Marius P. Schamschula

Fractional discrete Fourier transforms

Direct calculation of fractional Fourier transforms from the expressions derived for their optical implementation is laborious. An extension of the discrete Fourier transform would have only O(N(2)) computational complexity. We define such a system, offer a general way to compute the fractional discrete Fourier transform matrix, and numerically validate the algorithm.

OPTICAL SYNTACTIC PATTERN RECOGNITION BY FUZZY SCORING

A novel syntactic approach is introduced to treat particular problems in pattern recognition. The procedure is implemented by the use of optical correlation methods for identifying the various primitives that appear in the input pattern, and their importance is determined by fuzzy relational scoring. Robust pattern recognition with tolerance to normal variations is demonstrated, indicating an efficient new approach for optical pattern recognition.

Space-filling modular optics.

Repeated folding of the optical axis can be used to design space- and volume-efficient optical systems. We suggest that space-filling curves, such as the Peano and Hilbert curves, offer a useful way of realizing compact modular optics.

Multifeature fusion using Pulse Coupled Neural networks

We propose an unbiased multifeature fusion Pulse Coupled Neural Network (PCNN) algorithm. The method shares linking between several PCNNs running in parallel. We illustrate the PCNN fusion technique with a clean and noisy three-band color image example.

Hearing shapes: auditory recognition of two-dimensional spatial patterns

In the many years that pattern recognition has been of interest, there have ben many clever advances. One recent advance is the pulse-coupled neural network (PCNN). Due to recent developments in PCNNs, it is becoming increasingly possible to recognize images in space regardless of scale, rotation, translation. A continuation of this has been investigated which will allow images to be recognized audibly. In this paper, a general method for converting 2D spatial patterns into pattern-specific sound patterns will be discussed, along with some background information on PCNNs, and projections for his image-to-sound conversion.

Use of quantum indeterminacy in optical parallel genetic algorithms

The design and demonstration of a quantum optical genetic algorithms computer is described. We show that by avoiding the tedious computations of conventional genetic algorithms, time and energy could be saved. The role of quantum indeterminacy as a major component of the operation of this porcessor is emphasized.

Enhanced piezoelectric energy harvester performance using magnetic force and thermal energy

ABSTRACT One possible approach of improving the performance of energy harvesters is to use energy harvester with an external magnetic force to create a nonlinear coupling system. In this work, we report experimental results of a single piezoelectric cantilever beam (PCB) with tip mass or conventional piezoelectric energy harvester (CPEH), and the effect of applying an external magnetic force. The output voltage and power at optimal resistance was 7.62 V and 0.62 mW, respectively, at the resonance frequency of approximately 11 Hz of a CPEH. Also, the output voltage and average power at optimal resistance was 8.56 V and 0.44 mW, respectively, at resonance frequency of 7 Hz of a PCB with fixed opposing magnet. Furthermore, the output voltage and average power at optimal resistance was 13.31 V and 1.77 mW, respectively, at resonance frequency of 11 Hz of a PCB with opposing magnet attached at a second cantilever. In addition, comparison between the experimental results of all different configurations showed a reasonable enhancement of performance of energy harvester when an external magnetic force added over the main PCB. Finally, the performance of a multisource energy harvester with magnetic, thermal and mechanical sources is also presented in this study. In this case, it is demonstrated that increase in output voltage with temperature gradient under effect of magnetic force; the results of 2nd and 3rd model showed 44% and 99% enhancement of its original output voltage value at 1.2 °C and 2.7 °C temperature difference, respectively.

Disaggrregation of remotely sensed soil moisture using neural networks

Currently hydrological models are being developed that can be used to predict soil moisture conditions. However, these models suffer from drift due to nonlinearities in the dynamic system being modeled and due to roundoff errors in the computer hardware. We want use remotely sensed information to update the hydrological model. In order to sufficiently penetrate the soil to yield any useful information about the soil moisture of all but the very surface layer (< 1 cm) we need to choose from long wavelength microwave bands. Given the finite aperture of the antennas, this gives us a very low resolution. The problem we need to solve is how to match the low spatial resolution of the microwave sensor with the high resolution of the hydrological model. We developed an artificial neural network that is input the low-resolution remote sensor data along with information about the soil type, vegetation, and precipitation history at high resolution. The output is soil moisture information at high resolution. We can then use a Kalman filter to update the hydrological model.

Pulse-coupled neural network sensor fusion

Perception is assisted by sensed impressions of the outside world but not determined by them. The primary organ of perception is the brain and, in particular, the cortex. With that in mind, we have sought to see how a computer-modeled cortex--the PCNN or Pulse Coupled Neural Network--performs as a sensor fusing element. In essence, the PCNN is comprised of an array of integrate-and-fire neurons with one neuron for each input pixel. In such a system, the neurons corresponding to bright pixels reach firing threshold faster than the neurons corresponding to duller pixels. Thus, firing rate is proportional to brightness. In PCNNs, when a neuron fires it sends some of the resulting signal to its neighbors. This linking can cause a near-threshold neuron to fire earlier than it would have otherwise. This leads to synchronization of the pulses across large regions of the image. We can simplify the 3D PCNN output by integrating out the time dimension. Over a long enough time interval, the resulting 2D (x,y) pattern IS the input image. The PCNN has taken it apart and put it back together again. The shorter- term time integrals are interesting in themselves and will be commented upon in the paper. The main thrust of this paper is the use of multiple PCNNs mutually coupled in various ways to assemble a single 2D pattern or fused image. Results of experiments on PCNN image fusion and an evaluation of its advantages are our primary objectives.

Maximal limits on the information throughput of spatial light modulators

Spatial light modulators are optimized to operate with light of one polarization entering the device at normal incidence. Most optical processors, however, require light to enter at other angles, some far from normal incidence and with varying (angle-dependent) polarization. We discuss the implications of the limited field of view of spatial light modulators for optical processing and propose a solution to this problem.