Design of a configurable multispectral imaging system based on an AOTF.

In this paper, we present a configurable multispectral imaging system based on an acousto-optic tunable filter (AOTF). Typically, AOTFs are used to filter a single wavelength at a time, but thanks to the use of a versatile sweeping frequency generator implemented with a direct digital synthesizer, the imager may capture a configurable spectral range. Experimental results show a good spectral and imaging response of the system for spectral bandwidth up to a 50 nm.

Multi-Temporal Image Classification with Kernels

Non-linear RLS-based algorithm for pattern classification

A new non-linear recursive least squares (RLS) algorithm is presented in the context of pattern classification problems. The algorithm incorporates the non-linearity of the filter's output in the updating rules of the classical RLS algorithm. The proposed method yields lower stationary error levels when compared to the standard LMS and RLS algorithms in a classical application of pattern classification, such as the channel equalization problem.

Foetal ECG recovery using dynamic neural networks

Non-invasive electrocardiography has proven to be a very interesting method for obtaining information about the foetus state and thus to assure its well-being during pregnancy. One of the main applications in this field is foetal electrocardiogram (ECG) recovery by means of automatic methods. Evident problems found in the literature are the limited number of available registers, the lack of performance indicators, and the limited use of non-linear adaptive methods. In order to circumvent these problems, we first introduce the generation of synthetic registers and discuss the influence of different kinds of noise to the modelling. Second, a method which is based on numerical (correlation coe…

Analysis of acousto-optic tunable filter performance for imaging applications

Acousto-optic tunable filters (AOTFs) can be used as spec- tral filters in multispectral imaging applications. Acousto-optic crystals diffract a single wavelength from a broadband light beam, depending on the applied radio frequency signal. However, experimental measurements show that the actual performance is far from the expected behavior. We present an experimental characterization of several commercial off-the- shelf AOTFs for the implementation of multispectral imaging instruments. The diffraction performance of three bare crystals is compared, while a fourth AOTF crystal is mounted on the optical path of a multispectral im- ager to evaluate its performance. The experiments show that t…

400– to 1000–nm imaging spectrometer based on acousto-optic tunable filters

An imaging spectrometer covering the 400-1000 nm band has been conceived and developed. The system is based on an Acousto-Optic Tunable Filter (AOTF) attached to a high performance digital camera. The AOTF permits the selection of spectral bands with an RF signal in the range of 70-210 MHz. The range is covered using two transducers attached to a single crystal. Although the idea is not new it covers a broader spectrum than previous systems. It includes a telecentric optical system that enhances system efficiency, by ensuring that the chief ray of each light cone emerges out of this doublet parallel to the optical axes. Additionally, an smart choice of integration time reduces the dependenc…

Steady-state and tracking analysis of a robust adaptive filter with low computational cost

This paper analyses a new adaptive algorithm that is robust to impulse noise and has a low computational load [E. Soria, J.D. Martin, A.J. Serrano, J. Calpe, and J. Chambers, A new robust adaptive algorithm with low computacional cost, Electron. Lett. 42 (1) (2006) 60-62]. The algorithm is based on two premises: the use of the cost function often used in independent component analysis and a fuzzy modelling of the hyperbolic tangent function. The steady-state error and tracking capability of the algorithm are analysed using conservation methods [A. Sayed, Fundamentals of Adaptive Filtering, Wiley, New York, 2003], thus verifying the correspondence between theory and experimental results.

Semi-Supervised Remote Sensing Image Classification based on Clustering and the Mean Map Kernel

This paper presents a semi-supervised classifier based on the combination of the expectation-maximization (EM) algorithm for Gaussian mixture models (GMM) and the mean map kernel. The proposed method uses the most reliable samples in terms of maximum likelihood to compute a kernel function that accurately reflects the similarity between clusters in the kernel space. The proposed method improves classification accuracy in situations where the available labeled information does not properly describe the classes in the test image.

Regularized RBF Networks for Hyperspectral Data Classification

In this paper, we analyze several regularized types of Radial Basis Function (RBF) Networks for crop classification using hyperspectral images. We compare the regularized RBF neural network with Support Vector Machines (SVM) using the RBF kernel, and AdaBoost Regularized (ABR) algorithm using RBF bases, in terms of accuracy and robustness. Several scenarios of increasing input space dimensionality are tested for six images containing six crop classes. Also, regularization, sparseness, and knowledge extraction are paid attention.

Postgraduate level analog microelectronics course based on cadence IC software and AMS 0.35 µm technology

An analog microelectronics design course at the post-graduate level is described in this paper. In 30 h, students with different engineering background receive a basic but solid formation by implementing a complete microelectronics design flow, following current industry standards. The course deals with fundamental design blocks as well as specific computer aided design tools. This course can be taught, as it is, as a fundamental module in general electronics/communications masters, but it can be also tuned to match specific curriculum constraints, if required. It has been successfully taught for two years, with minor adjustments, within the Telecommunications Engineering Master, at the Un…

Mean Kernels for Semi-Supervised Remote Sensing Image Classification

Support Vector Machines for Crop Classification Using Hyperspectral Data

In this communication, we propose the use of Support Vector Machines (SVM) for crop classification using hyperspectral images. SVM are benchmarked to well–known neural networks such as multilayer perceptrons (MLP), Radial Basis Functions (RBF) and Co-Active Neural Fuzzy Inference Systems (CANFIS). Models are analyzed in terms of efficiency and robustness, which is tested according to their suitability to real–time working conditions whenever a preprocessing stage is not possible. This can be simulated by considering models with and without a preprocessing stage. Four scenarios (128, 6, 3 and 2 bands) are thus evaluated. Several conclusions are drawn: (1) SVM yield better outcomes than neura…

Configurable Passband Imaging Spectrometer Based on Acousto-optic Tunable Filter

This work presents a new configurable imaging spectrometer called Autonomous Tunable Filtering System (ATFS). The system can be configured to acquire a single narrow spectral band, a composite multispectral image, or a broad pass-band. This flexibility is given by the use of an Acousto-Optic Tunable Filter (AOTF) driven by a programmable radio frequency (rf) signal generator. The AOTF acts as a light-diffraction element which output wavelength is selected by the frequency of an rf signal applied to it. The designed rf driver is based on a high-speed Digital-to-Analog converter, which can synthesize any composite rf waveform formed by a combination of sine signals. The images are formed thro…

Gender and anthropometrics of patients undergoing cisplatin-containing chemotherapy as determinants of acute emesis over repeat courses

Objective. To characterize the anthropometric and pharmacotherapeutic variables related to acute emesis over repeat courses of cisplatin-containing regimens (≥50 mg/m survival analysis.2) by means of survival analysis. Methods. A prospective, cross-sectional non-controlled study was started to analyse acute vomiting in patients treated in a general hospital. The patients received an intravenous combination of drugs based on metoclopramide (two 3 mg/kg doses), diphenhydramine (20 mg) and dexamethasone (20 mg) as first choice antiemetic treatment. An intravenous regimen based on ondansetron (8 mg) and dexamethasone (20 mg) was given as an alternative regimen. Therapeutic failure was defined …

Biophysical parameter estimation with adaptive Gaussian Processes

We evaluate Gaussian Processes (GPs) for the estimation of biophysical parameters from acquired multispectral data. The standard GP formulation is used, and all hyperparameters (kernel parameters and noise variance) are optimized by maximizing the marginal likelihood. This gives rise to a fully-adaptive GP to data characteristics, both in terms of signal and noise properties. The good numerical results in the estimation of oceanic chlorophyll concentration and leaf membrane state confirm GPs as adequate, alternative non-parametric methods for biophysical parameter estimation. GPs are also analyzed by scrutinizing the predictive variance, the estimated noise variance, and the relevance of ea…

Configurable bandwidth imaging spectrometer based on acousto-optic tunable filter

This paper presents a new portable instrument called Autonomous Tunable Filtering System (ATFS), developed for highly customisable imaging spectrometry in the VIS-NIR range. The ATFS instrument consists of an Acousto-Optic Tunable Filter (AOTF), an optical system, a Radio Frequency (RF) driver based on a Direct Digital Synthesiser (DDS) and control software. The ATFS can be attached to a variety of high-performance monochrome cameras. The system works as a spectral bandpass filter whose wavelength can be selected between 400nm and 1000nm and whose bandwidth can be adjusted between 4nm and 50nm. The filter can be tuned electronically at a very high speed and accuracy, thanks to the DDS versa…

Cloud-screening algorithm for ENVISAT/MERIS multispectral images

This paper presents a methodology for cloud screening of multispectral images acquired with the Medium Resolution Imaging Spectrometer (MERIS) instrument on-board the Environmental Satellite (ENVISAT). The method yields both a discrete cloud mask and a cloud-abundance product from MERIS level-lb data on a per-pixel basis. The cloud-screening method relies on the extraction of meaningful physical features (e.g., brightness and whiteness), which are combined with atmospheric-absorption features at specific MERIS-band locations (oxygen and watervapor absorptions) to increase the cloud-detection accuracy. All these features are inputs to an unsupervised classification algorithm; the cloud-proba…

Retrieval of oceanic chlorophyll concentration with relevance vector machines

Abstract In this communication, we evaluate the performance of the relevance vector machine (RVM) for the estimation of biophysical parameters from remote sensing data. For illustration purposes, we focus on the estimation of chlorophyll-a concentrations from remote sensing reflectance just above the ocean surface. A variety of bio-optical algorithms have been developed to relate measurements of ocean radiance to in situ concentrations of phytoplankton pigments, and ultimately most of these algorithms demonstrate the potential of quantifying chlorophyll-a concentrations accurately from multispectral satellite ocean color data. Both satellite-derived data and in situ measurements are subject…

Efficient pruning of multilayer perceptrons using a fuzzy sigmoid activation function

This Letter presents a simple and powerful pruning method for multilayer feed forward neural networks based on the fuzzy sigmoid activation function presented in [E. Soria, J. Martin, G. Camps, A. Serrano, J. Calpe, L. Gomez, A low-complexity fuzzy activation function for artificial neural networks, IEEE Trans. Neural Networks 14(6) (2003) 1576-1579]. Successful performance is obtained in standard function approximation and channel equalization problems. Pruning allows to reduce network complexity considerably, achieving a similar performance to that obtained by unpruned networks.

Improving the performance of acousto-optic tunable filters in imaging applications

Acousto-optic tunable filters (AOTFs) can be used as spectral filters for the implementation of multispectral imaging systems. However, obtaining quality images is challenging. In this work, we propose several improvements that enable the use of these systems in quantitative spectroscopic imaging applications. The improvements are based on three pillars: 1. a finer spectral bandpass shaping by dynamically optimizing the radio frequency (rf) driving signal, 2. an extensive calibration process, and 3. careful image preprocessing that uses calibration data to correct some well known AOTF issues in imaging applications. A novel multispectral imaging instrument is built using commercial off-the-…

Semi-Supervised Support Vector Biophysical Parameter Estimation

Two kernel-based methods for semi-supervised regression are presented. The methods rely on building a graph or hypergraph Laplacian with both the labeled and unlabeled data, which is further used to deform the training kernel matrix. The deformed kernel is then used for support vector regression (SVR). The semi-supervised SVR methods are sucessfully tested in LAI estimation and ocean chlorophyll concentration prediction from remotely sensed images.