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The operational life of wireless sensor network based distributed sensing systems is limited by the energy provided by a portable battery pack. Owing to the inherently resource constrained nature of wireless sensor networks and nodes, a major research thrust in this field is the search for energy-aware methods of operation. Communication is among the most energy-intensive operations on a wireless device. It is therefore, the focus of our efforts to develop an energy-aware method of communication and to introduce a degree of reconfigurability to ensure autonomous operation of such devices. Given this background, three research tasks have been identified and investigated during the course of this research. 1) Devising an energy-efficient method of communication in a framework of reconfigurable operation: The dependence of the energy consumed during communication on the number of bits transmitted (and received) was identified from prior research work. A novel method of data compression was designed to exploit this dependence. This method uses the time-limited, orthonormal Walsh functions as basis functions for representing signals. The L2 norm of this representation is utilized to further compress the signals. From Parseval’s relation, the square of the L2 norm represents the energy content of a signal. The application of this theorem to our research makes it possible to use the L2 norm as a control knob. The operation of this control knob makes it possible to optimize the number of terms required to represent signals. The time-limited nature of the Walsh functions was leveraged to inject dynamic behaviour into our coding method. This time-limited nature allows decomposition of finite time-segments, without attendant limitations like loss of resolution that are inherent to derived, discrete transforms like the discrete Fourier transform or the discrete time Fourier transform. This decomposition over successive, finite time-segments, coupled with innovative operation of the previously mentioned control knob on every segment, gives us a dynamic scaling technique. The amount of data to be transmitted is in turn based on the magnitude of the coefficients of decomposition of each time-segment, leading to the realization of a variable word length coding method. This dynamic coding method can identify evolving changes or events in the quantity being sensed. The coefficients of decomposition represent features present in successive time-segments of signals and therefore enable identification of evolving events. The ability to identify events as they occur enables the algorithm to react to events as they evolve in the system. In other words the data transmission and the associated energy consumption are imparted a reconfigurable, event-driven nature by implementation of the coding algorithm. Performance evaluation of this method via simulations on machine generated (bearing vibration) and biometric (electro-cardio gram) signals shows it be a viable method for energy-aware communication. 2) Developing a framework for reconfigurable triggering: A framework for completely autonomous triggering of the coding method has been developed. This is achieved by estimating correlations of the signal with the representative Walsh functions. The correlation coefficient of a signal segment with a Walsh function gives a picture of the amount of energy localized by the function. This information is used to autonomously tune the abovementioned control knob or, in more proper terms, the degree of thresholding used in compression. Evaluation of this framework on bearing vibration and electro-cardio gram signals has shown results consistent with those of previous simulations. 3) Devising a computationally compact method of feature classification: A method of investigating time series measurements of dynamic systems in order to classify features buried in the signal measurements was investigated. The approach involves discretizing time-series measurements into strings of pre-defined symbols. These strings are transforms of the original time-series measurements and are a representation of the system dynamics. A method of statistically analyzing the symbol strings is presented and its efficacy is studied through representative simulations and experimental investigation of vibration signals recorded from a rolling bearing element. The method is computationally compact because it obviates the need for local signal processing tasks like denoising, detrending and amplification. Results indicate that the method can effectively classify deteriorating machine health, changing operating conditions and evolving defects. In addition to these major foci, another research task was the design and implementation of a wireless network testbed. This testbed consists of a network of netbooks, connected together wirelessly and was utilized for experimental verification of the variable word length coding method.

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The purpose of this thesis was to utilize existing technologies to create a development environment suitable for creating context-aware applications and systems specific to home and family living conditions. After outlining the history of context-aware applications and the challenges that face family-centric systems in this field, a development environment was implemented that solves the unique challenges that face application development for family-centric, context-aware applications. In particular, research cited in this document indicates that a browser-based user interface is the most appropriate interface for a family environment. The flexibility of the interface, as well as the familiarity of the application structure allows family members of varying levels of comprehension to use a given application. The use of a browser interface for a context-aware system creates unique challenges such as the ability to integrate with external applications and external devices. In addition to overcoming the restrictions of web browsers, the development environment was designed to support the unique user environment presented by a family structure. This includes mechanisms for the long-term adaptability of the system to the changing lifestyles of the family members, as well as the infrequent, but necessary ability to adjust the structure of the family unit due to the addition or prolonged absence of family members. Another problem that the development environment was required to solve was the varying levels of computer comprehension that exist among different family members. An application that targets an entire family unit must meet the usability needs of all levels of comprehension. The development environment was created to account for this wide array of usability requirements. The resulting development environment was implemented on a Windows XP Professional environment, utilizing existing technologies and software that were mostly cross-browser compatible. Although a new technology was not designed and created, existing technologies were combined to solve the aforementioned problems that are unique to developing systems and applications for a family-centric, context-aware environment. Recommendations are made for future research and development in the area of family-assistive application development.

Eye trackers have enabled users to interact with computers for a long time. Yet, there are still many challenges left to be solved to make the interaction easy for users with development disabilities. Especially, when it comes to setup eye tracking where calibration of the eye tracker is important to get accurate estimation of where users are looking. This paper presents a study in which three modified versions of a calibration interface for eye trackers has been designed and evaluated by nine participants with development disabilities. These calibration interfaces used regions that were aware of when a user gazed at them, gaze-aware regions, and varied in the speed of which a stimulus moved during the calibration and how quickly the regions around the stimulus grew. Data was collected for each interface about interaction with the gaze-aware regions, time to complete a calibration, how many calibration points that were completed and gaze offset from the stimulus. No statistically significance was found between the modified interfaces for its efficiency, effectiveness and accuracy. However, a stimulus moving quicker and a gaze-aware region growing faster indicated a more effective and efficient calibration method without loss in accuracy. Also, if only screen engagement is involved using smooth-pursuit calibration could improve the calibration process. / Ögonstyrning har under en längre tid möjliggjort interaktion för användare. Dock är det fortfarande många utmaningar för att göra interaktionen lätt för användare med intellektuella funktionsnedsättningar. Framförallt när det kommer till inställningar för ögonstyrning, där kalibrering har visat sig vara viktigt för att ge en noggrann uppskattning vart användarna fokuserar. Denna rapport presenterar en studie där tre modifierade versioner av ett kalibreringsgränsnitt för ögonstyrning har blivit designat och utvärderat av nio deltagare med låg fokuseringsförmåga. Dessa gränssnitt använde regioner som var medvetna när en användare tittade inom dom, så kallade blickmedvetna regioner, och varierade i vilken hastighet ett stimuli rörde sig och hur snabbt regionerna runt ett stimuli växte. Data samlades in för varje gränssnitt om interaktionen med de blickmedvetna regionerna, tiden för att genomföra kalibreringen, antal avklarade kalibreringspunkter och avståndet mellan användarnas blick och stimuli. Ingen statistisk signifikans hittades mellan de modifierade gränssnitten mellan tidseffektivitet, effektivitet och noggrannhet. Däremot indikerades en mer tidseffektiv och effektiv kalibreringsmetod, utan minskad noggrannhet, genom användningen av ett stimuli som rör sig snabbare med blickmedvetna regioner som växer. Dessutom skulle kalibreringsprocessen kunna förbättras om enbart engagemang med skärmen används genom smooth-pursuit kalibrering

Geo-localization is the problem of discovering the location where an image or video was captured. Recently, large scale geo-localization methods which are devised for ground-level imagery and employ techniques similar to image matching have attracted much interest. In these methods, given a reference dataset composed of geo-tagged images, the problem is to estimate the geo-location of a query by finding its matching reference images. In this dissertation, we address three questions central to geo-spatial analysis of ground-level imagery: 1) How to geo-localize images and videos captured at unknown locations? 2) How to refine the geo-location of already geo-tagged data? 3) How to utilize the extracted geo-tags? We present a new framework for geo-locating an image utilizing a novel multiple nearest neighbor feature matching method using Generalized Minimum Clique Graphs (GMCP). First, we extract local features (e.g., SIFT) from the query image and retrieve a number of nearest neighbors for each query feature from the reference data set. Next, we apply our GMCP-based feature matching to select a single nearest neighbor for each query feature such that all matches are globally consistent. Our approach to feature matching is based on the proposition that the first nearest neighbors are not necessarily the best choices for finding correspondences in image matching. Therefore, the proposed method considers multiple reference nearest neighbors as potential matches and selects the correct ones by enforcing the consistency among their global features (e.g., GIST) using GMCP. Our evaluations using a new data set of 102k Street View images shows the proposed method outperforms the state-of-the-art by 10 percent. Geo-localization of images can be extended to geo-localization of a video. We have developed a novel method for estimating the geo-spatial trajectory of a moving camera with unknown intrinsic parameters in a city-scale. The proposed method is based on a three step process: 1) individual geo-localization of video frames using Street View images to obtain the likelihood of the location (latitude and longitude) given the current observation, 2) Bayesian tracking to estimate the frame location and video's temporal evolution using previous state probabilities and current likelihood, and 3) applying a novel Minimum Spanning Trees based trajectory reconstruction to eliminate trajectory loops or noisy estimations. Thus far, we have assumed reliable geo-tags for reference imagery are available through crowdsourcing. However, crowdsourced images are well known to suffer from the acute shortcoming of having inaccurate geo-tags. We have developed the first method for refinement of GPS-tags which automatically discovers the subset of corrupted geo-tags and refines them. We employ Random Walks to discover the uncontaminated subset of location estimations and robustify Random Walks with a novel adaptive damping factor that conforms to the level of noise in the input. In location-aware image understanding, we are interested in improving the image analysis by putting it in the right geo-spatial context. This approach is of particular importance as the majority of cameras and mobile devices are now being equipped with GPS chips. Therefore, developing techniques which can leverage the geo-tags of images for improving the performance of traditional computer vision tasks is of particular interest. We have developed a location-aware multimodal approach which incorporates business directories, textual information, and web images to identify businesses in a geo-tagged query image.

Energy consumption is one of the most constrained requirements for the development and implementation of wireless sensor networks. Many design aspects affect energy consumption, ranging from the hardware components, operations of the sensors, the communication protocols, the application algorithms, duty cycles and others. Efficient simulation tool can be used to estimate the contribution to energy consumption of all of these factors, and significantly decrease the efforts and time spent to choose the right solution that fits best to a particular application. In this work we present design space exploration methodology for ultra low power embedded systems and wireless sensor networks. The methodology takes inspiration from Platform Based Design (PBD) paradigm and defines separate abstraction layers for all system aspects that directly contribute power consumption of target applications. To support presented methodology we built a SystemC-based discrete event simulation framework, called “PASES”, that provides power-aware simulation and analysis of wireless sensor networks and sensor nodes. Its modular architecture allows flexible, extensible and rapid modeling of custom HW platforms, SW application models, communication protocols, energy sources, environment dynamics and nodes mobility. Based on the feedback gained from PASES, the optimal and energy-efficient solution for the specific project of interest can be selected. The proposed approach improves state-of-the-art by providing fast and reliable power-aware system-level exploration for a wide range of custom applications

A growing number of applications involving autonomous mobile robots will require their navigation across environments in which spaces are shared with humans. In those situations, the robot’s actions are socially acceptable if they reflect the behaviours that humans would generate in similar conditions. Therefore, the robot must perceive people in the environment and correctly react based on their actions and relevance to its mission. In order to give a push forward to human-robot interaction, the proposed research is focused on efficient robot motion algorithms, covering all the tasks needed in the whole process, such as obstacle detection, human motion tracking and prediction, socially aware navigation, etc. The final framework presented in this thesis is a robust and efficient solution enabling the robot to correctly understand the human intentions and consequently perform safe, legible, and socially compliant actions. The thesis retraces in its structure all the different steps of the framework through the presentation of the algorithms and models developed, and the experimental evaluations carried out both with simulations and on real robotic platforms, showing the performance obtained in real–time in complex scenarios, where the humans are present and play a prominent role in the robot decisions. The proposed implementations are all based on insightful combinations of traditional model-based techniques and machine learning algorithms, that are adequately fused to effectively solve the human-aware navigation. The specific synergy of the two methodology gives us greater flexibility and generalization than the navigation approaches proposed so far, while maintaining accuracy and reliability which are not always displayed by learning methods.

This dissertation proposes a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The proposed methodology has been built upon the literature review, analyses, and findings from a number of published studies throughout the dissertation research. Namely, a Sig- nals of Opportunity Coherent Bistatic scattering model (SCoBi) has been first developed at MSU and then its simulator has been open-sourced. Simulated GNSS-Reflectometry (GNSS-R) analyses have been conducted by using SCoBi. Significant findings have been noted such that (1) Although the dominance of either the coherent reflections or incoher- ent scattering over land is a debate, we demonstrated that coherent reflections are stronger for flat and smooth surfaces covered by low-to-moderate vegetation canopy; (2) The influ- ence of several land geophysical parameters such as SM, vegetation water content (VWC), and surface roughness on the bistatic reflectivity was quantified, the dynamic ranges of reflectivity changes due to SM and VWC are much higher than the changes due to the surface roughness. Such findings of these analyses, combined with a comprehensive lit- erature survey, have led to the present inversion algorithm: Physics- and learning-based retrieval of soil moisture information from space-borne GNSS-R measurements that are taken by NASA’s CYGNSS mission. The study is the first work that proposes a machine learning-based, non-parametric, and non-linear regression algorithm for CYGNSS-based soil moisture estimation. The results over point-scale soil moisture observations demon- strate promising performance for applicability to large scales. Potential future work will be extension of the methodology to global scales by training the model with larger and diverse data sets.