Coherence Spectrotomography of Layered Medium with White-Light Continuum

An optical binary switch for aircraft applications is demonstrated. A fiber Fabry-Perot interferometer (FFPI) bonded to a cantilever is used as the sensing element. A white-light interferometry system with two bulk Michelson interferometers sharing the same motor-driven translation stage is utilized to monitor the elongation of the FFPI. The system exhibits excellent linearity as a force sensor; the experimental results are in good agreement with theoretical calculated values. With a properly set threshold value, the system produces a binary output.

The temporal behaviour and spectraldistribution of white-light continuum generation in H2O using a focusedintense 800 nm 130 fs Ti:sapphire laser were studied. The resultsindicated that the white-light continuum spectrally ranged from 1000 to400 nm, which was mainly attributed to self-phase modulation related tothe Kerr effect and ionization. The pulse duration of different spectralcomponents of the generated white-light continuum, which was measured withthe frequency up-conversion technique, was obtained around 137 fs. Itrevealed a comparable time resolution as the original laser pulse.

We present experimental results for the cross phase modulation (xpm) induced transient absorption signal in a 1 mm thick fused silica plate using a white light continuum as a probe. The fused silica plate mimics the entrance window of a commercial flow cell commonly used in liquid-phase transient absorption measurements. The experimental results are compared with those obtained theoretically by numerically solving the set of nonlinear coupled wave equations describing the propagation of the pump and the probe. The simulations allow for the different group velocities of the pump and probe pulses, and include the influence of the first and second order dispersion on the continuum probe. From the calculations the physical origin of the complex oscillatory feature observed around the zero time delay of each wavelength of the (chirped) continuum has been accurately identified. The influence of propagation effects arising from the finite thickness of the sample is discussed in great detail, and the necessity to work with thin samples, preferably free-flowing jets, is emphasized. The good agreement between theory and experiment indicates that the xpm artifact may be useful for characterizing the continuum probe, in particular its chirp.

This paper introduces a transmission line model analysis for dielectric-loaded self-resonant structures (SRSs). Equivalent circuit models, besides their fast computational time, usually offer some insights into the underlying physics of the electromagnetic structures, hence simplifying and improving the design and optimization criteria of these structures. A systematic approach for deriving impedance expressions of SRS on and within multi-layer dielectric media is presented. The process of extracting equivalent circuit parameters from impedance expressions is explained. The trends of equivalent circuit parameters are studied with different dielectric medium properties and various angles of incidence for an SRS with multiple resonant states highlighting the effect of changing the physical and electrical dimensions on the equivalent circuit parameters. The transmission line model responses are compared to fullwave simulation results, and model accuracy is assessed using the mean absolute error criteria. Furthermore, a new simplified method for calculating the forward transmission coefficient of an SRS within multi-layer media is proposed. This expression offers fundamental insights into the factors contributing to the S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sub> response. Finally, experimental measurements for SRS embedded in a multi-layer dielectric media inside a waveguide is conducted and compared to the equivalent model with very good agreement.

A novel multiplexing scheme based on a low-coherence reflectometry (LCR) is proposed for a sensing array (in parallel) of self-interfering long-period fiber gratings (SI-LPGs). Each SI-LPG sensor consists of an LPG and a section of fiber with a highly reflective end (mirror). The spectral information of each LPG is sensitive to some parameters of the surrounding measurand and can be reconstructed from the corresponding subreflectograms (obtained by the LCR) through a fast Fourier transformation. The sensing signals of multiple SI-LPG sensors can be multiplexed if the length of the fiber section in each sensor is set to a different value. Experiments of measuring the surrounding temperature at different sensors are demonstrated to show the good performance of our multiplexing system.

The analysis of a multitemporal sequence of images of a given site makes it possible to exploit temporal information in addition to spectral and spatial information, and therefore represents a way to improve the accuracy with respect to the non-contextual single-time classification. The proposed contextual multitemporal classification scheme consists of two stages of multilayer perceptron (MLP) neural networks for each single-time image of the multitemporal sequence. The first stage is a one-hidden layer MLP whose ro/spl circ/le is to estimate, for each pixel, the single-time posterior probability of each class, given the feature vector. These probability estimates represent spectral information; in addition, they are utilized to generate a non-contextual classification map. The neighboring class labels of a given pixel in the non-contextual classification map are exploited to extract spatial information, while temporal information is deduced from the non-contextual maps produced by the remaining single-time images in the multitemporal sequence. Spatial and temporal contextual information together with spectral information serve as inputs for the second stage network of the classification scheme where the fusion takes place. As the network configuration can influence the classification performances, three MLP-based configurations are investigated. Experimental results on a multitemporal data set consisting of two multisensor (Landsat TM and ERS-1 SAR) images are presented. The performances of the proposed methods are discussed and compared with those obtained by a reference classifier based on the Markov random fields fusion approach in terms of classification accuracy. The results show that the proposed fusion approach based on neural networks may represent an interesting solution to the problem of multitemporal contextual fusion.

The pansharpening algorithm often faces an imbalance between spatial sharpness and spectral preservation, resulting in spectral and intensity inhomogeneities in the fused image. In this paper, to overcome this problem, we present a robust pansharpening method for multiband images with adaptive spectral–intensity modulation. In this method, we propose an adaptive spectral modulation coefficient (ASMC) and an adaptive intensity modulation coefficient (AIMC) to modulate the spectral and spatial information in the fused image, respectively. Among these coefficients, the ASMC is constructed based on two aspects: first, the details extracted from the panchromatic (PAN) and multispectral (MS) images; and second, the spectral relationship between each MS band. The AIMC is calculated by assessing the correlation and standard deviation between the PAN image and each MS band. Finally, we propose a mathematically linear model to combine ASMC and AIMC to achieve the fused image. Various remote-sensing satellite images were used in the evaluations. Experimental results indicate that the proposed method achieves outstanding performance in balancing spatial and spectral information and outperforms several state-of-the-art fusion methods in terms of both full-reference and no-reference metrics, and on visual inspection.

This paper solves the image fusion problem by using a multi-object optimization strategy and a key energy function. The energy function mainly consists of two components. One ensures injection of more correlated detailed spatial information. The detailed information is extracted from gradient representation of the image to be fused. The other one guarantees that the spectral information is preserved by a data fitting term. By minimizing the proposed energy function, the fusion result can be obtained. Moreover, a key parameter is used in the energy function to adjust the weights of the spectral and spatial information during the image fusion. In this paper, a multi-object optimization is constructed to determine such a key parameter. The image fusion performance is evaluated through visional perception and some fusion indexes. Experimental results further demonstrate advantages of the proposed technique over the conventional fusion techniques.

Low coherence interferometry (LCI) is an optical technique in which light is used as an instrument to obtain high resolution optical images in a great diversity of materials. It is possible to find in the literature, a variety of configurations with different names, based on the same principle: low coherence reflectometry (OLCR), optical coherence tomography (OCT), white light interferometry (WLI), are only some examples of this. The main idea of this technique is to measure the echo time delay of backscattered light in the sample through the characterization of the interference intensity obtained when the light coming from the sample and the light reflected in a reference surface overlap. When a low coherence source is used, the interference signal is temporally and spatially localized, so it is possible to use this property to obtain distance values or parameters related to the time of flight of the light, reflected in different sections of a sample.

Spectral unmixing is an important problem for remotely sensed hyperspectral data exploitation. Automatic spectral unmixing can be viewed as a three-stage problem, where the first stage is subspace identification, the next one is endmember extraction, and the final one is abundance estimation. In this sequence, endmember extraction is the most challenging problem. Many researchers have attempted to extract endmembers from hyperspectral images using spectral information only. However, it is well known that the inclusion of spatial information can improve the endmember extraction task. In this article, we introduce a new endmember extraction algorithm that exploits both spectral and spatial information. A main innovation of the proposed algorithm is that spatial information is exploited using entropy, while spectral information is exploited using convex set optimization. In the literature, none of the spatial–spectral algorithms has used entropy as spatial information. The inclusion of this entropy-based spatial information improves the accuracy of the endmember extraction process. The results obtained by the proposed algorithm are compared (using a variety of metrics) with those obtained by other state-of-the-art methods, using both synthetic and real datasets. Our experimental results demonstrate that the proposed algorithm outperforms many available algorithms.

In order to quickly distinguish infertile eggs from fertile eggs, the hyperspectral imaging technology consisting of imaging and spectral information was used for detecting the fertile information of eggs. Before hatching eggs were incubated, a hyperspectral imaging system (wavelength between 400 to 1 000 nm) was used to acquire the images one-by-one manually. The characteristic information of ratios of length to short axis, elongation, roundness and the ratios of the yolk area to the whole area was extracted based on the images. The normalization method was used as the spectral data preprocessing, and then 155 spectral characteristic variables were extracted from 520 nm waveband through the correlation coefficient method. Principal component analysis (PCA) method was adopted to reduce the dimensions of image-spectrum fusion information; the top six principal components were extracted. Support vector machine (SVM) method was used to establish classification of fertile and infertile eggs models, which are based on image, spectrum and image-spectrum fusion information respectively. The accuracy rates of the SVM models were 84.00%, 90.00% and 93.00% respectively. The experimental results show that the model based on image-spectrum fusion information technology is superior to the single information model. Hyperspectral transmission imaging technology is effective and feasible to detect the fertile hatching eggs before incubation. Keywords: hyperspectral image, hatching eggs, information fusion, nondestructive detection DOI: 10.3965/j.ijabe.20150804.1672 Citation: Zhu Z H, Liu T, Xie D J, Wang Q H, Ma M H. Nondestructive detection of infertile hatching eggs based on spectral and imaging information. Int J Agric & Biol Eng, 2015; 8(4): 69－76.

We present theoretical and experimental results on an interference effect caused by boundary reflections on the optical scattering loss in high-index-contrast planar waveguides. Analytical expressions for the polarization-dependent scattering loss are derived using a surface Green's function. For high-index-contrast waveguides of submicrometer dimensions a significant deviation from accepted theory arises, including scattering loss suppression owing to a thin-film interference effect. Our theoretical predictions are confirmed by loss measurements on silicon-on-insulator channel waveguides.

The optical low-coherent interferometric technology for long-period grating (LPG) Mach-Zehnder interferometers is described. By including the coupling and recoupling behaviors of a LPG pair, a numerical model is developed to analyze the output reflectogram of the system. The effects of the grating interval, grating length, grating strength, and light source on the output reflectogram have been comprehensively discussed, which reveals that the low-coherence reflectometry offers the capability of interrogating the multiplexed sensors based on LPG pairs. A comparison of the calculated and experimental results is presented, and an excellent agreement between the simulation and the measurement is shown.

Regional clustering-based spatial preprocessing for hyperspectral unmixing

Spectro-polarimetric light-scattering, or merging spectral and polarimetric light-scattering information, is a growing interest in a wide range of scientific fields. Recently, it has been demonstrated how spectral polarimetric light-scattering could be employed to probe microphysical properties of particulate media, for instance mean particle size or relative complex refractive index. Fundamental advantages of merging spectral and polarimetric information for light-scattering are to address distinct scattering regimes, probe systems of particles at different scales, and extract useful information about a particulate media of interest. The challenge of simultaneous retrieval of microphysical properties of particulate media, without a priori knowledge or use of simplistic assumptions, is addressed throughout this review.