On the nature and cause of Specific Language Impairment: a view from sentence processing and infant research

This paper addresses the nature and cause of Specific Language Impairment (SLI) by reviewing recent research in sentence processing of children with SLI compared to typically developing (TD) children and research in infant speech perception. These studies have revealed that children with SLI are sensitive to syntactic, semantic, and real-world information, but do not show sensitivity to grammatical morphemes with low phonetic saliency, and they show longer reaction times than age-matched controls. TD children from the age of 4 show trace reactivation, but some children with SLI fail to show this effect, which resembles the pattern of adults and TD children with low working memory. Finally, findings from the German Language Development (GLAD) Project have revealed that a group of children at risk for SLI had a history of an auditory delay and impaired processing of prosodic information in the first months of their life, which is not detectable later in life. Although this is a single project that needs to be replicated with a larger group of children, it provides preliminary support for accounts of SLI which make an explicit link between an early deficit in the processing of phonology and later language deficits, and the Computational Complexity Hypothesis that argues that the language deficit in children with SLI lies in difficulties integrating different types of information at the interfaces.

Signal detection for orthogonal space-time block coding over time-selective fading channels: the H(i) systems

One major assumption in all orthogonal space-time block coding (O-STBC) schemes is that the channel remains static over the length of the code word. However, time-selective fading channels do exist, and in such case conventional O-STBC detectors can suffer from a large error floor in the high signal-to-noise ratio (SNR) cases. As a sequel to the authors' previous papers on this subject, this paper aims to eliminate the error floor of the H(i)-coded O-STBC system (i = 3 and 4) by employing the techniques of: 1) zero forcing (ZF) and 2) parallel interference cancellation (PIC). It is. shown that for an H(i)-coded system the PIC is a much better choice than the ZF in terms of both performance and computational complexity. Compared with the, conventional H(i) detector, the PIC detector incurs a moderately higher computational complexity, but this can well be justified by the enormous improvement.

Blind adaptive channel estimation for dual-rate DS/CDMA signals

This letter proposes the subspace-based blind adaptive channel estimation algorithm for dual-rate quasi-synchronous DS/CDMA systems, which can operate at the low-rate (LR) or high-rate (HR) mode. Simulation results show that the proposed blind adaptive algorithm at the LR mode has a better performance than that at the HR mode, with the cost of an increasing computational complexity.

Space-time multirate blind multiuser detection for synchronous DS/CDMA systems

This paper proposes the subspace-based space-time (ST) dual-rate blind linear detectors for synchronous DS/CDMA systems, which can be viewed as the ST extension of our previously presented purely temporal dual-rate blind linear detectors. The theoretical analyses on their performances are also carried out. Finally, the two-stage ST blind detectors are presented, which combine the adaptive purely temporal dual-rate blind MMSE filters with the non-adaptive beamformer. Their adaptive stages with parallel structure converge much faster than the corresponding adaptive ST dual-rate blind MMSE detectors, while having a comparable computational complexity to the latter.

Blind channel estimation for dual-rate DS/CDMA

This paper proposes a subspace based blind adaptive channel estimation algorithm for dual-rate DS-CDMA systems, which can operate at the low-rate (LR) or high-rate (HR) mode. Simulation results show that the proposed blind adaptive algorithm at the LR mode has a better performance than that at the HR mode, with the cost of an increased computational complexity.

Application of complex extreme learning machine to multiclass classification problems with high dimensionality: a THz spectra classification problem

We extend extreme learning machine (ELM) classifiers to complex Reproducing Kernel Hilbert Spaces (RKHS) where the input/output variables as well as the optimization variables are complex-valued. A new family of classifiers, called complex-valued ELM (CELM) suitable for complex-valued multiple-input–multiple-output processing is introduced. In the proposed method, the associated Lagrangian is computed using induced RKHS kernels, adopting a Wirtinger calculus approach formulated as a constrained optimization problem similarly to the conventional ELM classifier formulation. When training the CELM, the Karush–Khun–Tuker (KKT) theorem is used to solve the dual optimization problem that consists of satisfying simultaneously smallest training error as well as smallest norm of output weights criteria. The proposed formulation also addresses aspects of quaternary classification within a Clifford algebra context. For 2D complex-valued inputs, user-defined complex-coupled hyper-planes divide the classifier input space into four partitions. For 3D complex-valued inputs, the formulation generates three pairs of complex-coupled hyper-planes through orthogonal projections. The six hyper-planes then divide the 3D space into eight partitions. It is shown that the CELM problem formulation is equivalent to solving six real-valued ELM tasks, which are induced by projecting the chosen complex kernel across the different user-defined coordinate planes. A classification example of powdered samples on the basis of their terahertz spectral signatures is used to demonstrate the advantages of the CELM classifiers compared to their SVM counterparts. The proposed classifiers retain the advantages of their ELM counterparts, in that they can perform multiclass classification with lower computational complexity than SVM classifiers. Furthermore, because of their ability to perform classification tasks fast, the proposed formulations are of interest to real-time applications.

Nonlinear identification using orthogonal forward regression with nested optimal regularization

An efficient data based-modeling algorithm for nonlinear system identification is introduced for radial basis function (RBF) neural networks with the aim of maximizing generalization capability based on the concept of leave-one-out (LOO) cross validation. Each of the RBF kernels has its own kernel width parameter and the basic idea is to optimize the multiple pairs of regularization parameters and kernel widths, each of which is associated with a kernel, one at a time within the orthogonal forward regression (OFR) procedure. Thus, each OFR step consists of one model term selection based on the LOO mean square error (LOOMSE), followed by the optimization of the associated kernel width and regularization parameter, also based on the LOOMSE. Since like our previous state-of-the-art local regularization assisted orthogonal least squares (LROLS) algorithm, the same LOOMSE is adopted for model selection, our proposed new OFR algorithm is also capable of producing a very sparse RBF model with excellent generalization performance. Unlike our previous LROLS algorithm which requires an additional iterative loop to optimize the regularization parameters as well as an additional procedure to optimize the kernel width, the proposed new OFR algorithm optimizes both the kernel widths and regularization parameters within the single OFR procedure, and consequently the required computational complexity is dramatically reduced. Nonlinear system identification examples are included to demonstrate the effectiveness of this new approach in comparison to the well-known approaches of support vector machine and least absolute shrinkage and selection operator as well as the LROLS algorithm.

Dealing with complexity: an overview of the neural network approach

The problem of complexity is particularly relevant to the field of control engineering, since many engineering problems are inherently complex. The inherent complexity is such that straightforward computational problem solutions often produce very poor results. Although parallel processing can alleviate the problem to some extent, it is artificial neural networks (in various forms) which have recently proved particularly effective, even in dealing with the causes of the problem itself. This paper presents an overview of the current neural network research being undertaken. Such research aims to solve the complex problems found in many areas of science and engineering today.

Computational fluid dynamics modelling of the air movement in an environmental test chamber with a respiring manikin

In recent years, computational fluid dynamics (CFD) has been widely used as a method of simulating airflow and addressing indoor environment problems. The complexity of airflows within the indoor environment would make experimental investigation difficult to undertake and also imposes significant challenges on turbulence modelling for flow prediction. This research examines through CFD visualization how air is distributed within a room. Measurements of air temperature and air velocity have been performed at a number of points in an environmental test chamber with a human occupant. To complement the experimental results, CFD simulations were carried out and the results enabled detailed analysis and visualization of spatial distribution of airflow patterns and the effect of different parameters to be predicted. The results demonstrate the complexity of modelling human exhalation within a ventilated enclosure and shed some light into how to achieve more realistic predictions of the airflow within an occupied enclosure.