Multiple contour extraction from graylevel images using an artificial neural network

For active contour modeling (ACM), we propose a novel self-organizing map (SOM)-based approach, called the batch-SOM (BSOM), that attempts to integrate the advantages of SOM- and snake-based ACMs in order to extract the desired contours from images. We employ feature points, in the form of ail edge-map (as obtained from a standard edge-detection operation), to guide the contour (as in the case of SOM-based ACMs) along with the gradient and intensity variations in a local region to ensure that the contour does not "leak" into the object boundary in case of faulty feature points (weak or broken edges). In contrast with the snake-based ACMs, however, we do not use an explicit energy functional (based on gradient or intensity) for controlling the contour movement. We extend the BSOM to handle extraction of contours of multiple objects, by splitting a single contour into as many subcontours as the objects in the image. The BSOM and its extended version are tested on synthetic binary and gray-level images with both single and multiple objects. We also demonstrate the efficacy of the BSOM on images of objects having both convex and nonconvex boundaries. The results demonstrate the superiority of the BSOM over others. Finally, we analyze the limitations of the BSOM.

Optimal Weighted Antenna Selection For Imperfect Channel Knowledge From Training

Receive antenna selection (AS) reduces the hardware complexity of multi-antenna receivers by dynamically connecting an instantaneously best antenna element to the available radio frequency (RF) chain. Due to the hardware constraints, the channels at various antenna elements have to be sounded sequentially to obtain estimates that are required for selecting the ``best'' antenna and for coherently demodulating data. Consequently, the channel state information at different antennas is outdated by different amounts. We show that, for this reason, simply selecting the antenna with the highest estimated channel gain is not optimum. Rather, the channel estimates of different antennas should be weighted differently, depending on the training scheme. We derive closed-form expressions for the symbol error probability (SEP) of AS for MPSK and MQAM in time-varying Rayleigh fading channels for arbitrary selection weights, and validate them with simulations. We then derive an explicit formula for the optimal selection weights that minimize the SEP. We find that when selection weights are not used, the SEP need not improve as the number of antenna elements increases, which is in contrast to the ideal channel estimation case. However, the optimal selection weights remedy this situation and significantly improve performance.

Enantiodiscrimination and extraction of short and long range homo- and hetero-nuclear residual dipolar couplings by a spin selective correlation experiment

A two dimensional correlation experiment for the measurement of short and long range homo- and hetero- nuclear residual dipolar couplings (RDCs) from the broad and featureless proton NMR spectra including C-13 satellites is proposed. The method employs a single natural abundant C-13 spin as a spy nucleus to probe all the coupled protons and permits the determination of RDCs of negligible strengths. The technique has been demonstrated for the study of organic chiral molecules aligned in chiral liquid crystal, where additional challenge is to unravel the overlapped spectrum of enantiomers. The significant advantage of the method is demonstrated in better chiral discrimination using homonuclear RDCs as additional parameters. (C) 2010 Elsevier B.V. All rights reserved.

Formant extraction from linear-prediction phase spectra

Extraction of formant information from linear-prediction phase spectra is proposed. It is shown that the derivative of phase spectrum gives reliable formant information. Since the phase spectra for several resonators in cascade are additive, the resonance peaks are additive in the derivative of the phase spectrum unlike in the magnitude spectrum and hence the problem of identifying merged peaks is very easily solved by this method. Application of the method is illustrated through examples of linear-prediction spectra obtained for simulated models and for actural speech segments.