Neural networks for time-varying data

This paper reviews some basic issues and methods involved in using neural networks to respond in a desired fashion to a temporally-varying environment. Some popular network models and training methods are introduced. A speech recognition example is then used to illustrate the central difficulty of temporal data processing: learning to notice and remember relevant contextual information. Feedforward network methods are applicable to cases where this problem is not severe. The application of these methods are explained and applications are discussed in the areas of pure mathematics, chemical and physical systems, and economic systems. A more powerful but less practical algorithm for temporal problems, the moving targets algorithm, is sketched and discussed. For completeness, a few remarks are made on reinforcement learning.

Time-dependent evolution of tissue markers by MALDI-MS imaging

We have used MALDI-MS imaging (MALDI-MSI) to monitor the time dependent appearance and loss of signals when tissue slices are brought rapidly to room temperature for short to medium periods of time. Sections from mouse brain were cut in a cryostat microtome, placed on a MALDI target and allowed to warm to room temperature for 30 s to 3 h. Sections were then refrozen, fixed by ethanol treatment and analysed by MALDI-MSI. The intensity of a range of markers were seen to vary across the time course, both increasing and decreasing, with the intensity of some markers changing significantly within 30 s and markers also showed tissue location specific evolution. The markers resulting from this autolysis were compared directly to those that evolved in a comparable 16 h on-tissue trypsin digest, and the markers that evolved in the two studies were seen to be substantially different. These changes offer an important additional level of location-dependent information for mapping changes and seeking disease-dependent biomarkers in the tissue. They also indicate that considerable care is required to allow comparison of biomarkers between MALDI-MSI experiments and also has implications for the standard practice of thaw-mounting multiple tissue sections onto MALDI-MS targets.

Automatic processing, quality assurance and serving of real-time weather data over lightweight protocols

Recent advances in technology have produced a significant increase in the availability of free sensor data over the Internet. With affordable weather monitoring stations now available to individual meteorology enthusiasts a reservoir of real time data such as temperature, rainfall and wind speed can now be obtained for most of the United States and Europe. Despite the abundance of available data, obtaining useable information about the weather in your local neighbourhood requires complex processing that poses several challenges. This paper discusses a collection of technologies and applications that harvest, refine and process this data, culminating in information that has been tailored toward the user. In this case we are particularly interested in allowing a user to make direct queries about the weather at any location, even when this is not directly instrumented, using interpolation methods. We also consider how the uncertainty that the interpolation introduces can then be communicated to the user of the system, using UncertML, a developing standard for uncertainty representation.

Watermarking biomedical time series data

This thesis addresses the problem of information hiding in low dimensional digital data focussing on issues of privacy and security in Electronic Patient Health Records (EPHRs). The thesis proposes a new security protocol based on data hiding techniques for EPHRs. This thesis contends that embedding of sensitive patient information inside the EPHR is the most appropriate solution currently available to resolve the issues of security in EPHRs. Watermarking techniques are applied to one-dimensional time series data such as the electroencephalogram (EEG) to show that they add a level of confidence (in terms of privacy and security) in an individual’s diverse bio-profile (the digital fingerprint of an individual’s medical history), ensure belief that the data being analysed does indeed belong to the correct person, and also that it is not being accessed by unauthorised personnel. Embedding information inside single channel biomedical time series data is more difficult than the standard application for images due to the reduced redundancy. A data hiding approach which has an in built capability to protect against illegal data snooping is developed. The capability of this secure method is enhanced by embedding not just a single message but multiple messages into an example one-dimensional EEG signal. Embedding multiple messages of similar characteristics, for example identities of clinicians accessing the medical record helps in creating a log of access while embedding multiple messages of dissimilar characteristics into an EPHR enhances confidence in the use of the EPHR. The novel method of embedding multiple messages of both similar and dissimilar characteristics into a single channel EEG demonstrated in this thesis shows how this embedding of data boosts the implementation and use of the EPHR securely.

Placenta growth factor-1 exerts time-dependent stabilization of adherens junctions following VEGF-induced vascular permeability

Increased vascular permeability is an early event characteristic of tissue ischemia and angiogenesis. Although VEGF family members are potent promoters of endothelial permeability the role of placental growth factor (PlGF) is hotly debated. Here we investigated PlGF isoforms 1 and 2 and present in vitro and in vivo evidence that PlGF-1, but not PlGF-2, can inhibit VEGF-induced permeability but only during a critical window post-VEGF exposure. PlGF-1 promotes VE-cadherin expression via the trans-activating Sp1 and Sp3 interaction with the VE-cadherin promoter and subsequently stabilizes transendothelial junctions, but only after activation of endothelial cells by VEGF. PlGF-1 regulates vascular permeability associated with the rapid localization of VE-cadherin to the plasma membrane and dephosphorylation of tyrosine residues that precedes changes observed in claudin 5 tyrosine phosphorylation and membrane localization. The critical window during which PlGF-1 exerts its effect on VEGF-induced permeability highlights the importance of the translational significance of this work in that PLGF-1 likely serves as an endogenous anti-permeability factor whose effectiveness is limited to a precise time point following vascular injury. Clinical approaches that would pattern nature's approach would thus limit treatments to precise intervals following injury and bring attention to use of agents only during therapeutic windows.

Automatic processing, quality assurance and serving of real-time weather data

Recent advances in technology have produced a significant increase in the availability of free sensor data over the Internet. With affordable weather monitoring stations now available to individual meteorology enthusiasts a reservoir of real time data such as temperature, rainfall and wind speed can now be obtained for most of the United States and Europe. Despite the abundance of available data, obtaining useable information about the weather in your local neighbourhood requires complex processing that poses several challenges. This paper discusses a collection of technologies and applications that harvest, refine and process this data, culminating in information that has been tailored toward the user. In this case we are particularly interested in allowing a user to make direct queries about the weather at any location, even when this is not directly instrumented, using interpolation methods. We also consider how the uncertainty that the interpolation introduces can then be communicated to the user of the system, using UncertML, a developing standard for uncertainty representation.

Pragmatic development of service based real-time change data capture

This thesis makes a contribution to the Change Data Capture (CDC) field by providing an empirical evaluation on the performance of CDC architectures in the context of realtime data warehousing. CDC is a mechanism for providing data warehouse architectures with fresh data from Online Transaction Processing (OLTP) databases. There are two types of CDC architectures, pull architectures and push architectures. There is exiguous data on the performance of CDC architectures in a real-time environment. Performance data is required to determine the real-time viability of the two architectures. We propose that push CDC architectures are optimal for real-time CDC. However, push CDC architectures are seldom implemented because they are highly intrusive towards existing systems and arduous to maintain. As part of our contribution, we pragmatically develop a service based push CDC solution, which addresses the issues of intrusiveness and maintainability. Our solution uses Data Access Services (DAS) to decouple CDC logic from the applications. A requirement for the DAS is to place minimal overhead on a transaction in an OLTP environment. We synthesize DAS literature and pragmatically develop DAS that eciently execute transactions in an OLTP environment. Essentially we develop effeicient RESTful DAS, which expose Transactions As A Resource (TAAR). We evaluate the TAAR solution and three pull CDC mechanisms in a real-time environment, using the industry recognised TPC-C benchmark. The optimal CDC mechanism in a real-time environment, will capture change data with minimal latency and will have a negligible affect on the database's transactional throughput. Capture latency is the time it takes a CDC mechanism to capture a data change that has been applied to an OLTP database. A standard definition for capture latency and how to measure it does not exist in the field. We create this definition and extend the TPC-C benchmark to make the capture latency measurement. The results from our evaluation show that pull CDC is capable of real-time CDC at low levels of user concurrency. However, as the level of user concurrency scales upwards, pull CDC has a significant impact on the database's transaction rate, which affirms the theory that pull CDC architectures are not viable in a real-time architecture. TAAR CDC on the other hand is capable of real-time CDC, and places a minimal overhead on the transaction rate, although this performance is at the expense of CPU resources.

A near real-time water surface detection method based on HSV transformation of MODIS multi-Spectral time series data

In the face of global population growth and the uneven distribution of water supply, a better knowledge of the spatial and temporal distribution of surface water resources is critical. Remote sensing provides a synoptic view of ongoing processes, which addresses the intricate nature of water surfaces and allows an assessment of the pressures placed on aquatic ecosystems. However, the main challenge in identifying water surfaces from remotely sensed data is the high variability of spectral signatures, both in space and time. In the last 10 years only a few operational methods have been proposed to map or monitor surface water at continental or global scale, and each of them show limitations. The objective of this study is to develop and demonstrate the adequacy of a generic multi-temporal and multi-spectral image analysis method to detect water surfaces automatically, and to monitor them in near-real-time. The proposed approach, based on a transformation of the RGB color space into HSV, provides dynamic information at the continental scale. The validation of the algorithm showed very few omission errors and no commission errors. It demonstrates the ability of the proposed algorithm to perform as effectively as human interpretation of the images. The validation of the permanent water surface product with an independent dataset derived from high resolution imagery, showed an accuracy of 91.5% and few commission errors. Potential applications of the proposed method have been identified and discussed. The methodology that has been developed 27 is generic: it can be applied to sensors with similar bands with good reliability, and minimal effort. Moreover, this experiment at continental scale showed that the methodology is efficient for a large range of environmental conditions. Additional preliminary tests over other continents indicate that the proposed methodology could also be applied at the global scale without too many difficulties

The application of counter immunoelectrophoresis (CIE) in ocular protein studies. Part I:time dependent deposition patterns of immunoregulatory proteins on anionic hydrogel contact lenses

This paper focuses on the effects of wear regime on the deposition pattern of important immunoregulatory proteins on FDA Group IV etafilcon-A lenses. Specifically, the aim was to assess the extent to which the daily disposable wear modality produces a different deposition of proteins from the conventional daily wear regime which is coupled with cleaning and disinfection. Counter immunoelectrophoresis (CIE) was employed to detect individual proteins in lens extracts from individual patients and focused on the analysis of five proteins, IgA, IgG, lactoferrin, albumin and kininogen. Deposition was monitored as a function of time; significantly lower deposition was detected on the daily disposable lenses. cr 2002 British Contact Lens Association. Published by Elsevier Science Ltd. All rights reserved.

Time-dependent variation of fiber Bragg grating reflectivity in PMMA-based polymer optical fibers

In this paper we investigate the effects of viscoelasticity on both the strength and resonance wavelength of two fibre Bragg gratings (FBGs) inscribed in microstructured polymer optical fibre (mPOF) made of undoped PMMA. Both FBGs were inscribed under a strain of 1% in order to increase the material photosensitivity. After the inscription the strain was released and the FBGs spectra were monitored. We initially observed a decrease of the reflection down to zero after which it began to increase. After that, strain tests were carried out to confirm the results and finally the gratings were monitored for a further 120 days, with a stable reflection response being observed beyond 50 days.

Identification of a time-dependent bio-heat blood perfusion coefficient

In the paper the identification of the time-dependent blood perfusion coefficient is formulated as an inverse problem. The bio-heat conduction problem is transformed into the classical heat conduction problem. Then the transformed inverse problem is solved using the method of fundamental solutions together with the Tikhonov regularization. Some numerical results are presented in order to demonstrate the accuracy and the stability of the proposed meshless numerical algorithm.

True real-time change data capture with web service database encapsulation

This research is investigating the claim that Change Data Capture (CDC) technologies capture data changes in real-time. Based on theory, our hypothesis states that real-time CDC is not achievable with traditional approaches (log scanning, triggers and timestamps). Traditional approaches to CDC require a resource to be polled, which prevents true real-time CDC. We propose an approach to CDC that encapsulates the data source with a set of web services. These web services will propagate the changes to the targets and eliminate the need for polling. Additionally we propose a framework for CDC technologies that allow changes to flow from source to target. This paper discusses current CDC technologies and presents the theory about why they are unable to deliver changes in real-time. Following, we discuss our web service approach to CDC and accompanying framework, explaining how they can produce real-time CDC. The paper concludes with a discussion on the research required to investigate the real-time capabilities of CDC technologies. © 2010 IEEE.

Determining the temperature from Cauchy data in corner domains

We consider the problem of reconstruction of the temperature from knowledge of the temperature and heat flux on a part of the boundary of a bounded planar domain containing corner points. An iterative method is proposed involving the solution of mixed boundary value problems for the heat equation (with time-dependent conductivity). These mixed problems are shown to be well-posed in a weighted Sobolev space.

Reconstruction of an unsteady flow from incomplete boundary data:Part I. Theory

Purpose – To propose and investigate a stable numerical procedure for the reconstruction of the velocity of a viscous incompressible fluid flow in linear hydrodynamics from knowledge of the velocity and fluid stress force given on a part of the boundary of a bounded domain. Design/methodology/approach – Earlier works have involved the similar problem but for stationary case (time-independent fluid flow). Extending these ideas a procedure is proposed and investigated also for the time-dependent case. Findings – The paper finds a novel variation method for the Cauchy problem. It proves convergence and also proposes a new boundary element method. Research limitations/implications – The fluid flow domain is limited to annular domains; this restriction can be removed undertaking analyses in appropriate weighted spaces to incorporate singularities that can occur on general bounded domains. Future work involves numerical investigations and also to consider Oseen type flow. A challenging problem is to consider non-linear Navier-Stokes equation. Practical implications – Fluid flow problems where data are known only on a part of the boundary occur in a range of engineering situations such as colloidal suspension and swimming of microorganisms. For example, the solution domain can be the region between to spheres where only the outer sphere is accessible for measurements. Originality/value – A novel variational method for the Cauchy problem is proposed which preserves the unsteady Stokes operator, convergence is proved and using recent for the fundamental solution for unsteady Stokes system, a new boundary element method for this system is also proposed.

Upgrading legacy long-haul WDM systems through unbalancing the proportion of 1s and 0s in the transmitted data

We present experimental results for wavelength-division multiplexed (WDM) transmission performance using unbalanced proportions of 1s and 0s in pseudo-random bit sequence (PRBS) data. This investigation simulates the effect of local, in time, data unbalancing which occurs in some coding systems such as forward error correction when extra bits are added to the WDM data stream. We show that such local unbalancing, which would practically give a time-dependent error-rate, can be employed to improve the legacy long-haul WDM system performance if the system is allowed to operate in the nonlinear power region. We use a recirculating loop to simulate a long-haul fibre system.