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
Recent advances in wireless communication theory and semiconductor technology brought wireless to virtually every aspect of our life, and this trend is expected to continue to increase in the future. Unfortunately, as the number of wireless applications grows, the same scarce spectrum is reused over and over again, resulting in increased interference, which jeopardizes the prospect of wireless meeting its high expectations. Dynamic Spectrum Access proposes to mitigate this problem by adapting the operational parameters of wireless networks to varying interference conditions. However, the involved increase in cost threatens to reduce the benefit of wireless in different environments. In this paper we examine the economic balance between the added cost and the increased usability brought about by DSA. We focus on a particular real-life scenario - the production floor of an industrial installation - where there is typically extensive utilization of the ISM band. IEEE 802.15.4 wireless sensors monitor production machinery, and IEEE 802.11 WLAN is used as the data backbone. We model the benefit achieved by adding RF sensing technology in terms of reliability and battery lifetime, and qualitatively assess the cost of interference and the potential gain of introducing sensing technology. Based on this techno-economic analysis, we conclude that if implemented correctly, spectrum sensing can bring business gains in real-life applications. © 2011 IEEE.

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.

Abstract
Learning management systems are routinely used for presenting, solving and grading exercises with large classes. However, teachers are constrained to use questions with pre-defined answers, such as multiple-choice, to automatically correct the exercises of their students. Complex exercises cannot be evaluated automatically by the LMS and require the coordination of a set of heterogeneous systems. For instance, programming exercises require a specialized exercise resolution environment and automatic evaluation features, each provided by a different type of system. In this paper, the authors discuss an approach for the coordination of a network of eLearning systems supporting the resolution of exercises. The proposed approach is based on a pivot component embedded in the LMS and has two main roles: (1) provide an exercise resolution environment, and (2) coordinate communication between the LMS and other systems, exposing their functions as web services. The integration of the pivot component in the LMS relies on Learning Tools Interoperability (LTI). This paper presents an architecture to coordinate a network of eLearning systems and validate the proposed approach by creating such a network integrated with LMS from two different vendors.

Abstract
This article presents a new indirect identification method for continuous-time systems able to resolve the problem of fast sampling. To do this, a Subspace IDentification Down-Sampling (SIDDS) approach that takes into consideration the intermediate sampling instants of the input signal is proposed. This is done by partitioning the data set into m subsets, where m is the downsampling factor. Then, the discrete-time model is identified using a based subspace identification discrete-time algorithm where the data subsets are fused into a single one. Using the algebraic properties of the system, some of the parameters of the continuous-time model are directly estimated. A procedure that secures a prescribed number of zeros for the continuous-time model is used during the estimation process. The algorithm's performance is illustrated through an example of fast sampling, where its performance is compared with the direct methods implemented in Contsid.

Abstract
Supercooling is still today one of the most challenging physical phenomena to be modelled in food bioprocess engineering. In this study, we evaluate the capacity of a finite-element-cellular automata (FEM-CA) approach to model the propagation of nucleation inside supercooled strawberries with five different morphologies (higher and lower volumes of vascular tissue, pulp, and central air void) frozen inside an air blast freezer under different operational conditions: initial temperature (0 to +20 A degrees C), air temperature (-45 to -20 A degrees C), and velocity (1 to 10 m s (-aEuro parts per thousand 1)). Results show that nucleation is highly affected by the initial temperature and heat transfer rate during phase change. The stochastic nature of nucleation only allowed us to consider it a random variable inside the model temperature restriction interval, it not yet being possible to know what triggers nucleation. However, this study allowed us to conclude that: (1) the structure of liquid water in the supercooled region plays a very significant role during the supercooling effect, (2) nucleation temperatures increase in the supercooled region due to the release of latent heat, and (3) strawberry morphology and operational variables have a profound effect on the supercooling capacity. In our opinion, supercooling is still an open subject, and only a deeper understanding of the structuring of water and dynamics of nucleation at the molecular level may lead to significant advances in the quality of frozen foods and cryopreservation.