Muhammad Shahzad Younis

A Near-Optimal LLR Based Cooperative Spectrum Sensing Scheme for CRAHNs

In Cognitive Radio Ad Hoc Networks (CRAHNs), cooperative spectrum sensing schemes exploit spatial diversity of the Secondary Users (SUs), to reliably detect an unoccupied licensed spectrum. Soft energy combining schemes provide optimal detection performance by combining the actual sensed information from SUs. For reliable data fusion, these techniques mandate weight estimation for individual SUs in each sensing interval, resulting in high cooperation overhead in terms of time, processing and bandwidth. Alternately, a hard energy combining scheme offers lower cooperation overhead in which only local SU decisions are reported to the fusion center. However, it provides sub-optimal detection performance due to the information loss. In this paper, a Log-Likelihood Ratio (LLR) based cooperative spectrum sensing scheme is proposed in which each SU performs a local LLR based sensing test employing two threshold levels. The local decision and sequentially estimated SNR parameter values (for weight computation) are not reported to the fusion center if the local test result is in-between the two threshold levels. Thereby, cooperation overhead is reduced in proportion to the hard combining techniques; nevertheless simulation results show that the detection performance of the proposed scheme is close to the optimal soft combining techniques.

A Weighted Linear Combining Scheme for Cooperative Spectrum Sensing

Abstract Cooperative spectrum sensing exploits spatial diversity of secondary-users (SUs), to reliably detect the availability of a spectrum. Soft energy combining schemes have optimal detection performance at the cost of high cooperation overhead, since actual sensed data is required at the fusion center. To reduce cooperation overhead, in hard combining only local decisions are shared; however the detection performance is suboptimal due to the loss of information. In this paper, a weighted linear combining scheme is proposed in which a SU performs a local sensing test based on two threshold levels. If local test result lies between the two thresholds then the SU report neither its local decision nor sequentially estimated unknown SNR parameter values, to the fusion center. Thereby, uncertain decisions about the presence/absence of the primary-user signal are suppressed. Simulation results suggest that the detection performance of the proposed scheme is close to optimal soft combining schemes yet its overhead is similar to hard combining techniques.

Multiclass classification of initial stages of Alzheimer's disease using structural MRI phase images

Alzheimers disease (AD) is the most common type of dementia that is affecting the elderly population worldwide. We present here a novel method based on the progressive two class proximal support vector machine based decision (pTCDC- PSVM) classifier to distinguish between the elderly patients with AD, mild cognitive impairment (MCI) and normal controls (NC). Structural phase images are formed to extract useful features using independent component analysis (ICA) technique which are subsequently used for the classification purposes. The results obtained show the efficacy of our approach and the significant advantages associated with the use of structural magnetic resonance imaging (MRI) phase images in discriminating the early categories of Alzheimers disease.

Mobile Health in the Developing World: Review of Literature and Lessons From a Case Study

The mHealth trend, which uses mobile devices and associated technology for health interventions, offers unprecedented opportunity to transform the health services available to people across the globe. In particular, the mHealth transformation can be most disruptive in the developing countries, which is often characterized by a dysfunctional public health system. Despite this opportunity, the growth of mHealth in developing countries is rather slow and no existing studies have conducted an in-depth search to identify the reasons. We present a comprehensive report about the factors hindering the growth of mHealth in developing countries. Most importantly, we outline future strategies for making mHealth even more effective. We are also the first to conduct a case study on the public health system of Pakistan showing that mHealth can offer tremendous opportunities for a developing country with a severe scarcity of health infrastructure and resources. The findings of this paper will guide the development of policies and strategies for the sustainable adoption of mHealth not only in Pakistan but also for any developing country in general.

Performance analysis of Automatic Modulation Classification in multipath fading environment

Automatic Modulation Classification (AMC) is extremely desirable to realize the merits of cognitive radios in plethora of commercial and military applications. However, AMC is extremely challenging in real-world scenarios due to multipath fading and additive Gaussian noise on modulation schemes. Moreover, it becomes more difficult in blind environments where a little or no priori information about the received signal is available. Most of the available modulation classifiers do not consider the fading effects which results in performance degradation of classification in a blind channel environment. In this paper, we investigate the multipath fading effects on the AMC of some common modulation schemes i.e., BPSK, QPSK and 16-QAM for blind channels. In our work channel is assumed to be suffering from multipath and excessive additive noise resulting in low SNR of signal. The unknown channel and noise parameters are estimated using Hidden Markovian based Expectation Maximization algorithm. The estimated channel coefficients are then used in Maximum-Likelihood classifier for the classification of modulation scheme. Simulation results show that phase modulation schemes (i.e., BPSK and QPSK) perform better at low SNR compared to 16-QAM which includes the phase amplitude information.

A multi-hop angular routing protocol for wireless sensor networks

In this article, we propose two new routing protocols for wireless sensor networks. First one is AM-DisCNT (angular multi-hop distance–based clustering network transmission) protocol which uses circular deployment of sensors (nodes) for uniform energy consumption in the network. The protocol operates in such a way that nodes with maximum residual energy are selected as cluster heads for each round. Second one is iAM-DisCNT (improved AM-DisCNT) protocol which exploits both mobile and static base stations for throughput maximization. Besides the proposition of routing protocols, iAM-DisCNT is provided with three mathematical models: two linear-programming-based models for information flow maximization and packet drop rate minimization and one model for calculating energy consumption of nodes. Graphical analysis for linear-programming-based mathematical formulation is also part of this work. Simulation results show that AM-DisCNT has 32% and iAM-DisCNT has 48% improved stability period as compared to LEACH (low-energy adaptive clustering hierarchy) and DEEC (distributed energy-efficient clustering) routing protocols. Similarly, throughput of AM-DisCNT and iAM-DisCNT is improved by 16% and 80%, respectively, in comparison with the counterpart schemes.

A Log-likelihood based Cooperative Spectrum Sensing Scheme for Cognitive Radio Networks☆

Abstract In cognitive radio networks, cooperative spectrum sensing is used to exploit spatial diversity of secondary users, in order to reliably detect an unoccupied licensed spectrum. Since each secondary user experience different channel conditions, therefore observations of secondary users are weighted according to the reliability factor (weight) of individual secondary user. However, the weight estimation is done in each sensing interval resulting in high cooperation overhead in terms of time, processing and reporting channel bandwidth. In this paper, we propose a novel cooperative spectrum sensing scheme which is based on the Log-Likelihood ratio (LLR). The secondary users employ two threshold level to minimize false-alarms and miss-detection of the primary users signal. In addition, unknown parameters for weight computation are estimated/updated after certain number of sensing intervals instead of estimating them in every sensing interval. In this way, the detection performance is increased while the cooperation overhead reduces. To evaluate the detection performance of the proposed scheme we do a simulation study and compare it with the popular optimal detection schemes.

Investigating 3D echocardiography image fusion for improving image quality

3D echocardiography offers the ability to perform cardiac functional analysis by visualizing the full 3D geometry of the heart. The full potential of 3D echocardiography has still not been achieved due to problems with image quality and automated quantitative analysis. Native single-view images often lack sufficient anatomical information and are low in contrast and noisy in nature due to poor acoustic window and ultrasound physics limitations. In this work, we explore various ways of fusing the multiple single-view 3D echocardiography images in order to obtain a complete 3D view of the heart by preserving maximum salient information from individual images. Three fusion techniques have been explored for image fusion that include: maximum, averaging, and wavelet image fusion. A novel method of 3D echocardiography fusion utilizing principal component analysis is proposed and a comparative analysis of all discussed techniques is conducted Results obtained from 10 subjects demonstrate that 3D echocardiography image fusion helps in improving quantitative evaluation measures SNR, CNR and contrast while extending FOV and thus filling the missing information in the individual source images. It is hoped that this improved image quality leads to an improved cardiac functional analysis as the multi-view fused image shows the whole picture of the heart.

Community detection in networks: A multidisciplinary review

Abstract The modern science of networks has made significant advancement in the modeling of complex real-world systems. One of the most important features in these networks is the existence of community structure. In recent years, many community detection algorithms have been proposed to unveil the structural properties and dynamic behaviors of networks. In this study, we attempt a contemporary survey on the methods of community detection and its applications in the various domains of real life. Besides highlighting the strengths and weaknesses of each community detection approach, different aspects of algorithmic performance comparison and their testing on standard benchmarks are discussed. The challenges faced by community detection algorithms, open issues and future trends related to community detection are also postulated. The main goal of this paper is to put forth a review of prevailing community detection algorithms that range from traditional algorithms to state of the art algorithms for overlapping community detection. Algorithms based on dimensionality reduction techniques such as non-negative matrix factorization (NMF) and principal component analysis (PCA) are also focused. This study will serve as an up-to-date report on the evolution of community detection and its potential applications in various domains from real world networks.

Nonlinear Bayesian Estimation of BOLD Signal under Non-Gaussian Noise

Modeling the blood oxygenation level dependent (BOLD) signal has been a subject of study for over a decade in the neuroimaging community. Inspired from fluid dynamics, the hemodynamic model provides a plausible yet convincing interpretation of the BOLD signal by amalgamating effects of dynamic physiological changes in blood oxygenation, cerebral blood flow and volume. The nonautonomous, nonlinear set of differential equations of the hemodynamic model constitutes the process model while the weighted nonlinear sum of the physiological variables forms the measurement model. Plagued by various noise sources, the time series fMRI measurement data is mostly assumed to be affected by additive Gaussian noise. Though more feasible, the assumption may cause the designed filter to perform poorly if made to work under non-Gaussian environment. In this paper, we present a data assimilation scheme that assumes additive non-Gaussian noise, namely, the e-mixture noise, affecting the measurements. The proposed filter MAGSF and the celebrated EKF are put to test by performing joint optimal Bayesian filtering to estimate both the states and parameters governing the hemodynamic model under non-Gaussian environment. Analyses using both the synthetic and real data reveal superior performance of the MAGSF as compared to EKF.