This dissertation presents two lines of research that are superficially at opposite ends of the computational neuroscience spectrum. While models of adaptive motion detection in fruit flies and simulations inspired by monkeys that learn to control brain machine interfaces might seem like they have little in common, these projects both attempt to address the broad question of how real neural circuits flexibly compute. Sensory systems flexibly adapt their processing properties across a wide range of environmental and behavioral conditions. Such variable processing complicates attempts to extract mechanistic understanding of sensory computations. This is evident in the highly constrained, canonical Drosophila motion detection circuit, where the core computation underlying direction selectivity is still debated despite extensive studies. The first part of this dissertation analyzes the filtering properties of four neural inputs to the OFF motion-detecting T5 cell in Drosophila. These four neurons, Tm1, Tm2, Tm4 and Tm9, exhibit state- and stimulus-dependent changes in the shape of their temporal responses, which become more biphasic under specific conditions. Summing these inputs within the framework of a connectomic-constrained model of the circuit demonstrates that these shapes are sufficient to explain T5 responses to various motion stimuli. Thus, the stimulus- and state-dependent measurements reconcile motion computation with the anatomy of the circuit. These findings provide a clear example of how a basic circuit supports flexible sensory computation. The most flexible neural circuits are circuits that can learn. Despite extensive theoretical work on biologically plausible learning rules, however, it has been difficult to obtain clear evidence about whether and how such rules are implemented in the brain. In the second part of this dissertation, I consider biologically plausible supervised- and reinforcement-learning rules and ask whether biased changes in network activity during learning can be used to determine which learning rule is being used. Supervised learning requires a credit-assignment model estimating the mapping from neural activity to behavior, and, in a biological organism, this model will inevitably be an imperfect approximation of the ideal mapping, leading to a bias in the direction of the weight updates relative to the true gradient. Reinforcement learning, on the other hand, requires no credit-assignment model and tends to make weight updates following the true gradient direction. I derive a metric to distinguish between learning rules by observing biased changes in the network activity during learning, given that the mapping from brain to behavior is known by the experimenter. Because brain-machine interface (BMI) experiments allow for perfect knowledge of this mapping, I focus on modeling a cursor-control BMI task using recurrent neural networks, and show that learning rules can be distinguished in simulated experiments using only observations that a neuroscience experimenter would plausibly have access to.

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Thesis (MSc)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: Monocular structure from motion (SfM) is a widely researched problem, however many of the existing approaches prove to be too computationally expensive for use on mobile devices. In this thesis we investigate how inertial sensors can be used to increase the performance of SfM algorithms on mobile devices. Making use of the low cost inertial sensors found on most mobile devices we design and implement an extended Kalman filter (EKF) to exploit their complementary nature, in order to produce an accurate estimate of the attitude of the device. We make use of a quaternion based system model in order to linearise the measurement stage of the EKF, thus reducing its computational complexity. We use this attitude estimate to enhance the feature tracking and camera localisation stages in our SfM pipeline. In order to perform feature tracking we implement a hybrid tracking algorithm which makes use of Harris corners and an approximate nearest neighbour search to reduce the search space for possible correspondences. We increase the robustness of this approach by using inertial information to compensate for inter-frame camera rotation. We further develop an efficient bundle adjustment algorithm which only optimises the pose of the previous three key frames and the 3D map points common between at least two of these frames. We implement an optimisation based localisation algorithm which makes use of our EKF attitude estimate and the tracked features, in order to estimate the pose of the device relative to the 3D map points. This optimisation is performed in two steps, the first of which optimises only the translation and the second optimises the full pose. We integrate the aforementioned three sub-systems into an inertial assisted pose estimation pipeline. We evaluate our algorithms with the use of datasets captured on the iPhone 5 in the presence of a Vicon motion capture system for ground truth data. We find that our EKF can estimate the device’s attitude with an average dynamic accuracy of ±5°. Furthermore, we find that the inclusion of sensors into the visual pose estimation pipeline can lead to improvements in terms of robustness and computational efficiency of the algorithms and are unlikely to negatively affect the accuracy of such a system. Even though we managed to reduce execution time dramatically, compared to typical existing techniques, our full system is found to still be too computationally expensive for real-time performance and currently runs at 3 frames per second, however the ever improving computational power of mobile devices and our described future work will lead to improved performance. From this study we conclude that inertial sensors make a valuable addition into a visual pose estimation pipeline implemented on a mobile device. / AFRIKAANSE OPSOMMING: Enkel-kamera struktuur-vanaf-beweging (structure from motion, SfM) is ’n bekende navorsingsprobleem, maar baie van die bestaande benaderings is te berekeningsintensief vir gebruik op mobiele toestelle. In hierdie tesis ondersoek ons hoe traagheidsensors gebruik kan word om die prestasie van SfM algoritmes op mobiele toestelle te verbeter. Om van die lae-koste traagheidsensors wat op meeste mobiele toestelle gevind word gebruik te maak, ontwerp en implementeer ons ’n uitgebreide Kalman filter (extended Kalman filter, EKF) om hul komplementêre geaardhede te ontgin, en sodoende ’n akkurate skatting van die toestel se postuur te verkry. Ons maak van ’n kwaternioon-gebaseerde stelselmodel gebruik om die meetstadium van die EKF te lineariseer, en so die berekeningskompleksiteit te verminder. Hierdie afskatting van die toestel se postuur word gebruik om die fases van kenmerkvolging en kameralokalisering in ons SfM proses te verbeter. Vir kenmerkvolging implementeer ons ’n hibriede volgingsalgoritme wat gebruik maak van Harris-hoekpunte en ’n benaderde naaste-buurpunt-soektog om die soekruimte vir moontlike ooreenstemmings te verklein. Ons verhoog die robuustheid van hierdie benadering, deur traagheidsinligting te gebruik om vir kamerarotasies tussen raampies te kompenseer. Verder ontwikkel ons ’n doeltreffende bondelaanpassingsalgoritme wat slegs optimeer oor die vorige drie sleutelraampies, en die 3D punte gemeenskaplik tussen minstens twee van hierdie raampies. Ons implementeer ’n optimeringsgebaseerde lokaliseringsalgoritme, wat gebruik maak van ons EKF se postuurafskatting en die gevolgde kenmerke, om die posisie en oriëntasie van die toestel relatief tot die 3D punte in die kaart af te skat. Die optimering word in twee stappe uitgevoer: eerstens net oor die kamera se translasie, en tweedens oor beide die translasie en rotasie. Ons integreer die bogenoemde drie sub-stelsels in ’n pyplyn vir postuurafskatting met behulp van traagheidsensors. Ons evalueer ons algoritmes met die gebruik van datastelle wat met ’n iPhone 5 opgeneem is, terwyl dit in die teenwoordigheid van ’n Vicon bewegingsvasleggingstelsel was (vir die gelyktydige opneming van korrekte postuurdata). Ons vind dat die EKF die toestel se postuur kan afskat met ’n gemiddelde dinamiese akkuraatheid van ±5°. Verder vind ons dat die insluiting van sensors in die visuele postuurafskattingspyplyn kan lei tot verbeterings in terme van die robuustheid en berekeningsdoeltreffendheid van die algoritmes, en dat dit waarskynlik nie die akkuraatheid van so ’n stelsel negatief beïnvloed nie. Al het ons die uitvoertyd drasties verminder (in vergelyking met tipiese bestaande tegnieke) is ons volledige stelsel steeds te berekeningsintensief vir intydse verwerking op ’n mobiele toestel en hardloop tans teen 3 raampies per sekonde. Die voortdurende verbetering van mobiele toestelle se berekeningskrag en die toekomstige werk wat ons beskryf sal egter lei tot ’n verbetering in prestasie. Uit hierdie studie kan ons aflei dat traagheidsensors ’n waardevolle toevoeging tot ’n visuele postuurafskattingspyplyn kan maak.

People are increasingly recognizing how their health affects their quality of life, and health is most easily tracked through the use of wearable devices. The goal of this study is to detect and monitor human motion via gait analysis to provide information that will help people enhance their health. After reviewing a range of wearable health-tracking devices, the shoe has been chosen as one of the best device for observing human motion. / Most measurement systems currently used for motion and gait detection are disadvantaged in that they monitor and analyze motion in limited environments and not in real time. Hence, they cannot be used for long-term monitoring and detection. The design of a new, inexpensive, compact and lightweight shoe-integrated platform is elaborated in this thesis. The intelligent shoe system comprises a suite of sensors, a microprocessor board and a wireless communication module. This ideal platform requires no specialized equipment or lab setup, meaning data can be collected not only in the narrow confines of a research lab, but essentially anywhere, whether indoors or outdoors. / Our everyday lives are shaped by a wide variety of motions, some of which can cause injury. Injuries suffered due to falls account for a significant portion of accidents and immediate help should be provided. The intelligent shoe system offers an approach of detecting the user’s motion, especially the movement and direction of a fall. This study used principle component analysis (PCA) to decrease the number of sensors in the prototype (eight pairs) by half (four pairs), so as to reduce computational cost and enhance real-time performance. The resultant system can learn the patterns necessary to detect fall directions from abundant tilted-standing data instead of actual fall data. / Fatigue can result in an abnormal gait, making injury more probable. Hence, detecting fatigue is very important. Experiments have been conducted to determine the relationship between fatigue and gait, and the resultant data are used to analyze the relationship between force information and foot attitude. These findings can help a user detect fatigue and avoid injury. / People carry various kinds of loads in their daily lives, and long-term load-bearing activities can result in motion deformation. Another objective of this study is to determine a load-carrying approach that will decrease such deformation to a great extent. Resampling is used to partition the related data cycle by cycle. A support vector machine (SVM) is adopted to model a user’s normal walking gait and abnormal gaits without loads, which allows for the determination of whether a gait is normal when a load is carried. / To enhance overall health, exercise is commonly adopted, but many forms of exercise are dull. The proposed system’s shoe-computer interface not only helps people obtain detailed lower-body feedback, but can also be used to promote everyday exercise. People are analyzed while sitting for long periods in the workplace, and two interfaces are designed as a result: the shoe-keyboard, in which the feet are used to type words into a computer, and the shoe-write system, in which the foot is used like a hand to write on the ground, with the words displayed on the computer screen. Both of these applications use back-propagation (BP) networks to classify the motions involved. The shoe-keyboard is based on logical coding to map the motion-to-word relationship, and the shoe-write system incorporates an optical tracker to translate motion into information. / 人們現在越來越重視自己的生活質量，而健康方面是最為重要的。穿戴式設備是最好容易使用的檢測健康的設備。本文的目標是通過智能鞋，來檢測步態，對其進行分析和預測，已達到檢測和提高人們的健康水平的要求。 / 現在絕大多數的步態運動檢測系統都不是實時的且長時間工作的。在研究中，基於鞋子的智能系統被提出并得以實現，其具有便宜，緊湊，輕便等的優點。該系統包括壓力和加速度傳感器，處理芯片和無線傳輸模塊。這種設計將滿足日常步態信息的採集，并且把環境影響的因素放置最小，以達到室內室外都可長時間連續實時監測的要求。 / 在本論文中，對一系列日常生活的行為進行檢測和分類，尤其是最為危險的摔倒。本系統通過採用主成分分析，對已有的壓力傳感器進行的了分析，在保證了預測的準確性的前提下，將壓力傳感器由8對減少至4對，大大的降低了運算的次數，使得該系統實時性更好。同時本系統通過傾斜站立獲得的數據并應用于跌倒方向的檢測，並且有著良好的結果。 / 在本文中，對疲勞步態進行的分析，通過設計實驗，來區分不同疲勞程度下人們的步態。壓力信號較為明顯，同時加速度反映出每一步的劇烈程度。最終結果表明，壓力和加速度相輔相成，與疲勞程度的關係也很明顯，基於這種關係，本系統對疲勞程度進行了預測，通過壓力傳感器的信號，預測疲勞的程度，實驗結果也較為理想。 / 長時間的負責對身體的負擔很大，在本文中，著重的分析了在不同負重方式下，步態的變化，並且通過對比正常步態，採用支持向量機進行分類。在分類的過程中，通過重新採樣，將採集的數據轉變為一步為一組的數據，進行分類，最終得到的結果表明，平衡狀態下的負重是最好的。 / 對於健康而言，除了檢查受傷和疲勞，提升自身的身體素質也尤為重要。在本文中，介紹了兩種基於智能鞋的應用，在鍛煉下肢靈活度的同時，也避免了鍛煉的無聊。智能鞋鍵盤是通過腳踝的運動，基於一定的編碼方式，已達到在電腦上輸入文字的方式。智能鞋寫字系統是通過對下腳點的定位結合光電傳感器記錄位移，最終獲得文字輸入。應用BP神經網絡，對腳下點進行了分類，結合壓力傳感器和腿部建模，可以準確的區分30個的基本點的位置，從而獲得每筆的起點。最終完成寫字輸入。 / Tao, Yanbo. / Thesis Ph.D. Chinese University of Hong Kong 2014. / Includes bibliographical references (leaves 134-141). / Abstracts also in Chinese. / Title from PDF title page (viewed on 12, October, 2016). / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only.

In this dissertation, we propose a universal motion-based control framework that supports general functionalities on 2D and 3D user interfaces with a single integrated design. We develop a hybrid framework of optical and inertial sensing technologies to track 6-DOF (degrees of freedom) motion of a handheld device, which includes the explicit 6-DOF (position and orientation in the global coordinates) and the implicit 6-DOF (acceleration and angular speed in the device-wise coordinates). Motion recognition is another key function of the universal motion-based control and contains two parts: motion gesture recognition and air-handwriting recognition. The interaction technique of each task is carefully designed to follow a consistent mental model and ensure the usability. The universal motion-based control achieves seamless integration of 2D and 3D interactions, motion gestures, and air-handwriting. Motion recognition by itself is a challenging problem. For motion gesture recognition, we propose a normalization procedure to effectively address the large in-class motion variations among users. The main contribution is the investigation of the relative effectiveness of various feature dimensions (of tracking signals) for motion gesture recognition in both user-dependent and user-independent cases. For air-handwriting recognition, we first develop a strategy to model air-handwriting with basic elements of characters and ligatures. Then, we build word-based and letter-based decoding word networks for air-handwriting recognition. Moreover, we investigate the detection and recognition of air-fingerwriting as an extension to air-handwriting. To complete the evaluation of air-handwriting, we conduct usability study to support that air-handwriting is suitable for text input on a motion-based user interface.

A suite of signal processing algorithms designed for extracting information from brain electrophysiology and movement signals, along with new insights gained by applying these tools to understanding parkinsonism, were presented in this dissertation. The approach taken does not assume any particular stimulus, underlying activity, or synchronizing event, nor does it assume any particular encoding scheme. Instead, novel signal processing applications of complex continuous wavelet transforms, cross-frequency-coupling, feature selection, and canonical correlation were developed to discover the most significant electrophysiologic changes in the basal ganglia and cortex of parkinsonian rhesus monkeys and how these changes are related to the motor signs of parkinsonism. The resulting algorithms effectively characterize the severity of parkinsonism and, when combined with motor signal decoding algorithms, allow technology-assisted multi-modal grading of the primary pathological signs. Based on these results, parallel data collection algorithms were implemented in real-time embedded software and off-the-shelf hardware to develop a new system to facilitate monitoring of the severity of Parkinson's disease signs and symptoms in human patients. Off -line analysis of data collected with the system was subsequently shown to allow discrimination between normal and simulated parkinsonian conditions. The main contributions of the work were in three areas: 1) Evidence of the importance of optimally selecting multiple, non-redundant features for understanding neural information, 2) Discovery of signi ficant correlations between certain pathological motor signs and brain electrophysiology in different brain regions, and 3) Implementation and human subject testing of multi-modal monitoring technology.

This dissertation deals with the challenge of power utility in South Africa which is on proactive detection of fallen power line conductors and real time sagging measurement together with slipping of such conductors. Various methods which are currently used for sag detection were characterized and evaluated to the aim of the research. A mathematical reconstruction done to estimate the lowest point of the conductor in a span is presented. Practical simulations and application of radio frequency identification (RFID) for sag detection is attempted through matlab software. RFID radar system is then analyzed in different modes and found to give precision measurement for sag in real time as opposed to global positioning system (GPS) if one dimension of the tag assumed fixed on the power line. Lastly errors detected on the measurements are corrected using a trainable artificial neural network. A conclusion is made by making recommendations in the advancement of the research. / Electrical Engineering / M. Tech. (Electrical Engineering)