Assistive classification for improving the efficiency of avian species richness surveys

Avian species richness surveys, which measure the total number of unique avian species, can be conducted via remote acoustic sensors. An immense quantity of data can be collected, which, although rich in useful information, places a great workload on the scientists who manually inspect the audio. To deal with this big data problem, we calculated acoustic indices from audio data at a one-minute resolution and used them to classify one-minute recordings into five classes. By filtering out the non-avian minutes, we can reduce the amount of data by about 50% and improve the efficiency of determining avian species richness. The experimental results show that, given 60 one-minute samples, our approach enables to direct ecologists to find about 10% more avian species.

Semi-analytical finite element analysis of a strip footing on an elastic reinforced soil

A plane strain elastic interaction analysis of a strip footing resting on a reinforced soil bed has been made by using a combined analytical and finite element method (FEM). In this approach the stiffness matrix for the footing has been obtained using the FEM, For the reinforced soil bed (halfplane) the stiffness matrix has been obtained using an analytical solution. For the latter, the reinforced zone has been idealised as (i) an equivalent orthotropic infinite strip (composite approach) and (ii) a multilayered system (discrete approach). In the analysis, the interface between the strip footing and reinforced halfplane has been assumed as (i) frictionless and (ii) fully bonded. The contact pressure distribution and the settlement reduction have been given for different depths of footing and scheme of reinforcement in soil. The load-deformation behaviour of the reinforced soil obtained using the above modelling has been compared with some available analytical and model test results. The equivalent orthotropic approach proposed in this paper is easy to program and is shown to predict the reinforcing effects reasonably well.

Swelling behaviour of soil-bentonite mixtures

Studies on the swelling behaviour of mixtures of bentonite clay and nonswelling coarser fractions of different sizes and shapes reveal that observed swelling occurs only after the voids of the nonswelling particles are filled up with swollen clay particles. The magnitude of the swell within the voids, called intervoid swelling is large when the size and percentage of the nonswelling coarser fraction is large. The observable swell, after intervoid swelling, is called primary swelling and follows a rectangular hyperbolic relationship with time. The total swell per gram of the clay decreases with an increase in the size of the nonswelling fraction and with a decrease in the percentage of swelling clay. Time-swell relationships show that swelling continues to occur for a long time after the primary swelling, and this is called secondary swelling.

Steam-cured stabilised soil blocks for masonry construction

Energy-efficient, economical and durable building materials are essential for sustainable construction practices. The paper deals with production and properties of energy-efficient steam-cured stabilised soil blocks used fbr masonry construction. Problems of mixing expansive soil and lime, and production of blocks using soil-lime mixtures have been discussed briefly. Details of steam curing of stabilised soil blocks and properties of such blocks are given. A comparison of energy content of steam-cured soil blocks and burnt bricks is presented. It has been shown that energy-efficient steam cured soil blocks (consuming 35% less thermal energy compared to burnt clay bricks) having high compressive strength can be easily produced in a decentralised manner.

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.

Crop Classification using Biologically-inspired Techniques with High Resolution Satellite Image

Remote sensing provides a lucid and effective means for crop coverage identification. Crop coverage identification is a very important technique, as it provides vital information on the type and extent of crop cultivated in a particular area. This information has immense potential in the planning for further cultivation activities and for optimal usage of the available fertile land. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Further, image classification forms the core of the solution to the crop coverage identification problem. No single classifier can prove to satisfactorily classify all the basic crop cover mapping problems of a cultivated region. We present in this paper the experimental results of multiple classification techniques for the problem of crop cover mapping of a cultivated region. A detailed comparison of the algorithms inspired by social behaviour of insects and conventional statistical method for crop classification is presented in this paper. These include the Maximum Likelihood Classifier (MLC), Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) techniques. The high resolution satellite image has been used for the experiments.

A Classification of Homogeneous Operators in the Cowen-Douglas Class

A complete list of homogeneous operators in the Cowen-Douglas class B-n(D) is given. This classification is obtained from an explicit realization of all the homogeneous Hermitian holomorphic vector bundles on the unit disc under the action of the universal covering group of the bi-holomorphic automorphism group of the unit disc.

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).

Partial Loss Of Support And Frame-Soil Interaction

An interaction analysis has been conducted to study the effects of a local loss of support beneath the beam footing of a two-bay plane frame. The results of the study indicate that the magnitude of increase in the bending moment and axial force in the structure due to the presence of a void are dependent, not only on the extent of support loss, but also on the relative stiffnesses between foundation beam and soil, and between superstructure and soil. The increase in bending moment even for a void span of 1/12 of the foundation beam length can become so significant as to exceed the safety provisions. The study shows that the effect of a void on the superstructure moments can be greatly minimized by a combination of rigid foundation and flexible superstructure.

Dynamics of plant populations in Heteropogon contortus(black speargrass) pastures on a granite landscape in southern Queensland. 2. Seed production and soil seed banks of H. contortus

Seed production and soil seed hanks of H. contortus were studied in a subset of treatments within an extensive grazing study conducted in H. contortus pasture in southern Queensland between 1990 and 1996. Seed production of H. contortus in autumn ranged from 260 to 1800 seeds/m2 with much of this variation due to differences in rainfall between years. Seed production was generally higher in the silver-leaved ironbark than in the narrow-leaved ironbark land class and was also influenced by a consistent stocking rate x pasture type interaction. Inflorescence density was the main factor contributing to the variable seed production and was related to the rainfall received during February. The number of seeds per inflorescence was unaffected by seasonal rainfall, landscape position, stocking rate or legume oversowing. Seed viability was related to the rainfall received during March. Soil seed banks in spring varied from 130 to 520 seeds/m2 between 1990 and 1995 with generally more seed present in the silver-leaved ironbark than in the narrow-leaved ironbark land class. There were poor relationships between viable seed production and the size of the soil seed bank, and between the size of the soil seed bank and seedling recruitment. This study indicates that H. contortus has the potential to produce relatively large amounts of seed and showed that the seasonal pattern of rainfall plays a major role in achieving this potential

An overview of land application of pig effluent-P using soil P chemistry and mass balance calculations.

Attention is directed at land application of piggery effluent (containing urine, faeces, water, and wasted feed) as a potential source of water resource contamination with phosphorus (P). This paper summarises P-related properties of soil from 0-0.05 m depth at 11 piggery effluent application sites, in order to explore the impact that effluent application has had on the potential for run-off transport of P. The sites investigated were situated on Alfisol, Mollisol, Vertisol, and Spodosol soils in areas that received effluent for 1.5-30 years (estimated effluent-P applications of 100-310000 kg P/ha in total). Total (PT), bicarbonate extractable (PB), and soluble P forms were determined for the soil (0-0.05 m) at paired effluent and no-effluent sites, as well as texture, oxalate-extractable Fe and Al, organic carbon, and pH. All forms of soil P at 0-0.05 m depth increased with effluent application (PB at effluent sites was 1.7-15 times that at no-effluent sites) at 10 of the 11 sites. Increases in PB were strongly related to net P applications (regression analysis of log values for 7 sites with complete data sets: 82.6 % of variance accounted for, p <0.01). Effluent irrigation tended to increase the proportion of soil PT in dilute CaCl2-extractable forms (PTC: effluent average 2.0 %; no-effluent average 0.6%). The proportion of PTC in non-molybdate reactive forms (centrifuged supernatant) decreased (no-effluent average, 46.4 %; effluent average, 13.7 %). Anaerobic lagoon effluent did not reliably acidify soil, since no consistent relationship was observed for pH with effluent application. Soil organic carbon was increased in most of the effluent areas relative to the no-effluent areas. The four effluent areas where organic carbon was reduced had undergone intensive cultivation and cropping. Current effluent management at many of the piggeries failed to maximise the potential for waste P recapture. Ten of the case-study effluent application areas have received effluent-P in excess of crop uptake. While this may not represent a significant risk of leaching where sorption retains P, it has increased the risk of transport of P by run-off. Where such sites are close to surface water, run-off P loads should be managed.