Neural networks and spectra feature selection for retrival of hot gases temperature profiles

Proceeding of: International Conference on Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, Vienna, Austria 28-30 Nov. 2005Neural networks appear to be a promising tool to solve the so-called inverse problems focused to obtain a retrieval of certain physical properties related to the radiative transference of energy. In this paper the capability of neural networks to retrieve the temperature profile in a combustion environment is proposed. Temperature profile retrieval will be obtained from the measurement of the spectral distribution of energy radiated by the hot gases (combustion products) at wavelengths corresponding to the infrared region. High spectral resolution is usually needed to gain a certain accuracy in the retrieval process. However, this great amount of information makes mandatory a reduction of the dimensionality of the problem. In this sense a careful selection of wavelengths in the spectrum must be performed. With this purpose principal component analysis technique is used to automatically determine those wavelengths in the spectrum that carry relevant information on temperature distribution. A multilayer perceptron will be trained with the different energies associated to the selected wavelengths. The results presented show that multilayer perceptron combined with principal component analysis is a suitable alternative in this field.Publicad

On the transmission of light through a single rectangular hole

In this Letter we show that a single rectangular hole exhibits transmission resonances that appear near the cutoff wavelength of the hole waveguide. For light polarized with the electric field pointing along the short axis, it is shown that the normalized-to-area transmittance at resonance is proportional to the ratio between the long and short sides, and to the dielectric constant inside the hole. Importantly, this resonant transmission process is accompanied by a huge enhancement of the electric field at both entrance and exit interfaces of the hole. These findings open the possibility of using rectangular holes for spectroscopic purposes or for exploring non-linear effects.Comment: Submitted to PRL on Feb. 9th, 200

A holistic approach to the evaluation of sustainable housing

Residential housing is often evaluated against single or at best a limited number of similar criteria. These include quantifiable indicators such as energy use and its associated greenhouse gas emissions. It might also include material consumption from an embodied energy or resource use perspective. Social factors or qualitative indicators may be evaluated but are rarely placed or juxtaposed alongside these quantifiable indicators. A one-dimensional approach will be limiting because sustainable development includes both environmental and social factors. This paper describes the methodologies that have been developed to assess housing developments against five quite different criteria. These are: energy use, resource use, neighbourhood character, neighbourhood connectedness and diversity. In each case, high and low sustainability practice has been identified so that ranking is possible. These methodologies have then been tested by evaluating a typical precinct (approximately 400 m by 400 m) of a 1970-80s housing development in a suburb of Geelong. The rankings of the particular precinct have then been combined in a visual way to assist in the evaluation of the housing in a more holistic way. The results of this evaluation method are presented, along with a discussion of the strengths and weaknesses of the methodologies. The research is the outcome of collaboration by a cross-disciplinary group of academics within Deakin’s School of Architecture and Building

Motion Detection by Microcontroller for Panning Cameras

Motion detection is the first essential process in the extraction of information regarding moving objects. The approaches based on background difference are the most used with fixed cameras to perform motion detection, because of the high quality of the achieved segmentation. However, real time requirements and high costs prevent most of the algorithms proposed in literature to exploit the background difference with panning cameras in real world applications. This paper presents a new algorithm to detect moving objects within a scene acquired by panning cameras. The algorithm for motion detection is implemented on a Raspberry Pi microcontroller, which enables the design and implementation of a low-cost monitoring system.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras

Automatic video surveillance systems are usually designed to detect anomalous objects being present in a scene or behaving dangerously. In order to perform adequately, they must incorporate models able to achieve accurate pattern recognition in an image, and deep learning neural networks excel at this task. However, exhaustive scan of the full image results in multiple image blocks or windows to analyze, which could make the time performance of the system very poor when implemented on low cost devices. This paper presents a system which attempts to detect abnormal moving objects within an area covered by a PTZ camera while it is panning. The decision about the block of the image to analyze is based on a mixture distribution composed of two components: a uniform probability distribution, which represents a blind random selection, and a mixture of Gaussian probability distributions. Gaussian distributions represent windows in the image where anomalous objects were detected previously and contribute to generate the next window to analyze close to those windows of interest. The system is implemented on a Raspberry Pi microcontroller-based board, which enables the design and implementation of a low-cost monitoring system that is able to perform image processing.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Phase-locking of a Nonlinear Optical Cavity via Rocking: Transmuting Vortices into Phase Patterns

We report experimental observation of the conversion of a phase-invariant nonlinear system into a phase-locked one via the mechanism of rocking [G. J. de Valcarcel and K. Staliunas, Phys. Rev. E 67, 026604 (2003)]. This conversion results in that vortices of the phase-invariant system are being replaced by phase patterns such as domain walls. The experiment is carried out on a photorefractive oscillator in two-wave mixing configuration.A model for the experimental device is given that reproduces the observed behavior.Comment: 9 pages and 4 figure