Photonic processing for wideband cancellation and spectral discrimination of RF signals

Photonic signal processing is used to implement common mode signal cancellation across a very wide bandwidth utilising phase modulation of radio frequency (RF) signals onto a narrow linewidth laser carrier. RF spectra were observed using narrow-band, tunable optical filtering using a scanning Fabry Perot etalon. Thus functions conventionally performed using digital signal processing techniques in the electronic domain have been replaced by analog techniques in the photonic domain. This technique was able to observe simultaneous cancellation of signals across a bandwidth of 1400 MHz, limited only by the free spectral range of the etalon. © 2013 David M. Benton.

Effects of high-color-discrimination capability spectra on color-deficient vision

Light sources with three spectral bands in specific spectral positions are known to have high-color-discrimination capability. W. A. Thornton hypothesized that they may also enhance color discrimination for color-deficient observers. This hypothesis was tested here by comparing the Rösch–MacAdam color volume for color-deficient observers rendered by three of these singular spectra, two reported previously and one derived in this paper by maximization of the Rösch–MacAdam color solid. It was found that all illuminants tested enhance discriminability for deuteranomalous observers, but their impact on other congenital deficiencies was variable. The best illuminant was the one derived here, as it was clearly advantageous for the two red–green anomalies and for tritanopes and almost neutral for red–green dichromats. We conclude that three-band spectra with high-color-discrimination capability for normal observers do not necessarily produce comparable enhancements for color-deficient observers, but suitable spectral optimization clearly enhances the vision of the color deficient.

Utilização de imagem do sensor Aster para discriminação espectral de rochas carbonáticas aflorantes na região Noroeste do Estado do Rio de Janeiro

Este trabalho teve como objetivo geral avaliar o potencial das imagens do sensor ASTER, utilizando a região do infravermelho de ondas curtas (SWIR), para discriminação espectral de rochas carbonáticas aflorantes na região Noroeste do Estado do Rio de Janeiro, complementando produtos existentes de mapeamento geológico. As rochas carbonáticas servem de matéria-prima para produção de cimento, que atualmente apresenta forte demanda dado o crescimento de obras civis devido à expansão da infraestrutura do Estado do Rio de Janeiro. Este crescimento no consumo oferece desafios às companhias produtoras, tornando-se de vital importância a identificação de novas áreas para exploração de insumos para a indústria civil. Neste sentido, o carbonato tem sofrido grande pressão com relação a sua produção pois é a principal matéria-prima utilizada na fabricação do cimento. Imagens do sensor Aster vem sendo utilizadas na área da geologia com êxito, discriminando litologias e minerais como quartzo, óxido de ferro e calcita. Na região do intervalo de ondas entre 2,235-2,285 μm e 2,295-2,365 μm , as bandas 7 e 8 do sensor ASTER na região do SWIR, mostram-se adequadas para a identificação de minerais de calcita e dolomita. Como metodologia, foram aplicadas as técnicas de razões de bandas para separação de calcários e dolomitos e para a classificação espectral, foi utilizada a técnica SAM. Tornou-se como referência para a classificação espectral amostras de áreas de rochas carbonáticas aflorantes e espectros da biblioteca espectral da USGS. As classificações espectrais obtiveram resultados significativos na discriminação espectral das áreas carbonáticas, no entanto as técnicas de razões de bandas não obtiveram resultados suficientes para a discriminação de calcários e dolomitos. Para trabalhos futuros sugere-se a realização de trabalho de campo para a coleta de espectros, através da espectrorradiometria dos afloramentos dos carbonatos.

Detecting Submerged Macrophytes in a UK Chalk Stream Using Field Spectroscopy

Various studies using optical remote sensing in the marine environment have shown the possibilities of spectral discrimination of benthic macro and micro-algae. For in-land water bodies only very recently studies of have explored similar use of optical remote sensing to identify the taxonomic composition of algae and rooted plant communities. The importance of these communities for the functioning of river ecosystems warrants further research. In the study presented here, field spectroscopy is used to assess the possibilities of optically detecting macrophytes in a UK chalk streams. Spectral signatures of four common macrophytes were measured using a hand-held GER1500 spectroradiometer. Despite the strong absorption of near infrared in water, the results show that information on NIR can clearly contribute to the detection of submerged vegetation in shallow UK chalk stream environments. Observed spectra compare well with simulated submerged vegetation spectra, based on water absorption coefficients only. The field investigations, which were performed in the river Wylye, also indicate the confounding effects of specular reflection from riparian vegetation. The results of this study can inform remote sensing studies of the riverine environment using multi-spectral/low altitude sensors. Such larger scale studies will be highly beneficial for monitoring variation in chalk stream bioindicators, such as ranunculus.

Visibility of sparkle in metallic paints

For suitable illumination and observation conditions, sparkles may be observed in metallic coatings. The visibility of these sparkles depends critically on their intensity, and on the paint medium surrounding the metallic flakes. Based on previous perception studies from other disciplines, we derive equations for the threshold for sparkles to be visible. The resulting equations show how the visibility of sparkles varies with the luminosity and distance of the light source, the diameter of the metallic flakes, and the reflection properties of the paint medium. The predictions are confirmed by common observations on metallic sparkle. For example, under appropriate conditions even metallic flakes as small as 1 μm diameter may be visible as sparkle, whereas under intense spot light the finer grades of metallic coatings do not show sparkle. We show that in direct sunlight, dark coarse metallic coatings show sparkles that are brighter than the brightest stars and planets in the night sky. Finally, we give equations to predict the number of visually distinguishable flake intensities, depending on local conditions. These equations are confirmed by previous results. Several practical examples for applying the equations derived in this article are provided.

A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops

R. Zwiggelaar, 'A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops', Crop Protection 17 (3), 189-206 (1998)

Shape discrimination using invariants defined from higher-order spectra

An approach to pattern recognition using invariant parameters based on higher-order spectra is presented. In particular, bispectral invariants are used to classify one-dimensional shapes. The bispectrum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale- and amplification-invariant. A minimal set of these invariants is selected as the feature vector for pattern classification. Pattern recognition using higher-order spectral invariants is fast, suited for parallel implementation, and works for signals corrupted by Gaussian noise. The classification technique is shown to distinguish two similar but different bolts given their one-dimensional profiles

Pitch discrimination in optimal and suboptimal acoustic environments: electroencephalographic, magnetoencephalographic, and behavioral evidence

Pitch discrimination is a fundamental property of the human auditory system. Our understanding of pitch-discrimination mechanisms is important from both theoretical and clinical perspectives. The discrimination of spectrally complex sounds is crucial in the processing of music and speech. Current methods of cognitive neuroscience can track the brain processes underlying sound processing either with precise temporal (EEG and MEG) or spatial resolution (PET and fMRI). A combination of different techniques is therefore required in contemporary auditory research. One of the problems in comparing the EEG/MEG and fMRI methods, however, is the fMRI acoustic noise. In the present thesis, EEG and MEG in combination with behavioral techniques were used, first, to define the ERP correlates of automatic pitch discrimination across a wide frequency range in adults and neonates and, second, they were used to determine the effect of recorded acoustic fMRI noise on those adult ERP and ERF correlates during passive and active pitch discrimination. Pure tones and complex 3-harmonic sounds served as stimuli in the oddball and matching-to-sample paradigms. The results suggest that pitch discrimination in adults, as reflected by MMN latency, is most accurate in the 1000-2000 Hz frequency range, and that pitch discrimination is facilitated further by adding harmonics to the fundamental frequency. Newborn infants are able to discriminate a 20% frequency change in the 250-4000 Hz frequency range, whereas the discrimination of a 5% frequency change was unconfirmed. Furthermore, the effect of the fMRI gradient noise on the automatic processing of pitch change was more prominent for tones with frequencies exceeding 500 Hz, overlapping with the spectral maximum of the noise. When the fundamental frequency of the tones was lower than the spectral maximum of the noise, fMRI noise had no effect on MMN and P3a, whereas the noise delayed and suppressed N1 and exogenous N2. Noise also suppressed the N1 amplitude in a matching-to-sample working memory task. However, the task-related difference observed in the N1 component, suggesting a functional dissociation between the processing of spatial and non-spatial auditory information, was partially preserved in the noise condition. Noise hampered feature coding mechanisms more than it hampered the mechanisms of change detection, involuntary attention, and the segregation of the spatial and non-spatial domains of working-memory. The data presented in the thesis can be used to develop clinical ERP-based frequency-discrimination protocols and combined EEG and fMRI experimental paradigms.

Short-window spectral analysis using AMVAR and multitaper methods: a comparison

We compare two popular methods for estimating the power spectrum from short data windows, namely the adaptive multivariate autoregressive (AMVAR) method and the multitaper method. By analyzing a simulated signal (embedded in a background Ornstein-Uhlenbeck noise process) we demonstrate that the AMVAR method performs better at detecting short bursts of oscillations compared to the multitaper method. However, both methods are immune to jitter in the temporal location of the signal. We also show that coherence can still be detected in noisy bivariate time series data by the AMVAR method even if the individual power spectra fail to show any peaks. Finally, using data from two monkeys performing a visuomotor pattern discrimination task, we demonstrate that the AMVAR method is better able to determine the termination of the beta oscillations when compared to the multitaper method.

Application of z-COSY experiment and its variant for accurate chiral discrimination by H-1 NMR

We report the application of z-COSY experiment and a band selected version of it by employing a selective 90 degrees pulse entitled BASE-z-COSY for precise chiral discrimination, quantification of enantiomeric excess and the analyses of the H-1 NMR spectra of chiral molecules aligned in the chiral liquid crystalline solvent poly-gamma-benzyl-L-glutamate (PBLG). We have demonstrated their applicability for obtaining very high resolution in the H-1 NMR spectra of small organic molecules. It is well known that the commonly employed z-COSY experiment disentangles the spectral complexity, provides pure phase spectra with high resolution, aids in the complete spectral analyses, in addition to yielding information on relative signs of the Couplings. The BASE-z-COSY experiment possesses all these properties, permits the measure of enantiomeric excess, in addition to large saving of instrument time.

Chiral discrimination and the measurement of enantiomeric excess from a severely overcrowded NMR spectrum

NMR spectroscopic chiral visualization, unambiguous assignment of peaks pertaining to R and S enantiomers and the subsequent measurement of enantiomeric composition demands a highly resolved spectrum. The method fails when the spectrum is severely overcrowded or highly complex, thereby hampering the determination of enantiomeric excess. In order to circumvent such problems we propose the utility of pure shift spectrum obtained by resolving the chemical shift and coupling information in two orthogonal dimensions. The skew projected spectrum yields singlet's at the respective chemical shift positions, permitting the unravelling of the superimposed spectral transitions for each enantiomer and measurement of enantiomeric composition. (C) 2012 Elsevier B. V. All rights reserved.

Fast Discrimination of Commerical Corn Varieties Based on Near Infrared Spectra

The existing methods for the discrimination of varieties of commodity corn seed are unable to process batch data and speed up identification, and very time consuming and costly. The present paper developed a new approach to the fast discrimination of varieties of commodity corn by means of near infrared spectral data. Firstly, the experiment obtained spectral data of 37 varieties of commodity corn seed with the Fourier transform near infrared spectrometer in the wavenurnber range from 4 000 to 12 000 cm (1). Secondly, the original data were pretreated using statistics method of normalization in order to eliminate noise and improve the efficiency of models. Thirdly, a new way based on sample standard deviation was used to select the characteristic spectral regions, and it can search very different wavenumbers among all wavenumbers and reduce the amount of data in part. Fourthly, principal component analysis (PCA) was used to compress spectral data into several variables, and the cumulate reliabilities of the first ten components were more than 99.98%. Finally, according to the first ten components, recognition models were established based on BPR. For every 25 samples in each variety, 15 samples were randomly selected as the training set. The remaining 10 samples of the same variety were used as the first testing set, and all the 900 samples of the other varieties were used as the second testing set. Calculation results showed that the average correctness recognition rate of the 37 varieties of corn seed was 94.3%. Testing results indicate that the discrimination method had higher precision than the discrimination of various kinds of commodity corn seed. In short, it is feasible to discriminate various varieties of commodity corn seed based on near infrared spectroscopy and BPR.