Using virtual reality modelling in cultural management, archiving and research

This paper outlines how the Ortelia project’s 3D virtual reality models have the capacity to assist our understanding of sites of cultural heritage. The VR investigation of such spaces can be a valuable tool in 'real world' empirical research in theatre and spatiality. Through a demonstration of two of Ortelia's VR models (an art gallery and a theatre), we suggest how we might consider interpreting cultural space and sites as contributing significantly to cultural capital. We also introduce the potential for human interaction in such venues through motion-capture to discuss the potential for assessing how humans interact in such contexts.

Data collection, storage and retrieval with an underwater sensor network

In this paper we present a novel platform for underwater sensor networks to be used for long-term monitoring of coral reefs and �sheries. The sensor network consists of static and mobile underwater sensor nodes. The nodes communicate point-to-point using a novel high-speed optical communication system integrated into the TinyOS stack, and they broadcast using an acoustic protocol integrated in the TinyOS stack. The nodes have a variety of sensing capabilities, including cameras, water temperature, and pressure. The mobile nodes can locate and hover above the static nodes for data muling, and they can perform network maintenance functions such as deployment, relocation, and recovery. In this paper we describe the hardware and software architecture of this underwater sensor network. We then describe the optical and acoustic networking protocols and present experimental networking and data collected in a pool, in rivers, and in the ocean. Finally, we describe our experiments with mobility for data muling in this network.

Using virtual reality modelling in cultural management, archiving and research

This paper outlines how the Ortelia project’s 3D virtual reality models have the capacity to assist our understanding of sites of cultural heritage. The VR investigation of such spaces can be a valuable tool in 'real world' empirical research in theatre and spatiality. Through a demonstration of two of Ortelia's VR models (an art gallery and a theatre), we suggest how we might consider interpreting cultural space and sites as contributing significantly to cultural capital. We also introduce the potential for human interaction in such venues through motion-capture to discuss the potential for assessing how humans interact in such contexts.

An analysis of ranking principles and retrieval strategies

The assumptions underlying the Probability Ranking Principle (PRP) have led to a number of alternative approaches that cater or compensate for the PRP’s limitations. All alternatives deviate from the PRP by incorporating dependencies. This results in a re-ranking that promotes or demotes documents depending upon their relationship with the documents that have been already ranked. In this paper, we compare and contrast the behaviour of state-of-the-art ranking strategies and principles. To do so, we tease out analytical relationships between the ranking approaches and we investigate the document kinematics to visualise the effects of the different approaches on document ranking.

An analysis of ranking principles and retrieval strategies

The assumptions underlying the Probability Ranking Principle (PRP) have led to a number of alternative approaches that cater or compensate for the PRP’s limitations. All alternatives deviate from the PRP by incorporating dependencies. This results in a re-ranking that promotes or demotes documents depending upon their relationship with the documents that have been already ranked. In this paper, we compare and contrast the behaviour of state-of-the-art ranking strategies and principles. To do so, we tease out analytical relationships between the ranking approaches and we investigate the document kinematics to visualise the effects of the different approaches on document ranking.

Brain activity during the encoding, retention, and retrieval of stimulus representations

Studies of delayed nonmatching-to-sample (DNMS) performance following lesions of the monkey cortex have revealed a critical circuit of brain regions involved in forming memories and retaining and retrieving stimulus representations. Using event-related functional magnetic resonance imaging (fMRI), we measured brain activity in 10 healthy human participants during performance of a trial-unique visual DNMS task using novel barcode stimuli. The event-related design enabled the identification of activity during the different phases of the task (encoding, retention, and retrieval). Several brain regions identified by monkey studies as being important for successful DNMS performance showed selective activity during the different phases, including the mediodorsal thalamic nucleus (encoding), ventrolateral prefrontal cortex (retention), and perirhinal cortex (retrieval). Regions showing sustained activity within trials included the ventromedial and dorsal prefrontal cortices and occipital cortex. The present study shows the utility of investigating performance on tasks derived from animal models to assist in the identification of brain regions involved in human recognition memory.

Scalable document hashing and retrieval

This thesis studies document signatures, which are small representations of documents and other objects that can be stored compactly and compared for similarity. This research finds that document signatures can be effectively and efficiently used to both search and understand relationships between documents in large collections, scalable enough to search a billion documents in a fraction of a second. Deliverables arising from the research include an investigation of the representational capacity of document signatures, the publication of an open-source signature search platform and an approach for scaling signature retrieval to operate efficiently on collections containing hundreds of millions of documents.

Storage and retrieval of individual genomes

A repetitive sequence collection is one where portions of a base sequence of length n are repeated many times with small variations, forming a collection of total length N. Examples of such collections are version control data and genome sequences of individuals, where the differences can be expressed by lists of basic edit operations. Flexible and efficient data analysis on a such typically huge collection is plausible using suffix trees. However, suffix tree occupies O(N log N) bits, which very soon inhibits in-memory analyses. Recent advances in full-text self-indexing reduce the space of suffix tree to O(N log σ) bits, where σ is the alphabet size. In practice, the space reduction is more than 10-fold, for example on suffix tree of Human Genome. However, this reduction factor remains constant when more sequences are added to the collection. We develop a new family of self-indexes suited for the repetitive sequence collection setting. Their expected space requirement depends only on the length n of the base sequence and the number s of variations in its repeated copies. That is, the space reduction factor is no longer constant, but depends on N / n. We believe the structures developed in this work will provide a fundamental basis for storage and retrieval of individual genomes as they become available due to rapid progress in the sequencing technologies.

A PPCA based non-parametric modeling and retrieval of PD signal buried in excessive noise

The problem of on-line recognition and retrieval of relatively weak industrial signals such as partial discharges (PD), buried in excessive noise, has been addressed in this paper. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI) due to the overlapping broad band frequency spectrum of PI and PD pulses. Therefore, on-line, onsite, PD measurement is hardly possible in conventional frequency based DSP techniques. The observed PD signal is modeled as a linear combination of systematic and random components employing probabilistic principal component analysis (PPCA) and the pdf of the underlying stochastic process is obtained. The PD/PI pulses are assumed as the mean of the process and modeled instituting non-parametric methods, based on smooth FIR filters, and a maximum aposteriori probability (MAP) procedure employed therein, to estimate the filter coefficients. The classification of the pulses is undertaken using a simple PCA classifier. The methods proposed by the authors were found to be effective in automatic retrieval of PD pulses completely rejecting PI.