S. M. Riazul Islam

Bio: S. M. Riazul Islam is an academic researcher from Sejong University. The author has contributed to research in topics: Communication channel & Noma. The author has an hindex of 24, co-authored 125 publications receiving 5501 citations. Previous affiliations of S. M. Riazul Islam include University of Dhaka & Inha University.

Impact of Correlation and Pointing Error on Secure Outage Performance Over Arbitrary Correlated Nakagami- $m$ and $\mathcal {M}$ -Turbulent Fading Mixed RF-FSO Channel

Abstract: In Radio Frequency (RF)-Free Space Optical (FSO) mixed links, secrecy capacity (SC) can be improved by exploiting spatial diversity (i.e., antenna diversity) in the RF path. In addition to atmospheric turbulence and point error of the FSO link, antenna correlation in the RF link can significantly deteriorate the secrecy performance. In this paper, a secrecy rate of wiretap channels with a single source, relay, destination, and eavesdropper is analyzed under practical environments with the aforementioned impairments. The RF hop (source-to-relay) and the FSO hop (relay-to-destination) are modeled utilizing arbitrarily correlated Nakagami- $m$ and Malaga ( $\mathcal {M}$ ) distributions, respectively. The correlated signal branches of the RF hop are combined at the relay exploiting equal gain combining reception technique. We assume that the eavesdropper is capable of wiretapping via RF and FSO links separately. We derive novel closed-form expressions for secrecy outage probability (SOP) and strictly positive secrecy capacity (SPSC) considering heterodyne detection (HD) and intensity modulation with direct detection (IM/DD) techniques in order to examine the impact of atmospheric scintillation, pointing error, fading, and correlation on the system's secrecy performance. It is shown that the HD technique exhibits a better performance than an IM/DD technique. In addition, similar to the pointing error and turbulent fading, the correlation imposes a detrimental impact on SC. Finally, Monte-Carlo simulation results are provided for validation of the derived expressions.

Precision Medicine Informatics: Principles, Prospects, and Challenges

Abstract: Precision medicine (PM) is an emerging approach that appears with the impression of changing the existing paradigm of medical practice. Recent advances in technological innovations and genetics and the growing availability of health data have set a new pace of the research and impose a set of new requirements on the different stakeholders. Some studies are available that discuss about the different aspects of PM. Nevertheless, a holistic representation of those aspects deemed to confer with the technological perspective, in relation to the applications and challenges, have been mostly ignored. In this context, this paper surveys the advances in PM from the informatics viewpoint and reviews the enabling tools and techniques in a categorized manner. In addition, the study discusses how other technological paradigms, which include big data, artificial intelligence, and the internet of things, can be exploited to advance the potentials of PM. Furthermore, the paper provides some guidelines for future research for a seamless implementation and a wide-scale deployment of PM based on the identified open issues and the associated challenges. As a result, the paper proposes an integrated holistic framework for PM motivating informatics researchers to design their relevant research work in an appropriate context.

Asymmetrical D-Channel Photonic Crystal Fiber-based Plasmonic Sensor using the Wavelength Interrogation and Lower Birefringence Peak Method

Abstract: In this paper, an asymmetric photonic crystal fiber (PCF) working on surface plasmon resonance (SPR) has been proposed and demonstrated using the wavelength interrogation method and lower birefringence peak method. The proposed sensor contains a D-shaped analyte channel that can detect unknown analytes within the sensing range of 1.42–1.47 refractive index units (RIU) of the analytes. The structural asymmetry induces orthogonal x and y polarization modes. The numerical investigations with the finite element method (FEM) reveal that the sensor has a maximum wavelength sensitivity of 80,000 nm/RIU with a sensor resolution of 1.25 × 10−6 RIU for the y polarization mode and the maximum figure of merit (FOM) is found to be of 370.4 RIU−1. For the x polarization mode, the sensor exhibits a maximum wavelength sensitivity of 53,000 nm/RIU with a resolution of 1.89 × 10−6 RIU, having a maximum figure of merit (FOM) of 351 RIU−1. These results are found by using the wavelength interrogation method via confinement loss. On the other hand, the lower birefringence peak method-based analysis reveals a maximum wavelength sensitivity of 50,000 nm/RIU with a resolution of 2 × 10−6 RIU. As such, it is highly suitable for organic chemical detections and medical diagnostics. In addition, this paper studies the fabrication tolerance on the sensor performance.

Hybrid CNN-SVD Based Prominent Feature Extraction and Selection for Grading Diabetic Retinopathy Using Extreme Learning Machine Algorithm

Abstract: This paper exploits the extreme learning machine (ELM) approach to address diabetic retinopathy (DR), a medical condition in which impairment occurs to the retina caused by diabetes. DR, a leading cause of blindness worldwide, is a sort of swelling leakage due to excessive blood sugar in the retina vessels. An early-stage diagnosis is therefore beneficial to prevent diabetes patients from losing their sight. This study introduced a novel method to detect DR for binary class and multiclass classification based on the APTOS-2019 blindness detection and Messidor-2 datasets. First, DR images have been pre-processed using Ben Graham’s approach. After that, contrast limited adaptive histogram equalization (CLAHE) has been used to get contrast-enhanced images with lower noise and more distinguishing features. Then a novel hybrid convolutional neural network-singular value decomposition model has been developed to reduce input features for classifiers. Finally, the proposed method uses an ELM algorithm as the classifier that minimizes the training time cost. The experiments focus on accuracy, precision, recall, and F1-score and demonstrate the feasibility of adopting the proposed scheme for DR diagnosis. The method outperforms the existing techniques and shows an optimistic accuracy and recall of 99.73% and 100%, respectively, for binary class. For five stages of DR classification, the proposed model achieved an accuracy of 98.09% and 96.26% for APTOS-2019 and Messidor-2 datasets, respectively, which outperformed the existing state-of-art models.

A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction

Abstract: Early diagnosis of diabetes mellitus (DM) is critical to prevent its serious complications. An ensemble of classifiers is an effective way to enhance classification performance, which can be used to diagnose complex diseases, such as DM. This paper proposes an ensemble framework to diagnose DM by optimally employing multiple classifiers based on bagging and random subspace techniques. The proposed framework combines seven of the most suitable and heterogeneous data mining techniques, each with a separate set of suitable features. These techniques are k-nearest neighbors, naive Bayes, decision tree, support vector machine, fuzzy decision tree, artificial neural network, and logistic regression. The framework is designed accurately by selecting, for every sub-dataset, the most suitable feature set and the most accurate classifier. It was evaluated using a real dataset collected from electronic health records of Mansura University Hospitals (Mansura, Egypt). The resulting framework achieved 90% of accuracy, 90.2% of recall = 90.2%, and 94.9% of precision. We evaluated and compared the proposed framework with many other classification algorithms. An analysis of the results indicated that the proposed ensemble framework significantly outperforms all other classifiers. It is a successful step towards constructing a personalized decision support system, which could help physicians in daily clinical practice.

Wireless Body Area Networks: A Survey

Abstract: Recent developments and technological advancements in wireless communication, MicroElectroMechanical Systems (MEMS) technology and integrated circuits has enabled low-power, intelligent, miniaturized, invasive/non-invasive micro and nano-technology sensor nodes strategically placed in or around the human body to be used in various applications, such as personal health monitoring. This exciting new area of research is called Wireless Body Area Networks (WBANs) and leverages the emerging IEEE 802.15.6 and IEEE 802.15.4j standards, specifically standardized for medical WBANs. The aim of WBANs is to simplify and improve speed, accuracy, and reliability of communication of sensors/actuators within, on, and in the immediate proximity of a human body. The vast scope of challenges associated with WBANs has led to numerous publications. In this paper, we survey the current state-of-art of WBANs based on the latest standards and publications. Open issues and challenges within each area are also explored as a source of inspiration towards future developments in WBANs.

Nonorthogonal Multiple Access for 5G and Beyond

Abstract: Driven by the rapid escalation of the wireless capacity requirements imposed by advanced multimedia applications (e.g., ultrahigh-definition video, virtual reality, etc.), as well as the dramatically increasing demand for user access required for the Internet of Things (IoT), the fifth-generation (5G) networks face challenges in terms of supporting large-scale heterogeneous data traffic. Nonorthogonal multiple access (NOMA), which has been recently proposed for the third-generation partnership projects long-term evolution advanced (3GPP-LTE-A), constitutes a promising technology of addressing the aforementioned challenges in 5G networks by accommodating several users within the same orthogonal resource block. By doing so, significant bandwidth efficiency enhancement can be attained over conventional orthogonal multiple-access (OMA) techniques. This motivated numerous researchers to dedicate substantial research contributions to this field. In this context, we provide a comprehensive overview of the state of the art in power-domain multiplexing-aided NOMA, with a focus on the theoretical NOMA principles, multiple-antenna-aided NOMA design, on the interplay between NOMA and cooperative transmission, on the resource control of NOMA, on the coexistence of NOMA with other emerging potential 5G techniques and on the comparison with other NOMA variants. We highlight the main advantages of power-domain multiplexing NOMA compared to other existing NOMA techniques. We summarize the challenges of existing research contributions of NOMA and provide potential solutions. Finally, we offer some design guidelines for NOMA systems and identify promising research opportunities for the future.

Heart Disease: A Textbook of Cardiovascular Medicine

Abstract: This third edition of what has now become a well-established textbook in cardiovascular medicine is again edited by Dr Eugene Braunwald with the assistance of 65 other authors who read like a Who's Who of American Cardiology. Since the second edition, 12 new chapters have been added or substituted and others have been significantly revised. The first volume includes Part I on "Examination of the Patient" and Part II on "Normal and Abnormal Circulatory Function." The second volume deals with specific diseases. Part III, "Diseases of the Heart, Pericardium and Vascular System," includes new sections on "Risk Factors for Coronary Artery Disease," "The Pathogenesis of Atherosclerosis," and "Interventional Catheterization Techniques." Part IV, "Broader Perspectives on Heart Disease and Cardiologic Practice," includes new chapters on "Genetics and Cardiovascular Disease," "Aging in Cardiac Disease," and "Cost Effective Strategies in Cardiology." The last 200 pages of the book (Part V) are devoted to