The integration of brain monitoring into the man-machine interface holds great promise for real-time assessment of operator status and intelligent allocation of tasks between machines and humans. This article presents an integrated hardware and software solution for acquisition and real-time analysis of the electroencephalogram (EEG) to monitor indexes of alertness, cognition, and memory. Three experimental paradigms were evaluated in a total of 45 participants to identify EEG indexes associated with changes in cognitive workload: the Warship Commander Task (WCT), a simulated navy command and control environment that allowed workload levels to be systematically manipulated; a cognitive task with three levels of difficulty and consistent sensory inputs and motor outputs; and a multisession image learning and recognition memory test. Across tasks and participants, specific changes in the EEG were identified that were reliably associated with levels of cognitive workload. The EEG indexes were also shown to change as a function of training on the WCT and the learning and memory task. Future applications of the system to augment cognition in military and industrial environments are discussed.

Although most of the theoretical and implementation aspects of wavelet based algorithms in texture characterization are well studied and understood, many issues related to the choice of filter bank in texture processing remain unresolved. The impact of the wavelet basis has been mentioned in a few papers, whereas other more detailed investigations have considered only the choice of the wavelet basis in image coding. For example, regularity, number of vanishing moments and a shift variance degree have been used for the filter evaluation in image coding, but the utility of all these metrics in texture analysis has not yet been established. Therefore, the scope of this paper was to investigate whether the properties of decomposition filters play an important role in texture description, and which feature is dominant in the selection of an optimal filter bank. We performed classification experiments with 23 Brodatz textures. Our investigation shows that the selection of the decomposition filters has a significant influence on the result of texture characterization. Finally, the paper ranks 19 orthogonal and biorthogonal filters, and establishes the most relevant criteria for choice of decomposition filters in wavelet-based texture characterization algorithms.

This paper describes a new approach for texture characterization, based on nonseparable wavelet decomposition, and its application for the discrimination of visually similar diffuse diseases of liver. The proposed feature-extraction algorithm applies nonseparable quincunx wavelet transform and uses energies of the transformed regions to characterize textures. Classification experiments on a set of three different tissue types show that the scale/frequency approach, particularly one based on the nonseparable wavelet transform, could be a reliable method for a texture characterization and analysis of B-scan liver images. Comparison between the quincunx and the traditional wavelet decomposition suggests that the quincunx transform is more appropriate for characterization of noisy data, and practical applications, requiring description with lower rotational sensitivity.

This paper deals with the real-time implementation of IIR filters having linear phase. Powell and Chau (1991) have devised an efficient method for the design and realization of real-time linear phase IIR filters using suitable modifications of the well-known time reversing technique. In their method, the input signal is divided into L-sample segments, time-reversed, and twice filtered using two IIR filter blocks whose transfer functions are the same. The performance has been further improved by Willson and Orchard (see IEEE Trans. Signal Processing, vol.SP-42, no.10, p.2842-8, 1994), where double zeros on the unit circle have been separated, giving better amplitude response in the stop-band and slightly smaller amplitude and phase distortions. New improvements are described based on the reordering of polynomials in the numerator of the filter transfer function, or on the appropriate selection of the transfer function. The truncation noise, phase and amplitude errors of new realizations show considerable improvement over the previous solutions.

A new implementation of the two-dimensional extended lapped transform (2-D ELT) is proposed. Compared to the separable solution, proposed by Malvar (1992), the new realization of the 2-D ELT has a reduced arithmetic complexity. Computational savings are achieved because scaling and inverse scaling of the butterfly matrices, suggested by Malvar for the 1-D case, are, after some modifications of the basic separable algorithm, extended to 2-D case. The new implementation has the same frequency response as Malvar's. The structures for the ELT analysis filter bank and the ELT synthesis filter bank are shown.

This paper investigates the application of nonseparable wavelet transform for the texture characterization. On a set of 21 Brodatz textures we have performed traditional dyadic wavelet decomposition (in two levels) and nonseparable quincunx decomposition (in six levels), testing their ability to classify textures in: (a) normal working conditions, (b) noisy environment, and (c) with respect to rotation. Our experiments have shown that the quincunx transform is appropriate for characterization of noisy data, small number of resolution levels and shorter feature vectors and rotationally invariant description.

This paper describes the application of the nonseparable wavelet decomposition for the discrimination of diffuse diseases of liver. The proposed feature extraction algorithm uses the filter bank performing the quincunx transform and characterizes textures by a set of channel variances estimated at the output of each filter. Classification experiments on a set of three different tissue types show that this approach could be a reliable method for analysis of B-scan liver images.

The authors propose a new separable extension of the 1-D wavelet transform to the 2-D case and describe its application to the texture characterization problem. Comparing to previous decompositions, with the same resolution levels for each subband in horizontal and vertical directions, the new method has different resolutions for different directions. The new algorithm is applied to 84 ultrasound liver images, to detect liver cirrhosis in it's early stage. The classification accuracy was 92%. The method was compared to other texture description methods (gray level cooccurrence, Laws filters, pyramid and tree structured wavelet decompositions). The proposed 2N/spl times/1-D wavelet decomposition, gave the highest classification rate, showing its applicability for the approach based analysis of the large class of natural textures.

Some computer applications for tissue characterization in medicine operate with tissue samples taken from small area of interest so that only few methods can be used. These methods should be insensitive to noise and image distortions and yet reliable enough. Here, the authors propose a new approach for texture analysis, based on the wavelet transform. The idea of this method is to decompose analyzed image with the filter bank derived from an orthonormal wavelet basis and to form image approximations with higher resolution. Energies calculated at the outputs of the filter bank are used as texture features in an unsupervised classification procedure based on a modification of the statistical T-test. The method is tested clinically, for characterization of infarcted myocardium and reported classification results are very promising.

An improved implementation of the oddly stacked Princen-Bradley filter bank is proposed. The new implementation is based on the same approach as suggested by Duhamel et al. [1991], where the complex fast Fourier transform algorithm of length equal to half of the number of subbands is used in order to obtain a significant reduction of number of arithmetic operations. In the implementation, the analysis filter bank is similar to Duhamel's, with some minor differences. However, the synthesis filter bank is quite different, having easy and elegant merging of output rotations and windows and reduced arithmetic complexity.>