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.
Duration of euthymia and predominant polarity in bipolar disorder.
The concept of Predominant Polarity (PP) provides relevant information for clinical practice and has been widely described as course specifier for Bipolar Disorder (BD), however it has not been incorporated in DSM-5 yet. A descriptive study was conducted to identify clinical patterns associated with PP in outpatients attending a Mental Health Unit.Clinical and socio-demographic characteristics were assessed from a sample of 118 euthymic outpatients fulfilling DSM 5 criteria for BDI or II recruited at a catchment area. According to their PP, patients were divided into three subgroups: depressive (DPP; 39.0%), manic (MPP; 32.2%) or indeterminate (IPP; 28.8%). Subgroups of PP were compared reg…
Grip Strength, Neurocognition, and Social Functioning in People WithType-2 Diabetes Mellitus, Major Depressive Disorder, Bipolar Disorder, and Schizophrenia
[Background] Frailty is a common syndrome among older adults and patients with several comorbidities. Grip strength (GS) is a representative parameter of frailty because it is a valid indicator of current and long-term physical conditions in the general population and patients with severe mental illnesses (SMIs). Physical and cognitive capacities of people with SMIs are usually impaired; however, their relationship with frailty or social functioning have not been studied to date. The current study aimed to determine if GS is a valid predictor of changes in cognitive performance and social functioning in patients with type-2 diabetes mellitus and SMIs.
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.
Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation
This work presents the application of machine learning techniques to analyse the influence of physical exercise in the physiological properties of the heart, during ventricular fibrillation. To this end, different kinds of classifiers (linear and neural models) are used to classify between trained and sedentary rabbit hearts. The use of those classifiers in combination with a wrapper feature selection algorithm allows to extract knowledge about the most relevant features in the problem. The obtained results show that neural models outperform linear classifiers (better performance indices and a better dimensionality reduction). The most relevant features to describe the benefits of physical …
Diurnal Cycle Relationships between Passive Fluorescence, PRI and NPQ of Vegetation in a Controlled Stress Experiment
In order to estimate vegetation photosynthesis from remote sensing observations; some critical parameters need to be quantified. From all absorbed light; the plant needs to release any excess that is not used for photosynthesis; by non-photochemical quenching; by fluorescence emission and unregulated thermal dissipation. Non-photochemical quenching (NPQ) processes are controlled photoprotective mechanisms which; once activated; strongly control the dynamics of photochemical efficiency. With illumination conditions increasing and decreasing during a diurnal cycle; photoprotection mechanisms needs to change accordingly. The goal of this work is to quantify dynamic NPQ; measured from active fl…
Is processing speed a valid neurocognitive endophenotype in bipolar disorder? Evidence from a longitudinal, family study.
[Background] Substantial evidence supports the existence of neurocognitive endophenotypes in bipolar disorder (BD), but very few longitudinal studies have included unaffected relatives. In a 5-year, follow-up, family study, we have recently suggested that deficits in manual motor speed and visual memory could be endophenotype candidates for BD. We aimed to explore whether this also applies to processing speed.
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…
Evaluation of remote sensing of vegetation fluorescence by the analysis of diurnal cycles
Chlorophyll fluorescence (ChF) emission is a direct indicator of the photosynthetic activity of vegetation, which is a key parameter of the carbon cycle. This paper analyses chlorophyll fluorescence evolution at leaf level during a complete diurnal cycle in simulated and natural conditions, for two species under different stress conditions. Absolute spectral radiance of the ChF emission is obtained allowing a quantitative derivation of the fluorescence yield of the ChF, which correlates well with established fluorescence instruments. The studied cases show that the ChF emission is mainly driven by the photosynthetic active radiation during the whole cycle, but the fluorescence yield is seve…
LABCENTER. A remote laboratory system platform
Abstract A web system server especially suited for remote laboratories has been developed. Typical e-learning systems do not offer the possibility to perform a remote laboratory where real experiments can be done online, accessing real hardware located at the University facilities. Allowing students to connect to hardware systems remotely provides them with additional knowledge about real devices; very often, real laboratory devices are time or space restricted. The proposed LABCENTER platform is a general frame designed for remote laboratories connection. The platform is designed to allow an authorized student to connect to hardware systems. As direct hardware systems allow only a single u…
Analysis of computer user behavior, security incidents and fraud using Self-Organizing Maps
Abstract This paper addresses several topics of great interest in computer security in recent years: computer users’ behavior, security incidents and fraud exposure on the Internet, due to their high economic and social cost. Traditional research has been based mainly on gathering information about security incidents and fraud through surveys. The novelty of the present study is given by the use of Self-Organizing Maps (SOMs), a visual data mining technique. SOMs are applied to two data sets acquired using two different methodologies for collecting data about computer security. First, a traditional online survey about fraud exposure, security and user behavior was used. Second, in addition …
Sensitivity analysis of the fraunhofer line discrimination method for the measurement of chlorophyll fluorescence using a field spectroradiometer
The Fraunhofer Line Discrimination (FLD) principle is established as a good method for remote sensing of solar induced chlorophyll fluorescence. Some improvements to the method are analysed in order to determine and reduce the sources of error in the estimation of the fluorescence emission. A sensitivity analysis has been performed over simulated data generated from real diurnal cycle measurements.
Manual motor speed dysfunction as a neurocognitive endophenotype in euthymic bipolar disorder patients and their healthy relatives. Evidence from a 5-year follow-up study
Background: Few studies have examined Manual Motor Speed (MMS) in bipolar disorder (BD). The aim of this longitudinal, family study was to explore whether dysfunctional MMS represents a neurocognitive endophenotype of BD. Methods: A sample of 291 subjects, including 131 BD patients, 77 healthy first-degree relatives (BD-Rel), and 83 genetically-unrelated healthy controls (HC), was assessed with the Finger-Tapping Test (En) on three occasions over a 5-year period. Dependence of FTT on participants' age was removed by means of a lineal model of HC samples, while correcting simultaneously the time and learning effect. Differences between groups were evaluated with an ANOVA test. Results: The p…
Expert system for predicting unstable angina based on Bayesian networks
The use of computer-based clinical decision support (CDS) tools is growing significantly in recent years. These tools help reduce waiting lists, minimise patient risks and, at the same time, optimise the cost health resources. In this paper, we present a CDS application that predicts the probability of having unstable angina based on clinical data. Due to the characteristics of the variables (mostly binary) a Bayesian network model was chosen to support the system. Bayesian-network model was constructed using a population of 1164 patients, and subsequently was validated with a population of 103 patients. The validation results, with a negative predictive value (NPV) of 91%, demonstrate its …
Adaptive treatment of anemia on hemodialysis patients: A reinforcement learning approach
The aim of this work is to study the applicability of reinforcement learning methods to design adaptive treatment strategies that optimize, in the long-term, the dosage of erythropoiesis-stimulating agents (ESAs) in the management of anemia in patients undergoing hemodialysis. Adaptive treatment strategies are recently emerging as a new paradigm for the treatment and long-term management of the chronic disease. Reinforcement Learning (RL) can be useful to extract such strategies from clinical data, taking into account delayed effects and without requiring any mathematical model. In this work, we focus on the so-called Fitted Q Iteration algorithm, a RL approach that deals with the data very…
Discrimination of common defects in loquat fruit cv. ‘Algerie’ using hyperspectral imaging and machine learning techniques
Abstract Loquat (Eriobotrya japonica L.) is an important fruit for the economy of some regions of Spain that is very susceptible to mechanical damage and physiological disorders. These problems depreciate its value and prevent it from being exported. Visible (VIS) and near infrared (NIR) hyperspectral imaging was used to discriminate between external and internal common defects of loquat cv. ‘Algerie’. Two classifiers, random forest (RF) and extreme gradient boost (XGBoost), and different spectral pre-processing techniques were evaluated in terms of their capacity to distinguish between sound and defective features according to three approaches. In the first approach the fruit pixels were c…
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…
Somatometric and clinical cardiovascular risk factors in midlife and older women. A tale of four European countries
Study of the diurnal cycle of stressed vegetation for the improvement of fluorescence remote sensing
Chlorophyll fluorescence (Chf) emission allows estimating the photosynthetic activity of vegetation - a key parameter for the carbon cycle models - in a quite direct way. However, measuring Chf is difficult because it represents a small fraction of the radiance to be measured by the sensor. This paper analyzes the relationship between the solar induced Chf emission and the photosynthetically active radiation (PAR) in plants under water stress condition. The solar induced fluorescence emission is measured at leaf level by means of three different methodologies. Firstly, an active modulated light fluorometer gives the relative fluorescence yield. Secondly, a quantitative measurement of the Ch…
Neural networks for analysing the relevance of input variables in the prediction of tropospheric ozone concentration
Abstract This paper deals with tropospheric ozone modelling by using Artificial Neural Networks (ANNs). In this study, ambient ozone concentrations are estimated using surface meteorological variables and vehicle emission variables as predictors. The work is especially focused on analysing the importance of the input variables used by these models. This analysis is carried out in different time windows: all the time of study (April of 1997, 1999 and 2000), one month (April 1999), and finally, an hourly analysis. All the information extracted from these analyses can determine the most important factors in tropospheric ozone formation, thus achieving a qualitative model from the quantitative …
Remote sensing of chlorophyll fluorescence for estimation of stress in vegetation. Recommendations for future missions
Vegetation monitoring is a key issue in Earth Observation due to its relation with the global CO2 cycle. Chlorophyll fluorescence (ChF) emitted by the vegetation is an accurate indicator of the plant status and their photosynthetic activity. This work analyses the diurnal evolution of the ChF emission spectrum and the fluorescence yield in order to determine the best conditions for remote sensing of ChF from a satellite platform. The ChF evolution is studied at leaf level during several diurnal cycles, in simulated conditions, for two species under different stress conditions. The analysis of the signal levels gives an estimation of the values of ChF emission which could be observed from a …
BELM: Bayesian Extreme Learning Machine
The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap…
Optimization of anemia treatment in hemodialysis patients via reinforcement learning
Objective: Anemia is a frequent comorbidity in hemodialysis patients that can be successfully treated by administering erythropoiesis-stimulating agents (ESAs). ESAs dosing is currently based on clinical protocols that often do not account for the high inter- and intra-individual variability in the patient's response. As a result, the hemoglobin level of some patients oscillates around the target range, which is associated with multiple risks and side-effects. This work proposes a methodology based on reinforcement learning (RL) to optimize ESA therapy. Methods: RL is a data-driven approach for solving sequential decision-making problems that are formulated as Markov decision processes (MDP…
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…
Hardware implementation of a robust adaptive filter: Two approaches based in High-Level Synthesis design tools
Abstract Adaptive filters are used in a wide range of applications. Impulsive noise affects the proper operation of the filter and the adaptation process. This noise is one of the most damaging types of signal distortion, not always considered when implementing algorithms. Field Programmable Gate Array (FPGA) are widely used for applications where timing requirements are strict. Nowadays, two main design processes can be followed, namely, Hardware Description Language (HDL) and a High Level Synthesis (HLS) design tool for embedded system design. This paper describes the FPGA implementation of an adaptive filter robust to impulsive noise using two approaches based in HLS and the implementati…
Online fitted policy iteration based on extreme learning machines
Reinforcement learning (RL) is a learning paradigm that can be useful in a wide variety of real-world applications. However, its applicability to complex problems remains problematic due to different causes. Particularly important among these are the high quantity of data required by the agent to learn useful policies and the poor scalability to high-dimensional problems due to the use of local approximators. This paper presents a novel RL algorithm, called online fitted policy iteration (OFPI), that steps forward in both directions. OFPI is based on a semi-batch scheme that increases the convergence speed by reusing data and enables the use of global approximators by reformulating the valu…
Transdiagnostic neurocognitive deficits in patients with type 2 diabetes mellitus, major depressive disorder, bipolar disorder, and schizophrenia: A 1-year follow-up study
AbstractBackgroundImpairments in neurocognition are critical factors in patients with major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ), and also in those with somatic diseases such as type 2 diabetes mellitus (T2DM). Intriguingly, these severe mental illnesses are associated with an increased co-occurrence of diabetes (direct comorbidity). This study sought to investigate the neurocognition and social functioning across T2DM, MDD, BD, and SZ using a transdiagnostic and longitudinal approach.MethodsA total of 165 subjects, including 30 with SZ, 42 with BD, 35 with MDD, 30 with T2DM, and 28 healthy controls (HC), were assessed twice at a 1-year interval using a c…
Visual memory dysfunction as a neurocognitive endophenotype in bipolar disorder patients and their unaffected relatives. Evidence from a 5-year follow-up Valencia study.
BACKGROUND: Scarce research has focused on Visual Memory (VM) deficits as a possible neurocognitive endophenotype of bipolar disorder (BD). The main aim of this longitudinal, family study with healthy controls was to explore whether VM dysfunction represents a neurocognitive endophenotype of BD. METHODS: Assessment of VM by Rey-Osterrieth Complex Figure Test (ROCF) was carried out on a sample of 317 subjects, including 140 patients with BD, 60 unaffected first-degree relatives (BD-Rel), and 117 genetically-unrelated healthy controls (HC), on three occasions over a 5-year period (T1, T2, and T3). BD-Rel group scores were analyzed only at T1 and T2. RESULTS: Performance of BD patients was sig…
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-…
Cognitive impairment and consumption of mental healthcare resources in outpatients with bipolar disorder.
Cognitive dysfunction is a major predictor of functional outcomes, and loss of occupational functioning is usually linked with a higher cost of illness. However, the association between cognitive impairment and consumption of health resources has not been studied in bipolar disorder to date. This study aims to examine this relationship. This is an observational, retrospective study of a representative sample of euthymic outpatients between 18 and 55 years, fulfilling DSM 5 criteria for bipolar disorder and recruited at a catchment area in Spain. Cognitive performance was screened with the Spanish version of the Screen for Cognitive Impairment in Psychiatry (SCIP-S), and several variables of…