Short- and long-term movement patterns in the freshwater whipray (Himantura dalyensis) determined by the signal processing of passive acoustic telemetry data.

Patterns of movement in aquatic animals reflect ecologically important behaviours. Cyclical changes in the abiotic environment influence these movements, but when multiple processes occur simultaneously, identifying which is responsible for the observed movement can be complex. Here we used acoustic telemetry and signal processing to define the abiotic processes responsible for movement patterns in freshwater whiprays (Himantura dalyensis). Acoustic transmitters were implanted into the whiprays and their movements detected over 12 months by an array of passive acoustic receivers, deployed throughout 64 km of the Wenlock River, Qld, Australia. The time of an individual's arrival and departure from each receiver detection field was used to estimate whipray location continuously throughout the study. This created a linear-movement-waveform for each whipray and signal processing revealed periodic components within the waveform. Correlation of movement periodograms with those from abiotic processes categorically illustrated that the diel cycle dominated the pattern of whipray movement during the wet season, whereas tidal and lunar cycles dominated during the dry season. The study methodology represents a valuable tool for objectively defining the relationship between abiotic processes and the movement patterns of free-ranging aquatic animals and is particularly expedient when periods of no detection exist within the animal location data.

Lantibiotic Nisin and Its Detection Methods

The type A lantibiotic nisin produced by several Lactococcus lactis strains, and one Streptococcus uberis strainis a small antimicrobial peptide that inhibits the growth of a wide range of gram-positive bacteria, such as Bacillus, Clostridium, Listeria and Staphylococcus species. It is nontoxic to humans and used as a food preservative (E234) in more than 50 countries including the EU, the USA, and China. National legislations concerning maximum addition levels of nisin in different foods vary greatly. Therefore, there is a demand for non-laborious and sensitive methods to identify and quantify nisin reliably from different food matrices. The horizontal inhibition assay, based on the inhibitory effect of nisin to Micrococcus luteus is the base for most quantification methods developed so far. However, the sensitivity and accuracy of the agar diffusion method is affected by several parameters. Immunological tests have also been described. Taken into account the sensitivity of immunological methods to interfering substances within sample matrices, and possible cross-reactivities with lantibiotics structurally close to nisin, their usefulness for nisin detection from food samples remains limited. The proteins responsible for nisin biosynthesis, and producer self-immunity are encoded by genes arranged into two inducible operons, nisA/Z/QBTCIPRK and nisFEG, which also contain internal, constitutive promoters PnisI and PnisR. The transmembrane histidine kinase NisK and the response regulator NisR form a two-component signal transduction system, in which NisK autophosphorylates after exposure to extra cellular nisin, and subsequently transfers the phosphate to NisR. The phosphorylated NisR then relays the signal downstream by binding to two regulated promoters in the nisin gene cluster, i.e the nisA/Z/Qand the nisF promoters, thus activating transcription of the structural gene nisA/Z/Q and the downstream genes nisBTCIPRK from the nisA/Z/Q promoter, and the genes nisFEG from the nisF promoter. In this work two novel and highly sensitive nisin bioassays were developed. Both of these quantification methods were based on NisRK mediated, nisin induced Green Fluorescent Protein (GFP) fluorescence. The suitabilities of these assays for quantifica¬tion of nisin from food samples were evaluated in several food matrices. These bioassays had nisin sensitivities in the nanogram or picogram levels. In addition, shelf life of nisin in cooked sausages and retainment of the induction activity of nisin in intestinal chyme (intestinal content) was assessed.

Modern techniques in detection, identification and quantification of bacteria and peptides from foods

Standards have been placed to regulate the microbial and preservative contents to assure that foods are safe to the consumer. In a case of a food-related disease outbreak, it is crucial to be able to detect and identify quickly and accurately the cause of the disease. In addition, for every day control of food microbial and preservative contents, the detection methods must be easily performed for numerous food samples. In this present study, quicker alternative methods were studied for identification of bacteria by DNA fingerprinting. A flow cytometry method was developed as an alternative to pulsed-field gel electrophoresis, the golden method . DNA fragment sizing by an ultrasensitive flow cytometer was able to discriminate species and strains in a reproducible and comparable manner to pulsed-field gel electrophoresis. This new method was hundreds times faster and 200,000 times more sensitive. Additionally, another DNA fingerprinting identification method was developed based on single-enzyme amplified fragment length polymorphism (SE-AFLP). This method allowed the differentiation of genera, species, and strains of pathogenic bacteria of Bacilli, Staphylococci, Yersinia, and Escherichia coli. These fingerprinting patterns obtained by SE-AFLP were simpler and easier to analyze than those by the traditional amplified fragment length polymorphism by double enzyme digestion. Nisin (E234) is added as a preservative to different types of foods, especially dairy products, around the world. Various detection methods exist for nisin, but they lack in sensitivity, speed or specificity. In this present study, a sensitive nisin-induced green fluorescent protein (GFPuv) bioassay was developed using the Lactococcus lactis two-component signal system NisRK and the nisin-inducible nisA promoter. The bioassay was extremely sensitive with detection limit of 10 pg/ml in culture supernatant. In addition, it was compatible for quantification from various food matrices, such as milk, salad dressings, processed cheese, liquid eggs, and canned tomatoes. Wine has good antimicrobial properties due to its alcohol concentration, low pH, and organic content and therefore often assumed to be microbially safe to consume. Another aim of this thesis was to study the microbiota of wines returned by customers complaining of food-poisoning symptoms. By partial 16S rRNA gene sequence analysis, ribotyping, and boar spermatozoa motility assay, it was identified that one of the wines contained a Bacillus simplex BAC91, which produced a heat-stable substance toxic to the mitochondria of sperm cells. The antibacterial activity of wine was tested on the vegetative cells and spores of B. simplex BAC91, B. cereus type strain ATCC 14579 and cereulide-producing B. cereus F4810/72. Although the vegetative cells and spores of B. simplex BAC91 were sensitive to the antimicrobial effects of wine, the spores of B. cereus strains ATCC 14579 and F4810/72 stayed viable for at least 4 months. According to these results, Bacillus spp., more specifically spores, can be a possible risk to the wine consumer.

Lactic acid bacteria and their antimicrobial peptides : Induction,detection,partial characterization, and potential applications

Bacteriocin-producing lactic acid bacteria and their isolated peptide bacteriocins are of value to control pathogens and spoiling microorganisms in foods and feed. Nisin is the only bacteriocin that is commonly accepted as a food preservative and has a broad spectrum of activity against Gram-positive organisms including spore forming bacteria. In this study nisin induction was studied from two perspectives, induction from inside of the cell and selection of nisin inducible strains with increased nisin induction sensitivity. The results showed that a mutation in the nisin precursor transporter NisT rendered L. lactis incapable of nisin secretion and lead to nisin accumulation inside the cells. Intracellular proteolytic activity could cleave the N-terminal leader peptide of nisin precursor, resulting in active nisin in the cells. Using a nisin sensitive GFP bioassay it could be shown, that the active intracellular nisin could function as an inducer without any detectable release from the cells. The results suggested that nisin can be inserted into the cytoplasmic membrane from inside the cell and activate NisK. This model of two-component regulation may be a general mechanism of how amphiphilic signals activate the histidine kinase sensor and would represent a novel way for a signal transduction pathway to recognize its signal. In addition, nisin induction was studied through the isolation of natural mutants of the GFPuv nisin bioassay strain L. lactis LAC275 using fl uorescence-activated cell sorting (FACS). The isolated mutant strains represent second generation of GFPuv bioassay strains which can allow the detection of nisin at lower levels. The applied aspect of this thesis was focused on the potential of bacteriocins in chicken farming. One aim was to study nisin as a potential growth promoter in chicken feed. Therefore, the lactic acid bacteria of chicken crop and the nisin sensitivity of the isolated strains were tested. It was found that in the crop Lactobacillus reuteri, L. salivarius and L. crispatus were the dominating bacteria and variation in nisin resistance level of these strains was found. This suggested that nisin may be used as growth promoter without wiping out the dominating bacterial species in the crop. As the isolated lactobacilli may serve as bacteria promoting chicken health or reducing zoonoosis and bacteriocin production is one property associated with probiotics, the isolated strains were screened for bacteriocin activity against the pathogen Campylobacter jejuni. The results showed that many of the isolated L. salivarius strains could inhibit the growth of C. jejuni. The bacteriocin of the L. salivarius LAB47 strain, with the strongest activity, was further characterized. Salivaricin 47 is heat-stable and active in pH range 3 to 8, and the molecular mass was estimated to be approximately 3.2 kDa based on tricine SDS-PAGE analysis.

Economic theory informing approaches to phoenix activity in small business: A neo-Schumpeterian analysis

The adequacy and efficiency of existing legal and regulatory frameworks dealing with corporate phoenix activity have been repeatedly called into question over the past two decades through various reviews, inquiries, targeted regulatory operations and the implementation of piecemeal legislative reform. Despite these efforts, phoenix activity does not appear to have abated. While there is no law in Australia that declares ‘phoenix activity’ to be illegal, the behaviour that tends to manifest in phoenix activity can be capable of transgressing a vast array of law, including for example, corporate law, tax law, and employment law. This paper explores the notion that the persistence of phoenix activity despite the sheer extent of this law suggests that the law is not acting as powerfully as it might as a deterrent. Economic theories of entrepreneurship and innovation can to some extent explain why this is the case and also offer a sound basis for the evaluation and reconsideration of the existing law. The challenges facing key regulators are significant. Phoenix activity is not limited to particular corporate demographic: it occurs in SMEs, large companies and in corporate groups. The range of behaviour that can amount to phoenix activity is so broad, that not all phoenix activity is illegal. This paper will consider regulatory approaches to these challenges via analysis of approaches to detection and enforcement of the underlying law capturing illegal phoenix activity. Remedying the mischief of phoenix activity is of practical importance. The benefits include continued confidence in our economy, law that inspires best practice among directors, and law that is articulated in a manner such that penalties act as a sufficient deterrent and the regulatory system is able to detect offenders and bring them to account. Any further reforms must accommodate and tolerate legal phoenix activity, at least to some extent. Even then, phoenix activity pushes tolerance of repeated entrepreneurial failure to its absolute limit. The more limited liability is misused and abused, the stronger the argument to place some restrictions on access to limited liability. This paper proposes that such an approach is a legitimate next step for a robust and mature capitalist economy.

Robust and High-resolution Voiced/Unvoiced Classification in Noisy Speech Using A Signal Smoothness Criterion

We propose a novel technique for robust voiced/unvoiced segment detection in noisy speech, based on local polynomial regression. The local polynomial model is well-suited for voiced segments in speech. The unvoiced segments are noise-like and do not exhibit any smooth structure. This property of smoothness is used for devising a new metric called the variance ratio metric, which, after thresholding, indicates the voiced/unvoiced boundaries with 75% accuracy for 0dB global signal-to-noise ratio (SNR). A novelty of our algorithm is that it processes the signal continuously, sample-by-sample rather than frame-by-frame. Simulation results on TIMIT speech database (downsampled to 8kHz) for various SNRs are presented to illustrate the performance of the new algorithm. Results indicate that the algorithm is robust even in high noise levels.

Quarantine and surveillance strategies for plant pathogen detection and control

Incursions of plant pests and diseases pose serious threats to food security, agricultural productivity and the natural environment. One of the challenges in confidently delimiting and eradicating incursions is how to choose from an arsenal of surveillance and quarantine approaches in order to best control multiple dispersal pathways. Anthropogenic spread (propagules carried on humans or transported on produce or equipment) can be controlled with quarantine measures, which in turn can vary in intensity. In contrast, environmental spread processes are more difficult to control, but often have a temporal signal (e.g. seasonality) which can introduce both challenges and opportunities for surveillance and control. This leads to complex decisions regarding when, where and how to search. Recent modelling investigations of surveillance performance have optimised the output of simulation models, and found that a risk-weighted randomised search can perform close to optimally. However, exactly how quarantine and surveillance strategies should change to reflect different dispersal modes remains largely unaddressed. Here we develop a spatial simulation model of a plant fungal-pathogen incursion into an agricultural region, and its subsequent surveillance and control. We include structural differences in dispersal via the interplay of biological, environmental and anthropogenic connectivity between host sites (farms). Our objective was to gain broad insights into the relative roles played by different spread modes in propagating an invasion, and how incorporating knowledge of these spread risks may improve approaches to quarantine restrictions and surveillance. We find that broad heuristic rules for quarantine restrictions fail to contain the pathogen due to residual connectivity between sites, but surveillance measures enable early detection and successfully lead to suppression of the pathogen in all farms. Alternative surveillance strategies attain similar levels of performance by incorporating environmental or anthropogenic dispersal risk in the prioritisation of sites. Our model provides the basis to develop essential insights into the effectiveness of different surveillance and quarantine decisions for fungal pathogen control. Parameterised for authentic settings it will aid our understanding of how the extent and resolution of interventions should suitably reflect the spatial structure of dispersal processes.

A Generalized Approach to Multiresolution Complex SAR Signal Processing

Multiresolution synthetic aperture radar (SAR) image formation has been proven to be beneficial in a variety of applications such as improved imaging and target detection as well as speckle reduction. SAR signal processing traditionally carried out in the Fourier domain has inherent limitations in the context of image formation at hierarchical scales. We present a generalized approach to the formation of multiresolution SAR images using biorthogonal shift-invariant discrete wavelet transform (SIDWT) in both range and azimuth directions. Particularly in azimuth, the inherent subband decomposition property of wavelet packet transform is introduced to produce multiscale complex matched filtering without involving any approximations. This generalized approach also includes the formulation of multilook processing within the discrete wavelet transform (DWT) paradigm. The efficiency of the algorithm in parallel form of execution to generate hierarchical scale SAR images is shown. Analytical results and sample imagery of diffuse backscatter are presented to validate the method.

R1SVM: A randomised nonlinear approach to large-scale anomaly detection

The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning of kernel machines, there has been growing interest in random projection methods as an alternative to the computationally expensive problems of kernel matrix construction and sup-port vector optimisation. In this paper we leverage the theory of nonlinear random projections and propose the Randomised One-class SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life and synthetic datasets shows that our randomised 1SVM algorithm achieves comparable or better accuracy to deep auto encoder and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

High-Rate Space-Time Coded Large-MIMO Systems: Low-Complexity Detection and Channel Estimation

In this paper, we present a low-complexity algorithm for detection in high-rate, non-orthogonal space-time block coded (STBC) large-multiple-input multiple-output (MIMO) systems that achieve high spectral efficiencies of the order of tens of bps/Hz. We also present a training-based iterative detection/channel estimation scheme for such large STBC MIMO systems. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed multistage likelihood ascent search (M-LAS) detector in conjunction with the proposed iterative detection/channel estimation scheme at low complexities. The fact that we could show such good results for large STBCs like 16 X 16 and 32 X 32 STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot based training for channel estimation and turbo coding) establishes the effectiveness of the proposed detector and channel estimator. We decode perfect codes of large dimensions using the proposed detector. With the feasibility of such a low-complexity detection/channel estimation scheme, large-MIMO systems with tens of antennas operating at several tens of bps/Hz spectral efficiencies can become practical, enabling interesting high data rate wireless applications.

A Low-Complexity Near-ML Performance Achieving Algorithm for Large MIMO Detection

In this paper, we present a low-complexity, near maximum-likelihood (ML) performance achieving detector for large MIMO systems having tens of transmit and receive antennas. Such large MIMO systems are of interest because of the high spectral efficiencies possible in such systems. The proposed detection algorithm, termed as multistage likelihood-ascent search (M-LAS) algorithm, is rooted in Hopfield neural networks, and is shown to possess excellent performance as well as complexity attributes. In terms of performance, in a 64 x 64 V-BLAST system with 4-QAM, the proposed algorithm achieves an uncoded BER of 10(-3) at an SNR of just about 1 dB away from AWGN-only SISO performance given by Q(root SNR). In terms of coded BER, with a rate-3/4 turbo code at a spectral efficiency of 96 bps/Hz the algorithm performs close to within about 4.5 dB from theoretical capacity, which is remarkable in terms of both high spectral efficiency as well as nearness to theoretical capacity. Our simulation results show that the above performance is achieved with a complexity of just O(NtNt) per symbol, where N-t and N-tau denote the number of transmit and receive antennas.

Stationary processes for object detection and non-rigid structure-from-motion

Stationary processes are random variables whose value is a signal and whose distribution is invariant to translation in the domain of the signal. They are intimately connected to convolution, and therefore to the Fourier transform, since the covariance matrix of a stationary process is a Toeplitz matrix, and Toeplitz matrices are the expression of convolution as a linear operator. This thesis utilises this connection in the study of i) efficient training algorithms for object detection and ii) trajectory-based non-rigid structure-from-motion.

Energy efficient change detection over a MAC using physical layer fusion

We propose a simple and energy efficient distributed change detection scheme for sensor networks based on Page's parametric CUSUM algorithm. The sensor observations are IID over time and across the sensors conditioned on the change variable. Each sensor runs CUSUM and transmits only when the CUSUM is above some threshold. The transmissions from the sensors are fused at the physical layer. The channel is modeled as a multiple access channel (MAC) corrupted with IID noise. The fusion center which is the global decision maker, performs another CUSUM to detect the change. We provide the analysis and simulation results for our scheme and compare the performance with an existing scheme which ensures energy efficiency via optimal power selection.

Decentralized sequential change detection using physical layer fusion

We study the problem of decentralized sequential change detection with conditionally independent observations. The sensors form a star topology with a central node called fusion center as the hub. The sensors transmit a simple function of their observations in an analog fashion over a wireless Gaussian multiple access channel and operate under either a power constraint or an energy constraint. Simulations demonstrate that the proposed techniques have lower detection delays when compared with existing schemes. Moreover we demonstrate that the energy-constrained formulation enables better use of the total available energy than a power-constrained formulation.

Optimal sleep-wake scheduling for quickest intrusion detection using sensor networks

We consider the problem of quickest detection of an intrusion using a sensor network, keeping only a minimal number of sensors active. By using a minimal number of sensor devices, we ensure that the energy expenditure for sensing, computation and communication is minimized (and the lifetime of the network is maximized). We model the intrusion detection (or change detection) problem as a Markov decision process (MDP). Based on the theory of MDP, we develop the following closed loop sleep/wake scheduling algorithms: (1) optimal control of Mk+1, the number of sensors in the wake state in time slot k + 1, (2) optimal control of qk+1, the probability of a sensor in the wake state in time slot k + 1, and an open loop sleep/wake scheduling algorithm which (3) computes q, the optimal probability of a sensor in the wake state (which does not vary with time), based on the sensor observations obtained until time slot k. Our results show that an optimum closed loop control on Mk+1 significantly decreases the cost compared to keeping any number of sensors active all the time. Also, among the three algorithms described, we observe that the total cost is minimum for the optimum control on Mk+1 and is maximum for the optimum open loop control on q.