Amorphous piezoresistive and piezoelectric sensors for robotics applications

Ultrasonic transducers have often been used in the development of sensory systems for robotics applications. In most cases, these sensory systems are based on the determination of times of flight for signals from every transducer. In this work we have used piezoresistive and piezoelectric materials to measure the instant and position collision in metallic structures by using the difference of the times of propagation of an acoustic wave when it is produced over a ferromagnetic (iron, steel or another material) based structure. An immediate application of the proposed method is the detection and location of impacts over the metallic links of an industrial robot or the collision position in a metallic structure for an automated inspection

Piezoelectric sensing and non-parametric statistical signal processing for health monitoring of hysteretic dampers used in seismic-resistant structures

The paper proposes a new application of non-parametric statistical processing of signals recorded from vibration tests for damage detection and evaluation on I-section steel segments. The steel segments investigated constitute the energy dissipating part of a new type of hysteretic damper that is used for passive control of buildings and civil engineering structures subjected to earthquake-type dynamic loadings. Two I-section steel segments with different levels of damage were instrumented with piezoceramic sensors and subjected to controlled white noise random vibrations. The signals recorded during the tests were processed using two non-parametric methods (the power spectral density method and the frequency response function method) that had never previously been applied to hysteretic dampers. The appropriateness of these methods for quantifying the level of damage on the I-shape steel segments is validated experimentally. Based on the results of the random vibrations, the paper proposes a new index that predicts the level of damage and the proximity of failure of the hysteretic damper

Tunable mechanical resonator with aluminum nitride piezoelectric

The electromechanical response of piezoelectrically-actuated AlN micromachined bridge resonators has been characterized using laser interferometry and electrical admittance measurements. We compare the response of microbridges with different dimensions and buckling (induced by the initial residual stress of the layers). The resonance frequencies are in good agreement with numerical simulations of the electromechanical behavior of the structures. We show that it is possible to perform a rough tuning of the resonance frequencies by allowing a determined amount of builtin stress in the microbridge during its fabrication. Once the resonator is made, a DC bias added to the AC excitation signal allows to fine-tune the frequency. Our microbridges yield a tuning factor of around 88 Hz/V for a 500 ?m-long microbridge.

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on i) build reference models from a set of time series, based on the analysis of the events that they contain, is suitable for domains where the relevant information is concentrated in specific regions of the time series, known as events. In order to deal with events, each event is characterized by a set of attributes. ii) Discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time- that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. The performance of the model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals.

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The focus of this chapter is to study feature extraction and pattern classification methods from two medical areas, Stabilometry and Electroencephalography (EEG). Stabilometry is the branch of medicine responsible for examining balance in human beings. Balance and dizziness disorders are probably two of the most common illnesses that physicians have to deal with. In Stabilometry, the key nuggets of information in a time series signal are concentrated within definite time periods are known as events. In this chapter, two feature extraction schemes have been developed to identify and characterise the events in Stabilometry and EEG signals. Based on these extracted features, an Adaptive Fuzzy Inference Neural network has been applied for classification of Stabilometry and EEG signals.

A Stress-Detection System Based on Physiological Signals and Fuzzy Logic

A stress-detection system is proposed based on physiological signals. Concretely, galvanic skin response (GSR) and heart rate (HR) are proposed to provide information on the state of mind of an individual, due to their nonintrusiveness and noninvasiveness. Furthermore, specific psychological experiments were designed to induce properly stress on individuals in order to acquire a database for training, validating, and testing the proposed system. Such system is based on fuzzy logic, and it described the behavior of an individual under stressing stimuli in terms of HR and GSR. The stress-detection accuracy obtained is 99.5% by acquiring HR and GSR during a period of 10 s, and what is more, rates over 90% of success are achieved by decreasing that acquisition period to 3-5 s. Finally, this paper comes up with a proposal that an accurate stress detection only requires two physiological signals, namely, HR and GSR, and the fact that the proposed stress-detection system is suitable for real-time applications.

Quantitative evaluation of artifact removal in real magnetoencephalogram signals with blind source separation

The magnetoencephalogram (MEG) is contaminated with undesired signals, which are called artifacts. Some of the most important ones are the cardiac and the ocular artifacts (CA and OA, respectively), and the power line noise (PLN). Blind source separation (BSS) has been used to reduce the influence of the artifacts in the data. There is a plethora of BSS-based artifact removal approaches, but few comparative analyses. In this study, MEG background activity from 26 subjects was processed with five widespread BSS (AMUSE, SOBI, JADE, extended Infomax, and FastICA) and one constrained BSS (cBSS) techniques. Then, the ability of several combinations of BSS algorithm, epoch length, and artifact detection metric to automatically reduce the CA, OA, and PLN were quantified with objective criteria. The results pinpointed to cBSS as a very suitable approach to remove the CA. Additionally, a combination of AMUSE or SOBI and artifact detection metrics based on entropy or power criteria decreased the OA. Finally, the PLN was reduced by means of a spectral metric. These findings confirm the utility of BSS to help in the artifact removal for MEG background activity.

Modelado de materiales piezoeléctricos para aplicaciones de autoalimentación en entornos viales

Este proyecto continua con el estudio iniciado en los proyectos: Alimentación “Energy Harvesting” basada en fuentes piezoeléctricas, Alimentación autónoma: aplicación a fuente piezoeléctrica y Banco de pruebas para sistemas autoalimentados en aplicaciones viales, que se realizaron anteriormente. Este estudio se basa en la búsqueda de soluciones de alimentación Energy Harvesting centrados en fuentes piezoeléctricas. El objetivo de este estudio es conocer las posibilidades que pueden ofrecer los materiales piezoeléctricos para alimentación autónoma cuando son excitados por vehículos en diferentes circunstancias y entornos viales. Para la realización de este estudio se han utilizado señales provenientes del banco de ensayos, así como señales obtenidas de una instalación construida en un vial del Campus. Para poder realizar el estudio de los diversos materiales piezoeléctricos utilizados en este proyecto, se necesitan conocer las características de los modelos eléctricos. Para obtener estas características se ha diseñado un programa basado en LabView, este programa además puede modificar la señal para que simule las diversas circunstancias a las que pueden estar sometidos los diferentes materiales piezoeléctricos. En este estudio se ha tenido en cuenta la distancia de seguridad entre vehículos para diferentes velocidades y la posibilidad de poder amplificar la excitación que pueden recibir los diversos materiales. Utilizando el programa PSpice OrCAD® se realizan los diferentes modelos con el objetivo de observar el comportamiento de los materiales bajo diversas circunstancias. También se han utilizado varias topologías rectificadoras diferentes para poder comprobar en que situación se puede obtener la máxima tensión acumulada posible. Además también se ha evaluado la máxima potencia que puede entregar cada material en cada situación. Por ultimo se han realizado varias pruebas prácticas con el banco de ensayos para comprobar la acumulación energética real que se puede obtener de los materiales instalados en él. También se ha realizado el estudio de una aplicación práctica que sea capaz de alimentar pequeños circuitos o sensores de poco consumo. Abstract This Project goes on with the study started in the projects Feeding “Energy Harvesting” based on piezoelectric sources, independent feeding: application to piezoelectric source and testing bench to self fed systems in roads applications that were previously done. This study is based on the search of feeding solutions Energy Harvesting focus on piezoelectric sources. The aim of this study is know the different piezoelectric materials possibilities that can offer to independent feeding when different circumstances and road surroundings are excited by vehicles in. To develop this study several signals of the testing bench have been used and also signals get from an installation constructed on a Campus road. As far as the study of the different piezoelectric materials used in this project is concerned we should know the electrical models features. In order to obtain these characteristics a program based on LabView was designed, this program can modify the signal so that it simulates the diverse circumstances to which they can be put under the different piezoelectric materials. In this study I have bearded in mind the safe distance between vehicles for different speeds and the possibility of being able to amplify the excitation that can receive the diverse materials. Using the program PSpice OrCAD® the different models with the objective are made to observe the behaviour of the materials under diverse circumstances. Also several different rectifying topologies have been used to be able to verify the maximum overvoltage accumulated. In addition I have assessed the maximum power that can give each material in each situation. Last but not least different practical proofs with the testing bench have been done to check the actual energy accumulation that can be obtained from the materials installed on it. Also the study of a practical application has been made that is able to feed small circuits or low consumption sensor.

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50–100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

A new feature extraction method for signal classification applied to cat spinal cord signals

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

Cross-talk response analysis on a piezoelectric

The study of the cross-talk and its effects in the performance of a matrix array of piezoelectric elements is an important issue. This corresponds to the study of the cross mode of vibration of each one of the piezoelectric elements that form the ultrasonic array. The aim is to detect and measure the cross-talk that is generated for the cross mode of vibration. In order to accomplish this task, an array of 2x3 elements was designed and developed. This was constructed using 8 MHz piezoelectric ceramics. A number of configurations have been experimented, considering the excitation of an increasing number of elements, in order to detect and measure the propagation of wave interference. Initial results show the way cross-talk interferes the beam generated by the array, this causing attenuation of the main beam and other negative effects.

Piezoelectric and electroacoustic properties of Ti-doped AlN thin films as a function of Ti content

In this work we present the assessment of the structural and piezoelectric properties of Al(0.5-x)TixN0.5 compounds (titanium content menor que6% atomic), which are expected to possess improved properties than conventional AlN films, such as larger piezoelectric activity, thermal stability of frequency and temperature resistance. Al:Ti:N films were deposited from a twin concentric target of Al and Ti by reactive AC sputtering, which provided films with a radial gradient of the Ti concentration. The properties of the films were investigated as a function of their composition, which was measured by electron dispersive energy dispersive X-ray spectroscopy and Rutherford backscattering spectrometry. The microstructure and morphology of the films were assessed by X-ray diffraction and infrared reflectance. Their electroacoustic properties and dielectric constant were derived from the frequency response of BAW test resonators. Al:Ti:N films properties appear to be strongly dependent on the Ti content, which modifies the AlN wurtzite crystal structure leading to greater dielectric constant, lower sound velocities, lower electromechanical factor and moderately improved temperature coefficient of the resonant frequency.

Cinemómetro piezoeléctrico de alta exactitud (VUAE)

Se ha desarrollado un sistema electrónico computerizado, portátil y de bajo consumo, denominado Medidor de Velocidad de Vehículos por Ultrasonidos de Alta Exactitud, VUAE. La alta exactitud de la medida conseguida en el VUAE hace que pueda servir de medida de referencia de la velocidad de un vehículo circulando en carretera. Por tanto el VUAE puede usarse como medida de referencia que permita estimar el error de los cinemómetros comerciales. El VUAE está compuesto por n (n≥2) parejas de emisores y receptores piezoeléctricos de ultrasonidos, denominados E-Rult. Los emisores de las n parejas E-Rult generan n barreras de ultrasonidos, y los receptores piezoeléctricos captan la señal de los ecos cuando el vehículo atraviesa las barreras. Estos ecos se procesan digitalmente para conseguir señales representativas. Posteriormente, utilizando la técnica de la correlación cruzada de señales, se ha podido estimar con alta exactitud la diferencia de tiempos entre los ecos captados en cada barrera. Con los tiempos entre ecos y con la distancia entre cada una de las n barreras de ultrasonidos se puede realizar una estimación de la velocidad del vehículo con alta exactitud. El VUAE se ha contrastado con un sistema de velocidad de referencia, basado en cables piezoeléctricos. ABSTRACT We have developed a portable computerized and low consumption, our system is called High Accuracy Piezoelectric Kinemometer measurement, herein VUAE. By the high accuracy obtained by VUAE it make able to use the VUAE to obtain references measurements of system for measuring Speeds in Vehicles. Therefore VUAE could be used how reference equipment to estimate the error of installed kinemometers. The VUAE was created with n (n≥2) pairs of ultrasonic transmitter-receiver, herein E-Rult. The transmitters used in the n couples E-Rult generate n ultrasonic barriers and receivers receive the echoes when the vehicle crosses the barriers. Digital processing of the echoes signals let us to obtain acceptable signals. Later, by mean of cross correlation technics is possible make a highly exact estimation of speed of the vehicle. The log of the moments of interception and the distance between each of the n ultrasounds allows for a highly exact estimation of speed of the vehicle. VUAE speed measurements were compared to a speed reference system based on piezoelectric cables.

Analysis and design of multirate synchronous sampling schemes for sparse multiband signals

We consider the problem of developing efficient sampling schemes for multiband sparse signals. Previous results on multicoset sampling implementations that lead to universal sampling patterns (which guarantee perfect reconstruction), are based on a set of appropriate interleaved analog to digital converters, all of them operating at the same sampling frequency. In this paper we propose an alternative multirate synchronous implementation of multicoset codes, that is, all the analog to digital converters in the sampling scheme operate at different sampling frequencies, without need of introducing any delay. The interleaving is achieved through the usage of different rates, whose sum is significantly lower than the Nyquist rate of the multiband signal. To obtain universal patterns the sampling matrix is formulated and analyzed. Appropriate choices of the parameters, that is the block length and the sampling rates, are also proposed.

Design of universal multicoset sampling patterns for compressed sensing of multiband sparse signals

Many problems in digital communications involve wideband radio signals. As the most recent example, the impressive advances in Cognitive Radio systems make even more necessary the development of sampling schemes for wideband radio signals with spectral holes. This is equivalent to considering a sparse multiband signal in the framework of Compressive Sampling theory. Starting from previous results on multicoset sampling and recent advances in compressive sampling, we analyze the matrix involved in the corresponding reconstruction equation and define a new method for the design of universal multicoset codes, that is, codes guaranteeing perfect reconstruction of the sparse multiband signal.