Abstract—A novel cognitive classifier has been introduced to develop a trustable mammography Comp... more Abstract—A novel cognitive classifier has been introduced to develop a trustable mammography Computer Aided Diagnosis (CADx) system which is called Cognitive Weighted Linear Aggregation (CWLA). A group of in-depth analyzed features are extracted from the preprocessed Regions of Interest (ROIs) and mapped from set of real numbers to a set of linguistic terms. The proposed classifier primes a knowledge base which is developed according to a mammography expert. The semantic comparison of the extracted features with the expectations of the knowledge base, which is called cognitive resonance, leads to a primary clustering. Finally, the linguistic terms are remapped onto the set of real numbers and the final assessment comes out from the weighted linear aggregation of clustered categories. Since the output of the system comes with reason, the system is reliable. The achieved area under Receiver Operational Characteristics (ROC) curve (Az) and False Positive Rate (FPR) are 0.858 and 5.26%,...
Abstract—Despite technology advances in the field of antenna evaluating and measurement, some cou... more Abstract—Despite technology advances in the field of antenna evaluating and measurement, some countries still suffer from the lack of modern antenna laboratories. The old measurement tools do not provide digital outputs and only produce a plotted polar pattern which is incompatible with today’s simulation environments. Moreover, upgrading old measurement devices using suitable hardware is rather expensive. Current research is directed towards the development of an effective image processing algorithm that inputs the antenna polar patterns, traces the boundary, down samples the traced pixels and produces the magnitude and angle information in a software compatible format. The average achieved accuracy is 92.68%. More numerical results are reported in the paper.
An MLP network with a novel learning rule, called Opposite Weighted Back Propagation (OWBP), has ... more An MLP network with a novel learning rule, called Opposite Weighted Back Propagation (OWBP), has been utilized as the classifier which classifies breast masses into benign and malignant categories. Breast malignancy is one of the most common cancers among women. The risk of developing invasive breast cancer for a woman in her lifetime is approximately 11%.
AbstractIn this paper, a novel opposition-based classifier has been developed which classifies br... more AbstractIn this paper, a novel opposition-based classifier has been developed which classifies breast masses into benign and malignant categories. An MLP network with a novel learning rule, called Opposite Weighted Back Propagation (OWBP), has been utilized as the classifier. The objective is increasing the convergence rate of MLP learning rules as well as improving the mass diagnostic performance. The input ROI, which is a suspected part of mammogram, is segmented manually by expert radiologists and subjected to some preprocessing stages such as histogram equalization, translation and scaling. Then, a group of features which are appropriate descriptors of mass shape, margin and density have been extracted from the preprocessed ROIs. The proposed features include circularity, Zernike moments, contrast, average gray level, NRL derivatives and SP. The proposed classifier has been trained with both traditional BP and OWBP learning rules and the performance have been evaluated. The syst...
— In this paper, a novel Computer-aided Diagnosis (CADx) system has been proposed for mass diagno... more — In this paper, a novel Computer-aided Diagnosis (CADx) system has been proposed for mass diagnosis in mammography images. Zernike moments are utilized as descriptors of shape and density characteristics in order to improve the overall accuracy. The input Regions of Interest (ROI) are segmented and subjected to some preprocessing stages. The outcome of preprocessing stage is a gray-scale image containing co-scaled translated mass which contains both shape and density characteristics of the mass. Two groups of Zernike moments have been extracted from the preprocessed images. Considering the performance of the overall system the most effective moments have been chosen and applied to a Multi-layer Perceptron (MLP) classifier. The Receiver Operational Characteristics (ROC) plot and the performance of overall CADx system are analyzed for each group of features. The average achieved area under ROC curve (Az) and False Positive Rate (FPR) for high-order moments are 0.872 and 18.34%, respe...
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
This paper presents a newly developed laboratory paradigm for the laboratory component of signals... more This paper presents a newly developed laboratory paradigm for the laboratory component of signals and systems courses. It involves the implementation of signals and systems algorithms that are written in MATLAB on smartphones using their ARM processors. This smartphone-based approach enables an anywhere-anytime environment for students to conduct signals and systems experiments. The steps involved for running MATLAB codes on smartphones are discussed together with the laboratory experiments that are normally covered in signals and systems courses. It is shown that this paradigm still keeps the programming language for conducting signals and systems experiments to MATLAB while enabling a truly mobile laboratory environment for students to learn the implementation aspects of signals and systems concepts.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
This paper presents a voice activity detector (VAD) for automatic switching between a noise class... more This paper presents a voice activity detector (VAD) for automatic switching between a noise classifier and a speech enhancer as part of the signal processing pipeline of hearing aid devices. The developed VAD consists of a computationally efficient feature extractor and a random forest classifier. Previously used signal features as well as two newly introduced signal features are extracted and fed into the classifier to perform automatic switching. This switching approach is compared to two popular VADs. The results obtained indicate the introduced approach outperforms these existing approaches in terms of both detection rate and processing time.
Abstract This paper presents the real-time Simulink implementation of a noise adaptive speech pro... more Abstract This paper presents the real-time Simulink implementation of a noise adaptive speech processing pipeline for cochlear implants that was developed in a previous work. After providing an overview of each component or module in the pipeline, it is described how each module is implemented in Simulink so that the input audio frames are processed in real-time. This Simulink implementation allows the same code to be run on different hardware boards that are supported by Simulink. The performance of this implemented pipeline is evaluated in terms of six objective measures of speech quality. The results obtained indicate the effectiveness of suppressing noise in this speech processing pipeline when using the implemented automatic mechanism to identify the noise environment.
Abstract This paper presents a real-time hierarchical approach to sound signal classification for... more Abstract This paper presents a real-time hierarchical approach to sound signal classification for utilization in hearing improvement devices. The developed classification hierarchy consists of three levels to classify speech, music and different noise types. A distinguishing attribute of this hierarchical approach is that effective features are computed as needed at different levels of the hierarchy making the classification process computationally efficient. This approach is compared to the conventional one-step classification approach by examining both trained and non-trained sound signals. The results obtained show higher classification rates as well as higher computational efficiency of this hierarchical approach compared to the conventional one-step approach.
IEEE/ACM Transactions on Audio, Speech, and Language Processing
This paper presents a real-time unsupervised classification of environmental noise signals withou... more This paper presents a real-time unsupervised classification of environmental noise signals without knowing the number of noise classes or clusters. A previously developed online frame-based clustering algorithm is modified by adding feature extraction, a smoothing step and a fading step. The developed unsupervised classification or clustering is examined in terms of purity of clusters and normalized mutual information. The results obtained for actual noise signals exhibit the effectiveness of the introduced unsupervised classification in terms of both classification outcome and computational efficiency.
Abstract A typical undergraduate electrical engineering curriculum incorporates a signals and sys... more Abstract A typical undergraduate electrical engineering curriculum incorporates a signals and systems course. The widely used approach for the laboratory component of such courses involves the utilization of MATLAB to implement signals and systems concepts. This book presents a newly developed laboratory paradigm where MATLAB codes are made to run on smartphones, which most students already possess. This smartphone-based approach enables an anywhere-anytime platform for students to conduct signals and systems experiments. This book covers the laboratory experiments that are normally covered in signals and systems courses and discusses how to run MATLAB codes for these experiments on smartphones, thus enabling a truly mobile laboratory environment for students to learn the implementation aspects of signals and systems concepts. A zipped file of the codes discussed in the book can be acquired via the website http://sites.fastspring.com/bookcodes/product/SignalsSystemsBookcodes. Table of Contents: Preface / ...
International Journal of Polymer Analysis and Characterization, 2016
ABSTRACT In this article, we report on the chemical oxidative polymerization of 3-methylthiophene... more ABSTRACT In this article, we report on the chemical oxidative polymerization of 3-methylthiophene (3MTh) in a concentrated TiO2/CHCl3 homogeneous suspension with an oxidant/monomer mole ratio of 3 at room temperature. According to the scanning electron microscopy images, in this condition, poly(3-methylthiophene) (P3MTh) was prepared with fibrous morphology decorated by nano-dimensional TiO2 particles. P3MTh/TiO2 was also characterized by Fourier transform infrared spectroscopy and X-ray diffraction techniques. It was found that no aggregation of nanoparticles occurred during the polymerization process. In addition, the thermal stability of P3MTh/TiO2 nanocomposite was investigated by thermogravimetric analysis and compared with that of an analogously prepared neat P3MTh. The thermal degradation of P3MTh in the temperature range of 300–550°C decreases significantly due to the presence of the TiO2 nanoparticles in the polymer composite.
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016
This paper presents the real-time implementation and field testing of an app running on smartphon... more This paper presents the real-time implementation and field testing of an app running on smartphones for classifying noise signals involving subband features and a random forest classifier. This app is compared to a previously developed app utilizing mel-frequency cepstral coefficients features and a Gaussian mixture model classifier. The real-time implementation has been carried out on both the Android and iOS smartphones. The field testing results indicate the superiority of this newly developed app over the previously developed app in terms of classification rates.
This paper presents an online frame-based clustering algorithm (OFC) for unsupervised classificat... more This paper presents an online frame-based clustering algorithm (OFC) for unsupervised classification applications in which data are received in a streaming manner as time passes by with the number of clusters being unknown. This algorithm consists of a number of steps including density-based outlier removal, new cluster generation, and cluster update. It is designed for applications when data samples are received in an online manner in frames. Such frames are first passed through an outlier removal step to generate denoised frames with consistent data samples during transitions times between clusters. A classification step is then applied to find whether frames belong to any of existing clusters. When frames do not get matched to any of existing clusters and certain criteria are met, a new cluster is created in real time and in an on-the-fly manner by using support vector domain descriptors. Experiments involving four synthetic and two real datasets are conducted to show the performance of the introduced clustering algorithm in terms of cluster purity and normalized mutual information. Comparison results with similar clustering algorithms designed for streaming data are also reported exhibiting the effectiveness of the introduced online frame-based clustering algorithm. Online frame-based clustering algorithm without having any knowledge of number of clusters.For applications when samples of a class appear in streaming frames.Superior to existing algorithms applicable to online frame-based clustering.
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015
This paper presents the steps one needs to take in order to run a signal processing algorithm des... more This paper presents the steps one needs to take in order to run a signal processing algorithm designed in Simulink on the ARM processor of smartphones. The steps are conveyed by transitioning two signal processing application examples from Simulink to smartphone. The application examples involve background noise classification and lane departure detection. Considering that Simulink programming is widely used in signal processing, the approach presented in this paper is of benefit to practicing engineers and signal processing researchers/educators in terms of the process of making Simulink codes or models to run on smartphones.
Abstract—A novel cognitive classifier has been introduced to develop a trustable mammography Comp... more Abstract—A novel cognitive classifier has been introduced to develop a trustable mammography Computer Aided Diagnosis (CADx) system which is called Cognitive Weighted Linear Aggregation (CWLA). A group of in-depth analyzed features are extracted from the preprocessed Regions of Interest (ROIs) and mapped from set of real numbers to a set of linguistic terms. The proposed classifier primes a knowledge base which is developed according to a mammography expert. The semantic comparison of the extracted features with the expectations of the knowledge base, which is called cognitive resonance, leads to a primary clustering. Finally, the linguistic terms are remapped onto the set of real numbers and the final assessment comes out from the weighted linear aggregation of clustered categories. Since the output of the system comes with reason, the system is reliable. The achieved area under Receiver Operational Characteristics (ROC) curve (Az) and False Positive Rate (FPR) are 0.858 and 5.26%,...
Abstract—Despite technology advances in the field of antenna evaluating and measurement, some cou... more Abstract—Despite technology advances in the field of antenna evaluating and measurement, some countries still suffer from the lack of modern antenna laboratories. The old measurement tools do not provide digital outputs and only produce a plotted polar pattern which is incompatible with today’s simulation environments. Moreover, upgrading old measurement devices using suitable hardware is rather expensive. Current research is directed towards the development of an effective image processing algorithm that inputs the antenna polar patterns, traces the boundary, down samples the traced pixels and produces the magnitude and angle information in a software compatible format. The average achieved accuracy is 92.68%. More numerical results are reported in the paper.
An MLP network with a novel learning rule, called Opposite Weighted Back Propagation (OWBP), has ... more An MLP network with a novel learning rule, called Opposite Weighted Back Propagation (OWBP), has been utilized as the classifier which classifies breast masses into benign and malignant categories. Breast malignancy is one of the most common cancers among women. The risk of developing invasive breast cancer for a woman in her lifetime is approximately 11%.
AbstractIn this paper, a novel opposition-based classifier has been developed which classifies br... more AbstractIn this paper, a novel opposition-based classifier has been developed which classifies breast masses into benign and malignant categories. An MLP network with a novel learning rule, called Opposite Weighted Back Propagation (OWBP), has been utilized as the classifier. The objective is increasing the convergence rate of MLP learning rules as well as improving the mass diagnostic performance. The input ROI, which is a suspected part of mammogram, is segmented manually by expert radiologists and subjected to some preprocessing stages such as histogram equalization, translation and scaling. Then, a group of features which are appropriate descriptors of mass shape, margin and density have been extracted from the preprocessed ROIs. The proposed features include circularity, Zernike moments, contrast, average gray level, NRL derivatives and SP. The proposed classifier has been trained with both traditional BP and OWBP learning rules and the performance have been evaluated. The syst...
— In this paper, a novel Computer-aided Diagnosis (CADx) system has been proposed for mass diagno... more — In this paper, a novel Computer-aided Diagnosis (CADx) system has been proposed for mass diagnosis in mammography images. Zernike moments are utilized as descriptors of shape and density characteristics in order to improve the overall accuracy. The input Regions of Interest (ROI) are segmented and subjected to some preprocessing stages. The outcome of preprocessing stage is a gray-scale image containing co-scaled translated mass which contains both shape and density characteristics of the mass. Two groups of Zernike moments have been extracted from the preprocessed images. Considering the performance of the overall system the most effective moments have been chosen and applied to a Multi-layer Perceptron (MLP) classifier. The Receiver Operational Characteristics (ROC) plot and the performance of overall CADx system are analyzed for each group of features. The average achieved area under ROC curve (Az) and False Positive Rate (FPR) for high-order moments are 0.872 and 18.34%, respe...
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
This paper presents a newly developed laboratory paradigm for the laboratory component of signals... more This paper presents a newly developed laboratory paradigm for the laboratory component of signals and systems courses. It involves the implementation of signals and systems algorithms that are written in MATLAB on smartphones using their ARM processors. This smartphone-based approach enables an anywhere-anytime environment for students to conduct signals and systems experiments. The steps involved for running MATLAB codes on smartphones are discussed together with the laboratory experiments that are normally covered in signals and systems courses. It is shown that this paradigm still keeps the programming language for conducting signals and systems experiments to MATLAB while enabling a truly mobile laboratory environment for students to learn the implementation aspects of signals and systems concepts.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
This paper presents a voice activity detector (VAD) for automatic switching between a noise class... more This paper presents a voice activity detector (VAD) for automatic switching between a noise classifier and a speech enhancer as part of the signal processing pipeline of hearing aid devices. The developed VAD consists of a computationally efficient feature extractor and a random forest classifier. Previously used signal features as well as two newly introduced signal features are extracted and fed into the classifier to perform automatic switching. This switching approach is compared to two popular VADs. The results obtained indicate the introduced approach outperforms these existing approaches in terms of both detection rate and processing time.
Abstract This paper presents the real-time Simulink implementation of a noise adaptive speech pro... more Abstract This paper presents the real-time Simulink implementation of a noise adaptive speech processing pipeline for cochlear implants that was developed in a previous work. After providing an overview of each component or module in the pipeline, it is described how each module is implemented in Simulink so that the input audio frames are processed in real-time. This Simulink implementation allows the same code to be run on different hardware boards that are supported by Simulink. The performance of this implemented pipeline is evaluated in terms of six objective measures of speech quality. The results obtained indicate the effectiveness of suppressing noise in this speech processing pipeline when using the implemented automatic mechanism to identify the noise environment.
Abstract This paper presents a real-time hierarchical approach to sound signal classification for... more Abstract This paper presents a real-time hierarchical approach to sound signal classification for utilization in hearing improvement devices. The developed classification hierarchy consists of three levels to classify speech, music and different noise types. A distinguishing attribute of this hierarchical approach is that effective features are computed as needed at different levels of the hierarchy making the classification process computationally efficient. This approach is compared to the conventional one-step classification approach by examining both trained and non-trained sound signals. The results obtained show higher classification rates as well as higher computational efficiency of this hierarchical approach compared to the conventional one-step approach.
IEEE/ACM Transactions on Audio, Speech, and Language Processing
This paper presents a real-time unsupervised classification of environmental noise signals withou... more This paper presents a real-time unsupervised classification of environmental noise signals without knowing the number of noise classes or clusters. A previously developed online frame-based clustering algorithm is modified by adding feature extraction, a smoothing step and a fading step. The developed unsupervised classification or clustering is examined in terms of purity of clusters and normalized mutual information. The results obtained for actual noise signals exhibit the effectiveness of the introduced unsupervised classification in terms of both classification outcome and computational efficiency.
Abstract A typical undergraduate electrical engineering curriculum incorporates a signals and sys... more Abstract A typical undergraduate electrical engineering curriculum incorporates a signals and systems course. The widely used approach for the laboratory component of such courses involves the utilization of MATLAB to implement signals and systems concepts. This book presents a newly developed laboratory paradigm where MATLAB codes are made to run on smartphones, which most students already possess. This smartphone-based approach enables an anywhere-anytime platform for students to conduct signals and systems experiments. This book covers the laboratory experiments that are normally covered in signals and systems courses and discusses how to run MATLAB codes for these experiments on smartphones, thus enabling a truly mobile laboratory environment for students to learn the implementation aspects of signals and systems concepts. A zipped file of the codes discussed in the book can be acquired via the website http://sites.fastspring.com/bookcodes/product/SignalsSystemsBookcodes. Table of Contents: Preface / ...
International Journal of Polymer Analysis and Characterization, 2016
ABSTRACT In this article, we report on the chemical oxidative polymerization of 3-methylthiophene... more ABSTRACT In this article, we report on the chemical oxidative polymerization of 3-methylthiophene (3MTh) in a concentrated TiO2/CHCl3 homogeneous suspension with an oxidant/monomer mole ratio of 3 at room temperature. According to the scanning electron microscopy images, in this condition, poly(3-methylthiophene) (P3MTh) was prepared with fibrous morphology decorated by nano-dimensional TiO2 particles. P3MTh/TiO2 was also characterized by Fourier transform infrared spectroscopy and X-ray diffraction techniques. It was found that no aggregation of nanoparticles occurred during the polymerization process. In addition, the thermal stability of P3MTh/TiO2 nanocomposite was investigated by thermogravimetric analysis and compared with that of an analogously prepared neat P3MTh. The thermal degradation of P3MTh in the temperature range of 300–550°C decreases significantly due to the presence of the TiO2 nanoparticles in the polymer composite.
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016
This paper presents the real-time implementation and field testing of an app running on smartphon... more This paper presents the real-time implementation and field testing of an app running on smartphones for classifying noise signals involving subband features and a random forest classifier. This app is compared to a previously developed app utilizing mel-frequency cepstral coefficients features and a Gaussian mixture model classifier. The real-time implementation has been carried out on both the Android and iOS smartphones. The field testing results indicate the superiority of this newly developed app over the previously developed app in terms of classification rates.
This paper presents an online frame-based clustering algorithm (OFC) for unsupervised classificat... more This paper presents an online frame-based clustering algorithm (OFC) for unsupervised classification applications in which data are received in a streaming manner as time passes by with the number of clusters being unknown. This algorithm consists of a number of steps including density-based outlier removal, new cluster generation, and cluster update. It is designed for applications when data samples are received in an online manner in frames. Such frames are first passed through an outlier removal step to generate denoised frames with consistent data samples during transitions times between clusters. A classification step is then applied to find whether frames belong to any of existing clusters. When frames do not get matched to any of existing clusters and certain criteria are met, a new cluster is created in real time and in an on-the-fly manner by using support vector domain descriptors. Experiments involving four synthetic and two real datasets are conducted to show the performance of the introduced clustering algorithm in terms of cluster purity and normalized mutual information. Comparison results with similar clustering algorithms designed for streaming data are also reported exhibiting the effectiveness of the introduced online frame-based clustering algorithm. Online frame-based clustering algorithm without having any knowledge of number of clusters.For applications when samples of a class appear in streaming frames.Superior to existing algorithms applicable to online frame-based clustering.
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015
This paper presents the steps one needs to take in order to run a signal processing algorithm des... more This paper presents the steps one needs to take in order to run a signal processing algorithm designed in Simulink on the ARM processor of smartphones. The steps are conveyed by transitioning two signal processing application examples from Simulink to smartphone. The application examples involve background noise classification and lane departure detection. Considering that Simulink programming is widely used in signal processing, the approach presented in this paper is of benefit to practicing engineers and signal processing researchers/educators in terms of the process of making Simulink codes or models to run on smartphones.
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Papers by Fatemeh Saki