate of survival. One idea is to develop a device that women could use at home to perform a regular self-check. Ultrasound is a safe, radiation-free technology that could be used for this device. Ultrasound systems (B-mode) that are typically used in a hospital however, are too expensive for the general population. The older ultrasound technology called A-mode, could be repurposed for this device as it costs a lot less. A-mode scans are seldom used at the present time, as B-mode scans have proven to be much more powerful for use in medical diagnostics. However, the costs of manufacturing a B-mode probe is a lot higher than an A-mode probe. This is especially important in the development of a portable ultrasound probe where costs are constrained. This study attempts to investigate how an ultrasound probe that comprises multiple A-mode transducers could be used together for breast cancer detection. Specifically, the aim of this study is to characterize and verify an ultrasound probe that comprises multiple A-mode transducers. To this end, a simulation environment is developed in MATLAB using the k-Wave toolbox, where an ultrasound probe is described through a set of input parameters and outputs its response when placed on the skin of breast tissue. The breast phantom models are generated from software in the Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE) trial and have four different levels of density as defined in BI-RADS. Two masses, one of benign and the other malignant nature were examined. The input parameters distance between the transducers, input frequency, and number of transducers, are investigated by comparing their responses in terms of contrast ratio around a breast mass. Towards the end, comparison to B-mode imaging are also made. Based on the results, the distance between transducers yields better performance with a higher value, as long as it does not go beyond the size of the mass. Higher input frequency also contributed to a better contrast ratio value. The number of transducers did not seem to have a correlation to the performance, with respect to contrast ratio values. However, when converted to a pseudo (very narrow) B-mode image, a higher number of transducers seem to very slightly mimic what might appear in a B-mode image. Upsampling it to account for the distance between the transducers, as well as to create a wider image seemed to improve the results.

Technology is transforming almost all aspects of our lives, one of them is automation. The main motivation of automation is to help humans avoid performing tedious, high risk jobs. Automated driving, also known as autonomous driving, has been at the center of industrial and academic attention since a few decades now, thanks to its potential of making driving risk-free by enabling a highly efficient machine control the vehicle on roads. Apart from the common outdoor use-cases, several applications in indoor environments have also been extensively investigated. The primary ones include process automation and management in large factories and warehouses.

Localization of the autonomous vehicle is crucial to determine the path to be followed to reach the desired destination. Sensor fusion techniques are extensively investigated for this. However, the major challenge arising in indoor environment localization is obtaining accuracy in the scale of a few centimeters in real-time. In this thesis, we intend to address this challenge. The contributions of this thesis are two-fold. Firstly, we develop a low-cost testbed – Autonomous Ground Drone (AGD) – that enables us to develop sensor fusion and localization scheme for autonomous driving. Secondly, we employ Extended Kalman Filter (EKF) on the sensor combination of UWB, IMU, and Radar, and achieve a localization accuracy of 8 cm. Our localization scheme outperforms state of the art in this field in terms of accuracy, latency, and power consumption.

Keywords: Localization, Sensor Fusion, EKF, AGD, low-cost, real-time