Current robot manipulation and control paradigms have largely been developed for static or highly structured environments such as those common in factories. For most techniques in robot trajectory generation, such as heuristic-based geometric planning, this has led to putting a high cost on contact with the world. This approach and methodology can be prohibitive to robots operating in many unmodeled and dynamic environments. This dissertation presents work on using haptic based feedback (torque and tactile sensing) to formulate a controller for robot manipulation in clutter. We define “clutter” as any environment in which we expect the robot to make both incidental and purposeful contact while maneuvering and manipulating. The controllers developed in this dissertation take the form of single or multi-time step Model Predictive Control (a form of optimal control which incorporates feedback) which attempts to regulate contact forces at multiple locations on a robot arm while reaching to a goal. The results and conclusions in this dissertation are based on extensive testing in simulation (tens of thousands of trials) and testing in realistic scenarios with real robots incorporating tactile sensing. The approach is novel in the sense that it allows contact and explicitly incorporate the contact and predictive model of the robot arm in calculating control effort at every time step. The expected broader impact of this research is progress towards a new foundation of reactive feedback controllers that will include a higher likelihood of success in many constrained and dynamic scenarios such as reaching into containers without line of sight, maneuvering in cluttered search and rescue situations or working with unpredictable human co-workers.

<p>Beamforming of ultrasound signals in the presence of clutter, or partial aperture blockage by an acoustic obstacle can lead to reduced visibility of the structures of interest and diminished diagnostic value of the resulting image. We propose new beamforming methods to recover the quality of ultrasound images under such challenging conditions. Of special interest are the signals from large apertures, which are more susceptible to partial blockage, and from commercial matrix arrays that suffer from low sensitivity due to inherent design/hardware limitations. A coherence-based beamforming method designed for suppressing the in vivo clutter, namely Short-lag Spatial Coherence (SLSC) Imaging, is first implemented on a 1-D array to enhance visualization of liver vasculature in 17 human subjects. The SLSC images show statistically significant improvements in vessel contrast and contrast-to-noise ratio over the matched B-mode images. The concept of SLSC imaging is then extended to matrix arrays, and the first in vivo demonstration of volumetric SLSC imaging on a clinical ultrasound system is presented. The effective suppression of clutter via volumetric SLSC imaging indicates it could potentially compensate for the low sensitivity associated with most commercial matrix arrays. The rest of the dissertation assesses image degradation due to elements blocked by ribs in a transthoracic scan. A method to detect the blocked elements is demonstrated using simulated, ex vivo, and in vivo data from the fully-sampled 2-D apertures. The results show that turning off the blocked elements both reduces the near-field clutter and improves visibility of anechoic/hypoechoic targets. Most importantly, the ex vivo data from large synthetic apertures indicates that the adaptive weighing of the non-blocked elements can recover the loss of focus quality due to periodic rib structure, allowing large apertures to realize their full resolution potential in transthoracic ultrasound.</p> / Dissertation

Passive coherent location (PCL) radars employ illuminators of opportunity to detect and track targets. This silent operating mode provides many advantages such as low cost and interception immunity. Many radiation sources have been exploited as illumination sources such as broadcasting and telecommunication transmitters. The classical architecture of the bistatic PCL radars involves two receiving channels: a reference channel and a surveillance channel. The reference channel captures the direct-path signal from the transmitter, and the surveillancesignal collects the possible target echoes. The two major challenges for the PCL radars are the reference signal noise and the surveillance signal static clutter. A noisy reference signal degrades the detection probability by increasing the noise-floor level of the detection filter output. And the static clutter presence in the surveillance signal reduces the detector dynamic range and buries low magnitude echoes.In this thesis, we consider a PCL radar based on the digital video broadcasting-terrestrial (DVB-T) signals, and we propose a set of improved methods to deal with the reference signal noise and the static clutter in the surveillance signal. The DVB-T signals constitute an excellentcandidate as an illumination source for PCL radars; they are characterized by a wide bandwidth and a high radiated power. In addition, they provide the possibility of reconstructing the reference signal to enhance its quality, and they allow a straightforward static clutter suppressionin the frequency domain. This thesis proposes an optimum method for the reference signal reconstruction and an improved method for the static clutter suppression.The optimum reference signal reconstruction minimizes the mean square error between the reconstructed signal and the exact one. And the improved static clutter suppression method exploits the possibility of estimating the propagation channel. These two methods extend thefeasibility of a single receiver PCL radar, where the reference signal is extracted from the direct-path signal present in the surveillance signal. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

abstract: The radar performance of detecting a target and estimating its parameters can deteriorate rapidly in the presence of high clutter. This is because radar measurements due to clutter returns can be falsely detected as if originating from the actual target. Various data association methods and multiple hypothesis filtering approaches have been considered to solve this problem. Such methods, however, can be computationally intensive for real time radar processing. This work proposes a new approach that is based on the unsupervised clustering of target and clutter detections before target tracking using particle filtering. In particular, Gaussian mixture modeling is first used to separate detections into two Gaussian distinct mixtures. Using eigenvector analysis, the eccentricity of the covariance matrices of the Gaussian mixtures are computed and compared to threshold values that are obtained a priori. The thresholding allows only target detections to be used for target tracking. Simulations demonstrate the performance of the new algorithm and compare it with using k-means for clustering instead of Gaussian mixture modeling. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2016

Virtual reality has been a popular training tool for individuals with Autism Spectrum Disorder (ASD) in recent years. Although virtual reality was proven to be a promising tool for individuals with ASD by many previous studies, effects of virtual reality properties on user experience is still an unexplored area. More comparison studies and reliable data are needed to identify the benefits of different VR methods and properties, and leverage the future VR systems. In this dissertation, we explored effects of virtual reality properties on user experience of high functioning individuals with ASD with four different serious game experiments. The first experiment consisted of a virtual reality serious game system for vocational training of individuals with ASD. Although this experiment was focused on the effectiveness of virtual reality training on vocational skills of individuals with ASD and was not comparative; during the user study with 9 neurotypical and 9 high functioning ASD individuals, several observations regarding the effects of virtual reality properties on user experience have been performed. The next three experiments investigated the following: effects of instruction methods on user performance with virtual reality warehouse serious game, effects of visual fidelity and view zoom on user performance with a virtual reality investigation serious game, and effects of environmental clutter and motion on user performance with a virtual reality searching serious game. These three experiments were evaluated with user studies of 15 neurotypical and 15 high functioning ASD individuals. Our motivation was to provide positive contribution to the design and development of future virtual reality serious games targeting individuals with ASD so that more benefits could be gained from these applications. Results of the virtual reality for vocational rehabilitation experiment indicated that virtual reality provided effective training especially for the money management, cleaning and social skills of high functioning individuals with ASD. The distracters in the form of background motion and audio did not affect the performance of the participants significantly. Based on the results of the instruction methods experiment, using animated instructions and avoiding verbal instructions in virtual environments was recommended for an audience of high functioning individuals with ASD. The visual fidelity and view zoom experiment’s results indicated that using low visual fidelity and normal view zoom are better design principles for training applications targeting high functioning individuals with ASD. The results of the experiment on clutter and motion in virtual worksp aces suggested that using no clutter and no motion in training applications targeting high functioning individuals with ASD would provide better user experience. Several other design guidelines based on data analysis and observation were shared in the study, with the aim of leveraging future virtual reality serious games targeting high functioning individuals with ASD.

Cette thèse a pour objectif d’améliorer la détection de cibles lentes et de faible réflectivité dans le cas de radars de sol Doppler pulsés à fréquence de récurrence intermédiaire. Ces radars, hautement ambigus en distance et en vitesse, émettent de façon consécutive des trains d’impulsions de périodes de récurrence différentes, afin de lever les ambiguïtés.L’émission successive de trains d’impulsions de courtes durées conduit à une faible capacité de séparation sur l’axe Doppler. Par conséquent, les objets lents de faible réflectivité, comme les drones, sont difficiles à distinguer du fouillis de sol. A l’issue du traitement Doppler conventionnel qui vise à éliminer les échos de fouillis, les performances de détection de ces cibles sont fortement atténuées. Pour palier à ce problème, nous avons développé une nouvelle chaîne de traitement 2D distance/Doppler pour les radars à fréquence de récurrence intermédiaire. Celle-ci s’appuie, en premier lieu, sur un algorithme itératif permettant d’exploiter la diversité temporelle entre les trains d’impulsions émis, afin de lever les ambiguïtés en distance et en vitesse et de détecter les cibles rapides exo-fouillis. La détection des cibles lentes endo-fouillis est ensuite réalisée à l’aide d’un détecteur adaptatif. Une nouvelle approche, permettant d’associer les signaux issus de rafales de caractéristiques différentes pour l’estimation de la matrice de covariance, est utilisée en vue d’optimiser les performances de détection. Les différents tests effectués sur données simulées et réelles pour évaluer les traitements développés et la nouvelle chaîne de traitement, ont montré l’intérêt de ces derniers. / The aim of this thesis is to enhance the detection of slow-moving targets with low reflectivity in case of ground-based pulse Doppler radars operating in intermediate pulse repetition frequency. These radars are highly ambiguous in range and Doppler. To resolve ambiguities, they transmit successively short pulse trains with different pulse repetition intervals. The transmission of short pulse trains results in a poor Doppler resolution. As consequence, slow-moving targets with low reflectivity, such as unmanned aerial vehicles, are buried into clutter returns. One of the main drawbacks of the classical Doppler processing of intermediate pulse repetition frequency pulse Doppler radars is the low detection performance of small and slowly-moving targets after ground clutter rejection. In order to address this problem, a two-dimensional range / Dopper processing chain including new techniques is proposed in this thesis. First, an iterative algorithm allows to exploit transmitted pulse trains temporal diversity to resolve range and Doppler ambiguities and detect fast, exo-clutter, targets. The detection of slow, endo-clutter, targets is then performed by an adaptive detection scheme. It uses a new covariance matrix estimation approach allowing the association of pulse trains with different characteristics in order to enhance detection performance. The different tests performed on simulated and real data to evaluate the proposed techniques and the new processing chain have shown their effectiveness.

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This dissertation investigated the effects of complex backgrounds on mental rotation. Stimulus familiarity and background familiarity were manipulated. It systematically explored how familiarizing participants to objects and complex backgrounds affects their performance on a mental rotation task involving complex backgrounds. This study had 113 participants recruited through the UCF Psychology SONA system. Participants were familiarized with a stimulus in a task where they were told to distinguish the stimulus from 3 other stimuli. A similar procedure was used to familiarize the backgrounds. The research design was a 2 stimulus familiarity (Familiarized with the Target Stimulus, not familiarized with the Target Stimulus) by 2 background familiarity (Familiarized with Target Background, not familiarized with Target Background 1) by 2 stimulus response condition (Target Stimulus, Non-Target Stimulus) by 3 background response condition (Target Background, Non-Target Background, Blank Background) by 12 degree of rotation (0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330) mixed design. The study utilized target stimulus and target background familiarity conditions as the between-subjects variables. Background, stimulus, and degree of rotation were within-subjects variables. The participants' performance was measured using reaction time and percent of errors. Reaction time was computed using only the correct responses. After the familiarization task, participants engaged in a mental rotation task featuring stimuli and backgrounds that were present or not present in the familiarization task. A 2 (stimulus familiarization condition) by 2 (background familiarization condition) by 2 (stimulus response condition) by 3 (background response condition) by 12 (degree of rotation) mixed ANOVA was computed utilizing reaction time and percent of errors. Results suggest that familiarity with the Target Background had the largest effect on improving performance across response conditions. The results also suggest that familiarity with both the Target Stimulus and Target Background promoted inefficient mental rotation strategies which resulted in no significant differences between participants familiarized with neither the Target Stimulus nor the Target Background. Theoretical conclusions are drawn about stimulus familiarity and background familiarity. Future studies should investigate the effects of long term familiarity practice on mental rotation and complex backgrounds.

No description available.

<p>In this thesis, the problem of estimating the clutter spatial intensity function for the multitarget tracking algorithms has been considered. In many scenarios, after the signal detection process, measurement points provided by the sensor (e.g., sonar, infrared sensor, radar) are not distributed uniformly in the surveillance region as assumed by most tracking algorithms. On the other hand, in order to obtain accurate results, the multitarget tracking algorithm requires information about clutter’s spatial intensity. Thus, non-homogeneous clutter spatial intensity has to be estimated from the measurement set and the tracking filter’s output. Also, in order to take advantage of existing tracking algorithms, it is desirable for the clutter estimation method to be integrated into the tracker itself. In this thesis, the clutter is modeled by a non-homogeneous Poisson point (NHPP) process with a spatial intensity function g(z). To calculate the value of the clutter spatial intensity, all we need to do is estimating g(z). First, two new methods for joint spatial clutter intensity estimation and multitarget tracking using the Probability Hypothesis Density (PHD) Filter are presented. Then, based on NHPP process, multitarget multi-Bernoulli processes and set calculus, the approximated Bayesian method is extended to joint the non–homogeneous clutter background estimation and multitarget tracking with standard multitarget tracking algorithms, like the Multiple Hypothesis Tracking (MHT) and the Joint Integrated Probabilistic Data Association (JIPDA) tracker. Finally, a kernel density method is proposed for the clutter spatial intensity estimation problem. Simulation results illustrate the performance of the above algorithms, both in terms of the false track number and the true track initialization speed. All proposed algorithms show the ability to improve the performance of the multitarget tracker in the presence of slowly time varying non–homogeneous clutter background.</p> / Doctor of Philosophy (PhD)