QSpace Collection:
http://hdl.handle.net/1974/794
Sat, 22 Oct 2016 23:36:39 GMT2016-10-22T23:36:39ZCommunication-efficient decentralized sequential detection
http://hdl.handle.net/1974/15128
Title: Communication-efficient decentralized sequential detection
Authors: WANG, HONGFEI
Abstract: The problem of decentralized sequential detection is studied in this thesis, where local sensors are memoryless, receive independent observations, and no feedback from the fusion center. In addition to traditional criteria of detection delay and error probability, we introduce a new constraint: the number of communications between local sensors and the fusion center. This metric is able to reflect both the cost of establishing communication links as well as overall energy consumption over time. A new formulation for communication-efficient decentralized sequential detection is proposed where the overall detection delay is minimized with constraints on both error probabilities and the communication cost.
Two types of problems are investigated based on the communication-efficient formulation: decentralized hypothesis testing and decentralized change detection. In the former case, an asymptotically person-by-person optimum detection framework is developed, where the fusion center performs a sequential probability ratio test based on dependent observations. The proposed algorithm utilizes not only reported statistics from local sensors, but also the reporting times. The asymptotically relative efficiency of proposed algorithm with respect to the centralized strategy is expressed in closed form. When the probabilities of false alarm and missed detection are close to one another, a reduced-complexity algorithm is proposed based on a Poisson arrival approximation.
In addition, decentralized change detection with a communication cost constraint is also investigated. A person-by-person optimum change detection algorithm is proposed, where transmissions of sensing reports are modeled as a Poisson process. The optimum threshold value is obtained through dynamic programming. An alternative method with a simpler fusion rule is also proposed, where the threshold values in the algorithm are determined by a combination of sequential detection analysis and constrained optimization. In both decentralized hypothesis testing and change detection problems, tradeoffs in parameter choices are investigated through Monte Carlo simulations.
Description: Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2016-10-14 07:44:07.106Fri, 14 Oct 2016 04:00:00 GMThttp://hdl.handle.net/1974/151282016-10-14T04:00:00ZTowards Mapping of Rock Walls Using a UAV-Mounted 2D Laser Scanner in GPS Denied Environments
http://hdl.handle.net/1974/15074
Title: Towards Mapping of Rock Walls Using a UAV-Mounted 2D Laser Scanner in GPS Denied Environments
Authors: Turner, Glen
Abstract: In geotechnical engineering, the stability of rock excavations and walls is estimated by using tools that include a map of the orientations of exposed rock faces. However, measuring these orientations by using conventional methods can be time consuming, sometimes dangerous, and is limited to regions of the exposed rock that are reachable by a human. This thesis introduces a 2D, simulated, quadcopter-based rock wall mapping algorithm for GPS denied environments such as underground mines or near high walls on surface. The proposed algorithm employs techniques from the field of robotics known as simultaneous localization and mapping (SLAM) and is a step towards 3D rock wall mapping. Not only are quadcopters agile, but they can hover. This is very useful for confined spaces such as underground or near rock walls. The quadcopter requires sensors to enable self localization and mapping in dark, confined and GPS denied environments. However, these sensors are limited by the quadcopter payload and power restrictions. Because of these restrictions, a light weight 2D laser scanner is proposed. As a first step towards a 3D mapping algorithm, this thesis proposes a simplified scenario in which a simulated 1D laser range finder and 2D IMU are mounted on a quadcopter that is moving on a plane. Because the 1D laser does not provide enough information to map the 2D world from a single measurement, many measurements are combined over the trajectory of the quadcopter. Least Squares Optimization (LSO) is used to optimize the estimated trajectory and rock face for all data collected over the length of a light. Simulation results show that the mapping algorithm developed is a good first step. It shows that by combining measurements over a trajectory, the scanned rock face can be estimated using a lower-dimensional range sensor. A swathing manoeuvre is introduced as a way to promote loop closures within a short time period, thus reducing accumulated error. Some suggestions on how to improve the algorithm are also provided.
Description: Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2016-10-05 14:19:29.715Thu, 06 Oct 2016 04:00:00 GMThttp://hdl.handle.net/1974/150742016-10-06T04:00:00ZLinear Hyperspectral Unmixing Using L0-norm Approximations and Nonnegative Matrix Factorization
http://hdl.handle.net/1974/15072
Title: Linear Hyperspectral Unmixing Using L0-norm Approximations and Nonnegative Matrix Factorization
Authors: Esmaeili Salehani, Yaser
Abstract: Spectral unmixing (SU) is a technique to characterize mixed pixels of the hyperspectral images measured by remote sensors. Most of the existing spectral unmixing algorithms are developed using the linear mixing models. Since the number of endmembers/materials present at each mixed pixel is normally scanty compared with the number of total endmembers (the dimension of spectral library), the problem becomes sparse. This thesis introduces sparse hyperspectral unmixing methods for the linear mixing model through two different scenarios. In the first scenario, the library of spectral signatures is assumed to be known and the main problem is to find the minimum number of endmembers under a reasonable small approximation error. Mathematically, the corresponding problem is called the $\ell_0$-norm problem which is NP-hard problem. Our main study for the first part of thesis is to find more accurate and reliable approximations of $\ell_0$-norm term and propose sparse unmixing methods via such approximations. The resulting methods are shown considerable improvements to reconstruct the fractional abundances of endmembers in comparison with state-of-the-art methods such as having lower reconstruction errors. In the second part of the thesis, the first scenario (i.e., dictionary-aided semiblind unmixing scheme) will be generalized as the blind unmixing scenario that the library of spectral signatures is also estimated. We apply the nonnegative matrix factorization (NMF) method for proposing new unmixing methods due to its noticeable supports such as considering the nonnegativity constraints of two decomposed matrices. Furthermore, we introduce new cost functions through some statistical and physical features of spectral signatures of materials (SSoM) and hyperspectral pixels such as the collaborative property of hyperspectral pixels and the mathematical representation of the concentrated energy of SSoM for the first few subbands. Finally, we introduce sparse unmixing methods for the blind scenario and evaluate the efficiency of the proposed methods via simulations over synthetic and real hyperspectral data sets. The results illustrate considerable enhancements to estimate the spectral library of materials and their fractional abundances such as smaller values of spectral angle distance (SAD) and abundance angle distance (AAD) as well.
Description: Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2016-10-05 09:34:35.463Wed, 05 Oct 2016 04:00:00 GMThttp://hdl.handle.net/1974/150722016-10-05T04:00:00ZVibrational Feedback Training System for Use with SEMG Controlled Powered Prostheses
http://hdl.handle.net/1974/15034
Title: Vibrational Feedback Training System for Use with SEMG Controlled Powered Prostheses
Authors: Seagal, ILLYA
Abstract: Loss of limb results in loss of function and a partial loss of freedom. A powered prosthetic device can partially assist an individual with everyday tasks and therefore return some level of independence. Powered upper limb prostheses are often controlled by the user generating surface electromyographic (SEMG) signals. The goal of this thesis is to develop a virtual environment in which a user can control a virtual hand to safely grasp representations of everyday objects using EMG signals from his/her forearm muscles, and experience visual and vibrotactile feedback relevant to the grasping force in the process. This can then be used to train potential wearers of real EMG controlled prostheses, with or without vibrotactile feedback. To test this system an experiment was designed and executed involving ten subjects, twelve objects, and three feedback conditions. The tested feedback conditions were visual, vibrotactile, and both visual and vibrotactile. In each experimental exercise the subject attempted to grasp a virtual object on the screen using the virtual hand controlled by EMG electrodes placed on his/her forearm. Two metrics were used: score, and time to task completion, where score measured grasp dexterity. It was hypothesized that with the introduction of vibrotactile feedback, dexterity, and therefore score, would improve and time to task completion would decrease. Results showed that time to task completion increased, and score did not improve with vibrotactile feedback. Details on the developed system, the experiment, and the results are presented in this thesis.
Description: Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2016-09-29 19:39:24.391Sat, 01 Oct 2016 04:00:00 GMThttp://hdl.handle.net/1974/150342016-10-01T04:00:00Z