Modeling intrinsic noise and delays in chemical kinetics of coupled autoregulated oscillating cells

Delays are an important feature in temporal models of genetic regulation due to slow biochemical processes, such as transcription and translation. In this paper, we show how to model intrinsic noise effects in a delayed setting by either using a delay stochastic simulation algorithm (DSSA) or, for larger and more complex systems, a generalized Binomial τ-leap method (Bτ-DSSA). As a particular application, we apply these ideas to modeling somite segmentation in zebra fish across a number of cells in which two linked oscillatory genes (her1 and her7) are synchronized via Notch signaling between the cells.

Manufacturing Yield Improvement by Clustering

Dealing with product yield and quality in manufacturing industries is getting more difficult due to the increasing volume and complexity of data and quicker time to market expectations. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large databases. Growing self-organizing map (GSOM) is established as an efficient unsupervised datamining algorithm. In this study some modifications to the original GSOM are proposed for manufacturing yield improvement by clustering. These modifications include introduction of a clustering quality measure to evaluate the performance of the programme in separating good and faulty products and a filtering index to reduce noise from the dataset. Results show that the proposed method is able to effectively differentiate good and faulty products. It will help engineers construct the knowledge base to predict product quality automatically from collected data and provide insights for yield improvement.

Instantaneous phase of the autocorrelation and robustness to signal-dependent noise

The phase of an analytic signal constructed from the autocorrelation function of a signal contains significant information about the shape of the signal. Using Bedrosian's (1963) theorem for the Hilbert transform it is proved that this phase is robust to multiplicative noise if the signal is baseband and the spectra of the signal and the noise do not overlap. Higher-order spectral features are interpreted in this context and shown to extract nonlinear phase information while retaining robustness. The significance of the result is that prior knowledge of the spectra is not required.

On the behaviour of higher-order spectral features for object recognition in the presence of various types of noise

This paper presents results on the robustness of higher-order spectral features to Gaussian, Rayleigh, and uniform distributed noise. Based on cluster plots and accuracy results for various signal to noise conditions, the higher-order spectral features are shown to be better than moment invariant features.

Performance control of distributed generation using digital estimation of signal parameters

The Queensland University of Technology (QUT) allows the presentation of a thesis for the Degree of Doctor of Philosophy in the format of published or submitted papers, where such papers have been published, accepted or submitted during the period of candidature. This thesis is composed of seven published/submitted papers, of which one has been published, three accepted for publication and the other three are under review. This project is financially supported by an Australian Research Council (ARC) Discovery Grant with the aim of proposing strategies for the performance control of Distributed Generation (DG) system with digital estimation of power system signal parameters. Distributed Generation (DG) has been recently introduced as a new concept for the generation of power and the enhancement of conventionally produced electricity. Global warming issue calls for renewable energy resources in electricity production. Distributed generation based on solar energy (photovoltaic and solar thermal), wind, biomass, mini-hydro along with use of fuel cell and micro turbine will gain substantial momentum in the near future. Technically, DG can be a viable solution for the issue of the integration of renewable or non-conventional energy resources. Basically, DG sources can be connected to local power system through power electronic devices, i.e. inverters or ac-ac converters. The interconnection of DG systems to power system as a compensator or a power source with high quality performance is the main aim of this study. Source and load unbalance, load non-linearity, interharmonic distortion, supply voltage distortion, distortion at the point of common coupling in weak source cases, source current power factor, and synchronism of generated currents or voltages are the issues of concern. The interconnection of DG sources shall be carried out by using power electronics switching devices that inject high frequency components rather than the desired current. Also, noise and harmonic distortions can impact the performance of the control strategies. To be able to mitigate the negative effect of high frequency and harmonic as well as noise distortion to achieve satisfactory performance of DG systems, new methods of signal parameter estimation have been proposed in this thesis. These methods are based on processing the digital samples of power system signals. Thus, proposing advanced techniques for the digital estimation of signal parameters and methods for the generation of DG reference currents using the estimates provided is the targeted scope of this thesis. An introduction to this research – including a description of the research problem, the literature review and an account of the research progress linking the research papers – is presented in Chapter 1. One of the main parameters of a power system signal is its frequency. Phasor Measurement (PM) technique is one of the renowned and advanced techniques used for the estimation of power system frequency. Chapter 2 focuses on an in-depth analysis conducted on the PM technique to reveal its strengths and drawbacks. The analysis will be followed by a new technique proposed to enhance the speed of the PM technique while the input signal is free of even-order harmonics. The other techniques proposed in this thesis as the novel ones will be compared with the PM technique comprehensively studied in Chapter 2. An algorithm based on the concept of Kalman filtering is proposed in Chapter 3. The algorithm is intended to estimate signal parameters like amplitude, frequency and phase angle in the online mode. The Kalman filter is modified to operate on the output signal of a Finite Impulse Response (FIR) filter designed by a plain summation. The frequency estimation unit is independent from the Kalman filter and uses the samples refined by the FIR filter. The frequency estimated is given to the Kalman filter to be used in building the transition matrices. The initial settings for the modified Kalman filter are obtained through a trial and error exercise. Another algorithm again based on the concept of Kalman filtering is proposed in Chapter 4 for the estimation of signal parameters. The Kalman filter is also modified to operate on the output signal of the same FIR filter explained above. Nevertheless, the frequency estimation unit, unlike the one proposed in Chapter 3, is not segregated and it interacts with the Kalman filter. The frequency estimated is given to the Kalman filter and other parameters such as the amplitudes and phase angles estimated by the Kalman filter is taken to the frequency estimation unit. Chapter 5 proposes another algorithm based on the concept of Kalman filtering. This time, the state parameters are obtained through matrix arrangements where the noise level is reduced on the sample vector. The purified state vector is used to obtain a new measurement vector for a basic Kalman filter applied. The Kalman filter used has similar structure to a basic Kalman filter except the initial settings are computed through an extensive math-work with regards to the matrix arrangement utilized. Chapter 6 proposes another algorithm based on the concept of Kalman filtering similar to that of Chapter 3. However, this time the initial settings required for the better performance of the modified Kalman filter are calculated instead of being guessed by trial and error exercises. The simulations results for the parameters of signal estimated are enhanced due to the correct settings applied. Moreover, an enhanced Least Error Square (LES) technique is proposed to take on the estimation when a critical transient is detected in the input signal. In fact, some large, sudden changes in the parameters of the signal at these critical transients are not very well tracked by Kalman filtering. However, the proposed LES technique is found to be much faster in tracking these changes. Therefore, an appropriate combination of the LES and modified Kalman filtering is proposed in Chapter 6. Also, this time the ability of the proposed algorithm is verified on the real data obtained from a prototype test object. Chapter 7 proposes the other algorithm based on the concept of Kalman filtering similar to those of Chapter 3 and 6. However, this time an optimal digital filter is designed instead of the simple summation FIR filter. New initial settings for the modified Kalman filter are calculated based on the coefficients of the digital filter applied. Also, the ability of the proposed algorithm is verified on the real data obtained from a prototype test object. Chapter 8 uses the estimation algorithm proposed in Chapter 7 for the interconnection scheme of a DG to power network. Robust estimates of the signal amplitudes and phase angles obtained by the estimation approach are used in the reference generation of the compensation scheme. Several simulation tests provided in this chapter show that the proposed scheme can very well handle the source and load unbalance, load non-linearity, interharmonic distortion, supply voltage distortion, and synchronism of generated currents or voltages. The purposed compensation scheme also prevents distortion in voltage at the point of common coupling in weak source cases, balances the source currents, and makes the supply side power factor a desired value.

A review of road traffic noise indicators and their correlation with the LA10(18hour)

This paper outlines a study to determine the correlation between the LA10(18hour) and other road traffic noise indicators. It is based on a database comprising of 404 measurement locations including 947 individual days of valid noise measurements across numerous circumstances taken between November 2001 and November 2007. This paper firstly discusses the need and constraints on the indicators and their nature of matching a suitable indicator to the various road traffic noise dynamical characteristics. The paper then presents a statistical analysis of the road traffic noise monitoring data, correlating various indicators with the LA10(18hour) statistical indicator and provides a comprehensive table of linear correlations. There is an extended analysis on relationships across the night time period. The paper concludes with a discussion on the findings.

A two-stage decision model for information filtering

Information mismatch and overload are two fundamental issues influencing the effectiveness of information filtering systems. Even though both term-based and pattern-based approaches have been proposed to address the issues, neither of these approaches alone can provide a satisfactory decision for determining the relevant information. This paper presents a novel two-stage decision model for solving the issues. The first stage is a novel rough analysis model to address the overload problem. The second stage is a pattern taxonomy mining model to address the mismatch problem. The experimental results on RCV1 and TREC filtering topics show that the proposed model significantly outperforms the state-of-the-art filtering systems.

Integrating collaborative filtering and search-based techniques for personalized online product recommendation

The existing Collaborative Filtering (CF) technique that has been widely applied by e-commerce sites requires a large amount of ratings data to make meaningful recommendations. It is not directly applicable for recommending products that are not frequently purchased by users, such as cars and houses, as it is difficult to collect rating data for such products from the users. Many of the e-commerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user's query are retrieved and recommended to the user. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their online navigation behaviour. This paper proposes to integrate collaborative filtering and search-based techniques to provide personalized recommendations for infrequently purchased products. Two different techniques are proposed, namely CFRRobin and CFAg Query. Instead of using the target user's query to search for products as normal search based systems do, the CFRRobin technique uses the products in which the target user's neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAg Query technique uses the products that the user's neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAg Query perform better than the standard Collaborative Filtering (CF) and the Basic Search (BS) approaches, which are widely applied by the current e-commerce applications. The CFRRobin and CFAg Query approaches also outperform the e- isting query expansion (QE) technique that was proposed for recommending infrequently purchased products.

Structural damage identification with noise polluted Frequency Response Functions (FRFs)

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage with noise polluted data which is unavoidable effect in real world. The measurement data are contaminated by noise because of test environment as well as electronic devices and this noise tend to give error results with structural damage identification methods. Therefore it is important to investigate a method which can perform better with noise polluted data. This paper introduces a new damage index using principal component analysis (PCA) for damage detection of building structures being able to accept noise polluted frequency response functions (FRFs) as input. The FRF data are obtained from the function datagen of MATLAB program which is available on the web site of the IASC-ASCE (International Association for Structural Control– American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group. The proposed method involves a five-stage process: calculation of FRFs, calculation of damage index values using proposed algorithm, development of the artificial neural networks and introducing damage indices as input parameters and damage detection of the structure. This paper briefly describes the methodology and the results obtained in detecting damage in all six cases of the benchmark study with different noise levels. The proposed method is applied to a benchmark problem sponsored by the IASC-ASCE Task Group on Structural Health Monitoring, which was developed in order to facilitate the comparison of various damage identification methods. The illustrated results show that the PCA-based algorithm is effective for structural health monitoring with noise polluted FRFs which is of common occurrence when dealing with industrial structures.

Evolution of traffic management measures on road traffic noise

Road traffic noise affects the quality of life in the areas adjoining the road. The effect of traffic noise on people is wide ranging and may include sleep disturbance and negative impact on work efficiency. To address the problem of traffic noise, it is necessary to estimate the noise level. For this, a number of noise estimation models have been developed which can estimate noise at the receptor points, based on simple configuration of buildings. However, for a real world situation we have multiple buildings forming built-up area. In such a situation, it is almost impossible to consider multiple diffractions and reflections in sound propagation from the source to the receptor point. An engineering solution to such a real world problem is needed to estimate noise levels in built-up area.

Reduced sampling efficiency causes degraded Vernier hyperacuity with normal aging: Vernier acuity in position noise

Vernier acuity, a form of visual hyperacuity, is amongst the most precise forms of spatial vision. Under optimal conditions Vernier thresholds are much finer than the inter-photoreceptor distance. Achievement of such high precision is based substantially on cortical computations, most likely in the primary visual cortex. Using stimuli with added positional noise, we show that Vernier processing is reduced with advancing age across a wide range of noise levels. Using an ideal observer model, we are able to characterize the mechanisms underlying age-related loss, and show that the reduction in Vernier acuity can be mainly attributed to the reduction in efficiency of sampling, with no significant change in the level of internal position noise, or spatial distortion, in the visual system.

Adaptive bilateral filtering using saliency map for deblocking low bit rate videos

This paper proposes a novel approach to video deblocking which performs perceptually adaptive bilateral filtering by considering color, intensity, and motion features in a holistic manner. The method is based on bilateral filter which is an effective smoothing filter that preserves edges. The bilateral filter parameters are adaptive and avoid over-blurring of texture regions and at the same time eliminate blocking artefacts in the smooth region and areas of slow motion content. This is achieved by using a saliency map to control the strength of the filter for each individual point in the image based on its perceptual importance. The experimental results demonstrate that the proposed algorithm is effective in deblocking highly compressed video sequences and to avoid over-blurring of edges and textures in salient regions of image.

Study on road traffic noise prediction model taking into account the citywide network

For the further noise reduction in the future, the traffic management which controls traffic flow and physical distribution is important. To conduct the measure by the traffic management effectively, it is necessary to apply the model for predicting the traffic flow in the citywide road network. For this purpose, the existing model named AVENUE was used as a macro-traffic flow prediction model. The traffic flow model was integrated with the road vehicles' sound power model, and the new road traffic noise prediction model was established. By using this prediction model, the noise map of entire city can be made. In this study, first, the change of traffic flow on the road network after the establishment of new roads was estimated, and the change of the road traffic noise caused by the new roads was predicted. As a result, it has been found that this prediction model has the ability to estimate the change of noise map by the traffic management. In addition, the macro-traffic flow model and our conventional micro-traffic flow model were combined, and the coverage of the noise prediction model was expanded.

Noise detectives : design implications for mobile learning

In this paper, we present an account of children's interactions with a mobile technology prototype within the school context. The noise detectives trial was conducted in a school setting with the aim of better understanding the role of mobile resources as mediators within science and environmental learning activities. Over 80 children, aged between 10 and 12, completed an outdoor data-gathering activity, using a mobile learning prototype that included paper and software components. They measured and recorded noise levels in various locations throughout the school. We analysed the activity to determine how the components of the prototype were integrated into the learning activity, and to identify differences in behaviour that resulted from using these components. We present design implications that resulted from observed differences in prototype use and appropriation.