Liliana Raquel Castro

Diagnosis of mild Alzheimer disease through the analysis of eye movements during reading.

Reading requires the integration of several central cognitive subsystems, ranging from attention and oculomotor control to word identification and language comprehension. Reading saccades and fixations contain information that can be correlated with word properties. When reading a sentence, the brain must decide where to direct the next saccade according to what has been read up to the actual fixation. In this process, the retrieval memory brings information about the current word features and attributes into working memory. According to this information, the prefrontal cortex predicts and triggers the next saccade. The frequency and cloze predictability of the fixated word, the preceding words and the upcoming ones affect when and where the eyes will move next. In this paper we present a diagnostic technique for early stage cognitive impairment detection by analyzing eye movements during reading proverbs. We performed a case-control study involving 20 patients with probable Alzheimers disease and 40 age-matched, healthy control patients. The measurements were analyzed using linear mixed-effects models, revealing that eye movement behavior while reading can provide valuable information about whether a person is cognitively impaired. To the best of our knowledge, this is the first study using word-based properties, proverbs and linear mixed-effect models for identifying cognitive abnormalities.

Robust Model Predictive Control of a Wiener-like System

Abstract In this paper, a robust model predictive control for a Wiener-like system is presented. The proposed system consists of a lineal dynamic block represented by Laguerre or Kautz basis followed by a High Level Piecewise Linear function. The results are evaluated on the basis of a simulation of a distillation column.

Evaluation of Gunshot Detection Algorithms

Five pre-processing algorithms for the detection of firearm gunshots are statistically evaluated, using the receiver operating characteristic method, as a previous feasibility metric for their implementation on a low power VLSI circuit.

Patients with mild Alzheimer’s disease produced shorter outgoing saccades when reading sentences

In the present work we analyzed forward saccades of thirty five elderly subjects (Controls) and of thirty five mild Alzheimers disease (AD) during reading regular and high-predictable sentences. While they read, their eye movements were recorded. The pattern of forward saccade amplitudes as a function of word predictability was clearly longer in Controls. Our results suggest that Controls might use stored information of words for enhancing their reading performance. Further, cloze predictability increased outgoing saccades amplitudes, as this increase stronger in high-predictable sentences. Quite the contrary, patients with mild AD evidenced reduced forward saccades even at early stages of the disease. This reduction might reveal impairments in brain areas such as those corresponding to working memory, memory retrieval, and semantic memory functions that are already present at early stages of AD. Our findings might be relevant for expanding the options for the early detection and monitoring of in the early stages of AD. Furthermore, eye movements during reading could provide a new tool for measuring a drugs impact on patients behavior.

Distance of a complex coefficient stable polynomial from the boundary of the stability set

In this paper, the distance in the 1 ≤ 2p ≤ ∞ norm from a complex coefficient polynomial to the border of its Hurwitz region is analyzed. Simplified expressions for 2p=1, 2, ∞ are also obtained.

Degenerate bifurcations and catastrophe sets via frequency analysis

We present some bifurcation conditions using the well-known stability analysis of feedback systems. A general ordinary differential equation system is formulated in two parts: one that considers the linear part and the other that includes the memoryless nonlinear part, in a similar way as the describing function. The bifurcation conditions are obtained using the results of the generalized Nyquist stability criterion (GNSC) with some explicit formulae derived from some properties of the complex variable We analyse simultaneously both static and dynamic (Hopf) bifurcations and their degeneracies in a rich example, a continuous stirred-tank reactor (CSTR), in which two consecutive, irreversible, first-order reactions A→B→C occur

Set membership estimation theory for Wiener modelling using HLCPWL functions

In this paper, we propose a Wiener-like model structure where the dynamic linear part is represented by a finite set of discrete Laguerre or Kautz transfer functions, while the non-linear static part is realised by high level canonical piecewise linear basis functions (HLCPWL). In control applications, Laguerre functions are commonly used when dealing with overdamped systems, while Kautz systems are suitable for weakly damped ones. The approximation of the static non-linear functions using HLCPWL functions is based on that this representation uses the least number of parameters and the algorithm for computing the approximation is very efficient. In this model, we estimate the parameters of the HLCPWL using set membership estimation theory, under mild error constraints. It is shown that this structure allows to uniformly approximate any causal, time-invariant, non-linear discrete dynamic system with fading memory.

Rational wavelets in Wiener-like modeling

We use a Wiener-like approximation scheme using rational wavelets for the linear dynamical structure and a feedforward neural network for approximating the nonlinear static part. This class of structure allows us to approximate nonlinear oscillatory dynamic systems and has two main advantages: the time location of the dynamical components of the systems and the inclusion of the a priori knowledge of those components in the model.

Identification structures using rational wavelets: examples of application

In this paper we present three examples that show the applications of a black-box identification structure already defined. This structure can be described as a concatenation of a mapping from observed data to a finite set of linear filters realized using rational wavelets, and a nonlinear mapping from the output of the linear dynamic part to the system output represented by a hidden layer perceptron neural network, or a basis (that might be orthonormal) of high level canonical piecewise linear functions. The wavelets used for identifying the linear dynamic part are selected taking into account the linear dynamics of the system and consequently they can be considered as semiphysical regressors. Also, this structure allows to approximate the dynamic evolution of any nonlinear, causal, time-invariant system with fading memory.

Volterra kernels description using piecewise linear functions

In this paper, a technique for the input-output representation of nonlinear systems via Volterra series is presented. It consists of using piecewise linear functions for the kernel expressions. The form of the kernels in the transformed domain is developed using multidimensional Laplace transforms.