Onde de frange pour une discontinuité de courbure

The fringe wave is defined, following the physical theory of diffraction, as the difference between the total field and the field radiated by the physical optics currents. This concept is generalised to the case of the discontinuity in the curvature. It is necessary, to get a finite result for normal incidence, to define the fringe wave as the difference between the total field and the field radiated by the two first terms of the current given by the Luneburg-Kline development. Explicit formulas are given, for the perfectly conducting and for the impedance boundary condition cases.     

Integration of GPU Computing in a Software Radio Environment

This article studies the integration of Graphics Processing Units in a Software Defined Radio environment. Two main solutions are considered, based on two levels of granularity for the parallelization. First, a fine grain parallelism solution, which is an extension of the existing solutions but adapted to operations of large computational complexity, is proposed. Second, an original solution based on coarse grain approach allowing better usage of the computing resources and easier parallelism extraction is described. For both solutions, scheduling and communication design as well as implementation are given, along with integration in the environment. Both solutions have been implemented and compared on different operations types and on multi-operations sequences. It is clearly shown that using the second solution can provide performance improvement, while the first one is not adapted to SDR applications.     

Evolution of the AgCdO Contact Material Surface Microstructure with the Number of Arcs

This paper describes a study of the evolution of the AgCdO contact material surface microstructure as a function of the number of electrical arcs imposed on the switching surface. Five power switching devices were tested under different conditions. They were subjected to, respectively, 1, 2, 3, 10, and 100 electrical arcs under the same operating conditions: supply current of 400 A, circuit voltage of 28 V direct current (DC), and resistive load. For the analysis, a binocular microscope and a scanning electron microscope with an energy-dispersive x-ray spectrometer were used.     

Cryptanalysis of MD2

This paper considers the hash function MD2 which was developed by Ron Rivest in 1989. Despite its age, MD2 has withstood cryptanalytic attacks until recently. This paper contains the state-of-the-art cryptanalytic results on MD2, in particular collision and preimage attacks on the full hash function, the latter having complexity 2⁷³, which should be compared to a brute-force attack of complexity 2¹²⁸.     

Ultrasound elastography based on multiscale estimations of regularized displacement fields

Elasticity imaging is based on the measurements of local tissue deformation. The approach to ultrasound elasticity imaging presented in this paper relies on the estimation of dense displacement fields by a coarse-to-fine minimization of an energy function that combines constraints of conservation of echo amplitude and displacement field continuity. The multiscale optimization scheme presents several characteristics aimed at improving and accelerating the convergence of the minimization process. This includes the nonregularized initialization at the coarsest resolution and the use of adaptive configuration spaces. Parameters of the energy model and optimization were adjusted using data obtained from a tissue-like phantom material. Elasticity images from normal in vivo breast tissue were subsequently obtained with these parameters. Introducing a smoothness constraint into motion field estimation helped solve ambiguities due to incoherent motion, leading to elastograms less degraded by decorrelation noise than the ones obtained from correlation-based techniques.     

A CMOS adaptive RF front-end receiver for wireless applications

The performance of signal-processing algorithms implemented in hardware depends on the efficiency of datapath, memory speed and address computation. Pattern of data access in signal-processing applications is complex and it is desirable to execute the innermost loop of a kernel in a single-clock cycle. This necessitates the generation of typically three addresses per clock: two addresses for data sample/coefficient and one for the storage of processed data. Most of the Reconfigurable Processors, designed for multimedia, focus on mapping the multimedia applications written in a high-level language directly on to the reconfigurable fabric, implying the use of same datapath resources for kernel processing and address generation. This results in inconsistent and non-optimal use of finite datapath resources. Presence of a set of dedicated, efficient Address Generator Units (AGUs) helps in better utilisation of the datapath elements by using them only for kernel operations; and will certainly enhance the performance. This article focuses on the design and application-specific integrated circuit implementation of address generators for complex addressing modes required by multimedia signal-processing kernels. A novel algorithm and hardware for AGU is developed for accessing data and coefficients in a bit-reversed order for fast Fourier transform kernel spanning over log?₂ N stages, AGUs for zig-zag-ordered data access for entropy coding after Discrete Cosine Transform (DCT), convolution kernels with stored/streaming data, accessing data for motion estimation using the block-matching technique and other conventional addressing modes. When mapped to hardware, they scale linearly in gate complexity with increase in the size.     

Super-resolution in respiratory synchronized positron emission tomography

Respiratory motion is a major source of reduced quality in positron emission tomography (PET). In order to minimize its effects, the use of respiratory synchronized acquisitions, leading to gated frames, has been suggested. Such frames, however, are of low signal-to-noise ratio (SNR) as they contain reduced statistics. Super-resolution (SR) techniques make use of the motion in a sequence of images in order to improve their quality. They aim at enhancing a low-resolution image belonging to a sequence of images representing different views of the same scene. In this work, a maximum a posteriori (MAP) super-resolution algorithm has been implemented and applied to respiratory gated PET images for motion compensation. An edge preserving Huber regularization term was used to ensure convergence. Motion fields were recovered using a B-spline based elastic registration algorithm. The performance of the SR algorithm was evaluated through the use of both simulated and clinical datasets by assessing image SNR, as well as the contrast, position and extent of the different lesions. Results were compared to summing the registered synchronized frames on both simulated and clinical datasets. The super-resolution image had higher SNR (by a factor of over 4 on average) and lesion contrast (by a factor of 2) than the single respiratory synchronized frame using the same reconstruction matrix size. In comparison to the motion corrected or the motion free images a similar SNR was obtained, while improvements of up to 20% in the recovered lesion size and contrast were measured. Finally, the recovered lesion locations on the SR images were systematically closer to the true simulated lesion positions. These observations concerning the SNR, lesion contrast and size were confirmed on two clinical datasets included in the study. In conclusion, the use of SR techniques applied to respiratory motion synchronized images lead to motion compensation combined with improved image SNR and contrast, without any increase in the overall acquisition times.     

Bayesian separation of spectral sources under non-negativity and full additivity constraints

This paper addresses the problem of separating spectral sources which are linearly mixed with unknown proportions. The main difficulty of the problem is to ensure the full additivity (sum-to-one) of the mixing coefficients and non-negativity of sources and mixing coefficients. A Bayesian estimation approach based on Gamma priors was recently proposed to handle the non-negativity constraints in a linear mixture model. However, incorporating the full additivity constraint requires further developments. This paper studies a new hierarchical Bayesian model appropriate to the non-negativity and sum-to-one constraints associated to the sources and the mixing coefficients of linear mixtures. The estimation of the unknown parameters of this model is performed using samples obtained with an appropriate Gibbs algorithm. The performance of the proposed algorithm is evaluated through simulation results conducted on synthetic mixture data. The proposed approach is also applied to the processing of multicomponent chemical mixtures resulting from Raman spectroscopy.