The modified probabilistic neural network as a nonlinear correlator detector

A nonlinear correlator detector for the detection of a signal class with some intra class variance is developed using the modified probabilistic neural network and the general regression neural network. An application, involving the detection of regular tone bursts transmitted over a poor and noisy radio channel subjected to fading, random noise and impulse noise effects, is used to show the effectiveness of the method as compared to a linear correlato

State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state

Center for space microelectronics technology

The 1992 Technical Report of the Jet Propulsion Laboratory Center for Space Microelectronics Technology summarizes the technical accomplishments, publications, presentations, and patents of the center during the past year. The report lists 187 publications, 253 presentations, and 111 new technology reports and patents in the areas of solid-state devices, photonics, advanced computing, and custom microcircuits

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology

The extraction of accurate self-motion information from the visual world is a difficult problem that has been solved very efficiently by biological organisms utilizing non-linear processing. Previous bio-inspired models for motion detection based on a correlation mechanism have been dogged by issues that arise from their sensitivity to undesired properties of the image, such as contrast, which vary widely between images. Here we present a model with multiple levels of non-linear dynamic adaptive components based directly on the known or suspected responses of neurons within the visual motion pathway of the fly brain. By testing the model under realistic high-dynamic range conditions we show that the addition of these elements makes the motion detection model robust across a large variety of images, velocities and accelerations. Furthermore the performance of the entire system is more than the incremental improvements offered by the individual components, indicating beneficial non-linear interactions between processing stages. The algorithms underlying the model can be implemented in either digital or analog hardware, including neuromorphic analog VLSI, but defy an analytical solution due to their dynamic non-linear operation. The successful application of this algorithm has applications in the development of miniature autonomous systems in defense and civilian roles, including robotics, miniature unmanned aerial vehicles and collision avoidance sensors

NON-ORTHOGONAL SIGNAL-BASED OPTICAL COMMUNICATION SYSTEMS USING FUZZY LEARNING FOR INTERFERENCE CANCELLATION

Non-orthogonal signal-based systems are a type of communication system that uses signals that are not mutually perpendicular (i.e., not orthogonal) to transmit information. These types of systems can increase the spectral efficiency of communication systems by allowing for more data to be transmitted in the same bandwidth. Groups of signals with non-orthogonal waveforms can increase spectral efficiency, but they also increase the potential for interference. Spectrally efficient frequency division multiplexing (SEFDM) is a well-studied waveform that was originally proposed for use in wireless systems but has since found application in millimeter wave communications at 60 GHz, optical access network architecture, and long-distance optical fiber transmission. However, non-orthogonal signal-based systems are also more susceptible to interference from other sources, which can degrade the quality of the transmitted signal. To address this problem, this paper suggests using fuzzy learning techniques to cancel out interference and improve the signal-to-noise ratio. Fuzzy learning is a type of machine learning that uses fuzzy logic (FL) to handle uncertainty and imprecision in data. By using FL techniques to cancel out interference, the non-orthogonal signal-based optical communication (OC) system could potentially achieve better performance in noisy environments. Overall, this research topic has the potential to contribute to the development of more efficient and reliable OC systems that can operate in challenging environments

Measurements, Models, Systems and Design

531 s.