Non-destructive testing (NDT) and structural health monitoring (SHM) are widely used for this purpose. Different types of NDT techniques have been proposed for the damage detection, such as optical image, ultrasound wave, thermography, eddy current, and microwave. The focus in this study is on the wave-based detection method, which is grouped into two major categories: feature-based damage detection and model-assisted damage detection. Both damage detection approaches have their own pros and cons. Feature-based damage detection is usually very fast and doesn’t involve in the solution of the physical model. The key idea is the dimension reduction of signals to achieve efficient damage detection. The disadvantage is that the loss of information due to the feature extraction can induce significant uncertainties and reduces the resolution. The resolution of the feature-based approach highly depends on the sensing path density. Model-assisted damage detection is on the opposite side. Model-assisted damage detection has the ability for high resolution imaging with limited number of sensing paths since the entire signal histories are used for damage identification. Model-based methods are time-consuming due to the requirement for the inverse wave propagation solution, which is especially true for the large 3D structures.
The motivation of the proposed method is to develop efficient and accurate model-based damage imaging technique with limited data. The special focus is on the efficiency of the damage imaging algorithm as it is the major bottleneck of the model-assisted approach. The computational efficiency is achieved by two complimentary components. First, a fast forward wave propagation solver is developed, which is verified with the classical Finite Element(FEM) solution and the speed is 10-20 times faster. Next, efficient inverse wave propagation algorithms is proposed. Classical gradient-based optimization algorithms usually require finite difference method for gradient calculation, which is prohibitively expensive for large degree of freedoms. An adjoint method-based optimization algorithms is proposed, which avoids the repetitive finite difference calculations for every imaging variables. Thus, superior computational efficiency can be achieved by combining these two methods together for the damage imaging. A coupled Piezoelectric (PZT) damage imaging model is proposed to include the interaction between PZT and host structure. Following the formulation of the framework, experimental validation is performed on isotropic and anisotropic material with defects such as cracks, delamination, and voids. The results show that the proposed method can detect and reconstruct multiple damage simultaneously and efficiently, which is promising to be applied to complex large-scale engineering structures.
First, a pneumotach based flow sensing technique has been developed and integrated into a face mask for respiratory profile tracking. Algorithms have been developed to convert the pressure profile into respiratory flow rate profile. Gyroscope-based correction is used to remove motion artifacts that arise from daily activities. By using Principal Component Analysis, the follow-up work established a unique respiratory signature for each subject based on the flow profile and lung parameters computed using the wearable mask system.
Next, wristwatch devices to track transcutaneous gases like oxygen (TcO2) and carbon dioxide (TcCO2), and oximetry (SpO2) have been developed. Two chemical sensing approaches have been explored. In the first approach, miniaturized low-cost commercial sensors have been integrated into the wristwatch for transcutaneous gas sensing. In the second approach, CMOS camera-based colorimetric sensors are integrated into the wristwatch, where a part of camera frame is used for photoplethysmography while the remaining part tracks the optical signal from colorimetric sensors.
Finally, the wireless connectivity using Bluetooth Low Energy (BLE) in wearable systems has been explored and a data transmission protocol between wearables and host for reliable transfer has been developed. To improve the transmission reliability, the host is designed to use queue-based re-request routine to notify the wearable device of the missing packets that should be re-transmitted. This approach avoids the issue of host dependent packet losses and ensures that all the necessary information is received.
The works in this thesis have provided technical solutions to address challenges in wearable technologies, ranging from chemical sensing, flow sensing, data analysis, to wireless data transmission. These works have demonstrated transformation of traditional bench-top medical equipment into non-invasive, unobtrusive, ergonomic & stand-alone healthcare devices.
Background:
Ketogenic diets are high fat and low carbohydrate or very low carbohydrate diets, which render high production of ketones upon consumption known as nutritional ketosis (NK). Ketosis is also produced during fasting periods, which is known as fasting ketosis (FK). Recently, the combinations of NK and FK, as well as NK alone, have been used as resources for weight loss management and treatment of epilepsy.
Methods:
A crossover study design was applied to 11 healthy individuals, who maintained moderately sedentary lifestyle, and consumed three types of diet randomly assigned over a three-week period. All participants completed the diets in a randomized and counterbalanced fashion. Each weekly diet protocol included three phases: Phase 1 - A mixed diet with ratio of fat: (carbohydrate + protein) by mass of 0.18 or the equivalence of 29% energy from fat from Day 1 to Day 5. Phase 2- A mixed or a high-fat diet with ratio of fat: (carbohydrate + protein) by mass of approximately 0.18, 1.63, or 3.80 on Day 6 or the equivalence of 29%, 79%, or 90% energy from fat, respectively. Phase 3 - A fasting diet with no calorie intake on Day 7. Caloric intake from diets on Day 1 to Day 6 was equal to each individual’s energy expenditure. On Day 7, ketone buildup from FK was measured.
Results:
A statistically significant effect of Phase 2 (Day 6) diet was found on FK of Day 7, as indicated by repeated analysis of variance (ANOVA), F(2,20) = 6.73, p < 0.0058. Using a Fisher LDS pair-wise comparison, higher significant levels of acetone buildup were found for diets with 79% fat content and 90% fat content vs. 29% fat content (with p = 0.00159**, and 0.04435**, respectively), with no significant difference between diets with 79% fat content and 90% fat content. In addition, independent of the diet, a significantly higher ketone buildup capability of subjects with higher resting energy expenditure (R[superscript 2] = 0.92), and lower body mass index (R[superscript 2] = 0.71) was observed during FK.