The Design of a Mechanical Assembly System

This thesis describes a mechanical assembly system called LAMA (Language for Automatic Mechanical Assembly). The goal of the work was to create a mechanical assembly system that transforms a high-level description of an automatic assembly operation into a program or execution by a computer controlled manipulator. This system allows the initial description of the assembly to be in terms of the desired effects on the parts being assembled. Languages such as WAVE [Bolles & Paul] and MINI [Silver] fail to meet this goal by requiring the assembly operation to be described in terms of manipulator motions. This research concentrates on the spatial complexity of mechanical assembly operations. The assembly problem is seen as the problem of achieving a certain set of geometrical constraints between basic objects while avoiding unwanted collisions. The thesis explores how these two facets, desired constraints and unwanted collisions, affect the primitive operations of the domain.

Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma

X. Wang, J. Yang, R. Jensen and X. Liu, 'Rough Set Feature Selection and Rule Induction for Prediction of Malignancy Degree in Brain Glioma,' Computer Methods and Programs in Biomedicine, vol. 83, no. 2, pp. 147-156, 2006.

Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning

Q. Meng and M. H. Lee, 'Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning', Journal of Intelligent and Robotic Systems, 48(1), pp 97-114, 2007.

Speculative Concurrency Control for Real-Time Databases

In this paper, we propose a new class of Concurrency Control Algorithms that is especially suited for real-time database applications. Our approach relies on the use of (potentially) redundant computations to ensure that serializable schedules are found and executed as early as possible, thus, increasing the chances of a timely commitment of transactions with strict timing constraints. Due to its nature, we term our concurrency control algorithms Speculative. The aforementioned description encompasses many algorithms that we call collectively Speculative Concurrency Control (SCC) algorithms. SCC algorithms combine the advantages of both Pessimistic and Optimistic Concurrency Control (PCC and OCC) algorithms, while avoiding their disadvantages. On the one hand, SCC resembles PCC in that conflicts are detected as early as possible, thus making alternative schedules available in a timely fashion in case they are needed. On the other hand, SCC resembles OCC in that it allows conflicting transactions to proceed concurrently, thus avoiding unnecessary delays that may jeopardize their timely commitment.

Hidden Type Variables and Conditional Extension for More Expressive Generic Programs

Generic object-oriented programming languages combine parametric polymorphism and nominal subtype polymorphism, thereby providing better data abstraction, greater code reuse, and fewer run-time errors. However, most generic object-oriented languages provide a straightforward combination of the two kinds of polymorphism, which prevents the expression of advanced type relationships. Furthermore, most generic object-oriented languages have a type-erasure semantics: instantiations of type parameters are not available at run time, and thus may not be used by type-dependent operations. This dissertation shows that two features, which allow the expression of many advanced type relationships, can be added to a generic object-oriented programming language without type erasure: 1. type variables that are not parameters of the class that declares them, and 2. extension that is dependent on the satisfiability of one or more constraints. We refer to the first feature as hidden type variables and the second feature as conditional extension. Hidden type variables allow: covariance and contravariance without variance annotations or special type arguments such as wildcards; a single type to extend, and inherit methods from, infinitely many instantiations of another type; a limited capacity to augment the set of superclasses after that class is defined; and the omission of redundant type arguments. Conditional extension allows the properties of a collection type to be dependent on the properties of its element type. This dissertation describes the semantics and implementation of hidden type variables and conditional extension. A sound type system is presented. In addition, a sound and terminating type checking algorithm is presented. Although designed for the Fortress programming language, hidden type variables and conditional extension can be incorporated into other generic object-oriented languages. Many of the same problems would arise, and solutions analogous to those we present would apply.

Adaptive Reliable Multicast

An increasing number of applications, such as distributed interactive simulation, live auctions, distributed games and collaborative systems, require the network to provide a reliable multicast service. This service enables one sender to reliably transmit data to multiple receivers. Reliability is traditionally achieved by having receivers send negative acknowledgments (NACKs) to request from the sender the retransmission of lost (or missing) data packets. However, this Automatic Repeat reQuest (ARQ) approach results in the well-known NACK implosion problem at the sender. Many reliable multicast protocols have been recently proposed to reduce NACK implosion. But, the message overhead due to NACK requests remains significant. Another approach, based on Forward Error Correction (FEC), requires the sender to encode additional redundant information so that a receiver can independently recover from losses. However, due to the lack of feedback from receivers, it is impossible for the sender to determine how much redundancy is needed. In this paper, we propose a new reliable multicast protocol, called ARM for Adaptive Reliable Multicast. Our protocol integrates ARQ and FEC techniques. The objectives of ARM are (1) reduce the message overhead due to NACK requests, (2) reduce the amount of data transmission, and (3) reduce the time it takes for all receivers to receive the data intact (without loss). During data transmission, the sender periodically informs the receivers of the number of packets that are yet to be transmitted. Based on this information, each receiver predicts whether this amount is enough to recover its losses. Only if it is not enough, that the receiver requests the sender to encode additional redundant packets. Using ns simulations, we show the superiority of our hybrid ARQ-FEC protocol over the well-known Scalable Reliable Multicast (SRM) protocol.

The VITEWRITE Model of Handwriting Production

This article describes the VITEWRITE model for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in the outflow command to a given synergy occurs. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. Each synergy exhibits a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.

A Neural Network Model for Cursive Script Production

This article describes a neural network model, called the VITEWRITE model, for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a. hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The proposed controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in a given synergy is achieved. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. The separate "score" of onset times used in most prior models is hereby replaced by a self-scaling activity-released "motor program" that uses few memory resources, enables each synergy to exhibit a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless. connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data concerning band movements, such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.

Combining and choosing case base maintenance algorithms

Case-Based Reasoning (CBR) uses past experiences to solve new problems. The quality of the past experiences, which are stored as cases in a case base, is a big factor in the performance of a CBR system. The system's competence may be improved by adding problems to the case base after they have been solved and their solutions verified to be correct. However, from time to time, the case base may have to be refined to reduce redundancy and to get rid of any noisy cases that may have been introduced. Many case base maintenance algorithms have been developed to delete noisy and redundant cases. However, different algorithms work well in different situations and it may be difficult for a knowledge engineer to know which one is the best to use for a particular case base. In this thesis, we investigate ways to combine algorithms to produce better deletion decisions than the decisions made by individual algorithms, and ways to choose which algorithm is best for a given case base at a given time. We analyse five of the most commonly-used maintenance algorithms in detail and show how the different algorithms perform better on different datasets. This motivates us to develop a new approach: maintenance by a committee of experts (MACE). MACE allows us to combine maintenance algorithms to produce a composite algorithm which exploits the merits of each of the algorithms that it contains. By combining different algorithms in different ways we can also define algorithms that have different trade-offs between accuracy and deletion. While MACE allows us to define an infinite number of new composite algorithms, we still face the problem of choosing which algorithm to use. To make this choice, we need to be able to identify properties of a case base that are predictive of which maintenance algorithm is best. We examine a number of measures of dataset complexity for this purpose. These provide a numerical way to describe a case base at a given time. We use the numerical description to develop a meta-case-based classification system. This system uses previous experience about which maintenance algorithm was best to use for other case bases to predict which algorithm to use for a new case base. Finally, we give the knowledge engineer more control over the deletion process by creating incremental versions of the maintenance algorithms. These incremental algorithms suggest one case at a time for deletion rather than a group of cases, which allows the knowledge engineer to decide whether or not each case in turn should be deleted or kept. We also develop incremental versions of the complexity measures, allowing us to create an incremental version of our meta-case-based classification system. Since the case base changes after each deletion, the best algorithm to use may also change. The incremental system allows us to choose which algorithm is the best to use at each point in the deletion process.

The role played by angiotensin (1-7) in the regulation of renal haemodynamics and excretory function in anesthetized rats

Initial studies have demonstrated that intra- renal infusion of Ang (1-7) caused a diuresis and natriuresis that was proportional to the degree of activation of the Renin Angiotensin Aldosterone System (RAAS). This raised the question as why the magnitude of this diuresis and natriuresis was compromised in rats receiving a high sodium diet (suppressed RAAS) and enhanced in low sodium fed rats (activated RAAS)? Could the answer lie with changes in intra-renal AT1 or Mas receptor expression? Interestingly, the observed Ang (1-7) induced increases in sodium and water excretion in rats receiving either a low or normal sodium diet were and blocked in the presence of the AT 1 receptor antagonist (Losartan) in the presence of the, 'Mas' receptor antagonist (A-779). These data suggest that both AT1 and 'Mas' receptors need to be functional in order to fully mediate the renal responses to intra-renal Ang (1-7) infusion. Importantly, further experimentation also revealed that there is a proportional relationship between AT 1 receptor expression in the rat renal cortex and the magnitude of the excretory actions of intra renal Ang (1-7) infusion, which is only partially dependent on the level of 'Mas' receptor expression. These observations suggest that although Ang (1-7) induced increases in sodium and water excretion are mediated by the Mas receptor, the magnitude of these excretory responses appear to be dependent upon the level of AT 1 receptor expression and more specifically Ang II/ AT 1 receptor signalling. Thus in rats receiving a low sodium diet, Ang (1-7) acts via the Mas receptor to inhibit Ang II/ AT 1 receptor signalling. In rats receiving a high sodium diet the down regulated AT 1 receptor expression implies a reduction in Ang II/ AT 1 receptor signalling which renders the counter-regulatory effects of intra-renal Ang (1-7) infusion redundant.

The relevance of bacterial isoprenoid biosynthetic pathways for host-microbe interactions

This thesis was undertaken to investigate the relevance of two bacterial isoprenoid biosynthetic pathways (Mevalonate (MVAL) and 2-C-methyl-D-erythritol 4-phosphate (MEP)) for host-microbe interactions. We determined a significant reduction in microbial diversity in the murine gut microbiota (by next generation sequencing) following oral administration of a common anti-cholesterol drug Rosuvastatin (RSV) that targets mammalian and bacterial HMG-CoA reductase (HMG-R) for inhibition of MVAL formation. In tandem we identified significant hepatic and intestinal off-target alterations to the murine metabolome indicating alterations in inflammation, bile acid profiles and antimicrobial peptide synthesis with implications on community structure of the gastrointestinal microbiota in statin-treated animals. However we found no effect on local Short Chain Fatty Acid biosynthesis (metabolic health marker in our model). We demonstrated direct inhibition of bacterial growth in-vitro by RSV which correlated with reductions in bacterial MVAL formation. However this was only at high doses of RSV. Our observations demonstrate a significant RSV-associated impact on the gut microbiota prompting similar human analysis. Successful deletion of another MVAL pathway enzyme (HMG-CoA synthase (mvaS)) involved in Listeria monocytogenes EGDe isoprenoid biosynthesis determined that the enzyme is non-essential for normal growth and in-vivo pathogenesis of this pathogen. We highlight potential evidence for alternative means of synthesis of the HMG-CoA substrate that could render mvaS activity redundant under our test conditions. Finally, we showed by global gene expression analysis (Massive Analysis of cDNA Ends (MACE RNA-seq) a significant role for the penultimate MEP pathway metabolite (E)-4-hydroxy-3-methyl-2-but-2-enyl pyrophosphate (HMBPP) in significant up regulation of genes of immunity and antigen presentation in THP-1 cells at nanomolar levels. We infected THP-1 cells with wild type or HMBPP under/over-producing L. monoctyogenes EGDe mutants and determined subtle effects of HMBPP upon overall host responses to Listeria infection. Overall our findings provide greater insights regarding bacterial isoprenoid biosynthetic pathways for host-microbe/microbe-host dialogue.

Neonatal EEG classification using atomic decomposition

The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.

High-coverage sequencing and annotated assemblies of the budgerigar genome.

BACKGROUND: Parrots belong to a group of behaviorally advanced vertebrates and have an advanced ability of vocal learning relative to other vocal-learning birds. They can imitate human speech, synchronize their body movements to a rhythmic beat, and understand complex concepts of referential meaning to sounds. However, little is known about the genetics of these traits. Elucidating the genetic bases would require whole genome sequencing and a robust assembly of a parrot genome. FINDINGS: We present a genomic resource for the budgerigar, an Australian Parakeet (Melopsittacus undulatus) -- the most widely studied parrot species in neuroscience and behavior. We present genomic sequence data that includes over 300× raw read coverage from multiple sequencing technologies and chromosome optical maps from a single male animal. The reads and optical maps were used to create three hybrid assemblies representing some of the largest genomic scaffolds to date for a bird; two of which were annotated based on similarities to reference sets of non-redundant human, zebra finch and chicken proteins, and budgerigar transcriptome sequence assemblies. The sequence reads for this project were in part generated and used for both the Assemblathon 2 competition and the first de novo assembly of a giga-scale vertebrate genome utilizing PacBio single-molecule sequencing. CONCLUSIONS: Across several quality metrics, these budgerigar assemblies are comparable to or better than the chicken and zebra finch genome assemblies built from traditional Sanger sequencing reads, and are sufficient to analyze regions that are difficult to sequence and assemble, including those not yet assembled in prior bird genomes, and promoter regions of genes differentially regulated in vocal learning brain regions. This work provides valuable data and material for genome technology development and for investigating the genomics of complex behavioral traits.

Emergent self-organisation of wireless sensor networks

This paper describes a protocol for dynamically configuring wireless sensor nodes into logical clusters. The concept is to be able to inject an overlay configuration into an ad-hoc network of sensor nodes or similar devices, and have the network configure itself organically. The devices are arbitrarily deployed and have initially have no information whatsoever concerning physical location, topology, density or neighbourhood. The Emergent Cluster Overlay (ECO) protocol is totally self-configuring and has several novel features, including nodes self-determining their mobility based on patterns of neighbour discovery, and that the target cluster size is specified externally (by the sensor network application) and is not directly coupled to radio communication range or node packing density. Cluster head nodes are automatically assigned as part of the cluster configuration process, at no additional cost. ECO is ideally suited to applications of wireless sensor networks in which localized groups of sensors act cooperatively to provide a service. This includes situations where service dilution is used (dynamically identifying redundant nodes to conserve their resources).

Cooperative communications in ultra-wideband wireless body area networks: Channel modeling and system diversity analysis

In this paper, we explore the application of cooperative communications in ultra-wideband (UWB) wireless body area networks (BANs), where a group of on-body devices may collaborate together to communicate with other groups of on-body equipment. Firstly, time-domain UWB channel measurements are presented to characterize the body-centric multipath channel and to facilitate the diversity analysis in a cooperative BAN (CoBAN). We focus on the system deployment scenario when the human subject is in the sitting posture. Important channel parameters such as the pathloss, power variation, power delay profile (PDP), and effective received power (ERP) crosscorrelation are investigated and statistically analyzed. Provided with the model preliminaries, a detailed analysis on the diversity level in a CoBAN is provided. Specifically, an intuitive measure is proposed to quantify the diversity gains in a single-hop cooperative network, which is defined as the number of independent multipaths that can be averaged over to detect symbols. As this measure provides the largest number of redundant copies of transmitted information through the body-centric channel, it can be used as a benchmark to access the performance bound of various diversity-based cooperative schemes in futuristic body sensor systems.