Improving acoustic vehicle classification by information fusion

We present an information fusion approach for ground vehicle classification based on the emitted acoustic signal. Many acoustic factors can contribute to the classification accuracy of working ground vehicles. Classification relying on a single feature set may lose some useful information if its underlying sound production model is not comprehensive. To improve classification accuracy, we consider an information fusion diagram, in which various aspects of an acoustic signature are taken into account and emphasized separately by two different feature extraction methods. The first set of features aims to represent internal sound production, and a number of harmonic components are extracted to characterize the factors related to the vehicle’s resonance. The second set of features is extracted based on a computationally effective discriminatory analysis, and a group of key frequency components are selected by mutual information, accounting for the sound production from the vehicle’s exterior parts. In correspondence with this structure, we further put forward a modifiedBayesian fusion algorithm, which takes advantage of matching each specific feature set with its favored classifier. To assess the proposed approach, experiments are carried out based on a data set containing acoustic signals from different types of vehicles. Results indicate that the fusion approach can effectively increase classification accuracy compared to that achieved using each individual features set alone. The Bayesian-based decision level fusion is found fusion is found to be improved than a feature level fusion approac

Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

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Pathological Brain Detection Using Weiner Filtering, 2D-Discrete Wavelet Transform, Probabilistic PCA, and Random Subspace Ensemble Classifier

Accurate diagnosis of pathological brain images is important for patient care, particularly in the early phase of the disease. Although numerous studies have used machine-learning techniques for the computer-aided diagnosis (CAD) of pathological brain, previous methods encountered challenges in terms of the diagnostic efficiency owing to deficiencies in the choice of proper filtering techniques, neuroimaging biomarkers, and limited learning models. Magnetic resonance imaging (MRI) is capable of providing enhanced information regarding the soft tissues, and therefore MR images are included in the proposed approach. In this study, we propose a new model that includes Wiener filtering for noise reduction, 2D-discrete wavelet transform (2D-DWT) for feature extraction, probabilistic principal component analysis (PPCA) for dimensionality reduction, and a random subspace ensemble (RSE) classifier along with the K-nearest neighbors (KNN) algorithm as a base classifier to classify brain images as pathological or normal ones. The proposed methods provide a significant improvement in classification results when compared to other studies. Based on 5×5 cross-validation (CV), the proposed method outperforms 21 state-of-the-art algorithms in terms of classification accuracy, sensitivity, and specificity for all four datasets used in the study

Robust classification of advanced power quality disturbances in smart grids

The insertion of new devices, increased data flow, intermittent generation and massive computerization have considerably increased current electrical systems’ complexity. This increase resulted in necessary changes, such as the need for more intelligent electrical net works to adapt to this different reality. Artificial Intelligence (AI) plays an important role in society, especially the techniques based on the learning process, and it is extended to the power systems. In the context of Smart Grids (SG), where the information and innovative solutions in monitoring is a primary concern, those techniques based on AI can present several applications. This dissertation investigates the use of advanced signal processing and ML algorithms to create a Robust Classifier of Advanced Power Quality (PQ) Dis turbances in SG. For this purpose, known models of PQ disturbances were generated with random elements to approach real applications. From these models, thousands of signals were generated with the performance of these disturbances. Signal processing techniques using Discrete Wavelet Transform (DWT) were used to extract the signal’s main charac teristics. This research aims to use ML algorithms to classify these data according to their respective features. ML algorithms were trained, validated, and tested. Also, the accuracy and confusion matrix were analyzed, relating the logic behind the results. The stages of data generation, feature extraction and optimization techniques were performed in the MATLAB software. The Classification Learner toolbox was used for training, validation and testing the 27 different ML algorithms and assess each performance. All stages of the work were previously idealized, enabling their correct development and execution. The results show that the Cubic Support Vector Machine (SVM) classifier achieved the maximum accuracy of all algorithms, indicating the effectiveness of the proposed method for classification. Considerations about the results were interpreted as explaining the per formance of each technique, its relations and their respective justifications.A inserção de novos dispositivos na rede, aumento do fluxo de dados, geração intermitente e a informatização massiva aumentaram consideravelmente a complexidade dos sistemas elétricos atuais. Esse aumento resultou em mudanças necessárias, como a necessidade de redes elétricas mais inteligentes para se adaptarem a essa realidade diferente. A nova ger ação de técnicas de Inteligência Artificial, representada pelo "Big Data", Aprendizado de Máquina ("Machine Learning"), Aprendizagem Profunda e Reconhecimento de Padrões representa uma nova era na sociedade e no desenvolvimento global baseado na infor mação e no conhecimento. Com as mais recentes Redes Inteligentes, o uso de técnicas que utilizem esse tipo de inteligência será ainda mais necessário. Esta dissertação investiga o uso de processamento de sinais avançado e também algoritmos de Aprendizagem de Máquina para desenvolver um classificador robusto de distúrbios de qualidade de energia no contexto das Redes Inteligentes. Para isso, modelos já conhecidos de alguns proble mas de qualidade foram gerados junto com ruídos aleatórios para que o sistema fosse similar a aplicações reais. A partir desses modelos, milhares de sinais foram gerados e a Transformada Wavelet Discreta foi usada para extrair as principais características destas perturbações. Esta dissertação tem como objetivo utilizar algoritmos baseados no con ceito de Aprendizado de Máquina para classificar os dados gerados de acordo com suas classes. Todos estes algoritmos foram treinados, validados e por fim, testados. Além disso, a acurácia e a matriz de confusão de cada um dos modelos foi apresentada e analisada. As etapas de geração de dados, extração das principais características e otimização dos dados foram realizadas no software MATLAB. Uma toolbox específica deste programa foi us ada para treinar, validar e testar os 27 algoritmos diferentes e avaliar cada desempenho. Todas as etapas do trabalho foram previamente idealizadas, possibilitando seu correto desenvolvimento e execução. Os resultados mostram que o classificador "Cubic Support Vector Machine" obteve a máxima precisão entre todos os algoritmos, indicando a eficácia do método proposto para classificação. As considerações sobre os resultados foram inter pretadas, como por exemplo a explicação da performance de cada técnica, suas relações e suas justificativas

Application of spectral and spatial indices for specific class identification in Airborne Prism EXperiment (APEX) imaging spectrometer data for improved land cover classification

Hyperspectral remote sensing's ability to capture spectral information of targets in very narrow bandwidths gives rise to many intrinsic applications. However, the major limiting disadvantage to its applicability is its dimensionality, known as the Hughes Phenomenon. Traditional classification and image processing approaches fail to process data along many contiguous bands due to inadequate training samples. Another challenge of successful classification is to deal with the real world scenario of mixed pixels i.e. presence of more than one class within a single pixel. An attempt has been made to deal with the problems of dimensionality and mixed pixels, with an objective to improve the accuracy of class identification. In this paper, we discuss the application of indices to cope with the disadvantage of the dimensionality of the Airborne Prism EXperiment (APEX) hyperspectral Open Science Dataset (OSD) and to improve the classification accuracy using the Possibilistic c–Means (PCM) algorithm. This was used for the formulation of spectral and spatial indices to describe the information in the dataset in a lesser dimensionality. This reduced dimensionality is used for classification, attempting to improve the accuracy of determination of specific classes. Spectral indices are compiled from the spectral signatures of the target and spatial indices have been defined using texture analysis over defined neighbourhoods. The classification of 20 classes of varying spatial distributions was considered in order to evaluate the applicability of spectral and spatial indices in the extraction of specific class information. The classification of the dataset was performed in two stages; spectral and a combination of spectral and spatial indices individually as input for the PCM classifier. In addition to the reduction of entropy, while considering a spectral-spatial indices approach, an overall classification accuracy of 80.50% was achieved, against 65% (spectral indices only) and 59.50% (optimally determined principal component