Abstract
This study addresses the impact of satellite altimetry data processing on sea level studies at regional scale, with emphasis on the influence of various geophysical corrections and satellite orbit on the structure of the derived interannual signal and sea level trend. The work focuses on the analysis of TOPEX data for a period of over twelve years, for three regions in the North Atlantic: Tropical (0 degrees <= phi <= 25 degrees), Sub- Tropical (25 degrees <= phi <= 50 degrees) and Sub-Arctic (50 degrees <= phi <= 65 degrees). For this analysis corrected sea level anomalies with respect to a mean sea surface model have been derived from the GDR-Ms provided by AVISO by applying various state-of-the-art models for the geophysical corrections. Results show that sea level trend determined from TOPEX altimetry is dependent on the adopted models for the major geophysical corrections. The main effects come from the sea state bias (SSB), and from the application or not of the inverse barometer (IB) correction. After an appropriate modelling of the TOPEX A/B bias, the two analysed SSB models induce small variations in sea level trend, from 0.0 to 0.2 mm/yr, with a small latitude dependence. The difference in sea level trend determined by a non IB-corrected series and an IB-corrected one has a strong regional dependence with large differences in the shape of the interannual signals and in the derived linear trends. The use of two different drift models for the TOPEX Microwave Radiometer (TMR) has a small but non negligible effect on the North Atlantic sea level trend of about 0.1 mm/yr. The interannual signals of sea level time series derived with the NASA and the CNES orbits respectively, show a small departure in the middle of the series, which has no impact on the derived sea level trend. These results strike the need for a continuous improvement in the modelling of the various effects that influence the altimeter measurement.

Abstract
Larvae of the freshwater swan mussel, Anodonta cygnea, were cultured in artificial media at the controlled temperature of 23 degrees +/- 2 degrees C, with successful metamorphosis for the first time. The artificial medium contained a mixture of M199, common carp plasma, and antibiotics/antimycotics. Glochidia were reared to the juvenile stage in the medium after 10-11 d of culture. After 15 d of controlled feeding with phytoplankton, the juveniles showed an elongated shell with several growth lines. Larval survival was 34.3 +/- 9.3%, whereas the proportion undergoing metamorphosis was <= 60.8 +/- 4.2%. The ultrastructure of early developmental stages was observed by scanning electron microscopy, from the glochidial to the juvenile stage. Glochidia had a hooked shell, with two equal triangular valves formed by a calcareous layer with numerous pores and covered by a thin cuticle of chitin-keratin. The appearance of the complete foot within 11 d of in vitro culture was considered the final feature of metamorphosis to the juvenile stage. The main alteration during juvenile development was the formation, under the glochidial shell, of a new periostracum with growth lines. The prominent foot, gradually covered by long, dense cilia, showed rhythmical movements involved in the capture of particulate matter. Similarly, cilia and microvilli present in the mantle also performed the same role. Longer cilia, sparsely distributed in the mantle, may function as chemotactile sensors.

Abstract
The problem of estimating the motion and orientation parameters of a rigid object from two m - D point set patterns is of significant importance in medical imaging, electrocardiogram (ECG) alignment, and fingerprint matching. The rigid parameters can be defined by an m x m rotation matrix, a diagonal m x m scale matrix, and an m x 1 translation vector. All together, the total number of parameters to be found is m(m + 2). Several least squares based algorithms have recently appeared in the literature. These algorithms are all based on a singular value decomposition (SVD) of the m x m cross-covariance matrix between the two data sets. However, there are cases where the SVD based algorithms return a reflection matrix rather than a rotation matrix. Some authors have introduced a simple correction for guarding against such cases. Other types of algorithm are based on unit quaternions which guarantee obtaining a true rotation matrix. In this paper we introduce a principal component based registration algorithm which is solved in closed-form. By using matrix vectorization properties the problem can be cast as one of finding a rank-1 symmetric projection matrix. This is equivalent to solving a Sylvester equation with equality constraints. Once the solution is obtained, we apply the inverse vectorization operation to estimate the rotation and scale matrices, along with the translation vector. We apply the proposed algorithm to the alignment of ECG signals and compare the results to those obtained by the SVD and quaternion based algorithms.

Abstract
In this paper we analyse the estimates of the matrices produced by the non-biased deterministic-stochastic subspace identification algorithms (NBDSSI) proposed by Van Overschee and De Moor ( 1996). First, an alternate expression is derived for the A and C estimates. It is shown that the Chiuso and Picci result ( Chiuso and Picci 2004) stating that the A and C estimates delivered by this algorithm robust version and by the Verhaegen's MOESP (Verhaegen and Dewilde 1992a, Verhaegen and Dewilde 1992b, Verhaegen 1993, Verhaegen 1994) are equal, can be obtained from this expression. An alternative approach for the estimation of matrices B and D in subspace identification is also described. It is shown that the least squares approach for the estimation of these matrices estimation can be just expressed as an orthogonal projection of the future outputs on a lower dimension subspace in the orthogonal complement of the column space of the extended observability matrix. Since this subspace has a dimension equal to the number of outputs, a simpler and numerically more efficient ( but equally accurate) new subspace algorithm is provided.

Abstract
Real-time image processing is a computational intensive task with applications in various engineering fields. In several image processing applications, a significant amount of computing power is committed to image enhancement operations, basic segmentation and identification of regions of interest for further analysis. Such type of front-end processing can be done efficiently by custom data-flow processors closely coupled to an image sensor This paper proposes a visual design environment to support the high-level design of custom data-flow processors for real-time image analysis applications. The tool is embedded in Matlab/Simulink, and the system modeling is done using a library of blocks that implement common low-level image processing operations. Functional validation is performed efficiently by the simulation engine of Simulink in a frame by frame basis, using the functions provided by the image processing toolbox in Matlab. The automatic generation of a synthesizable RTL model guarantees a logic implementation of the system that complies to the high-level model validated, under constraints imposed by the user and the target reconfigurable device.

Abstract
This paper describes the design of a processor specific for testing cores embedded in system-on-chip. This processor which can be implemented within a system's reconfigurable area, shall be responsible for scheduling and control test operations and perform preliminary data processing, as well as to provide the interface with an external tester Building these test operations on-chip allows for simplifying external tester interface and to reduce testing time. The testing procedure and the infrastructure required to test an AID converter is described as an example.