Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

Computerized identification of motor complications in Parkinson's disease

Objective: To investigate whether spirography-based objective measures are able to effectively characterize the severity of unwanted symptom states (Off and dyskinesia) and discriminate them from motor state of healthy elderly subjects. Background: Sixty-five patients with advanced Parkinson’s disease (PD) and 10 healthy elderly (HE) subjects performed repeated assessments of spirography, using a touch screen telemetry device in their home environments. On inclusion, the patients were either treated with levodopa-carbidopa intestinal gel or were candidates for switching to this treatment. On each test occasion, the subjects were asked trace a pre-drawn Archimedes spiral shown on the screen, using an ergonomic pen stylus. The test was repeated three times and was performed using dominant hand. A clinician used a web interface which animated the spiral drawings, allowing him to observe different kinematic features, like accelerations and spatial changes, during the drawing process and to rate different motor impairments. Initially, the motor impairments of drawing speed, irregularity and hesitation were rated on a 0 (normal) to 4 (extremely severe) scales followed by marking the momentary motor state of the patient into 2 categories that is Off and Dyskinesia. A sample of spirals drawn by HE subjects was randomly selected and used in subsequent analysis. Methods: The raw spiral data, consisting of stylus position and timestamp, were processed using time series analysis techniques like discrete wavelet transform, approximate entropy and dynamic time warping in order to extract 13 quantitative measures for representing meaningful motor impairment information. A principal component analysis (PCA) was used to reduce the dimensions of the quantitative measures into 4 principal components (PC). In order to classify the motor states into 3 categories that is Off, HE and dyskinesia, a logistic regression model was used as a classifier to map the 4 PCs to the corresponding clinically assigned motor state categories. A stratified 10-fold cross-validation (also known as rotation estimation) was applied to assess the generalization ability of the logistic regression classifier to future independent data sets. To investigate mean differences of the 4 PCs across the three categories, a one-way ANOVA test followed by Tukey multiple comparisons was used. Results: The agreements between computed and clinician ratings were very good with a weighted area under the receiver operating characteristic curve (AUC) coefficient of 0.91. The mean PC scores were different across the three motor state categories, only at different levels. The first 2 PCs were good at discriminating between the motor states whereas the PC3 was good at discriminating between HE subjects and PD patients. The mean scores of PC4 showed a trend across the three states but without significant differences. The Spearman’s rank correlations between the first 2 PCs and clinically assessed motor impairments were as follows: drawing speed (PC1, 0.34; PC2, 0.83), irregularity (PC1, 0.17; PC2, 0.17), and hesitation (PC1, 0.27; PC2, 0.77). Conclusions: These findings suggest that spirography-based objective measures are valid measures of spatial- and time-dependent deficits and can be used to distinguish drug-related motor dysfunctions between Off and dyskinesia in PD. These measures can be potentially useful during clinical evaluation of individualized drug-related complications such as over- and under-medications thus maximizing the amount of time the patients spend in the On state.

Assessment of PD Speech Anomalies @ Home

Background: Voice processing in real-time is challenging. A drawback of previous work for Hypokinetic Dysarthria (HKD) recognition is the requirement of controlled settings in a laboratory environment. A personal digital assistant (PDA) has been developed for home assessment of PD patients. The PDA offers sound processing capabilities, which allow for developing a module for recognition and quantification HKD. Objective: To compose an algorithm for assessment of PD speech severity in the home environment based on a review synthesis. Methods: A two-tier review methodology is utilized. The first tier focuses on real-time problems in speech detection. In the second tier, acoustics features that are robust to medication changes in Levodopa-responsive patients are investigated for HKD recognition. Keywords such as Hypokinetic Dysarthria , and Speech recognition in real time were used in the search engines. IEEE explorer produced the most useful search hits as compared to Google Scholar, ELIN, EBRARY, PubMed and LIBRIS. Results: Vowel and consonant formants are the most relevant acoustic parameters to reflect PD medication changes. Since relevant speech segments (consonants and vowels) contains minority of speech energy, intelligibility can be improved by amplifying the voice signal using amplitude compression. Pause detection and peak to average power rate calculations for voice segmentation produce rich voice features in real time. Enhancements in voice segmentation can be done by inducing Zero-Crossing rate (ZCR). Consonants have high ZCR whereas vowels have low ZCR. Wavelet transform is found promising for voice analysis since it quantizes non-stationary voice signals over time-series using scale and translation parameters. In this way voice intelligibility in the waveforms can be analyzed in each time frame. Conclusions: This review evaluated HKD recognition algorithms to develop a tool for PD speech home-assessment using modern mobile technology. An algorithm that tackles realtime constraints in HKD recognition based on the review synthesis is proposed. We suggest that speech features may be further processed using wavelet transforms and used with a neural network for detection and quantification of speech anomalies related to PD. Based on this model, patients' speech can be automatically categorized according to UPDRS speech ratings.

Characterization and application of objective pilling classification to patterned fabrics

Previously, the authors proposed a new, simple method of frequency domain analysis based on the two-dimensional discrete wavelet transform to objectively measure the pilling intensity in sample fabric images. The method was further characterized, and the results obtained indicate that standard deviation and variance are the most appropriate measures of the dispersion of wavelet details coefficients for analysis, that the relationship between wavelet analysis scale and fabric inter-yarn pitch was empirically confirmed, and, that fabrics with random patterns do not appear to impact on the effectiveness of the analysis method.

New methods for objective evaluation of fabric pilling by frequency domain image processing

Fabric pilling is a serious problem for the apparel industry. Resistance to pilling is normally tested by simulated accelerated wear and manual assessment of degree of pilling based on a visual comparison of the sample to a set of test images. A number of automated systems based on image analysis have been developed. The authors propose new methods of image analysis based on the two-dimensional wavelet transform to objectively measure the pilling intensity in sample images. Initial work employed the detail coefficients of the two-dimensional discrete wavelet transform (2DDWT) as a measure of the pilling intensity of woven/knitted fabrics.

This method is shown to be robust to image translation and brightness variation. Using the approximation coefficients of the 2DDWT, the method is extended to non-woven pilling image sets. Wavelet texture analysis (WTA) combined with principal components analysis are shown to produce a richer texture description of pilling for analysis and classification. Finally, employing the two-dimensional dual-tree complex wavelet transform as the basis for the WTA feature vector is shown to produce good automated classification on a range of standard pilling image sets.

Intelligent clothing for automated recognition of human physical activities in free-living environment

This paper presents an intelligent clothing framework for human daily activity recognition using a single waist-worn tri-axial accelerometer sensor coupled with a robust pattern recognition system. The activity recognition algorithm is realized to distinguish six different physical activities through three major steps: acceleration signal collection/pre-processing, wavelet-based principle component analysis, and a support vector machine classifier. The proposed activity recognition method has been experimentally validated through two batches of trials with an overall mean classification accuracy of 95.25 and 94.87%, respectively. These results suggest that the intelligent clothing is not only able to learn the activity patterns but also capable of generalizing new data from both known and unknown subjects. This enables the proposed intelligent clothing to be applied in a comfortable and in situ assessment of human physical activities, which would open up new market segments to the textile industry.

Text-dependent speaker recognition using wavelets and neural networks

An intelligent system for text-dependent speaker recognition is proposed in this paper. The system consists of a wavelet-based module as the feature extractor of speech signals and a neural-network-based module as the signal classifier. The Daubechies wavelet is employed to filter and compress the speech signals. The fuzzy ARTMAP (FAM) neural network is used to classify the processed signals. A series of experiments on text-dependent gender and speaker recognition are conducted to assess the effectiveness of the proposed system using a collection of vowel signals from 100 speakers. A variety of operating strategies for improving the FAM performance are examined and compared. The experimental results are analyzed and discussed.

Development of a speaker recognition system using wavelets and artificial neural networks

This paper addresses the problem of speaker recognition from speech signals. The study focuses on the development of a speaker recognition system comprising two modules: a wavelet-based feature extractor, and a neural-network-based classifier. We have conducted a number of experiments to investigate the applicability of Discrete Wavelet Transform (D WT) in extracting discriminative features from the speech signals, and have examined various models from the Adaptive Resonance Theory (ART) family of neural networks in classijjing the extracted features. The results indicate that DWT could be a potential feature extraction tool for speaker recognition. In addition, the ART-based classijiers have yielded very promising recognition accuracy at more than 81%.

Robust patchwork-based watermarking method for stereo audio signals

This paper presents a patchwork-based watermarking method for stereo audio signals, which exploits the similarity of the two sound channels of stereo signals. Given a segment of stereo signal, we first compute the discrete Fourier transforms (DFTs) of the two sound channels, which yields two sets of DFT coefficients. The DFT coefficients corresponding to certain frequency range are divided into multiple subsegment pairs and a criterion is proposed to select those suitable for watermark embedding. Then a watermark is embedded into the selected subsegment pairs by modifying their DFT coefficients. The exact way of modification is determined by a secret key, the watermark to be embedded, and the DFT coefficients themselves. In the decoding process, the subsegment pairs containing watermarks are identified by another criterion. Then the secret key is used to extract the watermark from the watermarked subsegments. Compared to the existing patchwork methods for audio watermarking, the proposed method does not require knowledge of which segments of the watermarked audio signal contain watermarks and is more robust to conventional attacks.

Processamento Inteligente de Sinais de Pressão e Temperatura Adquiridos Através de Sensores Permanentes em Poços de Petróleo

Originally aimed at operational objectives, the continuous measurement of well bottomhole pressure and temperature, recorded by permanent downhole gauges (PDG), finds vast applicability in reservoir management. It contributes for the monitoring of well performance and makes it possible to estimate reservoir parameters on the long term. However, notwithstanding its unquestionable value, data from PDG is characterized by a large noise content. Moreover, the presence of outliers within valid signal measurements seems to be a major problem as well. In this work, the initial treatment of PDG signals is addressed, based on curve smoothing, self-organizing maps and the discrete wavelet transform. Additionally, a system based on the coupling of fuzzy clustering with feed-forward neural networks is proposed for transient detection. The obtained results were considered quite satisfactory for offshore wells and matched real requisites for utilization

Um modelo matemático baseado em wavelets para análise do método térmico de recuperação de óleo pesado aplicando irradiação eletromagnética

This work proposes a model to investigate the use of a cylindrical antenna used in the thermal method of recovering through electromagnetic radiation of high-viscosity oil. The antenna has a simple geometry, adapted dipole type, and it can be modelled by using Maxwell s equation. The wavelet transforms are used as basis functions and applied in conjunction with the method of moments to obtain the current distribution in the antenna. The electric field, power and temperature distribution are carefully calculated for the analysis of the antenna as electromagnetic heating. The energy performance is analyzed based on thermo-fluid dynamic simulations at field scale, and through the adaptation in the Steam Thermal and Advanced Processes Reservoir Simulator (STARS) by Computer Modelling Group (CMG). The model proposed and the numerical results obtained are stable and presented good agreement with the results reported in the specialized literature

Classificação de distúrbios na rede elétrica usando redes neurais e wavelets

Post dispatch analysis of signals obtained from digital disturbances registers provide important information to identify and classify disturbances in systems, looking for a more efficient management of the supply. In order to enhance the task of identifying and classifying the disturbances - providing an automatic assessment - techniques of digital signal processing can be helpful. The Wavelet Transform has become a very efficient tool for the analysis of voltage or current signals, obtained immediately after disturbance s occurrences in the network. This work presents a methodology based on the Discrete Wavelet Transform to implement this process. It uses a comparison between distribution curves of signals energy, with and without disturbance. This is done for different resolution levels of its decomposition in order to obtain descriptors that permit its classification, using artificial neural networks

Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Localização de faltas em linhas de transmissão usando morfologia matemática

This work an algorithm for fault location is proposed. It contains the following functions: fault detection, fault classification and fault location. Mathematical Morphology is used to process currents obtained in the monitored terminals. Unlike Fourier and Wavelet transforms that are usually applied to fault location, the Mathematical Morphology is a non-linear operation that uses only basic operation (sum, subtraction, maximum and minimum). Thus, Mathematical Morphology is computationally very efficient. For detection and classification functions, the Morphological Wavelet was used. On fault location module the Multiresolution Morphological Gradient was used to detect the traveling waves and their polarities. Hence, recorded the arrival in the two first traveling waves incident at the measured terminal and knowing the velocity of propagation, pinpoint the fault location can be estimated. The algorithm was applied in a 440 kV power transmission system, simulated on ATP. Several fault conditions where studied and the following parameters were evaluated: fault location, fault type, fault resistance, fault inception angle, noise level and sampling rate. The results show that the application of Mathematical Morphology in faults location is very promising

Application of wavelets to the evaluation of phantom images for mammography quality control

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)