Sensor Array Imaging Research Articles

Monitoring the biogenic amines in Chinese traditional salted pork in jelly (Yao‐meat) by colorimetric sensor array based on nine natural pigments

SummaryTo develop simple, cheap and convenient sensors for Yao‐meat freshness evaluation and biogenic amines (BAs) detection, a colorimetric sensor array was made by printing nine natural pigments on a hydrophobic nanoporous film. A colour change profile for each sample was obtained by differentiating the image of sensor array before and after exposure to volatile organic compounds (VOCs). The sensor array was exposed to atmospheres containing BAs vapours with concentrations over the range 10–30 ppm. Rosa chinensis extract possessed the highest sensitivity thanked to the highest anthocyanin contents. The results of principal component analysis (PCA) showed that the measurements corresponded to three freshness levels were separated clearly. The partial least‐squares prediction model was obtained with the correlation coefficients of 0.921 and 0.907 in calibration and validation sets, respectively. This research suggested that the anthocyanins content pigments maybe a very useful colorimetric sensor for quality evaluation of meat.

Sensing the quality parameters of Chinese traditional Yao-meat by using a colorimetric sensor combined with genetic algorithm partial least squares regression

Yao-meat is a traditional Chinese salted meat. Total volatile basic nitrogen content (TVB-N), total viable bacterial count (TVC), and residual nitrite (RN) level are important indexes of freshness for Yao-meat. This paper attempted the feasibility to determine TVB-N content, TVC and RN level in Yao-meat by a colorimetric sensor array chip. A color change profile for each sample was obtained by differentiating the image of sensor array before and after exposure to Yao-meat's volatile organic compounds. Genetic algorithm partial least squares regression (GA-PLS) was proposed to build the relationship between the TVB-N content, TVC, RN and the color change profiles of sensor array, and to select informative chemically responsive dyes for the three quality parameters. The GA-PLS models were achieved with RTVB-N=0.812, RTVC=0.856, RRN=0.855, in prediction set. This study demonstrated that colorimetric sensory array with GA-PLS algorithm could be used successfully to analyze the quality of Chinese traditional Yao-meat.

High frequency analysis of imaging with noise blending

We consider sensor array imaging for simultaneous noise blended sources. We study a migration imaging functional and we analyze its sensitivity to singular perturbations of the speed of propagation of the medium. We consider two kinds of random sources: randomly delayed pulses and stationary random processes, and three possible kinds of perturbations. Using high frequency analysis we prove the statistical stability (with respect to the realization of the noise blending) of the scheme and obtain quantitative results on the image contrast provided by the imaging functional, which strongly depends on the type of perturbations.

Classification of tea category using a portable electronic nose based on an odor imaging sensor array

A developed portable electronic nose (E-nose) based on an odor imaging sensor array was successfully used for classification of three different fermentation degrees of tea (i.e., green tea, black tea, and Oolong tea). The odor imaging sensor array was fabricated by printing nine dyes, including porphyrin and metalloporphyrins, on the hydrophobic porous membrane. A color change profile for each sample was obtained by differentiating the image of sensor array before and after exposure to tea's volatile organic compounds (VOCs). Multivariate analysis was used for the classification of tea categories, and linear discriminant analysis (LDA) achieved 100% classification rate by leave-one-out cross-validation (LOOCV). This study demonstrates that the E-nose based on odor imaging sensor array has a high potential in the classification of tea category according to different fermentation degrees.

Classification of rice wine according to different marked ages using a novel artificial olfactory technique based on colorimetric sensor array

A novel artificial olfactory technique based on colorimetric sensor array was developed for the classification of Chinese rice wine according to different marked ages. The sensor array was composed of nine porphyrins or metalloporphyrins materials and six pH indicators. When the sensor array was exposed to rice wine for several minutes, a colour change profile for each sample was obtained by differentiating the image of sensor array before and after exposure to the head-gas of the sample. The values of RGB (i.e., red, green, and blue) colour components were extracted from each dye in colour change profiles, and they were analysed using principal component analysis (PCA) and linear discriminant analysis (LDA). In contrast to PCA, LDA obtained an optimum classification result. This research shows that the artificial olfactory technique based on colorimetric sensor array has a powerful potential in the quality evaluation of rice wine.

A statistical approach to target detection and localization in the presence of noise

The problem addressed in this paper is the combined detection and localization of a point reflector embedded in a medium by sensor array imaging when the array response matrix is obtained in a noisy environment. We consider additive measurement noise or a clutter noise in the multiple scattering regime. We study a detection test based on reverse-time migration of the array response matrix that is the most powerful for a given false alarm rate and compare it with a test based on the singular values of the response matrix. Moreover, we show that reflector localization should be performed with reverse-time migration rather than any other form of weighted-subspace migration and we give the standard deviation of the localization error.

Filtering Deterministic Layer Effects in Imaging

Sensor array imaging arises in applications such as nondestructive evaluation of materials with ultrasonic waves, seismic exploration, and radar. The sensors probe a medium with signals and record the resulting echoes, which are then processed to determine the location and reflectivity of remote reflectors. These could be defects in materials such as voids, fault lines or salt bodies in the earth, and cars, buildings, or aircraft in radar applica- tions. Imaging is relatively well understood when the medium through which the signals propagate is smooth, and therefore nonscattering. But in many problems the medium is heterogeneous, with numerous small inhomogeneities that scatter the waves. We refer to the collection of inhomogeneities as clutter, which introduces an uncertainty in imaging because it is unknown and impossible to estimate in detail. We model the clutter as a random process. The array data is measured in one realization of the random medium, and the challenge is to mitigate cumulative clutter scattering so as to obtain robust images that are statistically stable with respect to different realizations of the inhomogeneities. Scatterers that are not buried too deep in clutter can be imaged reliably with the coher- ent interferometric (CINT) approach. But in heavy clutter the signal-to-noise ratio (SNR) is low and CINT alone does not work. The signal, the echoes from the scatterers to be imaged, is overwhelmed by the noise, the strong clutter reverberations. There are two existing approaches for imaging at low SNR: The first operates under the premise that data are incoherent so that only the intensity of the scattered field can be used. The unknown coherent scatterers that we want to image are modeled as changes in the coefficients of diffusion or radiative transport equations satisfied by the intensities, and the problem be- comes one of parameter estimation. Because the estimation is severely ill-posed, the results have poor resolution, unless very good prior information is available and large arrays are used. The second approach recognizes that if there is some residual coherence in the data, that is, some reliable phase information is available, it is worth trying to extract it and use it with well-posed coherent imaging methods to obtain images with better resolution. This paper takes the latter approach and presents a first attempt at enhancing the SNR of the array data by suppressing medium reverberations. It introduces filters, or annihila-

Minimization of sensing elements for full-range optical pH device formulation

The goal of this work is to find the minimum number of sensing elements that can be used to build a sensor array suitable for pH prediction for a full-range (0–14) optical pH sensor array based on hue (H) data from the HSV color space. The hue of each element coming from the pH sensor array imaging is used as input data for a neural network that provides the pH prediction approximation as the output response. This problem may be considered a multicriteria optimization task with the dual objectives of error minimization between the network pH prediction and the reference pH in the calibration data, and of minimizing the network complexity and the number of network inputs. To that end, this work proposes a multi-objective optimization method applied to a collection of 11 sensing elements that returns a set of optimal networks considering the Pareto optimality criterion. A solution from the Pareto front was selected to achieve a minimum pH prediction error with the minimum number of sensing elements. After this analysis, it can be concluded that the use of a sensor array made up of 4 sensing elements offers a good pH prediction (Mean Square Error of 0.052) over the full-range working with 4 hidden neurons.

Filtering Random Layering Effects in Imaging

Objects that are buried deep in heterogeneous media produce faint echoes which are difficult to distinguish from the backscattered field. Sensor array imaging in such media cannot work unless we filter out the backscattered echoes and enhance the coherent arrivals that carry information about the objects that we wish to image. We study such filters for imaging in strongly backscattering, finely layered media. The filters are based on a travel time transformation of the array data, the normal move-out, used frequently in connection with differential semblance velocity estimation in seismic imaging. In a previous paper [L. Borcea et al., Multiscale Model. Simul., 7 (2009), pp. 1267–1301] we showed that the filters can be used to remove coherent signals from strong plane reflectors. In this paper we show theoretically and with extensive numerical simulations that these filters, based on the normal move-out, can also remove the incoherent arrivals in the array data that are due to fine random layering in the ...

Studies on object recognition from degraded images using neural networks

The objective of this paper is to study the performance of artificial neural network models for recognition of objects from poorly resolved, noisy, and transformed (scaled, rotated, translated) images, such as images reconstructed from sparse and noisy data in a sensor array imaging context. Noise and sparsity of data in the imaging context result in degradation of quality of the reconstructed image as a whole, instead of affecting it in the form of local corruption of the image pixel information as in many image processing situations. Hence, (i) neighbourhood processing methods for noise cleaning may not be suitable, (ii) feature extraction cannot be reliably performed, and (iii) model-based methods for classification cannot easily be applied. In this paper, we show that neural network models can be used to overcome some of the difficulties in dealing with degraded images as obtained in an imaging context.

Image reconstruction from multiple frames of sparse data

High-resolution image reconstruction from sparse data is an important problem in sensor array imaging (SAI). The reconstructed images from such a data are poorly resolved. However, there may exist possibilities of making multiple measurements, for example, collecting many frames of data in a dynamic scene situation where there is relative motion between the object and the receiver. We discuss here a method of reconstructing good-quality images from multiple frames of sparse data obtained from a simulated dynamic scene situation. This method, based on projection onto convex sets (POCS), not only restricts the solution set by satisfying the constraints in the multiple measurements but also reconstructs a high-resolution image.

Signal reconstruction from partial data for sensor array imaging applications

In this paper, the problem of signal recovery from partial information with special reference to sensor array imaging situations is examined. In sensor array imaging, as used for viewing underwater objects, the complex wave field is measured at a discrete set of points on a receiver airier. This data is used to reconstruct an image of the object using a suitable transformation. This data can be viewed as partial magnitude and phase data, which after appropriate phase modification, is transformed to obtain the object information. Recently, algorithms have been proposed in the literature for reconstruction of signals from partial information such as magnitude or phase (1-bit phase in some cases) of the Fourier transform. We examine the relevance of these algorithms to sensor array imaging situations. We propose new algorithms that are applicable to these situations. In particular, we show that measurement at multiple frequencies with suitable phase quantization reduces the receiver complexity and sometimes the effects of noise. The proposed new algorithms are demonstrated with several illustrations of simulated field data for a simplified model of an imaging setup.

Image-plane processing of visual information

Shannon's theory of information is used to optimize the optical design of sensor-array imaging systems which use neighborhood image-plane signal processing for enhancing edges and compressing dynamic range during image formation. The resultant edge-enhancement, or band-pass-filter, response is found to be very similar to that of human vision. Comparisons of traits in human vision with results from information theory suggest that: (1) Image-plane processing, like preprocessing in human vision, can improve visual information acquisition for pattern recognition when resolving power, sensitivity, and dynamic range are constrained. Improvements include reduced sensitivity to changes in lighter levels, reduced signal dynamic range, reduced data transmission and processing, and reduced aliasing and photosensor noise degradation. (2) Information content can be an appropriate figure of merit for optimizing the optical design of imaging systems when visual information is acquired for pattern recognition. The design trade-offs involve spatial response, sensitivity, and sampling interval.

Imaging system design for improved information capacity

Shannon's theory of information for communication channels is used to assess the performance of line-scan and sensor-array imaging systems and to optimize the design trade-offs involving sensitivity, spatial response, and sampling intervals. Formulations and computational evaluations account for spatial responses typical of line-scan and sensor-array mechanisms, lens diffraction and transmittance shading, defocus blur, and square and hexagonal sampling lattices.

Application of information theory to the design of line-scan and sensor-array imaging systems

Information theory is used to assess the performance of line-scan and sensor-array imaging systems as a function of their spatial response, sensitivity, and sampling and quantization intervals. Computational results for the statistical properties of random radiance fields provide general guidelines for optimizing the information efficiency of image data acquisition, processing and reconstruction systems.