microgrid, distributed generation, electricity restructuring

Soft robots have rapidly evolved over the past decade due to significant advancements in sensing, actuation, control, material science, and manufacturing methods. These developments, combined with the inherent ability of soft robots to safely conform to delicate environments, have made them promising for a wide range of applications, including minimally invasive surgeries (MIS). However, to fully realize the potential of soft robots as effective systems for surgical applications, there are several remaining challenges, including: First, soft robots must be designed to meet the clinical needs of the specific target procedure, while simultaneously offering significant improvements in safety. However, the compliance offered by soft robots can also cause them to become unstable during surgical tasks that place significant loads on the robot's body. Second, highly deformable soft robots are less intuitive to control than rigid robots, complicating human-in-the-loop teleoperation for surgical applications. Finally, soft robots are generally pneumatically actuated to achieve the high forces and bandwidth needed for many surgical applications. However, this actuation method requires dedicated electromechanical components, such as pressure regulators, MOSFETs, and switches, for each actuator, making the system bulky, expensive, and limiting the robot's ability to operate untethered.

This dissertation presents several new robotic systems designed to address these challenges. First we introduce the InchIGRAB, a soft robot designed for colonoscopy. The InchIGRAB sits inside a vine robot (or growing robot) and is designed to enable navigation of the entire length of the colon during both forward motion and retraction, while minimizing interaction with the delicate colon environment. We also detail the design and modeling of soft, positive-pressure actuated suction cups for anchoring flexible and continuum robots during minimally invasive surgeries. Second, to enable intuitive control of soft and continuum robots, we present Hapstick, a soft, flexible joystick that can render stiffness information to users via fiber jamming. Hapstick was demonstrated as an input interface for teleoperating a continuum robot in colorectal cancer screening tasks. Finally, we present the PneuSIC Box, a pneumatic demultiplexer that enables independent control of multiple pneumatic actuators using a single pneumatic input and a motor. We demonstrated how PneuSIC Box could be used to provide independent control of actuators in an inchworm like robot, similar to the InchIGRAB, and a soft robotic hand. Overall, this dissertation presents several new robotic systems that advance the potential of soft robots for minimally invasive surgical applications

Pulmonary arterial hypertension (PAH) is a progressive disease characterized by an elevated mean pulmonary arterial pressure leading to right ventricular hypertrophy and failure. PAH is also associated with an increase in pulmonary vasculature resistance (PVR) due to the accumulation and proliferation of smooth muscle and endothelial cells in the intimal layer of the distal pulmonary arteries. This causes remodeling of the pulmonary vasculature, similar to the plexiform like lesions seen on the autopsy of PAH patients. Many studies have investigated the microstructural and mechanobiological properties of the pulmonary arteries however, the main cause initiating this remodeling process still remains unknown. In this thesis, the remodeling of pulmonary arteries that causes the changes in the resistance and compliance properties of pulmonary vasculature as PAH progresses overtime was studied, using three different Sugen Hypoxia treated rat models (male, female, ovariectomized female) of PAH. A lumped three-element Windkessel model was used to reproduce the in vivo measured blood pressure data efficiently by estimating the resistance and compliance parameters. The parameters estimated by the three-element Windkessel model exhibited statistically significant differences and reflected the relevant physiological changes as the disease progressed. For example, our model revealed that the resistance and compliance changes during PAH, and that the resistance significantly increases (p < 0.05) only in male however the compliance significantly decreases (p < 0.05) in all three groups of rats. Also, the remodeling was sex specific because the compliance changes significantly and early in all three groups of rats before any major changes in the resistances for female and ovariectomized females (OVX) were observed. On the other hand, no statistically significant differences in the parameters and pressures were found between the female and OVX rats. Future direction for this study includes the development of one-dimensional fluid dynamic model to better understand the sex specific remodeling and compare these changes to the structural and tissue-level changes of biomechanical properties in the pulmonary arteries of male, female and OVX female rats. Such insights can potentially help improve our understanding of treatment therapies in either sex of PAH patients in future.

Sparse Signal Recovery (SSR) problem has gained a lot of interest in recent times because of its significant impact on many engineering applications.

This thesis tackles this important problem in a Bayesian framework and discusses a generalized scale mixture distribution family, Power Exponential Scale Mixture (PESM) and analyzes its usefulness as a candidate for the sparsity promoting prior distribution. We also derive a unified MAP estimation or Type I framework for SSR by employing an appropriate member of PESM family and show that our unified framework encompasses several popular regularization based SSR algorithms. In addition to that, exploiting natural hierarchical framework induced by the PESM family, we utilize these priors in a Type II/ Empirical Bayes framework and develop corresponding EM based SSR algorithms. Multivariate extension of our proposed PESM family has also been discussed, which in turn resulted in a unified framework for imposing joint sparsity in Multiple Measurement Vector (MMV) recovery problem.

We have also shown three specific applications of SSR in audio signal processing, which includes problem specific algorithm enhancements but still utilizes the basic understanding of SSR. For example, by employing a source prior from M-PESM family in a joint blind source separation problem, we propose a realm of reweighted algorithms for Independent Vector Analysis (IVA) with the ability to exploit any intra-source correlation structure. An Empirical bayes based Impulse Response (IR) estimator has also been proposed, which exploits both sparse early reflections and exponential decay reverb tail structure in Room Impulse Response/ Relative Impulse Response as prior information. Sparsity in residual has also been exploited for a speech modeling application, which uses the prior block sparse structure of glottal excitation to find the all pole filter coefficients to model speech efficiently.