Mohsen Guizani

Bio: Mohsen Guizani is an academic researcher from Qatar University. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 79, co-authored 1110 publications receiving 31282 citations. Previous affiliations of Mohsen Guizani include Jaypee Institute of Information Technology & University College for Women.

Hybrid Rayleigh and Double-Weibull over Impaired RF/FSO System with Outdated CSI

Abstract: In this work, we present a global framework of a dual-hop RF/FSO system with multiple relays operating at the mode of amplify-and-forward (AF) with fixed gain. Partial relay selection (PRS) protocol with outdated channel state information (CSI) is assumed since the channels of the first hop are time-varying. The optical irradiance of the second hop are subject to the Double-Weibull model while the RF channels of the first hop experience the Rayleigh fading. The signal reception is achieved either by heterodyne or intensity modulation and direct detection (IM/DD). In addition, we introduce an aggregate model of hardware impairments to the source (S) and the relays since they are not perfect nodes. In order to quantify the impairment impact on the system, we derive closed-form, approximate, upper bound and high signal-to-noise ratio (SNR) asymptotic of the outage probability (OP) and the ergodic capacity (EC). Finally, analytical and numerical results are in agreement using Monte Carlo simulation.

Vanets Meet Autonomous Vehicles: Multimodal Surrounding Recognition Using Manifold Alignment

Abstract: In the past two years, calls for developing synergistic links between the two worlds of vehicular ad-hoc networks (VANETs) and autonomous vehicles have significantly gone up to achieve further on-road safety and benefits for end-users. In this paper, we present our vision to create such a beneficial link by designing a multimodal scheme for object detection, recognition, and mapping based on the fusion of stereo camera frames, point cloud Velodyne LIDAR scans, and vehicle-to-vehicle (V2V) basic safety messages (BSMs) exchanges using VANET protocols. Exploiting the high similarities in the underlying manifold properties of the three data sets, and their high neighborhood correlation, the proposed scheme employs semi-supervised manifold alignment to merge the key features of rich texture descriptions of objects from 2-D images, depth and distance between objects provided by 3-D point cloud, and the awareness of self-declared vehicles from BSMs’ 3-D information including the ones not seen by camera and LIDAR. The proposed scheme is applied to create joint pixel-to-point-cloud and pixel-to-V2V correspondences of objects in frames from the KITTI Vision Benchmark Suite, using a semi-supervised manifold alignment, to achieve camera-LIDAR and camera-V2V mapping of their recognized objects. We present the alignment accuracy results over two different driving sequences and show the additional acquired knowledge of objects from the various input modalities. We also study the effect of the number of neighbors employed in the alignment process on the alignment accuracy. With proper choice of parameters, the testing of our proposed scheme over two entire driving sequences exhibits 100% accuracy in the majority of cases, 74%–92% and 50%–72% average alignment accuracy for vehicles and pedestrians and up to 150% additional object recognition of the testing vehicle’s surrounding.

Trading wireless information and power transfer: Relay selection to minimize the outage probability

Abstract: This paper studies the outage probability minimization problem for a multiple relay network with energy harvesting constraints. The relays are hybrid nodes used for simultaneous wireless information and power transfer from the source radio frequency (RF) signals. There is a tradeoff associated with the amount of time a relay node is used for energy and information transfer. Large intervals of information transfer implies little time for energy harvesting from RF signals and thus, high probability of outage events. We propose relay selection schemes for a cooperative system with a fixed number of RF powered relays. We address both causal and non-causal channel state information cases at the relay-destination link and evaluate the tradeoff associated with information/power transfer in the context of minimization of outage probability.

Priority-Based Medium Access Control for Wireless Body Area Networks With High-Performance Design

Abstract: The wireless body area network (WBAN) has attracted considerable attention. Two main problems exist in WBANs: 1) the quality of service (QoS) requirements and 2) the energy efficiency of data transmission. To solve the above problems, in this paper, we carefully design the sensor priority according to the importance degree, sampling rate, timeout condition, and remaining energy. Then, considering the priority of the node and the channel factors, a utility function is introduced to characterize the value of a node transmitting data frames in a specific time period. Next, we model the time slot allocation problem, where the objective is to maximize the total utility of the data transmission of all nodes in a specified period of time by adjusting the transmission time and the transmission duration of each node. Finally, according to the problem model, we propose a time slot allocation scheme based on a greedy strategy, which effectively reduces the time complexity of the direct solution to the problem. In this scheme, nodes with higher priority are arranged to transmit data frames in the time slots with better channel conditions. The experimental results show that the proposed scheme achieves substantial improvements in QoS and energy efficiency relative to the comparison schemes.

An IoT and Blockchain-Based Multi-Sensory In-Home Quality of Life Framework for Cancer Patients

Abstract: Once a subject is diagnosed with cancer, a patient goes through a series of diagnosis and tests, referred to as after cancer treatment. Due to the nature of the treatment and side effects on regular lifestyles, maintaining quality of life in the home environment is a challenging task. Sometimes within a home environment, a cancer patient’s situation changes abruptly, as the functionality of certain organs deteriorate, which affects their quality of life. In this paper, we propose a Blockchain and off-chain based framework which will allow multiple medical and ambient intelligent IoT sensors to capture quality of life information from one’s home environment and securely share it with one’s community of interest. Using our proposed framework, both transactional records and multimedia big data – consisting of a user’s physiological as well as mental states – can be shared with an oncologist or palliative care unit for real-time decision support. We have also developed Blockchain-based data analytics, which will allow a clinician to visualize the immutable history of the patient’s data available from an in-home secure monitoring system for a better understanding of a patient’s current or historical states. We further designed a generic oncologist smart contract and digital wallet for different stakeholders to automate the treatment plan of a particular patient. Finally, we will present our current implementation status, which provides significant encouragement for further development.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.