Yannick Le Moullec

Design of a multimedia processor based on metrics computation

Media-processing applications, such as signal processing, 2D and 3D graphics rendering, and image compression, are the dominant workloads in many embedded systems today. The real-time constraints of those media applications have taxing demands on todays processor performances with low cost, low power and reduced design delay. To satisfy those challenges, a fast and efficient strategy consists in upgrading a low cost general purpose processor core. This approach is based on the personalization of a general RISC processor core according the target multimedia application requirements. Thus, if the extra cost is justified, the general purpose processor GPP core can be enforced with instruction level coprocessors, coarse grain dedicated hardware, ad hoc memories or new GPP cores. In this way the final design solution is tailored to the application requirements. The proposed approach is based on three main steps: the first one is the analysis of the targeted application using efficient metrics. The second step is the selection of the appropriate architecture template according to the first step results and recommendations. The third step is the architecture generation. This approach is experimented using various image and video algorithms showing its feasibility.

Development of a Bio-Impedance Signal Simulator on the Basis of the Regression Based Model of the Cardiac and Respiratory Impedance Signals

A software implemented bio-impedance signal simulator (BISS) is proposed, which can imitate real bioimpedance phenomena for analyzing the performance of various signal processing methods and algorithms. The underlying mathematical models are built by means of a curvefitting regression method. Three mathematical models were compared polynomial, Fourier series and sum of sine waves with four different measured impedance cardiography (ICG) datasets and two clean ICG and impedance respirography (IRG) datasets were taken as the basis of the signals. Statistical analysis (sum of squares error, correlation and execution time) implies that Fourier series is best suited. The models of the ICG and IRG signals are integrated into the proposed simulator.

Radio Resource Management Scheme in NB-IoT Systems

Narrowband Internet of Things (NB-IoT) is the prominent technology that fits the requirements of future IoT networks. However, due to the limited spectrum (i.e., 180 kHz) availability for NB-IoT systems, one of the key issues is how to efficiently use these resources to support massive IoT devices? Furthermore, in NB-IoT, to reduce the computation complexity and to provide coverage extension, the concept of time offset and repetition has been introduced. Considering these new features, the existing resource management schemes are no longer applicable. Moreover, the allocation of frequency band for NB-IoT within LTE band, or as a standalone, might not be synchronous in all the cells, resulting in intercell interference (ICI) from the neighboring cells’ LTE users or NB-IoT users (synchronous case). In this paper, first a theoretical framework for the upper bound on the achievable data rate is formulated in the presence of control channel and repetition factor. From the conducted analysis, it is shown that the maximum achievable data rates are 89.2 Kbps and 92 Kbps for downlink and uplink, respectively. Second, we propose an interference aware resource allocation for NB-IoT by formulating the rate maximization problem considering the overhead of control channels, time offset, and repetition factor. Due to the complexity of finding the globally optimum solution of the formulated problem, a sub-optimal solution with an iterative algorithm based on cooperative approaches is proposed. The proposed algorithm is then evaluated to investigate the impact of repetition factor, time offset and ICI on the NB-IoT data rate, and energy consumption. Furthermore, a detailed comparison between the non-cooperative, cooperative, and optimal scheme (i.e., no repetition) is also presented. It is shown through the simulation results that the cooperative scheme provides up to 8% rate improvement and 17% energy reduction as compared with the non-cooperative scheme.

EPICURE: A partitioning and co-design framework for reconfigurable computing

This paper presents a new global design methodology capable to bridge the gap between an abstract specification level and a heterogeneous reconfigurable architecture level. The Epicure contribution is the result of a joint study on abstraction/refinement methods and a smart reconfigurable architecture within the formal Esterel design tools suite. The original points of this work are : i) a generic HW/SW interface model, ii) a specification methodology that handles the control, includes efficient verification and HW/SW synthesis capabilities, iii) a method for parallelism exploration based on abstract resources/performance estimation expressed in terms of area/delay tradeoffs, iv) a HW/SW partitioning approach that refines the specification into explicit HW configurations and the associated SW control. The Epicure framework shows how a cooperation of complementary methodologies and CAD tools associated with a relevant architecture can significantly improve the designer productivity, especially in the context of reconfigurable architectures.

A parametric framework for the development of bioelectrical applications: application to a bio-impedance signal simulator

Extracting useful information from cardiac signals for the diagnosis of diseases and judgement of heart functioning is of special interest to medical personnel. However, exploiting such signals is subject to the availability of the signals themselves and to possible measurement errors. We thus argue that modelling such signals offers several advantages as compared to relying on measured data only. By using a formalized representation, the parameters of the signal model can be manipulated and/or modified, thus providing mechanisms that allow researchers to reproduce and control such signals by means of e.g. simulators. To guide both the signal modelling and simulator development phases, we propose a new generic framework. We then illustrate how it can be used to guide the modelling of the impedance cardiography and impedance respirography signals. We also show how the proposed framework has been used to guide the development of the corresponding Bio-Impedance Signal Simulator (BISS). As a result, the implemented BISS generates simulated Electrical Bio-Impedance (EBI) signals and gives freedom to the end-user to control the essential properties of the generated EBI signals depending on their needs. Predefined states of human conditions/ activities are also included for ease of use.

Frequency and power allocation schemes for heterogeneous networks including femto cells

In this paper we propose three novel schemes for spectrum management and compare their performance with that of the frequency reuse assignment technique. These schemes are based on reinforcement learning and power optimization algorithms. Simulation results show that our RL-based schemes yield up to 14% increase in capacity and provide better user satisfaction and capacity. Moreover, the RL-based schemes provide better performance in high cell loads and can accommodate femto cells with the same radio resources.

A bio-impedance signal simulator (BISS) for research and training purposes

A Bio-Impedance Signal Simulator (BISS) is developed based on the models of the impedance cardiography (ICG) and impedance respirography (IRG) signals. With the aim of imitating the real ICG and IRG phenomena, the ICG and IRG signals are modelled and combined with motion artefacts and Gaussian noise. The simulator allows the user to load different predefined human activity states such as resting, standing, walking, and running. Moreover, and importantly, the user can also control the parameters as per his/her needs and generate Electrical Bio-Impedance (EBI) datasets for further processing. Possible applications of BISS include research (e.g. performance evaluation of cardiac and respiratory separation algorithms) as well as teaching and training in physiological courses. To the best of our knowledge, BISS is the first EBI signal simulator that imitates the real ICG and IRG signals phenomena.

Fast feasibility estimation of reconfigurable architectures

Reconfigurable architectures are often said to be able to exploit the possibilities of resource savings by means of hardware time-sharing. However, existing literature does not point clearly at which conditions must be fulfilled for considering a reconfigurable architecture for the implementation of signal processing applications. Therefore, we propose a fast method to perform high-level pre-implementation feasibility-based evaluation of a reconfigurable hardware implementation. The method is based on a light architectural model to compute costs of a static reference as well as costs for globally and partially reconfigurable architectures. Two case studies have been performed for an FFT and an FPGA-based DAB application. Our results show that implementation on reconfigurable architectures is only feasible when the reconfiguration time is low, which generally means that a dynamically partially reconfigurable solution is preferred.

Scheduling Temporal Partitions in a Multiprocessing Paradigm for Reconfigurable Architectures

In this paper we describe a mapping methodology for heterogeneous reconfigurable architectures consisting of one or more SW processors and one or more reconfigurable units, FPGAs. The mapping methodology consists of a separated track for a) the generation of the configurations for the FPGA by level-based and clustering-based temporal partitioning, and b) the scheduling of those configurations as well as the software tasks, based on two multiprocessor scheduling algorithms: a simple list-based scheduler and the more complex extended dynamic level scheduling algorithm. The mapping methodology is benchmarked by means of randomly created task graphs on an architecture of one SW processor and one FPGA. The results are compared to a 0-1 integer linear programming solution in terms of exploration time as well as the finish-time of all tasks of the application. The results show that, in 90% of the investigated cases, the combination of level-based temporal partitioning and extended dynamic level scheduling gives the best performance in terms of finish-time of the full task-set.

A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications

Wireless body area networks are increasingly featuring cognitive capabilities. This work deals with the emerging concept of cognitive body area networks. In particular, the paper addresses two important issues, namely spectrum sharing and interferences. We propose methods for channel and power allocation. The former builds upon a reinforcement learning mechanism, whereas the latter is based on convex optimization. Furthermore, we also propose a mathematical channel model for off-body communication links in line with the IEEE 802.15.6 standard. Simulation results for a nursing home scenario show that the proposed approach yields the best performance in terms of throughput and QoS for dynamic environments. For example, in a highly demanding scenario our approach can provide throughput up to 7 Mbps, while giving an average of 97.2% of time QoS satisfaction in terms of throughput. Simulation results also show that the power optimization algorithm enables reducing transmission power by approximately 4.5 dBm, thereby sensibly and significantly reducing interference.