VISUAL-INERTIAL INTEGRATION FOR HUMAN MOTION TRACKING AND NAVIGATION IN FREE-LIVING ENVIRONMENTS

The aging population has become a burning issue for all modern societies around the world recently. There are two important issues existing now to be solved. One is how to continuously monitor the movements of those people having suffered a stroke in natural living environment for providing more valuable feedback to guide clinical interventions. The other one is how to guide those old people effectively when they are at home or inside other buildings and to make their life easier and convenient. Therefore, human motion tracking and navigation have been active research fields with the increasing number of elderly people. However, motion capture has been extremely challenging to go beyond laboratory environments and obtain accurate measurements of human physical activity especially in free-living environments, and navigation in free-living environments also poses some problems such as the denied GPS signal and the moving objects commonly presented in free-living environments. This thesis seeks to develop new technologies to enable accurate motion tracking and positioning in free-living environments. This thesis comprises three specific goals using our developed IMU board and the camera from the imaging source company: (1) to develop a robust and real-time orientation algorithm using only the measurements from IMU; (2) to develop a robust distance estimation in static free-living environments to estimate people’s position and navigate people in static free-living environments and simultaneously the scale ambiguity problem, usually appearing in the monocular camera tracking, is solved by integrating the data from the visual and inertial sensors; (3) in case of moving objects viewed by the camera existing in free-living environments, to firstly design a robust scene segmentation algorithm and then respectively estimate the motion of the vIMU system and moving objects. To achieve real-time orientation tracking, an Adaptive-Gain Orientation Filter (AGOF) is proposed in this thesis based on the basic theory of deterministic approach and frequency-based approach using only measurements from the newly developed MARG (Magnet, Angular Rate, and Gravity) sensors. To further obtain robust positioning, an adaptive frame-rate vision-aided IMU system is proposed to develop and implement fast vIMU ego-motion estimation algorithms, where the orientation is estimated in real time from MARG sensors in the first step and then used to estimate the position based on the data from visual and inertial sensors. In case of the moving objects viewed by the camera existing in free-living environments, a robust scene segmentation algorithm is firstly proposed to obtain position estimation and simultaneously the 3D motion of moving objects. Finally, corresponding simulations and experiments have been carried out.

Understanding volcanic processes using UV camera measurements of sulfur dioxide and coincident infrasound and seismicity

The exsolution of volatiles from magma maintains an important control on volcanic eruption styles. The nucleation, growth, and connectivity of bubbles during magma ascent provide the driving force behind eruptions, and the rate, volume, and ease of gas exsolution can affect eruptive activity. Volcanic plumes are the observable consequence of this magmatic degassing, and remote sensing techniques allow us to quantify changes in gas exsolution. However, until recently the methods used to measure volcanic plumes did not have the capability of detecting rapid changes in degassing on the scale of standard geophysical observations. The advent of the UV camera now makes high sample rate gas measurements possible. This type of dataset can then be compared to other volcanic observations to provide an in depth picture of degassing mechanisms in the shallow conduit. The goals of this research are to develop a robust methodology for UV camera field measurements of volcanic plumes, and utilize this data in conjunction with seismoacoustic records to illuminate degassing processes. Field and laboratory experiments were conducted to determine the effects of imaging conditions, vignetting, exposure time, calibration technique, and filter usage on the UV camera sulfur dioxide measurements. Using the best practices determined from these studies, a field campaign was undertaken at Volcán de Pacaya, Guatemala. Coincident plume sulfur dioxide measurements, acoustic recordings, and seismic observations were collected and analyzed jointly. The results provide insight into the small explosive features, variations in degassing rate, and plumbing system of this complex volcanic system. This research provides useful information for determining volcanic hazard at Pacaya, and demonstrates the potential of the UV camera in multiparameter studies.

Plenoptic camera : theory and experimental results

The purpose of this research was to develop a working physical model of the focused plenoptic camera and develop software that can process the measured image intensity, reconstruct this into a full resolution image, and to develop a depth map from its corresponding rendered image. The plenoptic camera is a specialized imaging system designed to acquire spatial, angular, and depth information in a single intensity measurement. This camera can also computationally refocus an image by adjusting the patch size used to reconstruct the image. The published methods have been vague and conflicting, so the motivation behind this research is to decipher the work that has been done in order to develop a working proof-of-concept model. This thesis outlines the theory behind the plenoptic camera operation and shows how the measured intensity from the image sensor can be turned into a full resolution rendered image with its corresponding depth map. The depth map can be created by a cross-correlation of adjacent sub-images created by the microlenslet array (MLA.) The full resolution image reconstruction can be done by taking a patch from each MLA sub-image and piecing them together like a puzzle. The patch size determines what object plane will be in-focus. This thesis also goes through a very rigorous explanation of the design constraints involved with building a plenoptic camera. Plenoptic camera data from Adobe © was used to help with the development of the algorithms written to create a rendered image and its depth map. Finally, using the algorithms developed from these tests and the knowledge for developing the plenoptic camera, a working experimental system was built, which successfully generated a rendered image and its corresponding depth map.

A STUDY ON REGIONAL CIRCULAR ECONOMY SYSTEM AND ITS CONSTRUCTION, OPERATION AND SUGGESTION FOR SHANGHAI

The characteristics of the traditional linear economic model are high consumption, high emission and low efficiency. Economic development is still largely at the expense of the environment and requires a natural resource investment. This can realize rapid economic development but resource depletion and environmental pollution become increasingly serious. In the 1990's a new economic model, circular economics, began to enter our vision. The circular economy maximizes production and minimizes the impact of economic activities on the ecological environment through organizing the activities through the closed-loop feedback cycle of "resources - production - renewable resource". Circular economy is a better way to solve the contradictions between the economic development and resource shortages. Developing circular economy has become the major strategic initiatives to achieving sustainable development in countries all over the world. The evaluation of the development of circular economics is a necessary step for regional circular economy development. Having a quantitative evaluation of circular economy can better monitor and reveal the contradictions and problems in the process of the development of recycling economy. This thesis will: 1) Create an evaluation model framework and new types of industries and 2) Make an evaluation of the Shanghai circular economy currently to analyze the situation of Shanghai in the development of circular economy. I will then propose suggestions about the structure and development of Shanghai circular economy.

Automatic Food Intake Assessment Using Camera Phones

Obesity is becoming an epidemic phenomenon in most developed countries. The fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories expended. It is essential to monitor everyday food intake for obesity prevention and management. Existing dietary assessment methods usually require manually recording and recall of food types and portions. Accuracy of the results largely relies on many uncertain factors such as user's memory, food knowledge, and portion estimations. As a result, the accuracy is often compromised. Accurate and convenient dietary assessment methods are still blank and needed in both population and research societies. In this thesis, an automatic food intake assessment method using cameras, inertial measurement units (IMUs) on smart phones was developed to help people foster a healthy life style. With this method, users use their smart phones before and after a meal to capture images or videos around the meal. The smart phone will recognize food items and calculate the volume of the food consumed and provide the results to users. The technical objective is to explore the feasibility of image based food recognition and image based volume estimation. This thesis comprises five publications that address four specific goals of this work: (1) to develop a prototype system with existing methods to review the literature methods, find their drawbacks and explore the feasibility to develop novel methods; (2) based on the prototype system, to investigate new food classification methods to improve the recognition accuracy to a field application level; (3) to design indexing methods for large-scale image database to facilitate the development of new food image recognition and retrieval algorithms; (4) to develop novel convenient and accurate food volume estimation methods using only smart phones with cameras and IMUs. A prototype system was implemented to review existing methods. Image feature detector and descriptor were developed and a nearest neighbor classifier were implemented to classify food items. A reedit card marker method was introduced for metric scale 3D reconstruction and volume calculation. To increase recognition accuracy, novel multi-view food recognition algorithms were developed to recognize regular shape food items. To further increase the accuracy and make the algorithm applicable to arbitrary food items, new food features, new classifiers were designed. The efficiency of the algorithm was increased by means of developing novel image indexing method in large-scale image database. Finally, the volume calculation was enhanced through reducing the marker and introducing IMUs. Sensor fusion technique to combine measurements from cameras and IMUs were explored to infer the metric scale of the 3D model as well as reduce noises from these sensors.