Houcine Hassan

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Sparse representation has gained popularity in the last few years as a technique to reconstruct a signal with few training examples. This reconstruction can be defined as adaptively finding a dictionary which best represents the signal on sample bases. Sparse representation establishes a more rigorous mathematical framework for studying high-dimensional data and ways to uncover the structures of the data, giving rise to a large repertoire of efficient algorithms. The sparse representation has just been applied to visual analysis for few years, while has shown its advantages in processing the visual information. Thus it will have a great potential in this field.

A simple power-aware scheduling for multicore systems when running real-time applications

High-performance microprocessors, e.g., multithreaded and multicore processors, are being implemented in embedded real-time systems because of the increasing computational requirements. These complex microprocessors have two major drawbacks when they are used for real-time purposes. First, their complexity difficults the calculation of the WCET (worst case execution time). Second, power consumption requirements are much larger, which is a major concern in these systems. In this paper we propose a novel soft power-aware real-time scheduler for a state-of-the-art multicore multithreaded processor, which implements dynamic voltage scaling techniques. The proposed scheduler reduces the energy consumption while satisfying the constraints of soft real-time applications. Different scheduling alternatives have been evaluated, and experimental results show that using a fair scheduling policy, the proposed algorithm provides, on average, energy savings ranging from 34% to 74%.

Remote Laboratory Architecture for the Validation of Industrial Control Applications

The seven engineering degrees of the Higher Technical School of Design Engineering (ETSID), Technical University of Valencia, include in their formation programs, subjects, and laboratory projects to instruct students in the aspects of the design, development, and validation of applications for process control, automation, industrial informatics, and embedded systems. Moreover, the authors participate in European projects of education such as the International Network of Embedded System (INES) and the European Project Semester (EPS), where exchange students remotely perform the first phase of their projects at ETSID from their home universities. To significantly reduce the cost of installing a huge number of real prototypes in labs and to fulfill the distance requirements of the exchange students, a remote laboratory architecture, i.e., simPROCes, has been designed. simPROCes not only permits the teleoperation of simulators/real prototypes but also allows that complete control applications be remotely tested and validated. simPROCes is precisely specified to be independent of the model of computer, data acquisition card, programming language, and operating system, and is transparent to the programmer and easy to use. This system is useful both for the education and development of control application purposes. A water tank process shows the installation procedure of simPROCes to test and validate control applications. The experience of applying simPROCes in the electronic engineering degree and within the framework of INES and EPS has been successfully rated by student surveys.

Smartphone-Based Industrial Informatics Projects and Laboratories

The use of IT technologies plays an important role in the training of future engineers. In this paper, smartphones and multimedia technologies are proposed as an innovative way to tackle the formation of students, at different levels, in the Industrial Informatics (II) subject of the Industrial Electronics Engineering (IEE) degree. II instructs future Engineers in the design of IT systems to control industrial processes. In the first level, smartphones are used to display a web-based multimedia tool that is implemented to register the lecture explanations regarding the design of II systems, so as it facilitates student to guide him/her self in the learning process. In the second level, the smartphone is proposed as the control system of a medium size industrial process (e.g., water tank). Since II uses a problem-based learning methodology (miniproject) to instruct the design of II systems, for each lecture, laboratory practices are tackled, and the solutions obtained are embedded in the smartphone to control the corresponding part of the miniproject. An application of the Smartphone multimedia tool is presented to show how students interact with the developed system. The successful evaluation of the proposed tools, by more than 900 IEE students during three years, is shown.

Power‐aware scheduling with effective task migration for real‐time multicore embedded systems

A major design issue in embedded systems is reducing the power consumption because batteries have a limited energy budget. For this purpose, several techniques such as dynamic voltage and frequency scaling (DVFS) or task migration are being used. DVFS allows reducing power by selecting the optimal voltage supply, whereas task migration achieves this effect by balancing the workload among cores. This paper focuses on power‐aware scheduling allowing task migration to reduce energy consumption in multicore embedded systems implementing DVFS capabilities. To address energy savings, the devised schedulers follow two main rules: migrations are allowed at specific points of time and only one task is allowed to migrate each time. Two algorithms have been proposed working under real‐time constraints. The simpler algorithm, namely, single option migration (SOM) only checks just one target core before performing a migration. In contrast, the multiple option migration (MOM) searches the optimal target core. In general, the MOM algorithm achieves better energy savings than the SOM algorithm, although differences are wider for a reduced number of cores and frequency/voltage levels. Moreover, the MOM algorithm reduces energy consumption as much as 40% over the worst fit algorithm. Copyright

A New Energy-Aware Dynamic Task Set Partitioning Algorithm for Soft and Hard Embedded Real-Time Systems

Power consumption is a major design concern in current embedded systems. To deal with consumption, many systems apply dynamic voltage scaling (DVS) techniques which dynamically change the system speed depending on the workload characteristics. DVS costs in a multicore system can be reduced by sharing the same DVS regulator among the cores. In this context, to handle energy efficiently, the workload must be properly balanced among the cores. This paper proposes a new heuristic algorithm to balance the workload in an embedded system with a coarse-grain multithreaded multicore processor. This heuristic is aimed at improving the overlapping time between the memory and the processor while keeping balanced core utilizations. To this end, the heuristic dynamically drives the frequency/voltage level to guarantee deadline fulfillment of the hard real-time tasks as well as to achieve a good trade-off between deadline losses and energy savings of the soft real-time tasks. The proposed technique has been evaluated on a model of a contemporary high-end ARM embedded microprocessor executing a set of standard embedded benchmarks. Energy savings depend on the range of frequency/voltage levels that the DVS regulator implements. Experimental results show that with the proposed heuristic, when working with hard real-time tasks, the energy consumption is about 33% the energy dissipated by a system without DVS regulator and balancing heuristic. Moreover, when soft real-time tasks are also considered, the normalized consumption presents values ranging in between 8 and 70% depending on the scheduler aggressiveness.

Flexible real-time mobile robotic architecture based on behavioural models

Abstract Behaviour-based models have been widely used to represent mobile robotic systems, which operate in uncertain dynamic environments and combine information from several sensory sources. The specification of complex mobile robotic applications is performed in such models by combining deliberative goal-oriented planning with reactive sensor driven operations. Consequently, the design of mobile robotic architectures requires the combination of time-constrained activities with deliberate time-consuming components. Furthermore, the temporal requirements of the reactive activities are variable and dependent on the environment (i.e. recognition processes) and/or on application parameters (i.e. process frequencies depend on robot speed). In this paper, a real-time mobile robotic architecture to cope with the functional and variable temporal characteristics of behaviour-based mobile robotic applications is proposed. Run-time flexibility is a main feature of the architecture that supports the variability of the temporal characteristics of the workload. The system has to be adapted to the environmental conditions, by adjusting robot control parameters (i.e. speed) or the system load (i.e. computational time), and take actions depending on it. This influence is focused on the ability of the system to select the appropriate activity to be executed depending on the available time, and, to change its behaviour depending on the environmental information. The flexibility of the system is allowed thanks to the definition of a real-time task model and the design of adaptation mechanisms for the regulation of the reactive load. The improvement of the robot quality of service (QoS) is another important aspect discussed in the paper. The architecture incorporates a quality of service manager (QoSM) that allows dynamically monitor analyse and improve the robot performances. Depending on the internal state, on the environment and on the objectives, the robot performance requirements vary (i.e. when the environment is overloaded, global map processes generating high-quality maps are required). The QoSM receives the performance requirements of the robot, and by adjusting the reactive load, the system enables the necessary slack time to schedule the more suitable deliberative processes and hence fulfilling the robot QoS. Moreover, the deliberative load can be scheduled by different heuristic strategies that provide answers of varying quality.

Leakage Current Reduction in Data Caches on Embedded Systems

Nowadays, embedded systems can be found in a wide range of pervasive devices (e.g., smart phones, PDAs, or video/digital cameras). These devices contain large cache memories, whose power consumption can reach about 50% of the total spent energy, from which leakage energy is the predominant fraction in current technologies. This paper proposes a technique to reduce leakage energy consumption in data caches on embedded systems, which is based on the fact that most stored bits take a logical value of zero. The proposed technique has been evaluated on a model of a contemporary high-end embedded microprocessor, namely the ARM Cortex A8 processor, executing a set of standard embedded benchmarks. Experimental results show that leakage energy savings reach about 40% with no IPC loss.

Strategies for scheduling optional tasks in intelligent real-time environments

Abstract Control systems have been the main application field of real-time system programming. While the complexity of the control system has been solved using classical theory, the theory to design the real-time system has follow some well known practices. However, when the complexity of the applications requires the combination of the control theory and techniques coming from the artificial intelligence field, the real-time design and implementation requires some new features. One of the main aspects to be considered is the non predictable behaviour of most of these techniques and, as consequence, their application to systems where the predictability plays a critical role. In this paper, a global organisation of a real time artificial intelligence system is presented. The proposal includes a task model to define real time tasks with components where the execution time is unbounded. To handle these tasks, a system organisation is proposed. The system includes an intelligent server that manages the execution of all unbounded parts. The proposal uses two scheduling levels, a static first level which is supported directly by the operating system, and a dynamic second level which handles the optional parts. Several scheduling algorithms are presented and evaluated.

E2FS: an elastic storage system for cloud computing

In cloud storage, replication technologies are essential to fault tolerance and high availability of data. While achieving the goal of high availability, replication brings extra number of active servers to the storage system. Extra active servers mean extra power consumption and capital expenditure. Furthermore, the lack of classification of data makes replication scheme fixed at the very beginning. This paper proposes an elastic and efficient file storage called E2FS for big data applications. E2FS can dynamically scale in/out the storage system based on real-time demands of big data applications. We adopt a novel replication scheme based on data blocks, which provides a fine-grained maintenance of the data in the storage system. E2FS analyzes features of data and makes dynamic replication decision to balance the cost and performance of cloud storage. To evaluate the performance of proposed work, we implement a prototype of E2FS and compare it with HDFS. Our experiments show E2FS can outperform HDFS in elasticity while achieving guaranteed performance for big data applications.