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Dr. Richard Povinelli
Opus College of Engineering, Marquette University, Milwaukee, WI 53233, USA

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0 Data Mining
0 Dynamical Systems
0 Machine Learning
0 Signal Processing
0 Chaos

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Research article
Published: 18 June 2020 in PLOS ONE
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After mating, female mosquitoes need animal blood to develop their eggs. In the process of acquiring blood, they may acquire pathogens, which may cause different diseases in humans such as malaria, zika, dengue, and chikungunya. Therefore, knowing the parity status of mosquitoes is useful in control and evaluation of infectious diseases transmitted by mosquitoes, where parous mosquitoes are assumed to be potentially infectious. Ovary dissections, which are currently used to determine the parity status of mosquitoes, are very tedious and limited to few experts. An alternative to ovary dissections is near-infrared spectroscopy (NIRS), which can estimate the age in days and the infectious state of laboratory and semi-field reared mosquitoes with accuracies between 80 and 99%. No study has tested the accuracy of NIRS for estimating the parity status of wild mosquitoes. In this study, we train an artificial neural network (ANN) models on NIR spectra to estimate the parity status of wild mosquitoes. We use four different datasets: An. arabiensis collected from Minepa, Tanzania (Minepa-ARA); An. gambiae s.s collected from Muleba, Tanzania (Muleba-GA); An. gambiae s.s collected from Burkina Faso (Burkina-GA); and An.gambiae s.s from Muleba and Burkina Faso combined (Muleba-Burkina-GA). We train ANN models on datasets with spectra preprocessed according to previous protocols. We then use autoencoders to reduce the spectra feature dimensions from 1851 to 10 and re-train the ANN models. Before the autoencoder was applied, ANN models estimated parity status of mosquitoes in Minepa-ARA, Muleba-GA, Burkina-GA and Muleba-Burkina-GA with out-of-sample accuracies of 81.9±2.8 (N = 274), 68.7±4.8 (N = 43), 80.3±2.0 (N = 48), and 75.7±2.5 (N = 91), respectively. With the autoencoder, ANN models tested on out-of-sample data achieved 97.1±2.2% (N = 274), 89.8 ± 1.7% (N = 43), 93.3±1.2% (N = 48), and 92.7±1.8% (N = 91) accuracies for Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA, respectively. These results show that a combination of an autoencoder and an ANN trained on NIR spectra to estimate the parity status of wild mosquitoes yields models that can be used as an alternative tool to estimate parity status of wild mosquitoes, especially since NIRS is a high-throughput, reagent-free, and simple-to-use technique compared to ovary dissections.

ACS Style

Masabho P. Milali; Samson S. Kiware; Nicodem J. Govella; Fredros Okumu; Naveen Bansal; Serdar Bozdag; Jacques D. Charlwood; Marta Ferreira Maia; Sheila B. Ogoma; Floyd E. Dowell; George F. Corliss; Maggy T. Sikulu-Lord; Richard J. Povinelli. An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra. PLOS ONE 2020, 15, e0234557 .

AMA Style

Masabho P. Milali, Samson S. Kiware, Nicodem J. Govella, Fredros Okumu, Naveen Bansal, Serdar Bozdag, Jacques D. Charlwood, Marta Ferreira Maia, Sheila B. Ogoma, Floyd E. Dowell, George F. Corliss, Maggy T. Sikulu-Lord, Richard J. Povinelli. An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra. PLOS ONE. 2020; 15 (6):e0234557.

Chicago/Turabian Style

Masabho P. Milali; Samson S. Kiware; Nicodem J. Govella; Fredros Okumu; Naveen Bansal; Serdar Bozdag; Jacques D. Charlwood; Marta Ferreira Maia; Sheila B. Ogoma; Floyd E. Dowell; George F. Corliss; Maggy T. Sikulu-Lord; Richard J. Povinelli. 2020. "An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra." PLOS ONE 15, no. 6: e0234557.

Research article
Published: 14 August 2019 in PLOS ONE
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Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into < or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier. We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. The ANN regression model scored root mean squared error (RMSE) of 1.6 ± 0.2 for An. gambiae and 2.8 ± 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 ± 0.2 for An. gambiae, and 4.5 ± 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the accuracy of the ANN regression model was 93.7 ± 1.0% for An. gambiae, and 90.2 ± 1.7% for An. arabiensis; while PLS regression model scored the accuracy of 83.9 ± 2.3% for An. gambiae, and 80.3 ± 2.1% for An. arabiensis. We also find that a directly trained binary classifier yields higher age estimation accuracy than a regression model interpreted as a binary classifier. A directly trained ANN binary classifier scored an accuracy of 99.4 ± 1.0 for An. gambiae and 99.0 ± 0.6% for An. arabiensis; while a directly trained PLS binary classifier scored 93.6 ± 1.2% for An. gambiae and 88.7 ± 1.1% for An. arabiensis. We further tested the reproducibility of these results on different independent mosquito datasets. ANNs scored higher estimation accuracies than when the same age models are trained using PLS. Regardless of the model architecture, directly trained binary classifiers scored higher accuracies on classifying age of mosquitoes than regression models translated as binary classifiers. We recommend training models to estimate age of An. arabiensis and An. gambiae using ANN model architectures (especially for datasets with at least 70 mosquitoes per age group) and direct training of binary classifier instead of training a regression model and interpreting it as a binary classifier.

ACS Style

Masabho P. Milali; Maggy T. Sikulu-Lord; Samson S. Kiware; Floyd E. Dowell; George F. Corliss; Richard J. Povinelli. Age grading An. gambiae and An. arabiensis using near infrared spectra and artificial neural networks. PLOS ONE 2019, 14, e0209451 .

AMA Style

Masabho P. Milali, Maggy T. Sikulu-Lord, Samson S. Kiware, Floyd E. Dowell, George F. Corliss, Richard J. Povinelli. Age grading An. gambiae and An. arabiensis using near infrared spectra and artificial neural networks. PLOS ONE. 2019; 14 (8):e0209451.

Chicago/Turabian Style

Masabho P. Milali; Maggy T. Sikulu-Lord; Samson S. Kiware; Floyd E. Dowell; George F. Corliss; Richard J. Povinelli. 2019. "Age grading An. gambiae and An. arabiensis using near infrared spectra and artificial neural networks." PLOS ONE 14, no. 8: e0209451.

Preprint content
Published: 07 December 2018
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Background Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into < or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier. Methods and findings We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. Based on ten-fold Monte Carlo cross-validation, an ANN regression model scored root mean squared error (RMSE) of 1.6 ± 0.2 for An. gambiae and 2.8 ± 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 ± 0.2 for An. gambiae, and 4.5 ± 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the accuracy of the ANN regression model was 93.7 ± 1.0 % for An. gambiae, and 90.2 ± 1.7 % for An. arabiensis; while PLS regression model scored the accuracy of 83.9 ± 2.3% for An. gambiae, and 80.3 ± 2.1% for An. arabiensis. We also find that a directly trained binary classifier yields higher age estimation accuracy than a regression model interpreted as a binary classifier. A directly trained ANN binary classifier scored an accuracy of 99.4 ± 1.0 for An. gambiae, and 99.0 ± 0.6% for An. arabiensis; while a directly trained PLS binary classifier scored 93.6 ± 1.2% for An. gambiae, and 88.7 ± 1.1% for An. arabiensis. Conclusion Training both regression and binary classification age models using ANNs yields models with higher estimation accuracies than when the same age models are trained using PLS. Regardless of the model architecture, directly trained binary classifiers score higher accuracy on classifying age of mosquitoes than a regression model translated as binary classifier. Therefore, we recommend training models to estimate age of An. gambiae and An. arabiensis using ANN model architectures and direct training of binary classifier instead of training a regression model and interpret it as a binary classifier.

ACS Style

Masabho P. Milali; Maggy T. Sikulu-Lord; Samson S. Kiware; Floyd E. Dowell; George F. Corliss; Richard J. Povinelli. Age Grading An. Gambiae and An. Arabiensis Using Near Infrared Spectra and Artificial Neural Networks. 2018, 490326 .

AMA Style

Masabho P. Milali, Maggy T. Sikulu-Lord, Samson S. Kiware, Floyd E. Dowell, George F. Corliss, Richard J. Povinelli. Age Grading An. Gambiae and An. Arabiensis Using Near Infrared Spectra and Artificial Neural Networks. . 2018; ():490326.

Chicago/Turabian Style

Masabho P. Milali; Maggy T. Sikulu-Lord; Samson S. Kiware; Floyd E. Dowell; George F. Corliss; Richard J. Povinelli. 2018. "Age Grading An. Gambiae and An. Arabiensis Using Near Infrared Spectra and Artificial Neural Networks." , no. : 490326.

Journal article
Published: 02 August 2018 in Energies
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Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech processing. We provide an overview of natural gas forecasting. Next, the deep learning method, contrastive divergence is explained. We compare our proposed deep neural network method to a linear regression model and a traditional artificial neural network on 62 operating areas, each of which has at least 10 years of data. The proposed deep network outperforms traditional artificial neural networks by 9.83% weighted mean absolute percent error (WMAPE).

ACS Style

Gregory D. Merkel; Richard J. Povinelli; Ronald H. Brown. Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †. Energies 2018, 11, 2008 .

AMA Style

Gregory D. Merkel, Richard J. Povinelli, Ronald H. Brown. Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †. Energies. 2018; 11 (8):2008.

Chicago/Turabian Style

Gregory D. Merkel; Richard J. Povinelli; Ronald H. Brown. 2018. "Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †." Energies 11, no. 8: 2008.

Journal article
Published: 03 March 2017 in Sensors
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We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL) and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA) and classification of events, activities and relationships for multi-object trackers (CLEAR MOT)). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

ACS Style

Anthony Hoak; Henry Medeiros; Richard J. Povinelli. Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods. Sensors 2017, 17, 501 .

AMA Style

Anthony Hoak, Henry Medeiros, Richard J. Povinelli. Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods. Sensors. 2017; 17 (3):501.

Chicago/Turabian Style

Anthony Hoak; Henry Medeiros; Richard J. Povinelli. 2017. "Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods." Sensors 17, no. 3: 501.

Journal article
Published: 22 September 2016 in Journal of Cheminformatics
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Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. This feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study.

ACS Style

Prachi Pradeep; Richard J. Povinelli; Shannon White; Stephen J. Merrill. An ensemble model of QSAR tools for regulatory risk assessment. Journal of Cheminformatics 2016, 8, 48 .

AMA Style

Prachi Pradeep, Richard J. Povinelli, Shannon White, Stephen J. Merrill. An ensemble model of QSAR tools for regulatory risk assessment. Journal of Cheminformatics. 2016; 8 (1):48.

Chicago/Turabian Style

Prachi Pradeep; Richard J. Povinelli; Shannon White; Stephen J. Merrill. 2016. "An ensemble model of QSAR tools for regulatory risk assessment." Journal of Cheminformatics 8, no. 1: 48.

Journal article
Published: 01 January 2016 in International Journal of Applied Pattern Recognition
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Extreme cold events in natural gas demand are characterised by unusual dynamics that makes modelling the characteristics of the gas demand during extreme cold events a challenging task. This unusual dynamics is in the form of hysteresis, possibly due to human behavioural response to extreme weather conditions. To natural gas distribution utilities, extreme cold events represent high risk events given the associated huge demand of gas by their customers. To understand the nature of the unusual dynamics and help utilities in their decision-making process, we present a semi-supervised learning algorithm that identifies extreme cold events in natural gas time series data. Using phase space reconstruction, the input space is mapped into a phase space. In the reconstructed phase space, events with similar dynamics are closer together, while events with different dynamics are far apart. A cluster containing extreme cold events is identified by finding the nearest neighbours to an observed cold event. The learning algorithm was tested on natural gas consumption data obtained from natural gas local distribution companies. Our RPS-kNN algorithm was able to identify extreme cold events in the data.

ACS Style

Babatunde I. Ishola; Richard J. Povinelli; George F. Corliss; Ronald H. Brown. Identifying extreme cold events using phase space reconstruction. International Journal of Applied Pattern Recognition 2016, 3, 259 .

AMA Style

Babatunde I. Ishola, Richard J. Povinelli, George F. Corliss, Ronald H. Brown. Identifying extreme cold events using phase space reconstruction. International Journal of Applied Pattern Recognition. 2016; 3 (3):259.

Chicago/Turabian Style

Babatunde I. Ishola; Richard J. Povinelli; George F. Corliss; Ronald H. Brown. 2016. "Identifying extreme cold events using phase space reconstruction." International Journal of Applied Pattern Recognition 3, no. 3: 259.

Journal article
Published: 01 January 2016 in International Journal of Applied Pattern Recognition
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Extreme cold events in natural gas demand are characterised by unusual dynamics that makes modelling the characteristics of the gas demand during extreme cold events a challenging task. This unusual dynamics is in the form of hysteresis, possibly due to human behavioural response to extreme weather conditions. To natural gas distribution utilities, extreme cold events represent high risk events given the associated huge demand of gas by their customers. To understand the nature of the unusual dynamics and help utilities in their decision-making process, we present a semi-supervised learning algorithm that identifies extreme cold events in natural gas time series data. Using phase space reconstruction, the input space is mapped into a phase space. In the reconstructed phase space, events with similar dynamics are closer together, while events with different dynamics are far apart. A cluster containing extreme cold events is identified by finding the nearest neighbours to an observed cold event. The learning algorithm was tested on natural gas consumption data obtained from natural gas local distribution companies. Our RPS-kNN algorithm was able to identify extreme cold events in the data.

ACS Style

Ronald H. Brown; George F. Corliss; Richard J. Povinelli; Babatunde I. Ishola. Identifying extreme cold events using phase space reconstruction. International Journal of Applied Pattern Recognition 2016, 3, 259 .

AMA Style

Ronald H. Brown, George F. Corliss, Richard J. Povinelli, Babatunde I. Ishola. Identifying extreme cold events using phase space reconstruction. International Journal of Applied Pattern Recognition. 2016; 3 (3):259.

Chicago/Turabian Style

Ronald H. Brown; George F. Corliss; Richard J. Povinelli; Babatunde I. Ishola. 2016. "Identifying extreme cold events using phase space reconstruction." International Journal of Applied Pattern Recognition 3, no. 3: 259.

Conference paper
Published: 01 July 2015 in 2015 IEEE Power & Energy Society General Meeting
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This paper presents a novel detrending algorithm that allows long-term natural gas demand signals to be used effectively to generate high quality short-term natural gas demand forecasting models. Short data sets in natural gas forecasting inadequately represent the range of consumption patterns necessary for accurate short-term forecasting. In contrast, longer data sets present a wide range of customer characteristics, but their long-term historical trends must be adjusted to resemble recent data before models can be developed. Our approach detrends historical natural gas data using domain knowledge. Forecasting models trained on data detrended using our algorithm are more accurate than models trained using nondetrended data or data detrended by benchmark methods. Forecasting accuracy improves using detrended longer-term signals, while forecast accuracy decreases using non-detrended long-term signals.

ACS Style

Ronald H. Brown; Steven R. Vitullo; George F. Corliss; Monica Adya; Paul E. Kaefer; Richard J. Povinelli. Detrending daily natural gas consumption series to improve short-term forecasts. 2015 IEEE Power & Energy Society General Meeting 2015, 1 -5.

AMA Style

Ronald H. Brown, Steven R. Vitullo, George F. Corliss, Monica Adya, Paul E. Kaefer, Richard J. Povinelli. Detrending daily natural gas consumption series to improve short-term forecasts. 2015 IEEE Power & Energy Society General Meeting. 2015; ():1-5.

Chicago/Turabian Style

Ronald H. Brown; Steven R. Vitullo; George F. Corliss; Monica Adya; Paul E. Kaefer; Richard J. Povinelli. 2015. "Detrending daily natural gas consumption series to improve short-term forecasts." 2015 IEEE Power & Energy Society General Meeting , no. : 1-5.

Journal article
Published: 31 March 2012 in Chaos, Solitons & Fractals
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Because of the mixing and aperiodic properties of chaotic maps, such maps have been used as the basis for pseudorandom number generators (PRNGs). However, when implemented on a finite precision computer, chaotic maps have finite and periodic orbits. This manuscript explores the consequences finite precision has on the periodicity of a PRNG based on the logistic map. A comparison is made with conventional methods of generating pseudorandom numbers. The approach used to determine the number, delay, and period of the orbits of the logistic map at varying degrees of precision (3 to 23 bits) is described in detail, including the use of the Condor high-throughput computing environment to parallelize independent tasks of analyzing a large initial seed space. Results demonstrate that in terms of pathological seeds and effective bit length, a PRNG based on the logistic map performs exponentially worse than conventional PRNGs.

ACS Style

K.J. Persohn; R.J. Povinelli. Analyzing logistic map pseudorandom number generators for periodicity induced by finite precision floating-point representation. Chaos, Solitons & Fractals 2012, 45, 238 -245.

AMA Style

K.J. Persohn, R.J. Povinelli. Analyzing logistic map pseudorandom number generators for periodicity induced by finite precision floating-point representation. Chaos, Solitons & Fractals. 2012; 45 (3):238-245.

Chicago/Turabian Style

K.J. Persohn; R.J. Povinelli. 2012. "Analyzing logistic map pseudorandom number generators for periodicity induced by finite precision floating-point representation." Chaos, Solitons & Fractals 45, no. 3: 238-245.

Conference paper
Published: 01 January 2006 in Computer Vision
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Novel speech features calculated from third-order statistics of subband-filtered speech signals are introduced and studied for robust speech recognition. These features have the potential to capture nonlinear information not represented by cepstral coefficients. Also, because the features presented in this paper are based on the third-order moments, they may be more immune to Gaussian noise than cepstrals, as Gaussian distributions have zero third-order moments. Experiments on the AURORA2 database studying these features in combination with Mel-frequency cepstral coefficients (MFCC’s) are presented, and some improvement over the MFCC-only baseline is shown when clean speech is used for training, though the same improvement is not seen when multi-condition training data is used.

ACS Style

Kevin M. IndrebO; Richard J. Povinelli; Michael T. Johnson. Third-Order Moments of Filtered Speech Signals for Robust Speech Recognition. Computer Vision 2006, 3817, 277 -283.

AMA Style

Kevin M. IndrebO, Richard J. Povinelli, Michael T. Johnson. Third-Order Moments of Filtered Speech Signals for Robust Speech Recognition. Computer Vision. 2006; 3817 ():277-283.

Chicago/Turabian Style

Kevin M. IndrebO; Richard J. Povinelli; Michael T. Johnson. 2006. "Third-Order Moments of Filtered Speech Signals for Robust Speech Recognition." Computer Vision 3817, no. : 277-283.

Conference paper
Published: 25 August 2005 in Proceedings of 2002 IEEE 10th Digital Signal Processing Workshop, 2002 and the 2nd Signal Processing Education Workshop.
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ACS Style

Xiaolin Liu; Richard J. Povinelli; Michael T. Johnson. Detecting determinism in speech phonemes. Proceedings of 2002 IEEE 10th Digital Signal Processing Workshop, 2002 and the 2nd Signal Processing Education Workshop. 2005, 1 .

AMA Style

Xiaolin Liu, Richard J. Povinelli, Michael T. Johnson. Detecting determinism in speech phonemes. Proceedings of 2002 IEEE 10th Digital Signal Processing Workshop, 2002 and the 2nd Signal Processing Education Workshop.. 2005; ():1.

Chicago/Turabian Style

Xiaolin Liu; Richard J. Povinelli; Michael T. Johnson. 2005. "Detecting determinism in speech phonemes." Proceedings of 2002 IEEE 10th Digital Signal Processing Workshop, 2002 and the 2nd Signal Processing Education Workshop. , no. : 1.

Conference paper
Published: 28 June 2005 in Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004.
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ACS Style

Kevin M. IndrebO; Richard J. Povinelli; Michael T. Johnson. A comparison of reconstructed phase spaces and cepstral coefficients for multi-band phoneme classification. Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004. 2005, 1 .

AMA Style

Kevin M. IndrebO, Richard J. Povinelli, Michael T. Johnson. A comparison of reconstructed phase spaces and cepstral coefficients for multi-band phoneme classification. Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004.. 2005; ():1.

Chicago/Turabian Style

Kevin M. IndrebO; Richard J. Povinelli; Michael T. Johnson. 2005. "A comparison of reconstructed phase spaces and cepstral coefficients for multi-band phoneme classification." Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004. , no. : 1.

Journal article
Published: 05 February 2005 in Speech Communication
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ACS Style

Kevin M. IndrebO; Richard J. Povinelli; Michael T. Johnson. Sub-banded reconstructed phase spaces for speech recognition. Speech Communication 2005, 48, 760 -774.

AMA Style

Kevin M. IndrebO, Richard J. Povinelli, Michael T. Johnson. Sub-banded reconstructed phase spaces for speech recognition. Speech Communication. 2005; 48 (7):760-774.

Chicago/Turabian Style

Kevin M. IndrebO; Richard J. Povinelli; Michael T. Johnson. 2005. "Sub-banded reconstructed phase spaces for speech recognition." Speech Communication 48, no. 7: 760-774.

Journal article
Published: 01 February 2005 in Physica D: Nonlinear Phenomena
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ACS Style

Michael T. Johnson; Richard J. Povinelli. Generalized phase space projection for nonlinear noise reduction. Physica D: Nonlinear Phenomena 2005, 201, 306 -317.

AMA Style

Michael T. Johnson, Richard J. Povinelli. Generalized phase space projection for nonlinear noise reduction. Physica D: Nonlinear Phenomena. 2005; 201 (3-4):306-317.

Chicago/Turabian Style

Michael T. Johnson; Richard J. Povinelli. 2005. "Generalized phase space projection for nonlinear noise reduction." Physica D: Nonlinear Phenomena 201, no. 3-4: 306-317.

Conference paper
Published: 21 November 2003 in 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).
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ACS Style

Michael T. Johnson; Andrew C. Lindgren; Richard J. Povinelli; Xiaolong Yuan. Performance of nonlinear speech enhancement using phase space reconstruction. 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003, 1 .

AMA Style

Michael T. Johnson, Andrew C. Lindgren, Richard J. Povinelli, Xiaolong Yuan. Performance of nonlinear speech enhancement using phase space reconstruction. 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).. 2003; ():1.

Chicago/Turabian Style

Michael T. Johnson; Andrew C. Lindgren; Richard J. Povinelli; Xiaolong Yuan. 2003. "Performance of nonlinear speech enhancement using phase space reconstruction." 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). , no. : 1.

Conference paper
Published: 13 November 2002 in IEMDC 2001 IEEE International Electric Machines and Drives Conference (Cat No 01EX485) IEMDC-01
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ACS Style

Richard J. Povinelli; John F. Bangura; Nabeel A O Demerdash; Ronald H. Brown. Diagnostics of bar and end-ring connector breakage faults in polyphase induction motors through a novel dual track of time-series data mining and time-stepping coupled FE-state space modeling. IEMDC 2001 IEEE International Electric Machines and Drives Conference (Cat No 01EX485) IEMDC-01 2002, 1 .

AMA Style

Richard J. Povinelli, John F. Bangura, Nabeel A O Demerdash, Ronald H. Brown. Diagnostics of bar and end-ring connector breakage faults in polyphase induction motors through a novel dual track of time-series data mining and time-stepping coupled FE-state space modeling. IEMDC 2001 IEEE International Electric Machines and Drives Conference (Cat No 01EX485) IEMDC-01. 2002; ():1.

Chicago/Turabian Style

Richard J. Povinelli; John F. Bangura; Nabeel A O Demerdash; Ronald H. Brown. 2002. "Diagnostics of bar and end-ring connector breakage faults in polyphase induction motors through a novel dual track of time-series data mining and time-stepping coupled FE-state space modeling." IEMDC 2001 IEEE International Electric Machines and Drives Conference (Cat No 01EX485) IEMDC-01 , no. : 1.

Conference paper
Published: 13 November 2002 in Conference Record of the 2001 IEEE Industry Applications Conference. 36th IAS Annual Meeting (Cat. No.01CH37248)
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ACS Style

John F. Bangura; Richard J. Povinelli; Nabeel A O Demerdash; Ronald H. Brown. Diagnostics of eccentricities and bar/end-ring connector breakages in polyphase induction motors through a combination of time-series data mining and time-stepping coupled FE-state space techniques. Conference Record of the 2001 IEEE Industry Applications Conference. 36th IAS Annual Meeting (Cat. No.01CH37248) 2002, 1 .

AMA Style

John F. Bangura, Richard J. Povinelli, Nabeel A O Demerdash, Ronald H. Brown. Diagnostics of eccentricities and bar/end-ring connector breakages in polyphase induction motors through a combination of time-series data mining and time-stepping coupled FE-state space techniques. Conference Record of the 2001 IEEE Industry Applications Conference. 36th IAS Annual Meeting (Cat. No.01CH37248). 2002; ():1.

Chicago/Turabian Style

John F. Bangura; Richard J. Povinelli; Nabeel A O Demerdash; Ronald H. Brown. 2002. "Diagnostics of eccentricities and bar/end-ring connector breakages in polyphase induction motors through a combination of time-series data mining and time-stepping coupled FE-state space techniques." Conference Record of the 2001 IEEE Industry Applications Conference. 36th IAS Annual Meeting (Cat. No.01CH37248) , no. : 1.