Biomedical subjects

Emanuel Marom

Publications and source records attributed to Emanuel Marom.

3 recordsLinked to original sources

PubMed2003

Speckle noise in bar-code scanning systems--power spectral density and SNR.

Laser-based flying-spot scanners are strongly affected by speckle that is intrinsic to coherent illumination of diffusing targets. In such systems information is usually extracted by processing the derivative of a photodetector signal that results from collecting over the detector's aperture the scattered light of a laser beam scanning a bar code. Because the scattered light exhibits a time-varying speckle pattern, the signal is corrupted by speckle noise. In this paper we investigate the power spectral density and total noise power of such signals. We also analyze the influence of speckle noise on edge detection and derive estimates for a signal-to-noise ratio when a laser beam scans different sequences of edges. The theory is illustrated by applying the results to Gaussian scanning beams for which we derive closed form expressions.

Journal Article↗

PubMed2002

Time multiplexing superresolution based on interference grating projection.

In a previous work done by the authors, it was shown that the superresolution concept based on two moving gratings could be effected by a physical grating attached to the object and a virtual grating. This concept was shown to be very efficient and exhibited features that are helpful in removing some artifacts caused when coherent illumination is used. Furthermore, it simplifies the optical and mechanical modules of the super-resolving system by removing the need for mechanical movement of one grating. However, the system still required the need for moving the first (encoding) grating attached to the input. In this study the encoding grating is replaced by use of a projected grating. This approach simplifies the need for attaching the grating to the input object and thus new applications, such as remote sensing can be considered. The theoretical concept is demonstrated and experimental results are shown.

Journal Article↗

PubMed1997

Efficient Training of Recurrent Neural Network with Time Delays.

Training recurrent neural networks to perform certain tasks is known to be difficult. The possibility of adding synaptic delays to the network properties makes the training task more difficult. However, the disadvantage of tough training procedure is diminished by the improved network performance. During our research of training neural networks with time delays we encountered a robust method for accomplishing the training task. The method is based on adaptive simulated annealing algorithm (ASA) which was found to be superior to other training algorithms. It requires no tuning and is fast enough to enable training to be held on low end platforms such as personal computers. The implementation of the algorithm is presented over a set of typical benchmark tests of training recurrent neural networks with time delays. Copyright 1996 Elsevier Science Ltd.

Journal Article↗