Image processing and classification procedure for the analysis of Australian frog vocalisations

Frog species have been declining worldwide at unprecedented rates in the past decades. There are many reasons for this decline including pollution, habitat loss, and invasive species [1]. To preserve, protect, and restore frog biodiversity, it is important to monitor and assess frog species. In this paper, a novel method using image processing techniques for analyzing Australian frog vocalisations is proposed. An FFT is applied to audio data to produce a spectrogram. Then, acoustic events are detected and isolated into corresponding segments through image processing techniques applied to the spectrogram. For each segment, spectral peak tracks are extracted with selected seeds and a region growing technique is utilised to obtain the contour of each frog vocalisation. Based on spectral peak tracks and the contour of each frog vocalisation, six feature sets are extracted. Principal component analysis reduces each feature set down to six principal components which are tested for classification performance with a k-nearest neighbor classifier. This experiment tests the proposed method of classification on fourteen frog species which are geographically well distributed throughout Queensland, Australia. The experimental results show that the best average classification accuracy for the fourteen frog species can be up to 87%.

Acoustic classification of Australian anurans using syllable features

Acoustic classification of anurans (frogs) has received increasing attention for its promising application in biological and environment studies. In this study, a novel feature extraction method for frog call classification is presented based on the analysis of spectrograms. The frog calls are first automatically segmented into syllables. Then, spectral peak tracks are extracted to separate desired signal (frog calls) from background noise. The spectral peak tracks are used to extract various syllable features, including: syllable duration, dominant frequency, oscillation rate, frequency modulation, and energy modulation. Finally, a k-nearest neighbor classifier is used for classifying frog calls based on the results of principal component analysis. The experiment results show that syllable features can achieve an average classification accuracy of 90.5% which outperforms Mel-frequency cepstral coefficients features (79.0%).

Acoustic feature extraction using perceptual wavelet packet decomposition for frog call classification

Frog protection has become increasingly essential due to the rapid decline of its biodiversity. Therefore, it is valuable to develop new methods for studying this biodiversity. In this paper, a novel feature extraction method is proposed based on perceptual wavelet packet decomposition for classifying frog calls in noisy environments. Pre-processing and syllable segmentation are first applied to the frog call. Then, a spectral peak track is extracted from each syllable if possible. Track duration, dominant frequency and oscillation rate are directly extracted from the track. With k-means clustering algorithm, the calculated dominant frequency of all frog species is clustered into k parts, which produce a frequency scale for wavelet packet decomposition. Based on the adaptive frequency scale, wavelet packet decomposition is applied to the frog calls. Using the wavelet packet decomposition coefficients, a new feature set named perceptual wavelet packet decomposition sub-band cepstral coefficients is extracted. Finally, a k-nearest neighbour (k-NN) classifier is used for the classification. The experiment results show that the proposed features can achieve an average classification accuracy of 97.45% which outperforms syllable features (86.87%) and Mel-frequency cepstral coefficients (MFCCs) feature (90.80%).

Application of image processing techniques for frog call classification

Frogs have received increasing attention due to their effectiveness for indicating the environment change. Therefore, it is important to monitor and assess frogs. With the development of sensor techniques, large volumes of audio data (including frog calls) have been collected and need to be analysed. After transforming the audio data into its spectrogram representation using short-time Fourier transform, the visual inspection of this representation motivates us to use image processing techniques for analysing audio data. Applying acoustic event detection (AED) method to spectrograms, acoustic events are firstly detected from which ridges are extracted. Three feature sets, Mel-frequency cepstral coefficients (MFCCs), AED feature set and ridge feature set, are then used for frog call classification with a support vector machine classifier. Fifteen frog species widely spread in Queensland, Australia, are selected to evaluate the proposed method. The experimental results show that ridge feature set can achieve an average classification accuracy of 74.73% which outperforms the MFCCs (38.99%) and AED feature set (67.78%).

Acoustic features for multi-level classification of Australian frogs

Over past few decades, frog species have been experiencing dramatic decline around the world. The reason for this decline includes habitat loss, invasive species, climate change and so on. To better know the status of frog species, classifying frogs has become increasingly important. In this study, acoustic features are investigated for multi-level classification of Australian frogs: family, genus and species, including three families, eleven genera and eighty five species which are collected from Queensland, Australia. For each frog species, six instances are selected from which ten acoustic features are calculated. Then, the multicollinearity between ten features are studied for selecting non-correlated features for subsequent analysis. A decision tree (DT) classifier is used to visually and explicitly determine which acoustic features are relatively important for classifying family, which for genus, and which for species. Finally, a weighted support vector machines (SVMs) classifier is used for the multi- level classification with three most important acoustic features respectively. Our experiment results indicate that using different acoustic feature sets can successfully classify frogs at different levels and the average classification accuracy can be up to 85.6%, 86.1% and 56.2% for family, genus and species respectively.

The critical role of water in spider silk and its consequence for protein mechanics

Due to its remarkable mechanical and biological properties, there is considerable interest in understanding, and replicating, spider silk's stress-processing mechanisms and structure-function relationships. Here, we investigate the role of water in the nanoscale mechanics of the different regions in the spider silk fibre, and their relative contributions to stress processing. We propose that the inner core region, rich in spidroin II, retains water due to its inherent disorder, thereby providing a mechanism to dissipate energy as it breaks a sacrificial amide-water bond and gains order under strain, forming a stronger amide-amide bond. The spidroin I-rich outer core is more ordered under ambient conditions and is inherently stiffer and stronger, yet does not on its own provide high toughness. The markedly different interactions of the two proteins with water, and their distribution across the fibre, produce a stiffness differential and provide a balance between stiffness, strength and toughness under ambient conditions. Under wet conditions, this balance is destroyed as the stiff outer core material reverts to the behaviour of the inner core.

Evaluation of ion exchange as a pre-treatment option for coal seam gas water

The Coal Seam Gas (CSG) industry in Australia has grown significantly in recent years. During the gas extraction process, water is also recovered which is brackish in character. In order to facilitate beneficial reuse of the water, the CSG industry has primarily invested in Reverse Osmosis (RO) as the primary method for associated water desalination. However, the presence of alkaline earth ions in the water combined with the inherent alkalinity of the water may result in RO membrane scaling. Consequently, weak acid cation (WAC) synthetic ion exchange resins were investigated as a potential solution to this potential problem. It was shown that resins were indeed highly efficient at treating single and multi-component solutions of alkaline earth ions. The interaction of the ions with the resin was found to be considerably more complex that previously reported.

Carbon nanodot decorated graphitic carbon nitride: New insights into the enhanced photocatalytic water splitting from ab initio studies

Interfacing carbon nanodots (C-dots) with graphitic carbon nitride (g-C3N4) produces a metal-free system that has recently demonstrated significant enhancement of photo-catalytic performance for water splitting into hydrogen [Science, 2015, 347, 970–974]. However, the underlying photo-catalytic mechanism is not fully established. Herein, we have carried out density functional theory (DFT) calculations to study the interactions between g-C3N4 and trigonal/hexagonal shaped C-dots. We find that hybrid C-dots/g-C3N4 can form a type-II van der Waals heterojunction, leading to significant reduction of band gap. The C-dot decorated g-C3N4 enhances the separation of photogenerated electron and hole pairs and the composite's visible light response. Interestingly, the band alignment of C-dots and g-C3N4 calculated by the hybrid functional method indicates that C-dots act as a spectral sensitizer in hybrid C-dots/g-C3N4 for water splitting. Our results offer new theoretical insights into this metal-free photocatalyst for water splitting.

A Bayesian Framework for the Assessment of Vision-based Weed and Fruit Detection and Classification Algorithms

This paper proposes new metrics and a performance-assessment framework for vision-based weed and fruit detection and classification algorithms. In order to compare algorithms, and make a decision on which one to use fora particular application, it is necessary to take into account that the performance obtained in a series of tests is subject to uncertainty. Such characterisation of uncertainty seems not to be captured by the performance metrics currently reported in the literature. Therefore, we pose the problem as a general problem of scientific inference, which arises out of incomplete information, and propose as a metric of performance the(posterior) predictive probabilities that the algorithms will provide a correct outcome for target and background detection. We detail the framework through which these predicted probabilities can be obtained, which is Bayesian in nature. As an illustration example, we apply the framework to the assessment of performance of four algorithms that could potentially be used in the detection of capsicums (peppers).

Rank regression analysis of correlated water quality data from South East Queensland

With growing population and fast urbanization in Australia, it is a challenging task to maintain our water quality. It is essential to develop an appropriate statistical methodology in analyzing water quality data in order to draw valid conclusions and hence provide useful advices in water management. This paper is to develop robust rank-based procedures for analyzing nonnormally distributed data collected over time at different sites. To take account of temporal correlations of the observations within sites, we consider the optimally combined estimating functions proposed by Wang and Zhu (Biometrika, 93:459-464, 2006) which leads to more efficient parameter estimation. Furthermore, we apply the induced smoothing method to reduce the computational burden. Smoothing leads to easy calculation of the parameter estimates and their variance-covariance matrix. Analysis of water quality data from Total Iron and Total Cyanophytes shows the differences between the traditional generalized linear mixed models and rank regression models. Our analysis also demonstrates the advantages of the rank regression models for analyzing nonnormal data.

Wavelet-based multiresolution analysis of Wivenhoe Dam water temperatures

Water temperature measurements from Wivenhoe Dam offer a unique opportunity for studying fluctuations of temperatures in a subtropical dam as a function of time and depth. Cursory examination of the data indicate a complicated structure across both time and depth. We propose simplifying the task of describing these data by breaking the time series at each depth into physically meaningful components that individually capture daily, subannual, and annual (DSA) variations. Precise definitions for each component are formulated in terms of a wavelet-based multiresolution analysis. The DSA components are approximately pairwise uncorrelated within a given depth and between different depths. They also satisfy an additive property in that their sum is exactly equal to the original time series. Each component is based upon a set of coefficients that decomposes the sample variance of each time series exactly across time and that can be used to study both time-varying variances of water temperature at each depth and time-varying correlations between temperatures at different depths. Each DSA component is amenable for studying a certain aspect of the relationship between the series at different depths. The daily component in general is weakly correlated between depths, including those that are adjacent to one another. The subannual component quantifies seasonal effects and in particular isolates phenomena associated with the thermocline, thus simplifying its study across time. The annual component can be used for a trend analysis. The descriptive analysis provided by the DSA decomposition is a useful precursor to a more formal statistical analysis.

Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits

Environmental data usually include measurements, such as water quality data, which fall below detection limits, because of limitations of the instruments or of certain analytical methods used. The fact that some responses are not detected needs to be properly taken into account in statistical analysis of such data. However, it is well-known that it is challenging to analyze a data set with detection limits, and we often have to rely on the traditional parametric methods or simple imputation methods. Distributional assumptions can lead to biased inference and justification of distributions is often not possible when the data are correlated and there is a large proportion of data below detection limits. The extent of bias is usually unknown. To draw valid conclusions and hence provide useful advice for environmental management authorities, it is essential to develop and apply an appropriate statistical methodology. This paper proposes rank-based procedures for analyzing non-normally distributed data collected at different sites over a period of time in the presence of multiple detection limits. To take account of temporal correlations within each site, we propose an optimal linear combination of estimating functions and apply the induced smoothing method to reduce the computational burden. Finally, we apply the proposed method to the water quality data collected at Susquehanna River Basin in United States of America, which dearly demonstrates the advantages of the rank regression models.

ESR measurements of the partitioning of some new spin probes in n-octanol-water

The stable free radical 1,1,3,3-tetramethylisoindolin-2-yloxyl (TMIO) has proved to be very suitable for use as a spin probe for a number of applications. Because it is soluble mainly in non-polar liquids, there is a need for new derivatives that can be used in a variety of environments. This has been done by introducing substituents in the 5-position of the aromatic ring, namely carboxyl (CTMIO), trimethylamino (TMTMIOI) and sodium sulphonate (NaTMIOS). An accurate ESR method was developed for the measurement of partition coefficients in n-octanol–water. For comparison purposes the method was also applied to some Tempo derivatives. The effect of temperature on the rotational correlation times and the nitrogen-14 hyperfine coupling constant of some of the spin probes was investigated. There is evidence for dimerization of CTMIO to form a biradical

Osteopenia and rickets in the extremely low birth weight infant: A survey of the incidence and a radiological classification

A review was carried out of the radiographs of twenty-five infants with birth weights under 1000 G, who survived for more than twenty-eight days; eighteen of these had enough suitable films for a survey of the progressive bone changes which occur in these infants, including estimation of humeral cortical cross-sectional area. The incidence of the changes has been assessed and a typical progression of radiographic appearances has been shown, with a suggested system of staging. All infants showed some loss of bone mineral, with frank changes of rickets occurring in forty-four percent. Aetiological factors are mainly concerned with the difficulty of supplying and ensuring absorption of sufficient bone mineral (calcium and phosphate) and vitamin D. Liver immaturity may be another factor. Disease states additional to prematurity accentuate the problem. Rib fractures occurring around 80–90 days post-nataEy commonly draw attention to the bone disorder and are probably the major clinical factor of importance; there is a high incidence of associated lung disease of uncertain pathology. Attention is drawn to possible confusion with other bone disorders in the post-natal period.

Optimized group replacement scheduling for water pipeline network

Replacement of deteriorated water pipes is a capital-intensive activity for utility companies. Replacement planning aims to minimize total costs while maintaining a satisfactory level of service and is usually conducted for individual pipes. Scheduling replacement in groups is seen to be a better method and has the potential to provide benefits such as the reduction of maintenance costs and service interruptions. However, developing group replacement schedules is a complex task and often beyond the ability of a human expert, especially when multiple or conflicting objectives need to be catered for, such as minimization of total costs and service interruptions. This paper describes the development of a novel replacement decision optimization model for group scheduling (RDOM-GS), which enables multiple group-scheduling criteria by integrating new cost functions, a service interruption model, and optimization algorithms into a unified procedure. An industry case study demonstrates that RDOM-GS can improve replacement planning significantly and reduce costs and service interruptions.