Abstract
An arterial pressure waveform recorder and analyser based on a Microchip PIC microcontroller (mu C), dsPIC33FJ256GP710 is described in this article. Our purpose is to develop a dsPIC based signal monitoring and processing system for cardiovascular studies, specially dedicated to arterial pressure waveform (APW) capture. We developed a piezoelectric (PZ) probe designed to reproduce the APW from the pulsatile activity taken non-invasively at the vicinity of a superficial artery. The advantages in developing a microcontroller based system show up in decreasing the associate cost, as well as in increasing the functionality of the system. Based on a MathWorks Simulink platform, the system supports the development and transfer of program code from a personal computer to the microcontroller, and evaluation of its execution on rapid prototyping hardware. Results demonstrate that embedded system can be an alternative to be used in autonomous cardiovascular probes. Although additional studies are still required, this probe seems to be a valid, low cost and easy to use alternative to expensive and hard to manipulate devices in the market.

Abstract
Over the last years, great emphasis has been placed on the role of arterial stiffness in the development of cardiovascular diseases. This hemodynamic parameter, generally associated to age and blood pressure increase, can be assessed by the measurement of pulse wave velocity (PWV). Currently available devices that measure PWV are expensive and need to be operated by skilled medical staff, reducing the potential of ambulatory setting. This research project aims at developing and testing the sensoring and algorithmic basis of an alternative and non-invasive device for PWV assessment. The proposed device is based on a double-headed sensor probe and allows the assessment of PWV in one single location, providing important information on local arterial hemodynamics. Although studies to validate the clinical use of this system are still required, it has already demonstrated good performance on a dedicated test bench system, capable of reproducing a range of relevant cardiovascular system's properties. © 2011 IEEE.

Abstract
The successive approximation Linear Parameter Varying systems subspace identification algorithm for discrete-time systems is based on a convergent sequence of linear time invariant deterministic-stochastic state-space approximations. In this chapter, this method is modified to cope with continuous-time LPV state-space models. To do this, the LPV system is discretised, the discrete-time model is identified by the successive approximations algorithm and then converted to a continuous-time model. Since affine dependence is preserved only for fast sampling, a subspace downsampling approach is used to estimate the linear time invariant deterministic-stochastic state-space approximations. A second order simulation example, with complex poles, illustrates the effectiveness of the new algorithm. © 2012 by World Scientific Publishing Co. Pte. Ltd.

Abstract

Abstract
The fitting of a causal dynamic model to an image is a fundamental problem in image processing, pattern recognition, and computer vision. There are numerous other applications that require a causal dynamic model, such as in scene analysis, machined parts inspection, and biometric analysis, to name only a few. There are many types of causal dynamic models that have been proposed in the literature, among which the autoregressive moving average (ARMA) and state-space models are the most widely known. In this paper we introduce a 2-D stochastic state-space system identification algorithm for obtaining stochastic 2-D, causal, recursive, and separable-in-denominator (CRSD) models in the Roesser state-space form. The algorithm is tested with a real image and the reconstructed image is shown to be almost indistinguishable to the true image.

Abstract
In this paper, the class of subspace system identification algorithms is used to derive a new identification algorithm for 2-D causal, recursive, and separable-in-denominator (CRSD) state space systems in the Roesser model form. The algorithm take a given deterministic input-output pair of 2-D signals and computes the system order (n) and system parameter matrices {A, B, C, D}. Since the CRSD model can be treated as two 1-D systems, the proposed algorithm first separates the vertical component from the state and output equations and then formulates an equivalent set of 1-D horizontal subspace equations. The solution to the horizontal subspace identification subproblem contains all the information necessary to compute the system order and parameter matrices, including those from the vertical subsystem.