Khawza I. Ahmed

Superimposed training-based compressed sensing of sparse multipath channels

In a number of wireless communication applications, the impulse response of multipath communication channels has sparse nature. In this study, physical model for various propagation environments exhibiting sparse channel structure is considered. A superimposed (SI) training-based compressed channel sensing (SI-CCS) technique is proposed for such sparse multipath channels. A non-random periodic pilot sequence is SI over the information sequence at the transmitter, which avoids the use of dedicated time slots for training sequence. At the receiver, first-order statistics and the theory of compressed sensing is applied to estimate the wireless communication channels with sparse impulse response. A simulation analysis is presented to demonstrate the effectiveness of the proposed-channel estimation technique, where mean-square error and bit-error rate are used as the performance measures. Exploiting the proposed SI-CCS technique, the simulation results along with the observations are presented, which illustrate the effect of various channel parameters on the performance of the proposed technique. Furthermore, obtained simulation results for the proposed SI-CCS technique along with its comparison with other techniques in literature are also presented. It is established that for the cases of sparse multipath channels, the proposed SI-CCS technique can potentially achieve significant improvement in the performance of channel estimator over the existing estimation techniques of such sparse channels.

Handwritten Bangla digit recognition using Sparse Representation Classifier

We present a framework for handwritten Bangla digit recognition using Sparse Representation Classifier. The classifier assumes that a test sample can be represented as a linear combination of the train samples from its native class. Hence, a test sample can be represented using a dictionary constructed from the train samples. The most sparse linear representation of the test sample in terms of this dictionary can be efficiently computed through ℓ1-minimization, and can be exploited to classify the test sample. We applied Sparse Representation Classifier on the image zone density, an image domain statistical feature extracted from the character image, to classify the Bangla numerals. This is a novel approach for Bangla Optical Character Recognition, and demonstrates an excellent accuracy of 94% on the off-line handwritten Bangla numeral database CMATERdb 3.1.1. This result is promising, and should be investigated further.

Suicidal Ideation Is Associated with Altered Variability of Fingertip Photo-Plethysmogram Signal in Depressed Patients

Physiological and psychological underpinnings of suicidal behavior remain ill-defined and lessen timely diagnostic identification of this subgroup of patients. Arterial stiffness is associated with autonomic dysregulation and may be linked to major depressive disorder (MDD). The aim of this study was to investigate the association between arterial stiffness by photo-plethysmogram (PPG) in MDD with and without suicidal ideation (SI) by applying multiscale tone entropy (T-E) variability analysis. Sixty-one 10-min PPG recordings were analyzed from 29 control, 16 MDD patients with (MDDSI+) and 16 patients without SI (MDDSI−). MDD was based on a psychiatric evaluation and the Mini-International Neuropsychiatric Interview (MINI). Severity of depression was assessed using the Hamilton Depression Rating Scale (HAM-D). PPG features included peak (systole), trough (diastole), pulse wave amplitude (PWA), pulse transit time (PTT) and pulse wave velocity (PWV). Tone (Diastole) at all lags and Tone (PWA) at lags 8, 9, and 10 were found to be significantly different between the MDDSI+ and MDDSI− group. However, Tone (PWA) at all lags and Tone (PTT) at scales 3–10 were also significantly different between the MDDSI+ and CONT group. In contrast, Entropy (Systole), Entropy (Diastole) and Tone (Diastole) were significantly different between MDDSI− and CONT groups. The suicidal score was also positively correlated (r = 0.39 ~ 0.47; p < 0.05) with systolic and diastolic entropy values at lags 2–10. Multivariate logistic regression analysis and leave-one-out cross-validation were performed to study the effectiveness of multi-lag T–E features in predicting SI risk. The accuracy of predicting SI was 93.33% in classifying MDDSI+ and MDDSI− with diastolic T-E and lag between 2 and 10. After including anthropometric variables (Age, body mass index, and Waist Circumference), that accuracy increased to 96.67% for MDDSI+/− classification. Our findings suggest that tone-entropy based PPG variability can be used as an additional accurate diagnostic tool for patients with depression to identify SI.

Estimation of direction of arrival (DOA) using real-time Array Signal Processing

Array Signal Processing (ASP) is a relatively new technique in Digital Signal Processing (DSP) with many potential applications in communication and speech processing. Direction of arrival (DOA) can be estimated using different techniques evolved with ASP. Spectral-based algorithm and subspace-based methods are implemented using two widely used softwares, MATLABTM and National Instrumentpsilas LabVIEWTM, to demonstrate the feasibility of introducing the topics in course curriculum of graduate or undergraduate program. It is observed that subspace method provides superior performance in resolving closely spaced sources. The blocks developed using LabVIEWTM can be used for processing signals obtained from data acquisition card in real time.

A novel smart metering system for loss reduction and efficient load management in the power distribution sector of Bangladesh

This paper presents a prototype implementation of smart meter that facilitates the migration of our existing conventional distribution systems towards the smart distribution system. This is a step forward towards implementation of smart grid system. The proposed smart meter that establishes a two-way wireless communication link between the consumer premise and service provider can provide a number of advantages and add-on features, such as, establishing flexible dynamic multirate tariff planning, identifying and locating the premise of system loss, and full time monitoring and subsequent decision-making. In particular, an improvement of the load shedding scenario in Bangladesh is proposed based on the analysis of the load shedding data furnished by Dhaka Power Distribution Company (DPDC). It is observed that a particular feeder can be operated with less frequent load shedding if the individual customer is allowed to operate within a reduced limit monitored by the novel smart meter.

Compressive parametric GLRT detector for airborne MIMO radar

In this paper, an improved target detection algorithm for MIMO airborne radar has been proposed namely, Compressive Parametric Generalized Likelihood Ratio Test (CP-GLRT). The Parametric Generalized Likelihood Ratio Test (P-GLRT) and Generalized likelihood ratio test (GLRT) are also studied considering the availability of secondary data. The signal is assumed to be spatially and temporally colored. The noise signal is modeled as an auto-regressive process. Performance of the proposed CP-GLRT detector is compared with existing P-GLRT and GLRT detectors. Detailed comparative results have been obtained for different compression ratios as well as different number of samples. It is observed that by using compressive parametric GLRT, the computational burden on the system can be reduced. However this reduction in computational burden is achieved as a trade off with the performance of the detector. Moreover, the performance of the CP-GLRT can be improved by choosing an adaptive measurement matrix from the transmitted signal subspace.

Subspace Compressive GLRT Detector for MIMO Radar in the Presence of Clutter.

The problem of optimising the target detection performance of MIMO radar in the presence of clutter is considered. The increased false alarm rate which is a consequence of the presence of clutter returns is known to seriously degrade the target detection performance of the radar target detector, especially under low SNR conditions. In this paper, a mathematical model is proposed to optimise the target detection performance of a MIMO radar detector in the presence of clutter. The number of samples that are required to be processed by a radar target detector regulates the amount of processing burden while achieving a given detection reliability. While Subspace Compressive GLRT (SSC-GLRT) detector is known to give optimised radar target detection performance with reduced computational complexity, it however suffers a significant deterioration in target detection performance in the presence of clutter. In this paper we provide evidence that the proposed mathematical model for SSC-GLRT detector outperforms the existing detectors in the presence of clutter. The performance analysis of the existing detectors and the proposed SSC-GLRT detector for MIMO radar in the presence of clutter are provided in this paper.

Analysis of propagation model performance in WiMAX (IEEE 802.16e)-based wireless mobile vehicular networks

WiMAX is an appropriate wireless technology for networked vehicular applications in intelligent transportation system because of its mobility support at vehicular speeds and its inherent wide coverage. In this paper, performance of a WiMAX (IEEE 802.16e Standard) mobile vehicular network is investigated under different existing empirical propagation models, hand-off scenarios and variation of speed for a moving MS. The study leads to the selection of a suitable propagation model for better performance with higher throughput and lower path loss. The network simulation is done by NCTUns simulation tool with design paradigms specified for WiMAX (IEEE 802.16e).

Reduced variability in pulse wave velocity in depressed patients with suicidal ideation

Recently arterial stiffness was found to be associated with depression. The aim of this study was to investigate the association between arterial pulse wave velocity (PWV) and Major Depressive Disorder (MDD) with or without suicidal ideation. Twenty unmedicated MDD patients with a history of suicidal ideations (Age: 32.37±9.53 years) and 20 unmedicated MDD patients without any history of suicidal ideations (Age: 36.84±8.66) were recruited for this study at a psychiatric clinic in the UAE. Depression severity was assessed with the Hamilton Depression Rating Scale and the Beck Depression Inventory. Pulse wave velocity (PWV) was estimated from the ratio of half of the height and Pulse Transit Time which is defined as the time delay between the R-wave of the ECG and the arrival of the pulse wave in the index finger respectively. MDD Patients with suicidal ideation were found to have reduced low frequency (LF) and high frequency (HF) power of PWV compared to MDD patients without suicidal ideations. Suicidal score was negatively correlated (r=-0.54;p<;0.05) with LF power and positively (r=0.54; p<;0.01) with HF power. No difference in the average PWV was found between the two groups of participants. Reduced variability in pulse wave velocity in MDD patients with suicidal ideation may lead to arterial stiffness and higher risk of future cardiovascular disease.

Error probability-based optimal training for linearly decoded orthogonal space–time block coded wireless systems

An optimal training strategy is devised for the linearly decoded orthogonal space-time block coded (OSTBC) wireless systems in quasi-static fading channel, based on the performance analysis using pairwise error probability (PEP) and symbol error probability (SEP). The PEP/SEP analyses allow us to find a generic expression for the performance improvement due to optimal training compared to the conventional case for OSTBC system equipped with any number of transmit and receive antennas and any linear modulation scheme. It is observed that the linear processing in the receiver, the most attractive feature of OSTBC, although destroys the orthogonality in the presence of channel estimation error, does not reduce diversity, but causes performance penalty as a loss of signal-to-noise ratio (LoSNR) due to training. This loss is quantified analytically and minimised by optimal allocation of power between training and data symbols. The performance of optimal power allocation improves with the higher number of space-time blocks in a frame. Furthermore, the LoSNR depends only on the OSTBC and is independent of any modulation scheme and the full rate Alamouti and other high rate OSTBCs suffer more in terms of performance due to training compared to the lower rate OSTBC.