A novel material is described for holographic light shaping diffusers based upon monolithic glass fabrication that is totally optically refractive. This diffuser has mechanical and optical properties far superior to plastic diffusers, making it suitable for use in optical systems that include high power lasers, ultraviolet applications, near infrared applications, and for high-temperature environments. We have designed and implemented an all glass diffuser. This novel diffuser is a monolithic optical element that controls the angular spread of transmitted light and homogenizes otherwise spatially noisy light sources such as LEDs and filamented light sources while maintaining damage thresholds consistent with any glass optical element. Preliminary analysis shows that this glass diffuser has a transmission efficiency of 90% from the ultraviolet wavelengths through the visible spectrum and into the near infrared.
This paper describes the general architecture of a hybrid neural network used to identify noisy and extremely complex spectra. A hybrid neural network has been built for environmental monitoring, medical diagnosis, and process control applications. The hybrid neural network consists of preprocessing algorithms to enhance the features of the spectra and an interconnect weight matrix for recognition. Results suggest that the hybrid neural network, through careful design of both the preprocessing algorithms and the neural network architecture, is capable of increasing the detection limit and speed of many analytical instruments.