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.

Cognitive Sensors Based on Ridge Phase-Smoothing Localization and Multiregional Histograms of Oriented Gradients

Abstract: This study presents a smart cognitive sensor “ i Recorder” that can spontaneously locate speakers among attendees at a boardroom using ubiquitous arrays of audiovisual sensors. The proposed system “ i Recorder” consists of two major components—Sound localization and mouth tracking. For acoustic processing, this work proposes ridge phase-smoothing direction-of-arrival (DOA) estimation, which refines the distorted phase of a signal and robustly determines acoustic directions. During visual detection, this study develops novel Multiregional Histograms of Oriented Gradients (MHOGs) to model an uttering mouth. Unlike HOGs, the proposed feature is no longer limited to fixed-sized windows or blocks. It relies on facial regions. Finally, the system uses a fusion mechanism that integrates both clues from audiovisual sensors based on majority voting to target an actual speaker. The experimental result of DOA estimation showed that the directional errors were successfully improved by 6.6 degree on average. Concerning detection of talking faces, the accuracy reached as high as a rate of 85.19 percent. The fusion test results also supported the effectiveness of the system. Such findings reveal that the proposed system is superior to the other approaches and establishes its feasibility.

Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization

Abstract: Unmanned Aerial Vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones, which is not the case of traditional systems relying on direct observations obtained from fixed cameras and sensors. Furthermore, thanks to the advancements in computer vision and machine learning, UAVs are being adopted for a broad range of solutions and applications. However, Deep Neural Networks (DNNs) are progressing toward deeper and complex models that prevent them from being executed on-board. In this paper, we propose a DNN distribution methodology within UAVs to enable data classification in resource-constrained devices and avoid extra delays introduced by the server-based solutions due to data communication over air-to-ground links. The proposed method is formulated as an optimization problem that aims to minimize the latency between data collection and decision-making while considering the mobility model and the resource constraints of the UAVs as part of the air-to-air communication. We also introduce the mobility prediction to adapt our system to the dynamics of UAVs and the network variation. The simulation conducted to evaluate the performance and benchmark the proposed methods, namely Optimal UAV-based Layer Distribution (OULD) and OULD with Mobility Prediction (OULD-MP), were run in an HPC cluster. The obtained results show that our optimization solution outperforms the existing and heuristic-based approaches.

Exploiting 4G mobile user cooperation for energy conservation: challenges and opportunities

Abstract: Recent years have witnessed tremendous success and popularity of mobile applications and services, resulting in an explosive growth in the number of mobile devices, as well as in the range and types of things these devices can do. People nowadays become extremely dependent on their smartphones and handheld devices to access and receive online services. While computing and processing powers of these handheld devices are keeping up with this demand, battery lifetime remains the performance bottleneck, and researchers are now more challenged than ever before to come up with new techniques that can make efficient use of the devices? energy resources. In this article, we focus on exploiting user cooperation as a way of conserving energy in 4G mobile networks. We first begin by overviewing user cooperation and illustrating its potential for reducing energy consumption. Then, we describe the key challenges 4G mobile users face vis-a-vis of cooperation. Finally, we discuss some of the techniques proposed in literature to address these challenges by highlighting their methodologies, advantages, and disadvantages.

Processing geo-dispersed big data in an advanced mapreduce framework

Abstract: Big data has emerged as a new era of information generation and processing. Big data applications are expected to provide a lot of benefits and convenience to our lives. Cloud computing is a popular infrastructure that has the resources for big data processing. As the number of mobile devices is fast increasing, mobile cloud computing is becoming an important part of many big data applications. In this article, we propose a novel MapReduce-based framework to process geo-dispersed big data in mobile cloud architecture. The proposed framework supports simple as well as complex operations on geo-dispersed big data, and uses various data aggregation schemes to satisfy different application requirements.

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.