Saeid Sanei - Academia.edu (original) (raw)

Books by Saeid Sanei

Signal Processing Techniques for Computational Health Informatics. Intelligent Systems Reference Library, vol 192. Springer, Cham., 2020

Biosignals have quite low signal-to-noise ratio and are often corrupted by different types of art... more Biosignals have quite low signal-to-noise ratio and are often corrupted by different types of artifacts and noises originated from both external and internal sources. The presence of such artifacts and noises poses a great challenge in proper analysis of the recorded signals and thus useful information extraction or classification in the subsequent stages becomes erroneous. This eventually results either in a wrong diagnosis of the diseases or misleading the feedback associated with such biosignal-based systems. Brain-Computer Interfaces (BCIs) and neural pros-theses are among the popular ones. There have been many signal processing-based algorithms proposed in the literature for reliable identification and removal of such artifacts from the biosignal recordings. The purpose of this chapter is to introduce different sources of artifacts and noises present in biosignal recordings, such as EEG, ECG, and EMG, describe how the artifact characteristics are different from signal-of-interest, and systematically analyze the state-of-the-art signal processing techniques for reliable identification of these offending artifacts and finally removing them from the raw recordings without distorting the signal-of-interest. The analysis of the biosignal recordings in time, frequency and tensor domains is of major interest. In addition, the impact of artifact and noise removal is examined for BCI and clinical diagnostic applications. Since most biosignals are recorded in low sampling rate, the noise removal algorithms can be often applied in real time. In the case of tensor domain systems, more care has to be taken to comply with real time applications. Therefore, in the final part of this chapter, both quantitative and qualitative measures

Papers by Saeid Sanei

IEEE Transactions on Network Science and Engineering, Apr 1, 2018

In partial diffusion-based least mean square (PDLMS) scheme, each node shares a part of its inter... more In partial diffusion-based least mean square (PDLMS) scheme, each node shares a part of its intermediate estimate vector with its neighbors at each iteration. In this paper, besides being involved in more general PDLMS scheme, we figure out how the noisy links affect deterioration of network performance during the exchange of weight estimates. We investigate the steady state mean square deviation (MSD) and derive a theoretical expression for it. We demonstrate that the PDLMS algorithm is stable and convergent in both mean and mean-square sense under non-ideal links. However, unlike the established statements on PDLMS scheme under ideal links, the trade-off between MSD performance and the number of selected entries of the intermediate estimate vectors as a sign of communication cost is mitigated. Strictly speaking, considering non-ideal links condition adds a new complexity to MSD relation that has a noticeable effect on its performance. This term violates the tradeoff between communication cost and estimation performance of the networks in comparison to noise-free condition on the links. Our simulation results substantiate the effect of noisy links on PDLMS algorithm and verify the theoretical findings. They match well with theory.

Zenodo (CERN European Organization for Nuclear Research), Jan 25, 2018

The recovery of periodic signals from their noisy single channel mixtures has made wide use of th... more The recovery of periodic signals from their noisy single channel mixtures has made wide use of the adaptive line enhancer (ALE). The ALE, however, is not designed for detection of two-(2-D) or three-dimensional (3-D) periodic signals such as tremor in an unconstrained hand motion. An ALE which can perform restoration of 3-D periodic signals is therefore required for such purposes. These signals may not exhibit periodicity in a single dimension. To address and solve this problem a quaternion adaptive line enhancer (QALE) is introduced in this paper for the first time which exploits the quaternion least mean square (QLMS) algorithm for the detection of 3-D (extendable to 4-D) periodic signals.

IEEE Transactions on Neural Systems and Rehabilitation Engineering

The application of machine learning-based tele-rehabilitation faces the challenge of limited avai... more The application of machine learning-based tele-rehabilitation faces the challenge of limited availability of data. To overcome this challenge, data augmentation techniques are commonly employed to generate synthetic data that reflect the configurations of real data. One such promising data augmentation technique is the Generative Adversarial Network (GAN). However, GANs have been found to suffer from mode collapse, a common issue where the generated data fails to capture all the relevant information from the original dataset. In this paper, we aim to address the problem of mode collapse in GAN-based data augmentation techniques for post-stroke assessment. We applied the GAN to generate synthetic data for two post-stroke rehabilitation datasets and observed that the original GAN suffered from mode collapse, as expected. To address this issue, we propose a Time Series Siamese GAN (TS-SGAN) that incorporates a Siamese network and an additional discriminator. Our analysis, using the longest common sub-sequence (LCSS), demonstrates that TS-SGAN generates data uniformly for all elements of two testing datasets, in contrast to the original GAN. To further evaluate the effectiveness of TS-SGAN, we encode the generated dataset into images using Gramian Angular Field and classify them using ResNet-18. Our results show that TS-SGAN achieves a significant accuracy increase of classification accuracy (35.2%-42.07%) for both selected

Circuits, Systems, and Signal Processing

This paper aims to extend the proportionate adaptation concept to the design of a class of diffus... more This paper aims to extend the proportionate adaptation concept to the design of a class of diffusion normalised subband adaptive filter (DNSAF) algorithms. This leads to four extensions of the algorithm associated with different step-size variation, namely, diffusion proportionate normalised subband adaptive filter (DPNSAF), diffusion µ-law PNSAF (DMPNSAF), diffusion improved PNSAF (DIPNSAF) and diffusion improved IPNSAF (DIIPNSAF). Subsequently, steady-state performance, stability conditions and computational complexity of the proposed algorithms are investigated. For each extension the performance has been evaluated using both real and simulated data, where the outcomes demonstrate the accuracy of the theoretical expressions and effectiveness of the proposed algorithms.

arXiv (Cornell University), May 24, 2017

In conventional distributed Kalman filtering, employing diffusion strategies, each node transmits... more In conventional distributed Kalman filtering, employing diffusion strategies, each node transmits its state estimate to all its direct neighbors in each iteration. In this paper we propose a partial diffusion Kalman filter (PDKF) for state estimation of linear dynamic systems. In the PDKF algorithm every node (agent) is allowed to share only a subset of its intermediate estimate vectors at each iteration among its neighbors, which reduces the amount of internode communications. We study the stability of the PDKF algorithm where our analysis reveals that the algorithm is stable and convergent in both mean and meansquare senses. We also investigate the steady-state mean-square deviation (MSD) of the PDKF algorithm and derive a closedform expression that describes how the algorithm performs at the steady-state. Experimental results validate the effectiveness of PDKF algorithm and demonstrate that the proposed algorithm provides a trade-off between communication cost and estimation performance that is extremely profitable.

IEEE Control Systems Letters

In this paper a robust incremental adaptation algorithm is presented to solve distributed estimat... more In this paper a robust incremental adaptation algorithm is presented to solve distributed estimation for a Hamiltonian network, where the measurements at each node may be corrupted by heavy-tailed impulsive noise. In the proposed algorithm, each node employs an error-nonlinearity into the update equation to mitigate the detrimental effects of impulsive noise. Moreover, the algorithm estimates both the optimal error non-linearity and the unknown parameter together, which in turn, obviates the requirement of prior knowledge about the statistical characteristics of measurement noise. In addition to algorithm development, its steady-state performance as well as convergence analysis have been provided. Simulation results validate the correctness of the analysis and reveal the superiority of the proposed algorithm over some existing algorithms. Index terms-Adaptive network, Hamilton, incremental, robust estimation.

Publication in the conference proceedings of EUSIPCO, Barcelona, Spain, 2011

Publication in the conference proceedings of EUSIPCO, Poznan, Poland, 2007

Publication in the conference proceedings of EUSIPCO, Poznan, Poland, 2007

Lecture Notes in Electrical Engineering, 2017

The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

2014 22nd European Signal Processing Conference (EUSIPCO), 2014

Biometrics are quantities obtained from analyses of biological measurements. For human based biom... more Biometrics are quantities obtained from analyses of biological measurements. For human based biometrics, the two main types are clinical and authentication. This paper presents a brief comparison between the two, showing that on many occasions clinical biometrics can motivate for its use in authentication applications. Since several clinical biometrics deal with temporal data and also involve several dimensions of movement, we also present a new application of Singular Spectrum Analysis, in particular its multivariate version, to obtain significant frequency information across these dimensions. We use the most significant frequency component as a biometric to distinguish between various types of human movements. The signals were collected from triaxial accelerometers mounted in an object that is handled by a user. Although this biometric was obtained in a clinical setting, it shows promise for authentication.

ArXiv, 2016

Partial diffusion-based recursive least squares (PDRLS) is an effective method for reducing compu... more Partial diffusion-based recursive least squares (PDRLS) is an effective method for reducing computational load and power consumption in adaptive network implementation. In this method, each node shares a part of its intermediate estimate vector with its neighbors at each iteration. PDRLS algorithm reduces the internode communications relative to the full-diffusion RLS algorithm. This selection of estimate entries becomes more appealing when the information fuse over noisy links. In this paper, we study the steady-state performance of PDRLS algorithm in presence of noisy links and investigate its convergence in both mean and mean-square senses. We also derive a theoretical expression for its steady-state meansquare deviation (MSD). The simulation results illustrate that the stability conditions for PDRLS under noisy links are not sufficient to guarantee its convergence. Strictly speaking, considering nonideal links condition adds a new complexity to the estimation problem for which t...

IEEE Internet of Things Journal, 2020

Demand-side management (DSM) involves a group of programs, initiatives, and technologies designed... more Demand-side management (DSM) involves a group of programs, initiatives, and technologies designed to encourage consumers to modify their level and pattern of electricity usage. This is performed following methods such as financial incentives and behavioral change through education. While the objective of the DSM is to achieve a balance between energy production and demand, effective and efficient implementation of the program rests within effective use of emerging Internet of things (IoT) concept for online interactions. Here, a novel DSM framework based on diffusion and alternating direction method of multipliers (ADMM) strategies, repeated under a model predictive control (MPC) protocol, is proposed. On the demand side, the customers autonomously and by cooperation with their immediate neighbors estimate the baseline price in real time. Based on the estimated price signal, the customers schedule their energy consumption using the ADMM cost-sharing strategy to minimize their incommodity level. On the supply side, the utility company determines the price parameters based on the customers real-time behavior to make a profit and prevent the infrastructure overload. The proposed mechanism is capable of tracking drifts in the optimal solution resulting from the changes in supply/demand sides. Moreover, it considers all classes of appliances by formulating the DSM problem as a mixed-integer programming (MIP) problem. Numerical examples are provided to show the effectiveness of the proposed framework.

IEEE Transactions on Network Science and Engineering, 2017

In partial diffusion-based least mean square (PDLMS) scheme, each node shares a part of its inter... more In partial diffusion-based least mean square (PDLMS) scheme, each node shares a part of its intermediate estimate vector with its neighbors at each iteration. In this paper, besides being involved in more general PDLMS scheme, we figure out how the noisy links affect deterioration of network performance during the exchange of weight estimates. We investigate the steady state mean square deviation (MSD) and derive a theoretical expression for it. We demonstrate that the PDLMS algorithm is stable and convergent in both mean and mean-square sense under non-ideal links. However, unlike the established statements on PDLMS scheme under ideal links, the trade-off between MSD performance and the number of selected entries of the intermediate estimate vectors as a sign of communication cost is mitigated. Strictly speaking, considering non-ideal links condition adds a new complexity to MSD relation that has a noticeable effect on its performance. This term violates the tradeoff between communication cost and estimation performance of the networks in comparison to noise-free condition on the links. Our simulation results substantiate the effect of noisy links on PDLMS algorithm and verify the theoretical findings. They match well with theory.

Computers & Electrical Engineering, 2016

In this paper, we propose a novel search-free localization method based on 3-D Bayesian saliency ... more In this paper, we propose a novel search-free localization method based on 3-D Bayesian saliency estimation. This method uses a new 3-D object tracking algorithm which includes: object detection, shadow detection and removal, and object recognition based on Bayesian methods. The algorithm is tested over three image datasets with different levels of complexities, and the results are compared with those of benchmark methods in terms of speed and accuracy. Unlike most search-based license-plate extraction methods, our proposed 3-D Bayesian saliency algorithm has lower execution time (less than 60 ms), more accuracy , and it is a search-free algorithm which works in noisy backgrounds.

IEEE Transactions on Biomedical Engineering, 2017

Reliable recognition of microaneurysms is an essential task when developing an automated analysis... more Reliable recognition of microaneurysms is an essential task when developing an automated analysis system for diabetic retinopathy detection. In this work, we propose an integrated approach for automated microaneurysm detection with high accuracy. Methods: Candidate objects are first located by applying a dark object filtering process. Their cross-section profiles along multiple directions are processed through singular spectrum analysis. The correlation coefficient between each processed profile and a typical microaneurysm profile is measured and used as a scale factor to adjust the shape of the candidate profile. This is to increase the difference in their profiles between true microaneurysms and other non-microaneurysm candidates. A set of statistical features of those profiles is then extracted for a K-Nearest Neighbour classifier. Results: Experiments show that by applying this process, microaneurysms can be separated well from the retinal background, the most common interfering objects and artefacts. Conclusion: The results have demonstrated the robustness of the approach when testing on large scale datasets with clinically acceptable sensitivity and specificity. Significance: The approach proposed in the evaluated system has great potential when used in an automated diabetic retinopathy screening tool or for large scale eye epidemiology studies.

2015 23rd European Signal Processing Conference (EUSIPCO), 2015

In rehabilitation, continual assessment of those with disabilities is needed to determine the eff... more In rehabilitation, continual assessment of those with disabilities is needed to determine the effectiveness of therapy and to prescribe the regimen and intensity of future treatment. Conducting assessments is challenging-there is a need to maintain objectivity and consistency across time. Also, repetitious tests can lull the assessor into lower levels of alertness. These motivate for automated scoring of rehabilitative tests. In this paper, we describe our work in automating the widely used and accepted Action Research Arm Test. We focus on the grasp subtest which employs a cube into which we embed sensors. Previously we have used live patient simulators and now the full set of patient trials have been completed. We employ Singular Spectrum Analysis on the signals, for which the resulting eigenvalues are then selected in a principled way to aid in signal filtering. The results show encouraging promise in our quest for automated scoring.

2008 19th International Conference on Pattern Recognition, 2008

Gait is an emerging biometric showing promise in its use. Most research focusses on fronto-parall... more Gait is an emerging biometric showing promise in its use. Most research focusses on fronto-parallel (FP) gait where people walk across a camera. In this paper, we present an original analysis, presenting the case for the use of frontonormal (FN) gait where motion is towards a camera. In FN gait, the image projected on a camera sensor will get larger, in a looming effect. This affects the data in a nonlinear and non stationary way, which will further complicate analysis of movement. By catering for this effect, we present new insights into perspective motion compensation for FN gait. Using an existing database which uses coloured markers, we compare two methods of compensation for looming. Initial examination of the resulting data shows a significant result, that fundamentally different approaches may be used for time series analyses on the a set of FN gait data. This opens up new avenues for biometric research in gait recognition.

2009 11th IEEE International Symposium on Multimedia, 2009

Fig. 2. FP vs FN gait-physical dimensions needed for video capture Fig. 1. Left, view of typical ... more Fig. 2. FP vs FN gait-physical dimensions needed for video capture Fig. 1. Left, view of typical security camera monitoring access point. Right, tracking multiple subjects

Signal Processing Techniques for Computational Health Informatics. Intelligent Systems Reference Library, vol 192. Springer, Cham., 2020

Biosignals have quite low signal-to-noise ratio and are often corrupted by different types of art... more Biosignals have quite low signal-to-noise ratio and are often corrupted by different types of artifacts and noises originated from both external and internal sources. The presence of such artifacts and noises poses a great challenge in proper analysis of the recorded signals and thus useful information extraction or classification in the subsequent stages becomes erroneous. This eventually results either in a wrong diagnosis of the diseases or misleading the feedback associated with such biosignal-based systems. Brain-Computer Interfaces (BCIs) and neural pros-theses are among the popular ones. There have been many signal processing-based algorithms proposed in the literature for reliable identification and removal of such artifacts from the biosignal recordings. The purpose of this chapter is to introduce different sources of artifacts and noises present in biosignal recordings, such as EEG, ECG, and EMG, describe how the artifact characteristics are different from signal-of-interest, and systematically analyze the state-of-the-art signal processing techniques for reliable identification of these offending artifacts and finally removing them from the raw recordings without distorting the signal-of-interest. The analysis of the biosignal recordings in time, frequency and tensor domains is of major interest. In addition, the impact of artifact and noise removal is examined for BCI and clinical diagnostic applications. Since most biosignals are recorded in low sampling rate, the noise removal algorithms can be often applied in real time. In the case of tensor domain systems, more care has to be taken to comply with real time applications. Therefore, in the final part of this chapter, both quantitative and qualitative measures

IEEE Transactions on Network Science and Engineering, Apr 1, 2018

In partial diffusion-based least mean square (PDLMS) scheme, each node shares a part of its inter... more In partial diffusion-based least mean square (PDLMS) scheme, each node shares a part of its intermediate estimate vector with its neighbors at each iteration. In this paper, besides being involved in more general PDLMS scheme, we figure out how the noisy links affect deterioration of network performance during the exchange of weight estimates. We investigate the steady state mean square deviation (MSD) and derive a theoretical expression for it. We demonstrate that the PDLMS algorithm is stable and convergent in both mean and mean-square sense under non-ideal links. However, unlike the established statements on PDLMS scheme under ideal links, the trade-off between MSD performance and the number of selected entries of the intermediate estimate vectors as a sign of communication cost is mitigated. Strictly speaking, considering non-ideal links condition adds a new complexity to MSD relation that has a noticeable effect on its performance. This term violates the tradeoff between communication cost and estimation performance of the networks in comparison to noise-free condition on the links. Our simulation results substantiate the effect of noisy links on PDLMS algorithm and verify the theoretical findings. They match well with theory.

Zenodo (CERN European Organization for Nuclear Research), Jan 25, 2018

The recovery of periodic signals from their noisy single channel mixtures has made wide use of th... more The recovery of periodic signals from their noisy single channel mixtures has made wide use of the adaptive line enhancer (ALE). The ALE, however, is not designed for detection of two-(2-D) or three-dimensional (3-D) periodic signals such as tremor in an unconstrained hand motion. An ALE which can perform restoration of 3-D periodic signals is therefore required for such purposes. These signals may not exhibit periodicity in a single dimension. To address and solve this problem a quaternion adaptive line enhancer (QALE) is introduced in this paper for the first time which exploits the quaternion least mean square (QLMS) algorithm for the detection of 3-D (extendable to 4-D) periodic signals.

IEEE Transactions on Neural Systems and Rehabilitation Engineering

The application of machine learning-based tele-rehabilitation faces the challenge of limited avai... more The application of machine learning-based tele-rehabilitation faces the challenge of limited availability of data. To overcome this challenge, data augmentation techniques are commonly employed to generate synthetic data that reflect the configurations of real data. One such promising data augmentation technique is the Generative Adversarial Network (GAN). However, GANs have been found to suffer from mode collapse, a common issue where the generated data fails to capture all the relevant information from the original dataset. In this paper, we aim to address the problem of mode collapse in GAN-based data augmentation techniques for post-stroke assessment. We applied the GAN to generate synthetic data for two post-stroke rehabilitation datasets and observed that the original GAN suffered from mode collapse, as expected. To address this issue, we propose a Time Series Siamese GAN (TS-SGAN) that incorporates a Siamese network and an additional discriminator. Our analysis, using the longest common sub-sequence (LCSS), demonstrates that TS-SGAN generates data uniformly for all elements of two testing datasets, in contrast to the original GAN. To further evaluate the effectiveness of TS-SGAN, we encode the generated dataset into images using Gramian Angular Field and classify them using ResNet-18. Our results show that TS-SGAN achieves a significant accuracy increase of classification accuracy (35.2%-42.07%) for both selected

Circuits, Systems, and Signal Processing

This paper aims to extend the proportionate adaptation concept to the design of a class of diffus... more This paper aims to extend the proportionate adaptation concept to the design of a class of diffusion normalised subband adaptive filter (DNSAF) algorithms. This leads to four extensions of the algorithm associated with different step-size variation, namely, diffusion proportionate normalised subband adaptive filter (DPNSAF), diffusion µ-law PNSAF (DMPNSAF), diffusion improved PNSAF (DIPNSAF) and diffusion improved IPNSAF (DIIPNSAF). Subsequently, steady-state performance, stability conditions and computational complexity of the proposed algorithms are investigated. For each extension the performance has been evaluated using both real and simulated data, where the outcomes demonstrate the accuracy of the theoretical expressions and effectiveness of the proposed algorithms.

arXiv (Cornell University), May 24, 2017

In conventional distributed Kalman filtering, employing diffusion strategies, each node transmits... more In conventional distributed Kalman filtering, employing diffusion strategies, each node transmits its state estimate to all its direct neighbors in each iteration. In this paper we propose a partial diffusion Kalman filter (PDKF) for state estimation of linear dynamic systems. In the PDKF algorithm every node (agent) is allowed to share only a subset of its intermediate estimate vectors at each iteration among its neighbors, which reduces the amount of internode communications. We study the stability of the PDKF algorithm where our analysis reveals that the algorithm is stable and convergent in both mean and meansquare senses. We also investigate the steady-state mean-square deviation (MSD) of the PDKF algorithm and derive a closedform expression that describes how the algorithm performs at the steady-state. Experimental results validate the effectiveness of PDKF algorithm and demonstrate that the proposed algorithm provides a trade-off between communication cost and estimation performance that is extremely profitable.

IEEE Control Systems Letters

In this paper a robust incremental adaptation algorithm is presented to solve distributed estimat... more In this paper a robust incremental adaptation algorithm is presented to solve distributed estimation for a Hamiltonian network, where the measurements at each node may be corrupted by heavy-tailed impulsive noise. In the proposed algorithm, each node employs an error-nonlinearity into the update equation to mitigate the detrimental effects of impulsive noise. Moreover, the algorithm estimates both the optimal error non-linearity and the unknown parameter together, which in turn, obviates the requirement of prior knowledge about the statistical characteristics of measurement noise. In addition to algorithm development, its steady-state performance as well as convergence analysis have been provided. Simulation results validate the correctness of the analysis and reveal the superiority of the proposed algorithm over some existing algorithms. Index terms-Adaptive network, Hamilton, incremental, robust estimation.

Publication in the conference proceedings of EUSIPCO, Barcelona, Spain, 2011

Publication in the conference proceedings of EUSIPCO, Poznan, Poland, 2007

Publication in the conference proceedings of EUSIPCO, Poznan, Poland, 2007

Lecture Notes in Electrical Engineering, 2017

The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

2014 22nd European Signal Processing Conference (EUSIPCO), 2014

Biometrics are quantities obtained from analyses of biological measurements. For human based biom... more Biometrics are quantities obtained from analyses of biological measurements. For human based biometrics, the two main types are clinical and authentication. This paper presents a brief comparison between the two, showing that on many occasions clinical biometrics can motivate for its use in authentication applications. Since several clinical biometrics deal with temporal data and also involve several dimensions of movement, we also present a new application of Singular Spectrum Analysis, in particular its multivariate version, to obtain significant frequency information across these dimensions. We use the most significant frequency component as a biometric to distinguish between various types of human movements. The signals were collected from triaxial accelerometers mounted in an object that is handled by a user. Although this biometric was obtained in a clinical setting, it shows promise for authentication.

ArXiv, 2016

Partial diffusion-based recursive least squares (PDRLS) is an effective method for reducing compu... more Partial diffusion-based recursive least squares (PDRLS) is an effective method for reducing computational load and power consumption in adaptive network implementation. In this method, each node shares a part of its intermediate estimate vector with its neighbors at each iteration. PDRLS algorithm reduces the internode communications relative to the full-diffusion RLS algorithm. This selection of estimate entries becomes more appealing when the information fuse over noisy links. In this paper, we study the steady-state performance of PDRLS algorithm in presence of noisy links and investigate its convergence in both mean and mean-square senses. We also derive a theoretical expression for its steady-state meansquare deviation (MSD). The simulation results illustrate that the stability conditions for PDRLS under noisy links are not sufficient to guarantee its convergence. Strictly speaking, considering nonideal links condition adds a new complexity to the estimation problem for which t...

IEEE Internet of Things Journal, 2020

Demand-side management (DSM) involves a group of programs, initiatives, and technologies designed... more Demand-side management (DSM) involves a group of programs, initiatives, and technologies designed to encourage consumers to modify their level and pattern of electricity usage. This is performed following methods such as financial incentives and behavioral change through education. While the objective of the DSM is to achieve a balance between energy production and demand, effective and efficient implementation of the program rests within effective use of emerging Internet of things (IoT) concept for online interactions. Here, a novel DSM framework based on diffusion and alternating direction method of multipliers (ADMM) strategies, repeated under a model predictive control (MPC) protocol, is proposed. On the demand side, the customers autonomously and by cooperation with their immediate neighbors estimate the baseline price in real time. Based on the estimated price signal, the customers schedule their energy consumption using the ADMM cost-sharing strategy to minimize their incommodity level. On the supply side, the utility company determines the price parameters based on the customers real-time behavior to make a profit and prevent the infrastructure overload. The proposed mechanism is capable of tracking drifts in the optimal solution resulting from the changes in supply/demand sides. Moreover, it considers all classes of appliances by formulating the DSM problem as a mixed-integer programming (MIP) problem. Numerical examples are provided to show the effectiveness of the proposed framework.

IEEE Transactions on Network Science and Engineering, 2017

In partial diffusion-based least mean square (PDLMS) scheme, each node shares a part of its inter... more In partial diffusion-based least mean square (PDLMS) scheme, each node shares a part of its intermediate estimate vector with its neighbors at each iteration. In this paper, besides being involved in more general PDLMS scheme, we figure out how the noisy links affect deterioration of network performance during the exchange of weight estimates. We investigate the steady state mean square deviation (MSD) and derive a theoretical expression for it. We demonstrate that the PDLMS algorithm is stable and convergent in both mean and mean-square sense under non-ideal links. However, unlike the established statements on PDLMS scheme under ideal links, the trade-off between MSD performance and the number of selected entries of the intermediate estimate vectors as a sign of communication cost is mitigated. Strictly speaking, considering non-ideal links condition adds a new complexity to MSD relation that has a noticeable effect on its performance. This term violates the tradeoff between communication cost and estimation performance of the networks in comparison to noise-free condition on the links. Our simulation results substantiate the effect of noisy links on PDLMS algorithm and verify the theoretical findings. They match well with theory.

Computers & Electrical Engineering, 2016

In this paper, we propose a novel search-free localization method based on 3-D Bayesian saliency ... more In this paper, we propose a novel search-free localization method based on 3-D Bayesian saliency estimation. This method uses a new 3-D object tracking algorithm which includes: object detection, shadow detection and removal, and object recognition based on Bayesian methods. The algorithm is tested over three image datasets with different levels of complexities, and the results are compared with those of benchmark methods in terms of speed and accuracy. Unlike most search-based license-plate extraction methods, our proposed 3-D Bayesian saliency algorithm has lower execution time (less than 60 ms), more accuracy , and it is a search-free algorithm which works in noisy backgrounds.

IEEE Transactions on Biomedical Engineering, 2017

Reliable recognition of microaneurysms is an essential task when developing an automated analysis... more Reliable recognition of microaneurysms is an essential task when developing an automated analysis system for diabetic retinopathy detection. In this work, we propose an integrated approach for automated microaneurysm detection with high accuracy. Methods: Candidate objects are first located by applying a dark object filtering process. Their cross-section profiles along multiple directions are processed through singular spectrum analysis. The correlation coefficient between each processed profile and a typical microaneurysm profile is measured and used as a scale factor to adjust the shape of the candidate profile. This is to increase the difference in their profiles between true microaneurysms and other non-microaneurysm candidates. A set of statistical features of those profiles is then extracted for a K-Nearest Neighbour classifier. Results: Experiments show that by applying this process, microaneurysms can be separated well from the retinal background, the most common interfering objects and artefacts. Conclusion: The results have demonstrated the robustness of the approach when testing on large scale datasets with clinically acceptable sensitivity and specificity. Significance: The approach proposed in the evaluated system has great potential when used in an automated diabetic retinopathy screening tool or for large scale eye epidemiology studies.

2015 23rd European Signal Processing Conference (EUSIPCO), 2015

In rehabilitation, continual assessment of those with disabilities is needed to determine the eff... more In rehabilitation, continual assessment of those with disabilities is needed to determine the effectiveness of therapy and to prescribe the regimen and intensity of future treatment. Conducting assessments is challenging-there is a need to maintain objectivity and consistency across time. Also, repetitious tests can lull the assessor into lower levels of alertness. These motivate for automated scoring of rehabilitative tests. In this paper, we describe our work in automating the widely used and accepted Action Research Arm Test. We focus on the grasp subtest which employs a cube into which we embed sensors. Previously we have used live patient simulators and now the full set of patient trials have been completed. We employ Singular Spectrum Analysis on the signals, for which the resulting eigenvalues are then selected in a principled way to aid in signal filtering. The results show encouraging promise in our quest for automated scoring.

2008 19th International Conference on Pattern Recognition, 2008

Gait is an emerging biometric showing promise in its use. Most research focusses on fronto-parall... more Gait is an emerging biometric showing promise in its use. Most research focusses on fronto-parallel (FP) gait where people walk across a camera. In this paper, we present an original analysis, presenting the case for the use of frontonormal (FN) gait where motion is towards a camera. In FN gait, the image projected on a camera sensor will get larger, in a looming effect. This affects the data in a nonlinear and non stationary way, which will further complicate analysis of movement. By catering for this effect, we present new insights into perspective motion compensation for FN gait. Using an existing database which uses coloured markers, we compare two methods of compensation for looming. Initial examination of the resulting data shows a significant result, that fundamentally different approaches may be used for time series analyses on the a set of FN gait data. This opens up new avenues for biometric research in gait recognition.

2009 11th IEEE International Symposium on Multimedia, 2009

Fig. 2. FP vs FN gait-physical dimensions needed for video capture Fig. 1. Left, view of typical ... more Fig. 2. FP vs FN gait-physical dimensions needed for video capture Fig. 1. Left, view of typical security camera monitoring access point. Right, tracking multiple subjects

Lecture Notes in Computer Science, 2008

We present a novel analysis of multimedia data that is useful in human computer interfacing. By a... more We present a novel analysis of multimedia data that is useful in human computer interfacing. By analyzing the video content of humans walking towards a camera, we establish the nonlinear nature of fronto-normal human gait which motivates the use of nonlinear dynamical analysis used in chaos theory to analyze human gait. In doing so, we obtain features that may be used as a biometric which can be used for automatic identification of humans using computers. We apply this in a multi-biometric experiment to demonstrate its effectiveness.