Linear Regression and Unsupervised Learning For Tracking and Embodied Robot Control.

Computer vision problems, such as tracking and robot navigation, tend to be solved using models of the objects of interest to the problem. These models are often either hard-coded, or learned in a supervised manner. In either case, an engineer is required to identify the visual information that is important to the task, which is both time consuming and problematic. Issues with these engineered systems relate to the ungrounded nature of the knowledge imparted by the engineer, where the systems have no meaning attached to the representations. This leads to systems that are brittle and are prone to failure when expected to act in environments not envisaged by the engineer. The work presented in this thesis removes the need for hard-coded or engineered models of either visual information representations or behaviour. This is achieved by developing novel approaches for learning from example, in both input (percept) and output (action) spaces. This approach leads to the development of novel feature tracking algorithms, and methods for robot control. Applying this approach to feature tracking, unsupervised learning is employed, in real time, to build appearance models of the target that represent the input space structure, and this structure is exploited to partition banks of computationally efficient, linear regression based target displacement estimators. This thesis presents the first application of regression based methods to the problem of simultaneously modeling and tracking a target object. The computationally efficient Linear Predictor (LP) tracker is investigated, along with methods for combining and weighting flocks of LP’s. The tracking algorithms developed operate with accuracy comparable to other state of the art online approaches and with a significant gain in computational efficiency. This is achieved as a result of two specific contributions. First, novel online approaches for the unsupervised learning of modes of target appearance that identify aspects of the target are introduced. Second, a general tracking framework is developed within which the identified aspects of the target are adaptively associated to subsets of a bank of LP trackers. This results in the partitioning of LP’s and the online creation of aspect specific LP flocks that facilitate tracking through significant appearance changes. Applying the approach to the percept action domain, unsupervised learning is employed to discover the structure of the action space, and this structure is used in the formation of meaningful perceptual categories, and to facilitate the use of localised input-output (percept-action) mappings. This approach provides a realisation of an embodied and embedded agent that organises its perceptual space and hence its cognitive process based on interactions with its environment. Central to the proposed approach is the technique of clustering an input-output exemplar set, based on output similarity, and using the resultant input exemplar groupings to characterise a perceptual category. All input exemplars that are coupled to a certain class of outputs form a category - the category of a given affordance, action or function. In this sense the formed perceptual categories have meaning and are grounded in the embodiment of the agent. The approach is shown to identify the relative importance of perceptual features and is able to solve percept-action tasks, defined only by demonstration, in previously unseen situations. Within this percept-action learning framework, two alternative approaches are developed. The first approach employs hierarchical output space clustering of point-to-point mappings, to achieve search efficiency and input and output space generalisation as well as a mechanism for identifying the important variance and invariance in the input space. The exemplar hierarchy provides, in a single structure, a mechanism for classifying previously unseen inputs and generating appropriate outputs. The second approach to a percept-action learning framework integrates the regression mappings used in the feature tracking domain, with the action space clustering and imitation learning techniques developed in the percept-action domain. These components are utilised within a novel percept-action data mining methodology, that is able to discover the visual entities that are important to a specific problem, and to map from these entities onto the action space. Applied to the robot control task, this approach allows for real-time generation of continuous action signals, without the use of any supervision or definition of representations or rules of behaviour

Effects of crosstalk in WDM optical label switching networks due to wavelength switching of a tunable laser

rosstalk caused by switching events in fast tunable lasers in an optical label switching (OLS) system is investigated for the first time. A wavelength-division-multiplexed OLS system based on subcarrier multiplexed labels is presented which employs a 40-Gb/s duobinary payload and a 155-Mb/s label on a 40-GHz subcarrier. Degradation in system performance as the transmitters switch between different channels is then characterized in terms of the frequency drift of the tunable laser

Time-resolved linewidth measurements of a wavelength switched SG-DBR laser for optical packet switched networks

We report time-resolved linewidth measurements of different sizes of optical packets under wavelength-switching for the first time and show that laser linewidth is significantly broadened during switching transition requiring ~3 mus to attain its minimum value

Time-resolved Q-factor measurement and its application in performance analysis of 42.6 Gbit/s packets generated by SGDBR lasers

We demonstrate a novel time-resolved Q-factor measurement technique and demonstrate its application in the analysis of optical packet switching systems with high information spectral density. For the first time, we report the time-resolved Q-factor measurement of 42.6 Gbit/s AM-PSK and DQPSK modulated packets, which were generated by a SGDBR laser under wavelength switching. The time dependent degradation of Q-factor performance during the switching transient was analyzed and was found to be correlated with different laser switching characteristics in each case

Cost efficient narrow linewidth laser transmitter for coherent detection

Authors present a cost efficient narrow linewidth laser transmitter for future coherent detection systems. The spectral purity of the laser allows the phase modulation of data signals at bit rates as low as 155 Mb/s

Fast switching tunable laser sources for wavelength division multiplexing in passive optical access networks

Tunable laser structures with nanosecond switching time between wavelength channels and low-power injection locking are demonstrated on a low-cost platform. These lasers are suitable as source or slave lasers in WDM passive optical access networks

Optimum bias point in broadband subcarrier multiplexing with optical IQ modulators

This paper develops a theoretical analysis of the tradeoff between carrier suppression and nonlinearities induced by optical IQ modulators in direct-detection subcarrier multiplexing systems. The tradeoff is obtained by examining the influence of the bias conditions of the modulator on the transmitted single side band signal. The frequency components in the electric field and the associated photocurrent at the output of the IQ modulator are derived mathematically. For any frequency plan, the optimum bias point can be identified by calculating the sensitivity gain for every subchannel. A setup composed of subcarriers located at multiples of the data rate ensures that the effects of intermodulation distortion are studied in the most suitable conditions. Experimental tests with up to five QPSK electrical subchannels are performed to verify the mathematical model and validate the predicted gains in sensitivity

Impact of band rejection in multichannel broadband subcarrier multiplexing

This paper explores experimentally the impairments in performance that are generated when multiple single-sideband (SSB) subcarrier multiplexing (SCM) signals are closely allocated in frequency to establish a spectrally efficient wavelength division multiplexing (WDM) link. The performance of cost-effective SSB WDM/ SCM implementations, without optical filters in the transmitter, presents a strong dependency on the imperfect sideband suppression ratio that can be directly achieved with the electro-optical modulator. A direct detected broadband multichannel SCM link composed of a state-of-the-art optical IQ modulator and five quadrature phase-shift keyed (QPSK) subcarriers per optical channel is presented, showing that a suppression ratio of 20 dB obtained directly with the modulator produced a penalty of 2 dB in overall performance, due to interference between adjacent optical channels

Calculation of receiver sensitivities in (orthogonal) subcarrier multiplexing microwave-optical links

Microwave-based all-analogue (orthogonal) subcarrier multiplexing (SCM) permits a direct processing of baseband data at Gbit/s while achieving low power consumption, low latency, low cost, and tolerance to dispersion. A key figure of merit in any SCM link is the sensitivity in the receiver, which depends on the transmitter, the link and the receiver. By analysing the impact of the nonlinearities of an optical IQ modulator in the presence of optical noise, sensitivities are mathematically estimated as a function of the optical modulation index (OMI) at the transmitter. The results are verified with simulations achieving a good agreement with the mathematical model. The theoretical model provided can be employed as a tool to predict the best achievable sensitivities and the optimum OMI in broadband SCM and orthogonal SCM links

The emergence of topographic steady state in a perpetually dynamic self-organized critical landscape

We conducted a series of four physical modeling experiments of mountain growth at differing rates of uplift and three distinct climates ranging from relatively wet to relatively dry. The spatial and temporal pattern of landscape behavior is characterized by ∼f−1 scaling in sediment discharge and power law scaling in the magnitude and frequency of ridge movement in all four experiments. We find that internally generated self-organized critical (SOC) processes generate dynamically stable catchment geometries after ∼1 relief depths of erosion: these regularly spaced catchments have an average outlet-spacing ratio of 2.16, well within the range of values reported in field studies. Once formed, large catchment bounding ridges oscillate about a critically balanced mean location, with occasional large-scale changes in catchment size. Ridge movement appears to be driven by the competition for discharge as landslides push ridges back and forth. These dynamics lead to the emergence of a complex twofold scaling in catchment dynamics that is fully established by 1.8 relief depths of erosion; at this stage, a clear threshold has emerged separating two distinct scaling regimes, where large ridge mobility is insensitive to relief and small ridge mobility is relief dependent. Overall, we demonstrate that the development of dynamically stable large-scale landforms is related to the emergence of a complex-system hierarchy in topographic dynamics. Once formed, these landscapes do not evolve; statistical properties such as average topography and discharge become stationary while topography remains highly dynamic at smaller length scales