Djamel Khadraoui

Bio: Djamel Khadraoui is an academic researcher from Metz. The author has contributed to research in topics: Critical infrastructure & Computer security model. The author has an hindex of 19, co-authored 135 publications receiving 1309 citations. Previous affiliations of Djamel Khadraoui include Citigroup & Blaise Pascal University.

Vision Based Control Law using 3D Visual Features

Abstract: This paper presents the use of 3D visual features in the so-called "Visual Servo-ing Approach". After having brie y recalled how the task function approach is used in visual servoing, we present the notion of 3D logical vision sensors which permit us to extract visual information. In particular, we a r e i n terested in those composed of the estimation of both a 3D point and a 3D attitude. We g i v e t h e c o n trol law expression with regard to the two kinds of visual features performed at video rate with our robotic platform. We present some of the experimental results and show the good convergence of the control laws.

Visual servoing in robotics scheme using a camera/laser-stripe sensor

Abstract: The work presented in this paper belongs to the realm of robotics and computer vision. The problem we seek to solve is the accomplishment of robotics tasks using visual features provided by a special sensor, mounted on a robot end effector. This sensor consists of two laser stripes fixed rigidly to a camera, projecting planar light on the scene. First, we briefly describe the classical visual servoing approach. We then generalize this approach to the case of our special sensor by considering its interaction with respect to a sphere. This interaction permits us to establish a kinematics relation between the sensor and the scene. Finally, both in simulation and in our experimental cell, the results are presented. They concern the positioning task with respect to a sphere, and show the robustness and the stability of the control scheme.

Comparison of Green Light Optimal Speed Advisory approaches

Abstract: Green Light Optimal Speed Advisory (GLOSA) systems provide drivers with speed advices that allow them to pass traffic lights during green interval. In this article we compare performance of single- and multi-segment GLOSA approaches. In a single-segment GLOSA traffic signals are considered independently, i.e. the system provides vehicles with the optimal speed for the segment ahead of the nearest traffic signals. In a multi-segment GLOSA several signals in a sequence on a vehicle's route are taken into account, that is, vehicles receive speed advices for a set of segments ahead of the vehicle. Traveling time and fuel efficiency are used as performance measures. We demonstrate that as long as traffic conditions allow drivers selecting a wide range of speed (e.g. during free-flow) a multi-segment GLOSA results in much better performance when compared with a single-segment approach.

Multi-segment Green Light Optimal Speed Advisory

Abstract: The problem of how to adjust speed of vehicles so that they can arrive at the intersection when the light is green can be solved by means of Green Light Optimal Speed Advisory (GLOSA). The existing GLOSA approaches are single segment, that is, they consider traffic lights independently by providing vehicles with the optimal speed for the segment ahead of the nearest traffic lights. In this article we introduce a new approach-a multi segment GLOSA-according to which several lights in sequence on a vehicle's route are taken into account. The speed optimisation process is performed using a genetic algorithm. We assume that a vehicle has access to all traffic light phase schedules that it will encounter on its route. The route is composed of segments divided by traffic lights. The proposed GLOSA provides a driver with speed advisory for each segment according to selected preferences like minimisation of total traveling time or fuel consumption. We demonstrate, that in free-flow conditions such multi-segment GLOSA results in much better results when compared with single-segment approach.

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.).

Internet of things (IoT) security: Current status, challenges and prospective measures

Abstract: The paper presents a survey and analysis on the current status and concerns of Internet of things (IoT) security. The IoT framework aspires to connect anyone with anything at anywhere. IoT typically has a three layers architecture consisting of Perception, Network, and Application layers. A number of security principles should be enforced at each layer to achieve a secure IoT realization. The future of IoT framework can only be ensured if the security issues associated with it are addressed and resolved. Many researchers have attempted to address the security concerns specific to IoT layers and devices by implementing corresponding countermeasures. This paper presents an overview of security principles, technological and security challenges, proposed countermeasures, and the future directions for securing the IoT.