An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

Computing the distinct features from input data, before the classification, is a part of complexity to the methods of Automatic Modulation Classification (AMC) which deals with modulation classification was a pattern recognition problem. Although the algorithms that focus on MultiLevel Quadrature Amplitude Modulation (M-QAM) which underneath different channel scenarios was well detailed. A search of the literature revealed indicates that few studies were done on the classification of high order M-QAM modulation schemes like128-QAM, 256-QAM, 512-QAM and1024-QAM. This work is focusing on the investigation of the powerful capability of the natural logarithmic properties and the possibility of extracting Higher-Order Cumulant's (HOC) features from input data received raw. The HOC signals were extracted under Additive White Gaussian Noise (AWGN) channel with four effective parameters which were defined to distinguished the types of modulation from the set; 4-QAM~1024-QAM. This approach makes the recognizer more intelligent and improves the success rate of classification. From simulation results, which was achieved under statistical models for noisy channels, manifest that recognized algorithm executes was recognizing in M-QAM, furthermore, most results were promising and showed that the logarithmic classifier works well over both AWGN and different fading channels, as well as it can achieve a reliable recognition rate even at a lower signal-to-noise ratio (less than zero), it can be considered as an Integrated Automatic Modulation Classification (AMC) system in order to identify high order of M-QAM signals that applied a unique logarithmic classifier, to represents higher versatility, hence it has a superior performance via all previous works in automatic modulation identification systemComment: 18 page

Classifiers accuracy improvement based on missing data imputation

In this paper we investigate further and extend our previous work on radar signal identification and classification based on a data set which comprises continuous, discrete and categorical data that represent radar pulse train characteristics such as signal frequencies, pulse repetition, type of modulation, intervals, scan period, scanning type, etc. As the most of the real world datasets, it also contains high percentage of missing values and to deal with this problem we investigate three imputation techniques: Multiple Imputation (MI); K-Nearest Neighbour Imputation (KNNI); and Bagged Tree Imputation (BTI). We apply these methods to data samples with up to 60% missingness, this way doubling the number of instances with complete values in the resulting dataset. The imputation models performance is assessed with Wilcoxon’s test for statistical significance and Cohen’s effect size metrics. To solve the classification task, we employ three intelligent approaches: Neural Networks (NN); Support Vector Machines (SVM); and Random Forests (RF). Subsequently, we critically analyse which imputation method influences most the classifiers’ performance, using a multiclass classification accuracy metric, based on the area under the ROC curves. We consider two superclasses (‘military’ and ‘civil’), each containing several ‘subclasses’, and introduce and propose two new metrics: inner class accuracy (IA); and outer class accuracy (OA), in addition to the overall classification accuracy (OCA) metric. We conclude that they can be used as complementary to the OCA when choosing the best classifier for the problem at hand

Automatic classification of digital communication signal modulations

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityAutomatic modulation classification detects the modulation type of received communication signals. It has important applications in military scenarios to facilitate jamming, intelligence, surveillance, and threat analysis. The renewed interest from civilian scenes has been fuelled by the development of intelligent communications systems such as cognitive radio and software defined radio. More specifically, it is complementary to adaptive modulation and coding where a modulation can be deployed from a set of candidates according to the channel condition and system specification for improved spectrum efficiency and link reliability. In this research, we started by improving some existing methods for higher classification accuracy but lower complexity. Machine learning techniques such as k-nearest neighbour and support vector machine have been adopted for simplified decision making using known features. Logistic regression, genetic algorithm and genetic programming have been incorporated for improved classification performance through feature selection and combination. We have also developed a new distribution test based classifier which is tailored for modulation classification with the inspiration from Kolmogorov-Smirnov test. The proposed classifier is shown to have improved accuracy and robustness over the standard distribution test. For blind classification in imperfect channels, we developed the combination of minimum distance centroid estimator and non-parametric likelihood function for blind modulation classification without the prior knowledge on channel noise. The centroid estimator provides joint estimation of channel gain and carrier phase o set where both can be compensated in the following nonparametric likelihood function. The non-parametric likelihood function, in the meantime, provide likelihood evaluation without a specifically assumed noise model. The combination has shown to have higher robustness when different noise types are considered. To push modulation classification techniques into a more timely setting, we also developed the principle for blind classification in MIMO systems. The classification is achieved through expectation maximization channel estimation and likelihood based classification. Early results have shown bright prospect for the method while more work is needed to further optimize the method and to provide a more thorough validation.School of Engineering and Design Brunel University London, the Faculty of Engineering University of Liverpool, and the University of Liverpool Graduate Association (Hong Kong)

The use of automated acoustic identification software for bat surveys in the neotropics : Gaps and opportunities

As populações de morcegos são conhecidas por serem afetadas por atividades antropogênicas,njá que os Chiroptera é um grupo extremamente diverso que ocupa quase todos os nichos disponíveis no meio terrestre. Assim, os morcegos são considerados bons bioindicadores para monitorar mudanças no meio ambiente, mas seu valor como tal também depende da facilidade de monitorar e detectar tendências demográficas em suas populações. O interesse a longo prazo dos pesquisadores na acústica dos morcegos resulta do fato de que é um método não-invasivo e eficiente em termos de tempo para monitorar os padrões espaço-temporais da diversidade e atividade de morcegos. A análise dos sons emitidos pelos organismos tem sido útil para a aquisição de conhecimento sobre as interações bióticas e abióticas específicas de cada espécie, e sua aplicação na conservação. Além das identificações manuais de chamados de morcegos, existe atualmente no mercado um conjunto de programas automatizados de identificação que utilizam bibliotecas regionais e se apresentam como uma ferramenta eficiente no monitoramento de populações de morcegos. A maioria desses programas não foi validada usando dados de campo. Este estudo avalia a confiabilidade de dois softwares automatizados, SonoChiro e Kaleidoscope Pro, em comparação com identificações manuais de dados de campo coletados da região Neotropical. Houve um baixo nível de concordância entre os dois métodos automatizados ao nível das identificações específicas, razoável ao nível do gênero e satisfatório ao nível a família. Houve também uma diferença significativa entre a proporção de chamados corretamente identificados entre os dois programas ao nível específico. Os principais desafios para o uso de software de identificação automatizada incluem a necessidade de bibliotecas de chamados abrangentes da diversidade existente nas regiões em foco dos estudos; as principais oportunidades, por outro lado, incluem a ampla possibilidade de monitorar os padrões espaço-temporais da atividade de morcegos. Existem ainda fortes lacunas que impedem uma aplicação generalizada de programas automatizados em estudos ecológicos e de conservação de morcegos, mas há potencial de melhoria. Considerando as limitações dos programas automatizados, é discutida uma estrutura para aplicação em estudos ecológicos e de conservação.Bat populations are known to be affected by anthropogenic activities because bats are an extremely diverse group occupying almost all available niches in terrestrial environment. Hence, bats are considered bioindicators to monitor changes in the environment, but their value as such also depends on the ease to monitor and detect demographic trends in their populations. The long term interest of researchers in the acoustic of bats results from the fact that it is a non-invasive, time-efficient methods to monitor spatiotemporal patterns of bat diversity and activity.The analysis of sounds emitted by organisms has been considered useful to gain insight into species-specific biotic and abiotic interactions, which can further be applied to conservation. Besides manual identifications of bat calls, a number of automated species identification programs using regional call classfiers have been introduced into the market as an efficient tool in monitoring of bat populations. Most of these programs have not been validated using field data. This study evaluates the reliability of two automated softwares, SonoChiro and Kaleidoscope Pro, in comparison to manual identifications of field data collected from the Neotropical region. There was low agreement between the two automated methods at the species level, fair agreement at the genus level and moderate agreement at the family level. There was also a significant difference between the proportion of correctly identified calls of the two-automated software at the species level identifications. Major challenges for using automated identification software include the need for comprehensive call libraries of the regions under scope; major opportunities, on the other hand, include the widespread possibility to monitor spatiotemporal patterns of bat activity. Overall, there are serious gaps that preclude a widespread application of automated programs in ecological and conservation studies of bats, but there is a potential for improvement. Considering the limitations of the automated programs, a framework for application in ecological and conservation studies is discussed

Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/ licenses/by/4.0

Condition monitoring of bearing faults using the stator current and shrinkage methods

Producción CientíficaCondition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults, the use of vibrations or sound generally offers better results in the accuracy of the detection, although with some disadvantages related to the sensors used for monitoring. To improve the performance of bearing monitoring, it is proposed to take advantage of more information available in the current spectra, beyond the usually employed, incorporating the amplitude of a significant number of sidebands around the first eleven harmonics, growing exponentially the number of fault signatures. This is especially interesting for inverter-fed motors. But, in turn, this leads to the problem of overfitting when applying a classifier to perform the fault diagnosis. To overcome this problem, and still exploit all the useful information available in the spectra, it is proposed to use shrinkage methods, which have been lately proposed in machine learning to solve the overfitting issue when the problem has many more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal.CAPES (process BEX552269/2011-5

Comparative analysis of methods for microbiome study

Microbiome analysis is garnering much interest with benefits including improved treatment options, enhanced capabilities for personalized medicine, greater understanding of the human body, and contributions to ecological study. Data from these communities of bacteria, viruses, and fungi are feature rich, sparse, and have sample sizes not appreciably larger than the feature space, making analysis challenging and necessitating a coordinated approach utilizing multiple techniques alongside domain expertise. This thesis provides an overview and comparative analysis of these methods, with a case study on cirrhosis and hepatic encephalopathy demonstrating a selection of methods. Approaches are considered in a medically motivated context where relationships between microbes in the human body and diseases or conditions are of primary interest, with additional objectives being the identification of how microbes influence each other and how these influences relate to the diseases and conditions being studied. These analysis methods are partitioned into three categories: univariate statistical methods, classifier-based methods, and joint analysis methods. Univariate statistical methods provide results corresponding to how much a single variable or feature differs between groups in the data. Classifier-based approaches can be generalized as those where a classification model with microbe abundance as inputs and disease states as outputs is used, resulting in a predictive model which is then analyzed to learn about the data. The joint analysis category corresponds to techniques which specifically target relationships between microbes and compare those relationships among subpopulations within the data. Despite significant differences between these categories and the individual methods, each has strengths and weaknesses and plays an important role in microbiome analysis

Integrating Diverse Datasets Improves Developmental Enhancer Prediction

Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through in vivo validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology. © 2014 Erwin et al