Khalid Al-Mutib

Audio-Visual Emotion Recognition Using Big Data Towards 5G

With the advent of future generation mobile communication technologies (5G), there is the potential to allow mobile users to have access to big data processing over different clouds and networks. The increasing numbers of mobile users come with additional expectations for personalized services (e.g., social networking, smart home, health monitoring) at any time, from anywhere, and through any means of connectivity. Because of the expected massive amount of complex data generated by such services and networks from heterogeneous multiple sources, an infrastructure is required to recognize a user’s sentiments (e.g., emotion) and behavioral patterns to provide a high quality mobile user experience. To this end, this paper proposes an infrastructure that combines the potential of emotion-aware big data and cloud technology towards 5G. With this proposed infrastructure, a bimodal system of big data emotion recognition is proposed, where the modalities consist of speech and face video. Experimental results show that the proposed approach achieves 83.10 % emotion recognition accuracy using bimodal inputs. To show the suitability and validity of the proposed approach, Hadoop-based distributed processing is used to speed up the processing for heterogeneous mobile clients.

Fuzzy Logic Navigation and Obstacle Avoidance by a Mobile Robot in an Unknown Dynamic Environment

Mobile robot navigation has remained an open problem over the last two decades. Mobile robots are required to navigate in unknown and dynamic environments, and in recent years the use of mobile robots in material handling has considerably increased. Usually workers push carts around warehouses and manually handle orders which is not very cost-effective. To this end, a potential method to control a swarm of mobile robots in a warehouse with static and dynamic obstacles is to use the wireless control approach. Further, to be able to control different types of mobile robots in the warehouse, the fuzzy logic control approach has been chosen. Therefore, in this paper, an on-line navigation technique for a wheeled mobile robot (WMR) in an unknown dynamic environment using fuzzy logic techniques is investigated. In this paper, we aim to use the robot in application in a warehouse. Experimental results show the effectiveness of the proposed algorithm.

A Hierarchical Fuzzy Control Design for Indoor Mobile Robot

This paper presents a motion control for an autonomous robot navigation using fuzzy logic motion control and stereo vision based path-planning module. This requires the capability to maneuver in a complex unknown environment. The mobile robot uses intuitive fuzzy rules and is expected to reach a specific target or follow a prespecified trajectory while moving among unforeseen obstacles. The robots mission depends on the choice of the task. In this paper, behavioral-based control architecture is adopted, and each local navigational task is analyzed in terms of primitive behaviors. Our approach is systematic and original in the sense that some of the fuzzy rules are not triggered in face of critical situations for which the stereo vision camera can intervene to unblock the mobile robot.

Implementation of Fuzzy Decision Based Mobile Robot Navigation Using Stereo Vision

Abstract In this article, we discuss implementation phases for an autonomous navigation of a mobile robotic system using SLAM data, while relying on the features of learned navigation maps. The adopted SLAM based learned maps, was relying entirely on an active stereo vision for observing features of the navigation environment. We show the framework for the adopted lower-level software coding, that was necessary once a vision is used for multiple purposes, distance measurements, and obstacle discovery. In addition, the article describes the adopted upper-level of system intelligence using fuzzy based decision system. The proposed map based fuzzy autonomous navigation was trained from data patterns gathered during numerous navigation tasks. Autonomous navigation was further validated and verified on a mobile robot platform.

Stereo vision SLAM based indoor autonomous mobile robot navigation

This manuscript presents an autonomous navigation of a mobile robot using SLAM, while relying on an active stereo vision. We show a framework of low-level software coding which is necessary when the vision is used for multiple purposes such as obstacle discovery. The built system incorporated a number of SLAM based routines while replying on stereo vision mechanism. The system was implemented and tested on a mobile robot platform, and perform an experiment of autonomous navigation in an indoor environment.

Motion periodicity-based pedestrian detection and particle filter-based pedestrian tracking using stereo vision camera

A novel method is proposed that adapts a previously proposed LADAR based pedestrian detection and tracking technique by introducing a stereo-vision based segmentation technique for the purpose of pedestrian detection and tracking. The proposed method detects the harmonic motions of limbs and body during a typical human walk and temporally propagates the position, stride, direction and phase using a particle filter. The particle-filter uses a human limb-motion model and is able to track the walking pedestrians in a heavily occluded environment. Potential 3D point clusters belonging to arms and feet are extracted employing an adapted version of RANSAC based segmentation algorithm. A Fourier-transform based periodogram confirms the periodicity for each point-cluster representing limbs. Since RGB or intensity data from the stereo-vision input is ignored and the proposed method completely relies upon 3D data produced by the stereo-vision sensor, reliable illumination invariant pedestrian detection and tracking results are achieved using Daimler-Stereo-Pedestrian-Detection-Dataset. Further lab experiments also confirm the viability of the method within the indoor environment.

Obstacle avoidance using wall-following strategy for indoor mobile robots

This paper proposes a fuzzy logic methodology to control an indoor mobile robot for a complete navigation in an unknown environment. The methodology incorporates two basic behaviors, namely: reaching the goal and avoiding obstacles. The obstacle avoidance behavior is treated using wall-following scheme based on a fuzzy technique proposed for this proposed. The mobile robot control mechanism uses some sort of knowledge-base arranged in a set of fuzzy-rule-base to implement the wanted behavior that makes the mobile robot follow the boundary of an obstacle or a wall. A constant distance to the obstacle/wall is maintained while the robot tries successfully to get around this difficulty. Once the path is clear, the obstacle avoidance behavior is inhibited and reaching the goal behavior is activated using a second fuzzy controller. In order to handle data uncertainties, Type-2 fuzzy sets are considered. This methodology was successfully tested on a real mobile robot for different sort of scenarios.

Towards software engineering process for C4I systems

Command Control, Communication Computer and Intelligence (C4I) systems enables modern military forces to achieve information superiority in the battlefield. C4I are complex System of systems (SOS) where individual systems interact locally to achieve global SOS behaviors. To build software for C4I systems conventional software engineering SwE process and practices have shortcomings and are not capable to support certain aspect of these systems. If C4I systems fail to operate as required due to the fact that SwE process was unable to fulfill its requirements, the consequences may not be tolerated because of the criticality of the mission of these systems in information warfare (IW). This paper highlights the distinguished characteristics and operational requirements of C4I systems which poses challenges to SwE process and practices. This paper also discuss the possible future research areas in order to enhance SwE process so that better software could drive these complex systems as required.

Understanding of Metric-Topological maps for mobile robot navigation

The research is presenting a technique through which to learn, hence understand mobile robot Metric-Topological navigation maps for the purpose of much understanding of navigated environments. The adopted learning technique is based on using Principles Components Analysis (PCA) technique. PCA is used to reduce navigated maps dimensionality, capture maps only important details, hence to learn inherent details and characteristics of the environment. Navigation maps were created as based on using a stereo vision measurement techniques (VSLAM), Al-Mutib et al. [1]. Maps sizes are fixed, however their inside details are not static, as the environment is a moving dynamic space. The adopted technique was useful in terms of learning and understanding the environments inherent characterizations. This will help to support an enhanced and improved mobile navigation.

Optimization of Fuzzy Logic Controller Using PSO for Mobile Robot Navigation in an Unknown Environment

The control of autonomous mobile robot in an unknown environments include many challenge. Fuzzy logic controller is one of the useful tool in this field. Performance of fuzzy logic controlling depends on the membership function, so the membership function adjusting is a time consuming process. In this paper, we optimized a fuzzy logic controller (Fuzzy) by automatic adjusting the membership function using a particle swarm optimization (PSO). The proposed method (PSO-Fuzzy) is implemented and compared with Fuzzy using Khepera simulator. Moreover, the performance of these approaches compared through experiments using a real Khepera III platform.