Purpose: Trigger points may occur when muscle is subject to direct trauma, sustained tension, fatigue, radiculopathy, joint dysfunction and emotional stress, which may cause aberrant nerve conduction and dysfunction of the motor neurons. Any of these factors may increase the possibility of overload stress to a muscle and may convert a latent trigger point to an active one. In humans, due to muscle attachments, spinal manipulation causes reflex relaxation of associated and distal musculature. The purpose of this study was to assess the effects of upper cervical spine manipulation, specifically C1, on pain tolerance of trigger points over the erector spinae muscles in show-jumping horses. Method: This study consisted of two groups, the experimental and the control group, each consisting of ten horses. Potential candidates were examined and accepted based on the inclusion and exclusion criteria. The only method of treatment that was administered to each horse was chiropractic manipulation to the most restricted side of the UPPER CERVICAL SPINE joint complex, from which the objective findings were based. Procedure: Both groups were examined for trigger points within the Erector Spinae muscles and these trigger points were assessed, via an algometer, for spot tenderness. All horses were then examined for a cervical restriction of the upper cervical spine. Only the experiment group had the restriction corrected by a chiropractic manipulation and thereafter both groups were reassessed two minutes later, and then again two weeks later, via an algometer, for spot tenderness within the same trigger points. Results: Statistically significant changes were found when comparing the algometer readings before the adjustment with the algometer readings after the adjustment on the right. Otherwise no statistically significant differences were found when comparing algometer readings before the adjustment with the algometer readings after the adjustment on the left, or when comparing the algometer readings before the adjustment with the algometer reading two weeks later bilaterally. Conclusion: The results were inconclusive with regards to immediate and prolonged effects of upper cervical spine manipulation, specifically C1, on pain tolerances over the erector spinae muscle. As this study was directed to a small group of subjects, accurate conclusions cannot be formulated due to the insignificant findings obtained from the study and further research needs to be performed on the effects of upper cervical spine manipulation on trigger points in horses. / Dr. Ashleigh Deall Dr. Alex Niven Dr. Chris Yelverton

M.Tech.

This thesis presents contributions to applied robotic control and manipulation in the areas of motion algorithm design, hardware, and software robotic system design. Mobile robotic systems are widely used in several applications. Control of such systems poses many challenges caused by system modeling uncertainty. Complex physics phenomena and environmental effects are usually neglected to simplify analysis and control design. In motion planning, this thesis introduces an algorithm for navigation learning in mobile robots that aims to reduce the effect of modeling uncertainties on control performance. Starting from an initial feasible state and input trajectories, the objective is to reduce navigation time through iterative trials. A nominal model of the actual system and the experimental system output are used to update the control input in every iteration for incremental improvement. The navigation problem is formulated as an optimal control problem that is solved after each trial to generate a vector of input deviations for the next trial. The formulation of the approach, simulation, and experimental results shows the effectiveness of the presented method. The design part focuses on developing hardware and software systems for manipulation and aerial robots. A software tool for automated generation of multirotor simulation models is developed utilizing CAD software API and Matlab. In the area of human-robot interaction, a human-supervised UAV inspection system has been developed and tested. The UAV is guided by a human operator using a handheld laser pointing device that is designed and fabricated in-house. In the field of robotic manipulation, a novel gripper mechanism is designed and implemented. The proposed mechanism targets applications where a grasped object lies in areas with limited surrounding clearance and where external torques affect the grasped object. This design was implemented on a mobile manipulation platform and tested during an international robotics competition.

Robotic grasping and manipulation of three-dimensional deformable objects is a complex task that currently does not have robust and flexible solutions. Deformable objects include a wide variety of elastic and inelastic objects that change size and shape during manipulation. The development of adaptable methods for grasping and autonomously controlling the shape of three-dimensional deformable objects will benefit many commercial applications, including shaping parts for assembly in manufacturing, manipulating food for packaging and controlling tissues during robotic surgery. Controlling a deformable object to a desired shape requires first choosing contact points on the object's surface. Next, the robotic hand is positioned in the correct position and orientation to grasp and deform the object. After deformation, the object is assessed to evaluate the quality of the shape control procedure. In many cases, this process is completed without knowing the object's properties or behaviour before deformation. This work proposes and implements the framework for a robotic arm and hand system to control the shape of a previously unseen deformable object autonomously. Significant original contributions are made in developing an original algorithm to plan contact points on a three-dimensional object for grasping and shape control. This research uses a novel object representation to reduce the dimensionality of the deformable object manipulation problem. A path planning algorithm guides the robot arm to the optimal valid grasp pose to deform the object at the determined contact points. Additional contributions include developing a multi-view assessment strategy to determine the quality of the deformation towards the desired shape. The system completes the objectives using depth and colour images captured from a single point of view to locate and identify a previously unseen three-dimensional object within a robotic workspace. After estimating the unknown object's geometry, initial grasp contact points are planned to control the object to the desired shape. The grasp points are used to plan and execute a collision-free trajectory for the robot manipulator to place the robotic hand in the optimal position and orientation to grasp and deform the object. After the deformation is complete, the object is moved to a variety of assessment positions to determine the success of the shape control procedure. The system is validated experimentally on a variety of deformable three-dimensional objects.

This thesis deals with the creation of optimalization data of rollway frame for transport handling units. Optimalization is performed on the basis of the size of load handling unit and the subsequent deflection of the profile. The solution is carried out analytically and the results are compared with FEM program outputs.

The purpose of this thesis is the layout optimalization of workplaces within the firm Slovarm Ltd., which was successfully done. By using the procedures of technological design was made the layout, which is based on requirements of company management. The process of solving problems was preceded by a theoretical clarification and explanation of basic concepts, as well as the characterization and technical description of the company as a whole. Subsequent analyses showed different variants from which was chosen the best one. It was elaborated in details and its positive effects were measured in technical-economic evaluation.

Dexterous robotic manipulation of non-rigid objects is a challenging problem but necessary to explore as robots are increasingly interacting with more complex environments in which such objects are frequently present. In particular, common manipulation tasks such as molding clay to a target shape or picking fruits and vegetables for use in the kitchen, require a high-level understanding of the scene and objects. Commonly, the behavior of non-rigid objects is described by a model. Although, well-established modeling techniques are difficult to apply in robotic tasks since objects and their properties are unknown in such unstructured environments. This work proposes a sensing and modeling framework to measure the 3D shape deformation of non-rigid objects. Unlike traditional methods, this framework explores data-driven learning techniques focused on shape representation and deformation dynamics prediction using a graph-based approach. The proposal is validated experimentally, analyzing the performance of the representation model to capture the current state of the non-rigid object shape. In addition, the performance of the prediction model is analyzed in terms of its ability to produce future states of the non-rigid object shape due to the manipulation actions of the robotic system. The results suggest that the representation model is able to produce graphs that closely capture the deformation behavior of the non-rigid object. Whereas, the prediction model produces visually plausible graphs when short-term predictions are required.

Dynamic standing balance is essential to perform functional activities and is included in the treatment of many lower extremity injuries. Physiotherapists utilize many methods to restore standing balance including stability exercises, functional retraining, and manual therapy. The purpose of this study was to investigate the effects of a rearfoot distraction manipulation on dynamic standing balance. Twenty healthy participants (age: 24.4 ± 2.8 years; height: 162.9 ± 37.7 cm; mass: 68.0 ± 4.8 kg; right leg dominant = 20) completed this study. Following familiarization, dynamic standing balance was assessed during: (1) an experimental condition immediately following a rearfoot distraction manipulation, and (2) a control condition. Dominant leg balance was quantified using the Y-balance test which measures lower extremity reach distances. Reach distances were normalized to leg length and measured in the anterior, posteromedial and posterolateral directions. Overall balance was calculated through the summing of all normalized directions. Paired t-tests and Wilcoxon rank tests were used to compare balance scores for parametric and non-parametric data as appropriate. Significance was set at 0.05 a priori. Effect size (ES) was calculated to determine the clinical impact of the manipulation. Increased reach distances (indicating improved balance) were noted following manipulation for overall balance (p = 0.03, ES = 0.26) and in the posteromedial direction (p = 0.01, ES = 0.42). Reach distances did not differ for the anterior (p = 0.11, ES = 0.16) or posterolateral (p = 0.11, ES = 0.25) components. Dynamic standing balance improved after a rearfoot distraction manipulation in healthy participants. It is hypothesized that manual therapy applied to the foot and ankle may be beneficial to augment other therapeutic modalities when working with patients to improve dynamic standing balance.

The advancement of computer architecture and chip design has set the stage for the deep learning revolution by supplying enormous computational power. In general, deep learning is built upon neural networks that can be regarded as a universal approximator of any mathematical function. In contrast to model-based machine learning where the representative features are designed by human engineers, deep learning enables the automatic discovery of desirable feature representations based on a data-driven manner. In this thesis, the applications of deep learning to visual content processing and analysis are discussed. For visual content processing, two novel approaches, named LCVSR and RawVSR, are proposed to address the common issues in the filed of Video Super-Resolution (VSR). In LCVSR, a new mechanism based on local dynamic filters via Locally Connected (LC) layers is proposed to implicitly estimate and compensate motions. It avoids the errors caused by the inaccurate explicit estimation of flow maps. Moreover, a global refinement network is proposed to exploit non-local correlations and enhance the spatial consistency of super-resolved frames. In RawVSR, the superiority of camera raw data (where the primitive radiance information is recorded) is harnessed to benefit the reconstruction of High-Resolution (HR) frames. The developed network is in line with the real imaging pipeline, where the super-resolution process serves as a pre-processing unit of ISP. Moreover, a Successive Deep Inference (SDI) module is designed in accordance with the architectural principle suggested by a canonical decomposition result for Hidden Markov Model (HMM) inference, and a reconstruction module is built with elaborately designed Attention based Residual Dense Blocks (ARDBs). For visual content analysis, a new approach, named PSCC-Net, is proposed to detect and localize image manipulations. It consists of two paths: a top-down path that extracts the local and global features from an input image, and a bottom-up path that first distinguishes manipulated images from pristine ones via a detection head, and then localizes forged regions via a progressive mechanism, where manipulation masks are estimated from small scales to large ones, each serving as a prior of the next-scale estimation. Moreover, a Spatio-Channel Correlation Module (SCCM) is proposed to capture both spatial and channel-wise correlations among extracted features, enabling the network to cope with a wide range of manipulation attacks. Extensive experiments validate that the proposed methods in this thesis have achieved the SOTA results and partially addressed the existing issues in previous works. / Dissertation / Doctor of Philosophy (PhD)

<p>  </p> <p>The implementation of ASC 606 significantly changed revenue recognition practices for many firms in the capital market by increasing the level of discretion required to record revenue amounts. I examine the impact of this increase in discretion on revenue manipulation around external targets. I find that firms near analyst revenue targets have increased levels of discretionary revenues under ASC 606 relative to their peers, indicating increased revenue manipulation. Further analysis reveals that these increased levels of discretionary revenues are concentrated in firms characterized as having increased opportunity or incentive to use ASC 606 opportunistically to manipulate revenues upwards. Specifically, firms with more complex revenue operations and firms in earlier life cycles are associated with increased discretionary revenues under ASC 606. I also provide preliminary evidence of revenue manipulation varying systematically with industry characteristics. This paper is one of the first to provide evidence of capital market consequences stemming from ASC 606, while also highlighting the impact of firm characteristics on the choice to manage revenues.</p>