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The recently proposed hierarchical temporal memory (HTM) paradigm of soft computing is applied to the detection and classification of foreign materials in a conveyor belt carrying tobacco leaves in a cigarette manufacturing industry. The HTM has been exposed to hyperspectral imaging data from 10 types of unwanted materials intermingled with tobacco leaves. The impact of the HTM architecture and the configuration of internal parameters on its classification performance have been explored. Classification results match or surpass those attained with other methods, such as Artificial Neural Networks (ANNs), with the advantage that HTM are able to handle raw spectral data and no preprocessing, spectral compression, or reflectance correction is required. It is also demonstrated that an optimized configuration of the HTM architecture and internal values can be derived from the statistical properties of the hyperspectral data, allowing the extension of the approach to other classification problems.

A method for automatic qualitative classification of liquid samples based on their absorption spectrum in the ultraviolet, visible and near-infrared regions is presented. A fiber-optic setup has been employed for material characterization. A simple technique based on image matching is proposed to perform spectra comparison. This alternative implementation of conventional spectrum matching methodologies can be easily translated to hardware platforms thus improving the time response of the classification system. The proposed method does not make any assumption on the probability density function of the data and it is also capable of automatic outlier removal. “Spectral marks” based on the polar representation of the absorption spectra and their “optimized marks” improved by means of Principal Component Analysis have been implemented. Preliminary discrimination results have been obtained on the classification of different oil samples from seeds and olives.

The validity of a processing technique, based on feature selection and artificial neural networks, when applied to arc-welding on-line quality monitoring is analyzed. An optical fiber embedded within the welding torch captures the plasma radiation, which is delivered to a CCD spectrometer. In the proposed solution the spectral analysis is performed in two stages: dimensionality reduction is accomplished using a feature selection algorithm and, after that, an artificial neural network carries out the spectral identification task. The validity of the technique has been successfully demonstrated by means of several experimental welding tests.

A new spectral processing technique designed for application in the on-line detection and classification of arc-welding defects is presented in this paper. A noninvasive fiber sensor embedded within a TIG torch collects the plasma radiation originated during the welding process. The spectral information is then processed in two consecutive stages. A compression algorithm is first applied to the data, allowing real-time analysis. The selected spectral bands are then used to feed a classification algorithm, which will be demonstrated to provide an efficient weld defect detection and classification. The results obtained with the proposed technique are compared to a similar processing scheme presented in previous works, giving rise to an improvement in the performance of the monitoring system.

A novel system which allows arc-welding defect detection and classification is presented in this paper. The spectroscopic analysis of the plasma spectra produced during the welding process is a well-known technique to monitor the quality of the resulting weld seams. The analysis of specific emission lines and the subsequent estimation of the electronic temperature T e profile offers a direct correlation between this parameter and the corresponding weld seams. However, the automatic identification and classification of weld defects has proven to be difficult, and it is usually performed by means of statistical studies of the electronic temperature profile. In this paper, a new approach that allows automatic weld defect detection and classification based in the combined use of principal component analysis (PCA) and an artificial neural network (ANN) is proposed. The plasma spectra captured from the welding process is processed with PCA, which reduces the processing complexity, by performing a data compression in the spectral dimension. The designed ANN, after the selection of a proper data training set, allows automatic detection of weld defects. The proposed technique has been successfully checked. Arc-weld tests on stainless steel are reported, showing a good correlation between the ANN outputs and the classical interpretation of the electronic temperature profile.

Agricultural and industrial sectors are more competitive and quality conscious than ever before. To be profitable, industry requires equipment that would ensure pure, high-quality production, and efficient work and cost. This fact is even more important in developed countries to aid companies to defend their position and competitiveness against others where labour cost does not account for such a significant part of the overall manufacturing cost. Raw material sorters, conveyors and processing systems easy to install, energy efficient, reliable, low-maintenance and simple to adjust and control are sought. Even the inclusion of network connectivity capabilities for remote monitoring and communication is also desirable. Optical Spectroscopy (OS) becomes highly appropriate for this kind of applications because it covers all types of qualitative and quantitative analytical methods based on the interaction of light with living and non-living matter (Schmidt, 2005). For more than 200 years it has been utilized in various fields of science, industry and medicine, particularly in (bio-) chemistry, biology, physics and astronomy. OS is highly specific since each substance is discernible from all others by its spectral properties. In addition, the requirements of the samples are not particularly restrictive. Measurements of different optical parameters as a function of wavelength/energy (“spectrum”) provide valuable insights that are not, or not readily, attainable by other analytical methods. Traditional OS techniques generate information on the bulk properties of a sample or a portion taken from it (Millar, 2008). However, there are many aspects of sample properties which result from heterogeneity in composition and the monitoring of this aspect of quality is considerably more challenging. In the last few years Hyperspectral Imaging Spectroscopy (HIS) that integrates conventional imaging and moderate resolution spectroscopy, which was primarily developed for applications in remote sensing and astronomy, has been developed for laboratory use and it has even slowly transitioned into other areas as life sciences or industrial production. The key difference is that, in this technique, entire spectra are collected for each pixel within the image so its advantages in agricultural and industrial procedures such as material discrimination are obvious, i.e. reduced measurement time due to the simultaneous acquisition without the need for scanning mechanics. By assessing specific spectral features at each pixel corresponding to a material point, or by using calibration to quantify

A method for the automatic qualitative discrimination of liquid samples based on their absorption spectrum in the ultraviolet, visible and near-infrared regions is presented. An alternative implementation of conventional spectrum matching methodologies is proposed working towards the improvement of the response time of the discrimination system. The method takes advantage of not making assumptions on the probability density function of the data and it is also capable of automatic outlier removal. Optimized marks are obtained after PCA application on the absorption spectral data. The feasibility of the algorithm has been validated with the automatic discrimination of different oil samples (seeds and olive) that have been measured with an optical fiber absorption spectroscopy set-up. The system here proposed could be easily and efficiently implemented in hardware platforms improving the system performance.

In a previous paper a new approach was explored where the output parameters of a welding monitoring system based on plasma spectroscopy were the participation profiles of plasma ions and neutral atoms. They were obtained by the generation of synthetic spectra and the use of an optimization algorithm, showing correlation to the appearance of defects on the seams. In this work a feature selection algorithm is included in the model to determine the most discriminant wavelengths in terms of defect detection, thus allowing to reduce the spectral range where the synthetic spectra are generated. This should also give rise to an improvement in the overall computational performance of the algorithm. Alternatives to the use of controlled randomn search algorithms will be also explored, and the resulting model will be checked by means of experimental and field tests of arc-welding processes. This work has been co-supported by the Spanish TEC2007-67987-C02-01 projects.

In this paper a new spectroscopic monitoring parameter is proposed for the on-line monitoring of welding processes, the plasma RMS signal, which is determined by considering the contribution from the spectral samples over a particular spectral window. This parameter is directly related to the heat input that can be estimated by measuring both welding voltage and current, but it exhibits a higher sensitivity to the appearance of weld defects. A comparison between the results obtained from the different spectroscopic parameters will be presented, with data from both experimental and field arc-welding tests. This work has been co-supported by the Spanish TEC2007-67987-C02-01 projects.

An optical fibre transducer able to work in high temperatures environments is experimentally demonstrated in the laboratory. It is based on a permanent Long period gratings (PLPG) written using a thermo-mechanical technique. The fabrication technique, the experimental works, their results and the conclusions are presented and discussed in this paper.

Plasma optical spectroscopy is a technique widely used for on-line welding diagnostics, given the rich information to be found within the plasma spectra generated during the process. One of the key factors in this regard is the computational performance of the whole monitoring system, as it determines the resulting spatial resolution. The electronic temperature of the welding plasma is typically used as the output monitoring parameter, but it requires an identification of the emission lines, what implies additional processing stages. In this paper we propose the use of the line-to-continuum method to generate the system output profiles, previously choosing the required spectral band by means of the SFFS algorithm.

An automated algorithm and methodology is presented to pathologically classify the scattering changes encountered in the raster scanning of normal and tumor pancreatic tissues using microsampling reflectance spectroscopy. A quasi-confocal reflectance imaging system was used to directly measure the tissue scatter reflectance in situ, and the spectrum was used to identify the scattering power, amplitude and total wavelength-integrated intensity. Pancreatic tumor and normal samples were characterized using the instrument and subtle changes in the scatter signal were encountered within regions of each sample. Discrimination between normal vs. tumor tissue was readily performed using an Artificial Neural Network (ANN) classifier algorithm. A similar approach has worked also for regions of tumor morphology when statistical pre-processing of the scattering parameters was included to create additional data features. This automated interpretation methodology can provide a tool for guiding surgical resection in areas where microscopy imaging do not reach enough contrast to assist the surgeon.

The search for an efficient on-line monitoring system focused on the real-time analysis of the welding quality is an active area of research, mainly due to the widespread use of both arc and laser welding processes in relevant industrial scenarios such as aeronautics or nuclear. In this work, an improvement in the performance of a previously designed monitor system is presented. This improvement is accomplished by the employment of a dual spatial-spectral technique, namely imaging spectroscopy. This technique allows the simultaneous determination of the optical spectrum components and the spatial location of an object in a surface. In this way, the spatially characterization of the plasma emitted during a tungsten inert gas (TIG) welding is performed. The main advantage of this technique is that the spectra of all the points in the line of vision are measured at the same time. Not only are all the spectra captured simultaneously, but they are also processed as a batch, allowing the investigation of the welding quality. Moreover, imaging spectroscopy provides the desired real-time operation. To simultaneously acquire the information of both domains, spectral and spatial, a passive Prism-Grating-Prism (PGP) device can be used. In this paper the plasma spectra is captured during the welding test by means of a near infrared imaging spectroscopic system which consists of input optics, an imaging spectrograph and a monochrome camera. Technique features regarding on-line welding quality monitoring are discussed by means of several experimental welding tests.

A new spectral processing technique designed for its application in the on-line detection and classification of arc-welding defects is presented in this paper. A non-invasive fiber sensor embedded within a TIG torch collects the plasma radiation originated during the welding process. The spectral information is then processed by means of two consecutive stages. A compression algorithm is first applied to the data allowing real-time analysis. The selected spectral bands are then used to feed a classification algorithm, which will be demonstrated to provide an efficient weld defect detection and classification. The results obtained with the proposed technique are compared to a similar processing scheme presented in a previous paper, giving rise to an improvement in the performance of the monitoring system.

The introduction of arc and laser welding in the aerospace, automotive and nuclear sectors, among others, has led to a great effort in research concerning quality assurance of these processes. Hence, an on-line, real-time welding monitor system able to detect instabilities affecting the welding quality would be of great interest, as it would allow to reduce the use of off-line inspection techniques, some of them by means of destructive-testing evaluation, improving process productivity. Among several different approaches, plasma optical spectroscopy has proved to be a feasible solution for the on-line detection of weld defects. However, the direct interpretation of the results offered by this technique can be difficult. Therefore, Artificial Neural Networks (ANN), due to their ability to handle non-linearity, is a highly suitable solution to identify and detect disturbances along the seam. In this paper plasma spectra captured during welding tests are compressed by means of Principal Component Analysis (PCA) and, then, processed in a back propagation ANN. Experimental tests performed on stainless steel plates show the feasibility of the proposed solution to be implemented as an on-line arc-welding quality monitor system.

Absorption spectroscopy in the wide 200- 1700 nm spectral range is carried out by means of optical fiber instrumentation to achieve a digital mapping of liquids according to their differences or similarities. Extra virgin olive oils from different Italian regions and lubricant oils from tubines with different degrees of degradation were considered as 'case studies'. The spectral data were processed by means of multivariate analysis so as to obtain a correlation to qualtity parameters. In practice, the wide range absorption spectra were considered as an optical signature of the liquids from which to extract product quality information. The optical signatures of extra virgin olive oils were used to predict the content of the most important fatty acids. The optical signatures of lubricant oils were used to predict the concentration of the most important parameters for indicating the oil's degree of degradation, such as TAN, JOAP antiwear index, and water content.

Absorption spectroscopy in the wide 200-1700 nm spectral range is carried out by means of optical fiber instrumentation to achieve an optical signature for a digital mapping of liquids according to their differences or similarities. Extra virgin olive oils from different Italian regions and lubricant oils from turbines with different degrees of degradation were considered as dasia case studies psila . The spectral data were processed by means of multivariate analysis so as to obtain a correlation to liquid psila s quality parameters. The optical signatures of extra virgin olive oils were used to predict the content of the most important fatty acids. The optical signatures of lubricant oils were used to predict the concentration of the most important parameters for indicating the oil psila s degree of degradation, such as total acid number, JOAP anti-wear index, and water content.