Three carrier ambiguity resolution : distance-independent performance demonstrated using semi-generated triple frequency GPS signals

In spite of significant research in the development of efficient algorithms for three carrier ambiguity resolution, full performance potential of the additional frequency signals cannot be demonstrated effectively without actual triple frequency data. In addition, all the proposed algorithms showed their difficulties in reliable resolution of the medium-lane and narrow-lane ambiguities in different long-range scenarios. In this contribution, we will investigate the effects of various distance-dependent biases, identifying the tropospheric delay to be the key limitation for long-range three carrier ambiguity resolution. In order to achieve reliable ambiguity resolution in regional networks with the inter-station distances of hundreds of kilometers, a new geometry-free and ionosphere-free model is proposed to fix the integer ambiguities of the medium-lane or narrow-lane observables over just several minutes without distance constraint. Finally, the semi-simulation method is introduced to generate the third frequency signals from dual-frequency GPS data and experimentally demonstrate the research findings of this paper.

The role of insulin and IGF2 signalling on metabolic pathways in prostate cancer progression

Prostate cancer (CaP) is the most commonly diagnosed cancer in males in Australia, North America, and Europe. If found early and locally confined, CaP can be treated with radical prostatectomy or radiation therapy; however, 25-40% patients will relapse and go on to advanced disease. The most common therapy in these cases is androgen deprivation therapy (ADT), which suppresses androgen production from the testis. Lack of the testicular androgen supply causes cells of the prostate to undergo apoptosis. However, in some cases the regression initially seen with ADT eventually gives way to a growth of a population of cancerous cells that no longer require testicular androgens. This phenotype is essentially fatal and is termed castrate resistant prostate cancer (CRPC). In addition to eventual regression, there are many undesirable side effects which accompany ADT, including development of a metabolic syndrome, which is defined by the U.S. National Library of Medicine as “a combination of medical disorders that increase the risk of developing cardiovascular disease and diabetes.” This project will focus on the effect of ADT induced hyperinsulinemia, as mimicked by treating androgen receptor positive CaP cells with insulin in a serum (hormone) deprived environment. While this side effect is not widely explored, in this thesis it is demonstrated for the first time that insulin upregulates pathways important to CaP progression. Our group has previously shown that during CaP progression, the enzymes necessary for de novo steroidogenesis are upregulated in the LNCaP xenograft model, total steroid levels are increased in tumours compared to pre castrate levels, and de novo steroidogenesis from radio-labelled acetate has been demonstrated. Because of the CaP dependence on AR for survival, we and other groups believe that CaP cells carry out de novo steroidogenesis to survive in androgen deprived conditions. Because (a) men on ADT often develop metabolic syndrome, and (b) men with lifestyle-induced obesity and hyperinsulinemia have worse prognosis and faster disease progression, and because (c) insulin causes steroidogenesis in other cell lines, the hypothesis that insulin may contribute to CaP progression through upregulation of steroidogenesis was explored. Insulin upregulates steroidogenesis enzymes at the mRNA level in three AR positive cell lines, as well as upregulating these enzymes at the protein level in two cell lines. It has also been demonstrated that insulin increases mitochondrial (functional) levels of steroid acute regulatory protein (StAR). Furthermore, insulin causes increased levels of total steroids in and induction of de novo steroid synthesis by insulin has been demonstrated at levels induced sufficient to activate AR. The effect of insulin analogs on CaP steroidogenesis in LNCaP and VCaP cells has also been investigated because epidemiological studies suggest that some of the analogs developed may have more cancer stimulatory effects than normal insulin. In this project, despite the signalling differences between glargine, X10, and insulin, these analogs did not appear to induce steroidogenesis any more potently that normal insulin. The effect of insulin of MCF7breast cancer cells was also investigated with results suggesting that breast cancer cells may be capable of de novo steroidogenesis, and that increase in estradiol production may be exacerbated by insulin. Insulin has also been long known to stimulate lipogenesis in the liver and adipocytes, and has been demonstrated to increase lipogenesis in breast cancer cells; therefore, investigation of the effect of insulin on lipogenesis, which is a hallmark of aggressive cancers, was investigated. In CaP progression sterol regulatory element binding protein (SREBP) is dysregulated and upregulates fatty acid synthase (FASN), acetyl CoA-carboxylase, and other lipogenesis genes. SREBP is important for steroidogenesis and in this project has been shown to be upregulated by insulin in CaP cells. Fatty acid synthesis provides building blocks of membrane growth, provides substrates for acid oxidation, the main energy source for CaP cells, provides building blocks for anti-apoptotic and proinflammatory molecules, and provides molecules that stimulate steroidogenesis. In this project it has been shown that insulin upregulates FASN and ACC, which synthesize fatty acids, as well as upregulating hormone sensitive lipase (HSL), diazepam-binding inhibitor (DBI), and long-chain acyl-CoA synthetase 3 (ACSL3), which contribute to lipid activation of steroidogenesis. Insulin also upregulates total lipid levels and de novo lipogenesis, which can be suppressed by inhibition of the insulin receptor (INSR). The fatty acids synthesized after insulin treatment are those that have been associated with CaP; furthermore, microarray data suggests insulin may upregulate fatty acid biosynthesis, metabolism and arachidonic acid metabolism pathways, which have been implicated in CaP growth and survival. Pharmacological agents used to treat patients with hyperinsulinemia/ hyperlipidemia have gained much interest in regards to CaP risk and treatment; however, the scientific rationale behind these clinical applications has not been examined. This thesis explores whether the use of metformin or simvastatin would decrease either lipogenesis or steroidogenesis or both in CaP cells. Simvastatin is a 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) inhibitor, which blocks synthesis of cholesterol, the building block of steroids/ androgens. It has also been postulated to down regulate SREBP in other metabolic disorders. It has been shown in this thesis, in LNCaP cells, that simvastatin inhibited and decreased insulin induced steroidogenesis and lipogenesis, respectively, but increased these pathways in the absence of insulin. Conversely, metformin, which activates AMP-activated protein kinase (AMPK) to shut down lipogenesis, cholesterol synthesis, and protein synthesis, highly suppresses both steroidogenesis and lipogenesis in the presence and absence of insulin. Lastly, because it has been demonstrated to increase steroidogenesis in other cell lines, and because the elucidation of any factors affecting steroidogenesis is important to understanding CaP, the effect of IGF2 on steroidogenesis in CaP cells was investigated. In patient samples, as men progress to CRPC, IGF2 mRNA and the protein levels of the receptors it may signal through are upregulated. It has also been demonstrated that IGF2 upregulates steroidogenic enzymes at both the mRNA and protein levels in LNCaP cells, increases intracellular and secreted steroid/androgen levels in LNCaPs to levels sufficient to stimulate the AR, and upregulated de novo steroidogenesis in LNCaPs and VCaPs. As well, inhibition of INSR and insulin-like growth factor 1 receptor (IGF1R), which IGF2 signals through, suggests that induction of steroidogenesis may be occurring predominantly through IGF1R. In summary, this project has illuminated for the first time that insulin is likely to play a large role in cancer progression, through upregulation of the steroidogenesis and lipogenesis pathways at the mRNA and protein levels, and production levels, and demonstrates a novel role for IGF-II in CaP progression through stimulation of steroidogenesis. It has also been demonstrated that metformin and simvastatin drugs may be useful in suppressing the insulin induction of these pathways. This project affirms the pathways by which ADT- induced metabolic syndrome may exacerbate CaP progression and strongly suggests that the monitoring and modulation of the metabolic state of CaP patients could have a strong impact on their therapeutic outcomes.

Separation of acoustic emission signals from small size multi-cylinder diesel engine

This paper presents techniques which can lead to diagnosis of faults in a small size multi-cylinder diesel engine. Preliminary analysis of the acoustic emission (AE) signals is outline, including time-frequency analysis and selection of optimum frequency band.The results of applying mean field independent component analysis (MFICA) to separate the AE root mean square (RMS) signals and the effects of changing parameter values are also outlined. The results on separation of RMS signals show thsi technique has the potential of increasing the probability to successfully identify the AE events associated with the various mechanical events within the combustion process of multi-cylinder diesel engines.

A Bias in ML Estimates of Branch Lengths in the Presence of Multiple Signals

Sequence data often have competing signals that are detected by network programs or Lento plots. Such data can be formed by generating sequences on more than one tree, and combining the results, a mixture model. We report that with such mixture models, the estimates of edge (branch) lengths from maximum likelihood (ML) methods that assume a single tree are biased. Based on the observed number of competing signals in real data, such a bias of ML is expected to occur frequently. Because network methods can recover competing signals more accurately, there is a need for ML methods allowing a network. A fundamental problem is that mixture models can have more parameters than can be recovered from the data, so that some mixtures are not, in principle, identifiable. We recommend that network programs be incorporated into best practice analysis, along with ML and Bayesian trees.

Diesel knock combustion and its detection using acoustic emission

This paper presents an experimental investigation into the detection of excessive Diesel knock using acoustic emission signals. Three different dual-fuel Diesel engine operating regimes were induced into a compression ignition (Diesel) engine operating on both straight Diesel fuel and two different mixtures of fumigated ethanol and Diesel. The experimentally induced engine operating regimes were; normal, or Diesel only operation, acceptable dual-fuel operation and dual-fuel operation with excessive Diesel knock. During the excessive Diesel knock operating regime, high rates of ethanol substitution induced potentially damaging levels of Diesel knock. Acoustic emission data was captured along with cylinder pressure, crank-angle encoder, and top-dead centre signals for the different engine operating regimes. Using these signals, it was found that acoustic emission signals clearly distinguished between the two acceptable operating regimes and the operating regime experiencing excessive Diesel knock. It was also found that acoustic emission sensor position is critical. The acoustic emission sensor positioned on the block of the engine clearly related information concerning the level of Diesel knock occurring in the engine whist the sensor positioned on the head of the engine gave no indication concerning Diesel knock severity levels.

Diesel engine problems, acoustic emission signals and simulated misfire faults

This paper discusses commonly encountered diesel engine problems and the underlying combustion related faults. Also discussed are the methods used in previous studies to simulate diesel engine faults and the initial results of an experimental simulation of a common combustion related diesel engine fault, namely diesel engine misfire. This experimental fault simulation represents the first step towards a comprehensive investigation and analysis into the characteristics of acoustic emission signals arising from combustion related diesel engine faults. Data corresponding to different engine running conditions was captured using in-cylinder pressure, vibration and acoustic emission transducers along with both crank-angle encoder and top-dead centre signals. Using these signals, it was possible to characterise the diesel engine in-cylinder pressure profiles and the effect of different combustion conditions on both vibration and acoustic emission signals.

Effective discrimination of acoustic emission source signals for structural health monitoring

Acoustic emission (AE) is the phenomenon where stress waves are generated due to rapid release of energy within a material caused by sources such as crack initiation or growth. AE technique involves recording the stress waves by means of sensors and subsequent analysis of the recorded signals to gather information about the nature of the source. Though AE technique is one of the popular non destructive evaluation (NDE) techniques for structural health monitoring of mechanical, aerospace and civil structures; several challenges still exist in successful application of this technique. Presence of spurious noise signals can mask genuine damage‐related AE signals; hence a major challenge identified is finding ways to discriminate signals from different sources. Analysis of parameters of recorded AE signals, comparison of amplitudes of AE wave modes and investigation of uniqueness of recorded AE signals have been mentioned as possible criteria for source differentiation. This paper reviews common approaches currently in use for source discrimination, particularly focusing on structural health monitoring of civil engineering structural components such as beams; and further investigates the applications of some of these methods by analyzing AE data from laboratory tests.

Spectrum sensing, detection and optimisation in cognitive radio for non-stationary primary user signals

Cognitive radio is an emerging technology proposing the concept of dynamic spec- trum access as a solution to the looming problem of spectrum scarcity caused by the growth in wireless communication systems. Under the proposed concept, non- licensed, secondary users (SU) can access spectrum owned by licensed, primary users (PU) so long as interference to PU are kept minimal. Spectrum sensing is a crucial task in cognitive radio whereby the SU senses the spectrum to detect the presence or absence of any PU signal. Conventional spectrum sensing assumes the PU signal as ‘stationary’ and remains in the same activity state during the sensing cycle, while an emerging trend models PU as ‘non-stationary’ and undergoes state changes. Existing studies have focused on non-stationary PU during the transmission period, however very little research considered the impact on spectrum sensing when the PU is non-stationary during the sensing period. The concept of PU duty cycle is developed as a tool to analyse the performance of spectrum sensing detectors when detecting non-stationary PU signals. New detectors are also proposed to optimise detection with respect to duty cycle ex- hibited by the PU. This research consists of two major investigations. The first stage investigates the impact of duty cycle on the performance of existing detec- tors and the extent of the problem in existing studies. The second stage develops new detection models and frameworks to ensure the integrity of spectrum sensing when detecting non-stationary PU signals. The first investigation demonstrates that conventional signal model formulated for stationary PU does not accurately reflect the behaviour of a non-stationary PU. Therefore the performance calculated and assumed to be achievable by the conventional detector does not reflect actual performance achieved. Through analysing the statistical properties of duty cycle, performance degradation is proved to be a problem that cannot be easily neglected in existing sensing studies when PU is modelled as non-stationary. The second investigation presents detectors that are aware of the duty cycle ex- hibited by a non-stationary PU. A two stage detection model is proposed to improve the detection performance and robustness to changes in duty cycle. This detector is most suitable for applications that require long sensing periods. A second detector, the duty cycle based energy detector is formulated by integrat- ing the distribution of duty cycle into the test statistic of the energy detector and suitable for short sensing periods. The decision threshold is optimised with respect to the traffic model of the PU, hence the proposed detector can calculate average detection performance that reflect realistic results. A detection framework for the application of spectrum sensing optimisation is proposed to provide clear guidance on the constraints on sensing and detection model. Following this framework will ensure the signal model accurately reflects practical behaviour while the detection model implemented is also suitable for the desired detection assumption. Based on this framework, a spectrum sensing optimisation algorithm is further developed to maximise the sensing efficiency for non-stationary PU. New optimisation constraints are derived to account for any PU state changes within the sensing cycle while implementing the proposed duty cycle based detector.

Improved foreground detection via block-based classifier cascade with probabilistic decision integration

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.

Detection of excessive diesel engine piston slap using acoustic emission signals

This paper presents a study whereby a series of tests was undertaken using a naturally aspirated 4 cylinder, 2.216 litre, Perkins Diesel engine fitted with a piston having an undersized skirt. This experimental simulation resulted in engine running conditions that included abnormally high levels of piston slap occurring in one of the cylinders. The detectability of the resultant Diesel engine piston slap was investigated using acoustic emission signals. Data corresponding to both normal and piston slap engine running conditions was captured using acoustic emission transducers along with both; in-cylinder pressure and top-dead centre reference signals. Using these signals it was possible to demonstrate that the increased piston slap running conditions were distinguishable by monitoring the piston slap events occurring near the piston mid-stroke positions. However, when monitoring the piston slap events occurring near the TDC/BDC piston stroke positions, the normal and excessive piston slap engine running condition were not clearly distinguishable.

Determination of vertical hydraulic conductivity of aquitards in a multilayered leaky system using water-level signals in adjacent aquifers

This paper presents a methodology for determining the vertical hydraulic conductivity (Kv) of an aquitard, in a multilayered leaky system, based on the harmonic analysis of arbitrary water-level fluctuations in aquifers. As a result, Kv of the aquitard is expressed as a function of the phase-shift of water-level signals measured in the two adjacent aquifers. Based on this expression, we propose a robust method to calculate Kv by employing linear regression analysis of logarithm transformed frequencies and phases. The frequencies, where the Kv are calculated, are identified by coherence analysis. The proposed methods are validated by a synthetic case study and are then applied to the Westbourne and Birkhead aquitards, which form part of a five-layered leaky system in the Eromanga Basin, Australia.

Counterfeiting attacks on block-wise dependent fragile watermarking schemes

In this paper, we present three counterfeiting attacks on the block-wise dependent fragile watermarking schemes. We consider vulnerabilities such as the exploitation of a weak correlation among block-wise dependent watermarks to modify valid watermarked %(medical or other digital) images, where they could still be verified as authentic, though they are actually not. Experimental results successfully demonstrate the practicability and consequences of the proposed attacks for some relevant schemes. The development of the proposed attack models can be used as a means to systematically examine the security levels of similar watermarking schemes.

Unique X-linked familial FSGS with co-segregating heart block disorder is associated with a mutation in the NXF5 gene

Focal segmental glomerulosclerosis (FSGS) is the consequence of a disease process that attacks the kidney's filtering system, causing serious scarring. More than half of FSGS patients develop chronic kidney failure within 10 years, ultimately requiring dialysis or renal transplantation. There are currently several genes known to cause the hereditary forms of FSGS (ACTN4, TRPC6, CD2AP, INF2, MYO1E and NPHS2). This study involves a large, unique, multigenerational Australian pedigree in which FSGS co-segregates with progressive heart block with apparent X-linked recessive inheritance. Through a classical combined approach of linkage and haplotype analysis, we identified a 21.19 cM interval implicated on the X chromosome. We then used a whole exome sequencing approach to identify two mutated genes, NXF5 and ALG13, which are located within this linkage interval. The two mutations NXF5-R113W and ALG13-T141L segregated perfectly with the disease phenotype in the pedigree and were not found in a large healthy control cohort. Analysis using bioinformatics tools predicted the R113W mutation in the NXF5 gene to be deleterious and cellular studies support a role in the stability and localization of the protein suggesting a causative role of this mutation in these co-morbid disorders. Further studies are now required to determine the functional consequence of these novel mutations to development of FSGS and heart block in this pedigree and to determine whether these mutations have implications for more common forms of these diseases in the general population.