ishmam zabir | BUET - Academia.edu (original) (raw)

Papers by ishmam zabir

2022 IEEE Wireless Communications and Networking Conference (WCNC)

ICC 2022 - IEEE International Conference on Communications

Healthcare technology letters, Feb 1, 2018

Photoplethysmographic (PPG) signal is getting popularity for monitoring heart rate in wearable de... more Photoplethysmographic (PPG) signal is getting popularity for monitoring heart rate in wearable devices because of simplicity of construction and low cost of the sensor. The task becomes very difficult due to the presence of various motion artefacts. In this study, an algorithm based on cascade and parallel combination (CPC) of adaptive filters is proposed in order to reduce the effect of motion artefacts. First, preliminary noise reduction is performed by averaging two channel PPG signals. Next in order to reduce the effect of motion artefacts, a cascaded filter structure consisting of three cascaded adaptive filter blocks is developed where three-channel accelerometer signals are used as references to motion artefacts. To further reduce the affect of noise, a scheme based on convex combination of two such cascaded adaptive noise cancelers is introduced, where two widely used adaptive filters namely recursive least squares and least mean squares filters are employed. Heart rates are estimated from the noise reduced PPG signal in spectral domain. Finally, an efficient heart rate tracking algorithm is designed based on the nature of the heart rate variability. The performance of the proposed CPC method is tested on a widely used public database. It is found that the proposed method offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach.

Glioma is a type of brain tumor, originates from glial cells. Approximately 80% of them are malig... more Glioma is a type of brain tumor, originates from glial cells. Approximately 80% of them are malignant. Based on pathological evolution of tumor, they can be classified into two types of tumor - high grade & low grade glioma. In this paper, the segmented area obtained from the conventional region-growing approach is automatically selected as the the initial contour to the iterative distance regularized level set evolution method thus removing the need of selecting the initial region of interest by the user. Therefore, a computer aided fully automated technique is developed to detect glioma from multimodal MRI images & segment the tumor region from whole image. The proposed method is capable of improving the overall detection and segmentation performance of tumor for different glioma cases of BRATS 2012 publicly available database.

2019 IEEE Global Communications Conference (GLOBECOM)

We present a statistical analysis of the secrecy capacity for two downlink transmission schemes: ... more We present a statistical analysis of the secrecy capacity for two downlink transmission schemes: transmitantenna selection (TAS) and transmit-antenna beamforming (TAB), where the transmitter (Alice) has multiple antennas, the receiver (Bob) is a single-antenna full-duplex radio, and the eavesdroppers are randomly distributed each with a single antenna. We focus on the secrecy outage probability (SOP) or its related measure, show closed-form expressions of SOP for the two schemes, and present insights into how various system parameters such as the jamming powers from both Alice and Bob affect SOP. The SOP performance of TAB is shown to be significantly better than that of TAS although TAB requires more on channel estimation than TAS.

IEEE Transactions on Information Forensics and Security, 2021

This paper considers the secrecy performance of several schemes for multi-antenna transmission to... more This paper considers the secrecy performance of several schemes for multi-antenna transmission to single-antenna users with full-duplex (FD) capability against randomly distributed single-antenna eavesdroppers (EDs). These schemes and related scenarios include transmit antenna selection (TAS), transmit antenna beamforming (TAB), artificial noise (AN) from the transmitter, user selection based their distances to the transmitter, and colluding and non-colluding EDs. The locations of randomly distributed EDs and users are assumed to be distributed as Poisson Point Process (PPP). We derive closed form expressions for the secrecy outage probabilities (SOP) of all these schemes and scenarios. The derived expressions are useful to reveal the impacts of various environmental parameters and user’s choices on the SOP, and hence useful for network design purposes. Examples of such numerical results are discussed.

OF THE THESIS Blind Source Separation of Speech Signals: Exploiting Second Order Statistics by Vi... more OF THE THESIS Blind Source Separation of Speech Signals: Exploiting Second Order Statistics by Vishaal Madanagopal Master of Science, Graduate Program in Electrical Engineering University of California, Riverside, March 2018 Dr. Yingbo Hua, Chairperson Blind source separation is a popular technique which is used in the fields of signal processing, audio, video and image processing. BSS is used to separate the mixed signals with only knowing the mixed signals and knowing very little about original signal characteristics. The separated signals should be very good approximations of the source signals. In particular, the blind source separation algorithm tries to estimate the Mixing Matrix. In my thesis, I have studied the blind source separation of signals based on its second order statistics. The problem of blind source separation is studied considering the following cases: when the signal is modelled as non-stationary, cyclo-stationary and quasi-stationary. A closed form solution to ...

This paper proposes an efficient and effective method to classify motor tasks from ECoG signals i... more This paper proposes an efficient and effective method to classify motor tasks from ECoG signals in session-to-session transfer. Real time BCI systems require the classification algorithm to be able to overcome the difficulties due to session-to-session transfer. The feature vector proposed here consists of the higher order statistical properties of the bispectrum of the signal itself and of the extracted MRP signal. A KNN classifier with bagging algorithm is employed to ensure optimum number of K nearest neighbors. In order to evaluate the performance of the proposed method and to perform comparison the Dataset I of the BCI competition III is used. It is found that the proposed method is able to provide an accuracy of 87% which is higher than some of the state-of-the-art methods.

Healthcare Technology Letters

Photoplethysmographic (PPG) signal is getting popularity for monitoring heart rate in wearable de... more Photoplethysmographic (PPG) signal is getting popularity for monitoring heart rate in wearable devices because of simplicity of construction and low cost of the sensor. The task becomes very difficult due to the presence of various motion artefacts. In this study, an algorithm based on cascade and parallel combination (CPC) of adaptive filters is proposed in order to reduce the effect of motion artefacts. First, preliminary noise reduction is performed by averaging two channel PPG signals. Next in order to reduce the effect of motion artefacts, a cascaded filter structure consisting of three cascaded adaptive filter blocks is developed where three-channel accelerometer signals are used as references to motion artefacts. To further reduce the affect of noise, a scheme based on convex combination of two such cascaded adaptive noise cancelers is introduced, where two widely used adaptive filters namely recursive least squares and least mean squares filters are employed. Heart rates are estimated from the noise reduced PPG signal in spectral domain. Finally, an efficient heart rate tracking algorithm is designed based on the nature of the heart rate variability. The performance of the proposed CPC method is tested on a widely used public database. It is found that the proposed method offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach.

Biomedical Signal Processing and Control

Objective-Heart rate monitoring using wrist type Photoplethysmographic (PPG) signals is getting p... more Objective-Heart rate monitoring using wrist type Photoplethysmographic (PPG) signals is getting popularity because of construction simplicity and low cost of wearable devices. The task becomes very difficult due to the presence of various motion artifacts. The objective is to develop algorithms to reduce the effect of motion artifacts and thus obtain accurate heart rate estimation. Methods-Proposed heart rate estimation scheme utilizes both time and frequency domain analyses. Unlike conventional single stage adaptive filter, multi-stage cascaded adaptive filtering is introduced by using three channel accelerometer data to reduce the effect of motion artifacts. Both recursive least squares (RLS) and least mean squares (LMS) adaptive filters are tested. Moreover, singular spectrum analysis (SSA) is employed to obtain improved spectral peak tracking. The outputs from the filter block and SSA operation are logically combined and used for spectral domain heart rate estimation. Finally, a tracking algorithm is incorporated considering neighbouring estimates. Results-The proposed method provides an average absolute error of 1.16 beat per minute (BPM) with a standard deviation of 1.74 BPM while tested on publicly available database consisting of recordings from 12 subjects during physical activities. Conclusion-It is found that the proposed method provides consistently better heart rate estimation performance in comparison to that recently reported by TROIKA, JOSS and SPECTRAP methods. Significance-The proposed method offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach and thus feasible for implementing in wearable devices to monitor heart rate for fitness and clinical purpose.

The PARAFAC tensor decomposition has enjoyed an increasing success in exploratory multi-aspect da... more The PARAFAC tensor decomposition has enjoyed an increasing success in exploratory multi-aspect data mining scenarios. A major challenge remains the estimation of the number of latent factors (i.e., the rank) of the decomposition, which is known to yield high-quality, interpretable results. Previously, AutoTen, an automated tensor mining method which leverages a well-known quality heuristic from the field of Chemometrics, the Core Consistency Diagnostic (CORCONDIA), in order to automatically determine the rank for the PARAFAC decomposition, was proposed. In this work, building upon AutoTen, we set out to explore the trade-off between 1) the interpretability of the results (as expressed by CORCONDIA), and 2) the predictive accuracy of the decomposition, towards improving rank estimation quality. Our preliminary results indicate that striking a good balance in that trade-off yields high-quality rank estimation, towards achieving unsupervised tensor mining.

2022 IEEE Wireless Communications and Networking Conference (WCNC)

ICC 2022 - IEEE International Conference on Communications

Healthcare technology letters, Feb 1, 2018

Photoplethysmographic (PPG) signal is getting popularity for monitoring heart rate in wearable de... more Photoplethysmographic (PPG) signal is getting popularity for monitoring heart rate in wearable devices because of simplicity of construction and low cost of the sensor. The task becomes very difficult due to the presence of various motion artefacts. In this study, an algorithm based on cascade and parallel combination (CPC) of adaptive filters is proposed in order to reduce the effect of motion artefacts. First, preliminary noise reduction is performed by averaging two channel PPG signals. Next in order to reduce the effect of motion artefacts, a cascaded filter structure consisting of three cascaded adaptive filter blocks is developed where three-channel accelerometer signals are used as references to motion artefacts. To further reduce the affect of noise, a scheme based on convex combination of two such cascaded adaptive noise cancelers is introduced, where two widely used adaptive filters namely recursive least squares and least mean squares filters are employed. Heart rates are estimated from the noise reduced PPG signal in spectral domain. Finally, an efficient heart rate tracking algorithm is designed based on the nature of the heart rate variability. The performance of the proposed CPC method is tested on a widely used public database. It is found that the proposed method offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach.

Glioma is a type of brain tumor, originates from glial cells. Approximately 80% of them are malig... more Glioma is a type of brain tumor, originates from glial cells. Approximately 80% of them are malignant. Based on pathological evolution of tumor, they can be classified into two types of tumor - high grade & low grade glioma. In this paper, the segmented area obtained from the conventional region-growing approach is automatically selected as the the initial contour to the iterative distance regularized level set evolution method thus removing the need of selecting the initial region of interest by the user. Therefore, a computer aided fully automated technique is developed to detect glioma from multimodal MRI images & segment the tumor region from whole image. The proposed method is capable of improving the overall detection and segmentation performance of tumor for different glioma cases of BRATS 2012 publicly available database.

2019 IEEE Global Communications Conference (GLOBECOM)

We present a statistical analysis of the secrecy capacity for two downlink transmission schemes: ... more We present a statistical analysis of the secrecy capacity for two downlink transmission schemes: transmitantenna selection (TAS) and transmit-antenna beamforming (TAB), where the transmitter (Alice) has multiple antennas, the receiver (Bob) is a single-antenna full-duplex radio, and the eavesdroppers are randomly distributed each with a single antenna. We focus on the secrecy outage probability (SOP) or its related measure, show closed-form expressions of SOP for the two schemes, and present insights into how various system parameters such as the jamming powers from both Alice and Bob affect SOP. The SOP performance of TAB is shown to be significantly better than that of TAS although TAB requires more on channel estimation than TAS.

IEEE Transactions on Information Forensics and Security, 2021

This paper considers the secrecy performance of several schemes for multi-antenna transmission to... more This paper considers the secrecy performance of several schemes for multi-antenna transmission to single-antenna users with full-duplex (FD) capability against randomly distributed single-antenna eavesdroppers (EDs). These schemes and related scenarios include transmit antenna selection (TAS), transmit antenna beamforming (TAB), artificial noise (AN) from the transmitter, user selection based their distances to the transmitter, and colluding and non-colluding EDs. The locations of randomly distributed EDs and users are assumed to be distributed as Poisson Point Process (PPP). We derive closed form expressions for the secrecy outage probabilities (SOP) of all these schemes and scenarios. The derived expressions are useful to reveal the impacts of various environmental parameters and user’s choices on the SOP, and hence useful for network design purposes. Examples of such numerical results are discussed.

OF THE THESIS Blind Source Separation of Speech Signals: Exploiting Second Order Statistics by Vi... more OF THE THESIS Blind Source Separation of Speech Signals: Exploiting Second Order Statistics by Vishaal Madanagopal Master of Science, Graduate Program in Electrical Engineering University of California, Riverside, March 2018 Dr. Yingbo Hua, Chairperson Blind source separation is a popular technique which is used in the fields of signal processing, audio, video and image processing. BSS is used to separate the mixed signals with only knowing the mixed signals and knowing very little about original signal characteristics. The separated signals should be very good approximations of the source signals. In particular, the blind source separation algorithm tries to estimate the Mixing Matrix. In my thesis, I have studied the blind source separation of signals based on its second order statistics. The problem of blind source separation is studied considering the following cases: when the signal is modelled as non-stationary, cyclo-stationary and quasi-stationary. A closed form solution to ...

This paper proposes an efficient and effective method to classify motor tasks from ECoG signals i... more This paper proposes an efficient and effective method to classify motor tasks from ECoG signals in session-to-session transfer. Real time BCI systems require the classification algorithm to be able to overcome the difficulties due to session-to-session transfer. The feature vector proposed here consists of the higher order statistical properties of the bispectrum of the signal itself and of the extracted MRP signal. A KNN classifier with bagging algorithm is employed to ensure optimum number of K nearest neighbors. In order to evaluate the performance of the proposed method and to perform comparison the Dataset I of the BCI competition III is used. It is found that the proposed method is able to provide an accuracy of 87% which is higher than some of the state-of-the-art methods.

Healthcare Technology Letters

Photoplethysmographic (PPG) signal is getting popularity for monitoring heart rate in wearable de... more Photoplethysmographic (PPG) signal is getting popularity for monitoring heart rate in wearable devices because of simplicity of construction and low cost of the sensor. The task becomes very difficult due to the presence of various motion artefacts. In this study, an algorithm based on cascade and parallel combination (CPC) of adaptive filters is proposed in order to reduce the effect of motion artefacts. First, preliminary noise reduction is performed by averaging two channel PPG signals. Next in order to reduce the effect of motion artefacts, a cascaded filter structure consisting of three cascaded adaptive filter blocks is developed where three-channel accelerometer signals are used as references to motion artefacts. To further reduce the affect of noise, a scheme based on convex combination of two such cascaded adaptive noise cancelers is introduced, where two widely used adaptive filters namely recursive least squares and least mean squares filters are employed. Heart rates are estimated from the noise reduced PPG signal in spectral domain. Finally, an efficient heart rate tracking algorithm is designed based on the nature of the heart rate variability. The performance of the proposed CPC method is tested on a widely used public database. It is found that the proposed method offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach.

Biomedical Signal Processing and Control

Objective-Heart rate monitoring using wrist type Photoplethysmographic (PPG) signals is getting p... more Objective-Heart rate monitoring using wrist type Photoplethysmographic (PPG) signals is getting popularity because of construction simplicity and low cost of wearable devices. The task becomes very difficult due to the presence of various motion artifacts. The objective is to develop algorithms to reduce the effect of motion artifacts and thus obtain accurate heart rate estimation. Methods-Proposed heart rate estimation scheme utilizes both time and frequency domain analyses. Unlike conventional single stage adaptive filter, multi-stage cascaded adaptive filtering is introduced by using three channel accelerometer data to reduce the effect of motion artifacts. Both recursive least squares (RLS) and least mean squares (LMS) adaptive filters are tested. Moreover, singular spectrum analysis (SSA) is employed to obtain improved spectral peak tracking. The outputs from the filter block and SSA operation are logically combined and used for spectral domain heart rate estimation. Finally, a tracking algorithm is incorporated considering neighbouring estimates. Results-The proposed method provides an average absolute error of 1.16 beat per minute (BPM) with a standard deviation of 1.74 BPM while tested on publicly available database consisting of recordings from 12 subjects during physical activities. Conclusion-It is found that the proposed method provides consistently better heart rate estimation performance in comparison to that recently reported by TROIKA, JOSS and SPECTRAP methods. Significance-The proposed method offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach and thus feasible for implementing in wearable devices to monitor heart rate for fitness and clinical purpose.

The PARAFAC tensor decomposition has enjoyed an increasing success in exploratory multi-aspect da... more The PARAFAC tensor decomposition has enjoyed an increasing success in exploratory multi-aspect data mining scenarios. A major challenge remains the estimation of the number of latent factors (i.e., the rank) of the decomposition, which is known to yield high-quality, interpretable results. Previously, AutoTen, an automated tensor mining method which leverages a well-known quality heuristic from the field of Chemometrics, the Core Consistency Diagnostic (CORCONDIA), in order to automatically determine the rank for the PARAFAC decomposition, was proposed. In this work, building upon AutoTen, we set out to explore the trade-off between 1) the interpretability of the results (as expressed by CORCONDIA), and 2) the predictive accuracy of the decomposition, towards improving rank estimation quality. Our preliminary results indicate that striking a good balance in that trade-off yields high-quality rank estimation, towards achieving unsupervised tensor mining.