Optoelectronic implementation of a 256-channel sonar adaptive-array processor

We present an optoelectronic implementation of an adaptive-array processor that is capable of performing beam forming and jammer nulling in signals of wide fractional bandwidth that are detected by an array of arbitrary topology. The optical system makes use of a two-dimensional scrolling spatial light modulator to represent an array of input signals in 256 tapped delay lines, two acousto-optic modulators for modulating the feedback error signal, and a photorefractive crystal for representing the adaptive weights as holographic gratings. Gradient-descent learning is used to dynamically adapt the holographic weights to optimally form multiple beams and to null out multiple interference sources, either in the near field or in the far field. Space-integration followed by differential heterodyne detection is used for generating the system's output. The processor is analyzed to show the effects of exponential weight decay on the optimum solution and on the convergence conditions. Several experimental results are presented that validate the system's capacity for broadband beam forming and jammer nulling for linear and circular arrays.     

Optimized Prototype Filter Based on the FRM Approach for Cosine-Modulated Filter Banks

A design procedure for frequency-response masking (FRM) prototype filters of cosine-modulated filter banks (CMFBs) is proposed. In the given method, we perform minimization of the maximum attenuation level in the filters stopband, subject to intersymbol interference (ISI) and intercarrier interference (ICI) constraints. For optimization, a quasi-Newton algorithm with line search is used, and we provide simplified analytical expressions to impose the interference constraints, which greatly reduce the computational complexity of the optimization procedure. The result is lower levels of ISI and ICI for a predetermined filter order, or a reduced filter complexity for given levels of interferences. It is then illustrated how the FRM-CMFB structure is suitable for implementing filter banks with a large number of bands, yielding sharp transition bands and small roll-off factors, which is an attractive feature for a wide range of practical applications.     

Spatio-temporal operator formalism for holographic recording and diffraction in a photorefractive-based true-time-delay phased-array processor

We present a spatio-temporal operator formalism and beam propagation simulations that describe the broadband efficient adaptive method for a true-time-delay array processing (BEAMTAP) algorithm for an optical beamformer by use of a photorefractive crystal. The optical system consists of a tapped-delay line implemented with an acoustooptic Bragg cell, an accumulating scrolling time-delay detector achieved with a traveling-fringes detector, and a photorefractive crystal to store the adaptive spatio-temporal weights as volume holographic gratings. In this analysis, linear shift-invariant integral operators are used to describe the propagation, interference, grating accumulation, and volume holographic diffraction of the spatio-temporally modulated optical fields in the system to compute the adaptive array processing operation. In addition, it is shown that the random fluctuation in time and phase delays of the optically modulated and transmitted array signals produced by fiber perturbations (temperature fluctuations, vibrations, or bending) are dynamically compensated for through the process of holographic wavefront reconstruction as a byproduct of the adaptive beam-forming and jammer-excision operation. The complexity of the cascaded spatial-temporal integrals describing the holographic formation, and subsequent readout processes, is shown to collapse to a simple imaging condition through standard operator manipulation. We also present spatio-temporal beam propagation simulation results as an illustrative demonstration of our analysis and the operation of a BEAMTAP beamformer.     

Reduction of electromyographic noise in the signal-averaged electrocardiogram by spectral decomposition

This paper proposes a technique to improve the quality of high-resolution electrocardiogram by weighting the coherent average of beats by a function of the energy of the corrupting myoelectric noise, prior to subsequent detection of ventricular late potentials. The results obtained with 20 patients indicate the method requires fewer beats than conventional nonweighted average to achieve the same noise level.     

Carbamazepine/betaCD/HPMC solid dispersions. I. Influence of the spray-drying process and betaCD/HPMC on the drug dissolution profile

The aim of this study was to compare carbamazepine (CBZ) solid dispersions prepared by spray-drying of aqueous dispersions with the corresponding physical mixtures. The influence of the association of β-cyclodextrin (βCD) and hydroxypropyl methylcellulose (HPMC) on the CBZ dissolution profile of the preparations was investigated. Results demonstrated that CBZ release from solid dispersions is dependent on the ratio of βCD and HPMC. The spray-drying process confers better homogeneity to CBZ polymeric dispersions than the physical mixture process. In summary, we demonstrated the feasibility of obtaining a homogeneous polymeric solid dispersion of CBZ from an aqueous media by spray-drying and a clear influence of the βCD:HPMC ratio on the release profile of CBZ.     

Learning, capability accumulation and firms differences: evidence from latecomer steel

This paper is concerned with the application of frameworks inorder to explain how key features of the intra-firm learningprocesses influence inter-firm differences in technologicalcapability accumulation in the late-industrializing or latecomercontext. This relationship is examined in two of the largeststeel firms in Brazil over their lifetimes of 40 and 60 years.These issues have been addressed, individually or together,in different studies of ‘technological capability’and ‘learning’ in industrialized and latecomer firms.However, there is a scarcity of analytical frameworks and supportingempirical evidence to explore the practical implications ofthe underlying learning processes for inter-firm differencesin technological capability-accumulation paths, particularlywithin the latecomer context. The framework for capability accumulationidentifies different types and levels of technological capabilities.The framework for learning identifies four processes: externaland internal knowledge acquisition, knowledge socialization,and knowledge codification. These are examined on the basisof key features: variety, intensity and functioning. Drawingon in-depth comparative case study methodology, the study hasfound that: (i) the technological capability accumulation pathsfollowed by the two firms diverged and have proceeded at differingrates over time, and (ii) key features of the intra-firm learningprocesses have played a substantial part in influencing thesedifferences. The paper suggests that, at least within largelatecomer steel firms, purposeful, continuous and effectiveefforts to improve on key features of the underlying learningprocesses are likely to generate positive implications for themanner and rate of technological capability accumulation.     

Reconstruction of 3D Object Meshes from Silhouette Images

In this work we propose a method for computing mesh representations of 3D objects reconstructed from a set of silhouette images. Our method is based on the polygonization of volumetric reconstructions by using a modified version of the dual contouring method. In order to apply dual contouring on volumetric reconstruction from silhouettes we devised a method that is able to determine the discrete topology of the surface in relation to the octree cells. We also developed a new scheme for computing hermitian data representing the intersections of conic volumes with the octree cells and their corresponding normals with subpixel accuracy. Due to the discrete and extremely noisy nature of the data used in the reconstruction we had to devise a different criterion for mesh simplification that applies topological consistency tests only when the geometric error measure is beyond a given tolerance. We present results of the application of the proposed method in the extraction of a mesh corresponding to the surface of objects of a real scene.     

Evolution of neural networks for classification and regression

Although Artificial Neural Networks (ANNs) are importantdata mining techniques, the search for the optimal ANN is a challenging task: the ANN should learn the input–output mapping without overfitting the data and training algorithms may get trapped in local minima. The use of Evolutionary Computation (EC) is a promising alternative for ANN optimization. This work presents two hybrid EC/ANN algorithms: the first evolves neural topologies while the latter performs simultaneous optimization of architectures and weights. Sixteen real-world tasks were used to test these strategies. Competitive results were achieved when compared with a heuristic model selection and other Data Mining algorithms.