Jérôme Galy

Highly Scalable Dynamically Reconfigurable Systolic Ring-Architecture for DSP Applications

New parallel execution based machine paradigms must be considered. Thanks to their high level of flexibility structurally programmable architectures are potentially interesting candidates to overcome classical CPUs limitations. Based on a parallel execution model, we present in this paper a new dynamically reconfigurable architecture, dedicated to data oriented applications acceleration. Principles, realizations and comparative results will be exposed for some classical applications, targeted on different architectures.

Performance bounds under misspecification model for MIMO radar application

Recent tools established on misspecified lower bound on the mean square error allow to predict more accurately the mean square error behavior than the classical lower bounds in presence of model. errors. These bounds are helpful since model errors exist in practice due to system imperfections. In this paper, we are interested in the direction of arrival and direction of departure estimation in MIMO radar context with array elements position error. A closed-form expression is derived for the misspecified Cramér-Rao bound (or Huber limit) for any antennas geometry. A comparison of the misspecified Cramér-Rao bound with the classical Cramér-Rao bound and with the maximum likelihood estimator mean square error highlights the tightness improvement resulting from the use of the proposed bound.

Hybrid lower bound on the MSE based on the Barankin and Weiss-Weinstein bounds

This article investigates hybrid lower bounds in order to predict the estimators mean square error threshold effect. A tractable and computationally efficient form is derived. This form combines the Barankin and the Weiss-Weinstein bounds. This bound is applied to a frequency estimation problem for which a closed-form expression is provided. A comparison with results on the hybrid Barankin bound shows the superiority of this new bound to predict the mean square error threshold.

A dynamically reconfigurable architecture for embedded systems

The Internet is becoming one of the key features of tomorrows communication world. The evolution of mobile telephone networks, such as UMTS, will soon allow everyone to be connected everywhere. These new network technologies bring the ability to deal not only with classical voice or text messages, but also with improved content, i.e. multimedia. At the mobile level, this kind of data-oriented content requires highly efficient architectures; and nowadays, embedded system-on-chip solutions are no longer able to manage critical constraints like area, power and data-computing efficiency. In this paper, we propose a new, dynamically reconfigurable network, dedicated to data-oriented applications such as, for instance, one targeted on third-generation networks. Principles, realisations and comparative results are exposed for some classical applications, targeted on different architectures.

A Ziv-Zakaï type bound for hybrid parameter estimation

In statistical signal processing, hybrid parameter estimation refers to the case where the parameters vector to estimate contains both non-random and random parameters. In this communication, we propose a new hybrid lower bound which, for the first time, includes the Ziv-Zakaï bound well known for its tightness in the Bayesian context (random parameters only). For the general case of parameterized mean model with Gaussian noise, closed-form expressions of the proposed bound are provided.

The Systolic Ring: A Dynamically Reconfigurable Architecture for Embedded Systems

Internet is becoming one of the key features of tomorrows communication world. The evolution of mobile phones networks, such as UMTS will soon allow everyone to be connected, everywhere. These new network technologies bring the ability to deal not only with classical voice or text messages, but also with improved content: multimedia. At the mobile level, this kind of data oriented content requires highly efficient architectures; and nowadays mobile system-on-chip solutions will no longer be able to manage the critical constraints like area, power and data computing efficiency. In this paper we will propose a new dynamically reconfigurable network, dedicated to data oriented applications such as the one allowed on third generation networks. Principles, realizations and comparative results will be exposed for some classical applications targeted on different architectures.

Blind methods for interference cancellation in array processing

In this paper, we address several methods which permit to suppress the interference signals received at the antenna level. The classical method to suppress interference uses second order statistics and consists in forming a spatial filter or in calculating the opposition coefficients to apply on different channels. An original solution consists in using higher order statistics for the blind source separation. These algorithms do not use any a priori knowledge on the array manifold. One of the benefits of such blind separation is that source separation is essentially unaffected by errors in the propagation model or in array calibration. Only the statistical independence and the non-Gaussian source signals are important. An other method: the canonical correlation analysis is also used and compared to the others.

Lower bounds for non standard deterministic estimation

In this paper, non standard deterministic parameters estimation is considered, i.e. the situation where the probability density function (p.d.f.) parameterized by unknown deterministic parameters results from the marginalization of a joint p.d.f. depending on additional random variables. Unfortunately, in the general case, this marginalization is mathematically intractable, which prevents from using the known deterministic lower bounds on the mean-squared-error (MSE). However an embedding mechanism allows to transpose all the known lowers bounds into modified lower bounds fitted with non-standard deterministic estimation, encompassing the modified Cramér-Rao/Bhattacharyya bounds and hybrid lower bounds.

Hybrid Barankin–Weiss–Weinstein Bounds

This letter investigates hybrid lower bounds on the mean square error in order to predict the so-called threshold effect. A new family of tighter hybrid large error bounds based on linear transformations (discrete or integral) of a mixture of the McAulay-Seidman bound and the Weiss-Weinstein bound is provided in multivariate parameters case with multiple test points. For use in applications, we give a closed-form expression of the proposed bound for a set of Gaussian observation models with parameterized mean, including tones estimation which exemplifies the threshold prediction capability of the proposed bound.

Recursive Hybrid Cramér–Rao Bound for Discrete-Time Markovian Dynamic Systems

In statistical signal processing, hybrid parameter estimation refers to the case where the parameters vector to estimate contains both non-random and random parameters. As a contribution to the hybrid estimation framework, we introduce a recursive hybrid Cramér-Rao lower bound for discrete-time Markovian dynamic systems depending on unknown deterministic parameters. Additionally, the regularity conditions required for its existence and its use are clarified.