Aplicaciones de filtrado adaptativo LMS para mejorar la respuesta de acelerómetros

El proyecto, “Aplicaciones de filtrado adaptativo LMS para mejorar la respuesta de acelerómetros”, se realizó con el objetivo de eliminar señales no deseadas de la señal de información procedentes de los acelerómetros para aplicaciones automovilísticas, mediante los algoritmos de los filtros adaptativos LMS. Dicho proyecto, está comprendido en tres áreas para su realización y ejecución, los cuales fueron ejecutados desde el inicio hasta el último día de trabajo. En la primera área de aplicación, diseñamos filtros paso bajo, paso alto, paso banda y paso banda eliminada, en lo que son los filtros de butterworth, filtros Chebyshev, de tipo uno como de tipo dos y filtros elípticos. Con esta primera parte, lo que se quiere es conocer, o en nuestro caso, recordar el entorno de Matlab, en sus distintas ecuaciones prediseñadas que nos ofrece el mencionado entorno, como también nos permite conocer un poco las características de estos filtros. Para posteriormente probar dichos filtros en el DSP. En la segunda etapa, y tras recordar un poco el entorno de Matlab, nos centramos en la elaboración y/o diseño de nuestro filtro adaptativo LMS; experimentado primero con Matlab, para como ya se dijo, entender y comprender el comportamiento del mismo. Cuando ya teníamos claro esta parte, procedimos a “cargar” el código en el DSP, compilarlo y depurarlo, realizando estas últimas acciones gracias al Visual DSP. Resaltaremos que durante esta segunda etapa se empezó a excitar las entradas del sistema, con señales provenientes del Cool Edit Pro, y además para saber cómo se comportaba el filtro adaptativo LMS, se utilizó señales provenientes de un generador de funciones, para obtener de esta manera un desfase entre las dos señales de entrada; aunque también se utilizó el propio Cool Edit Pro para obtener señales desfasadas, pero debido que la fase tres no podíamos usar el mencionado software, realizamos pruebas con el generador de funciones. Finalmente, en la tercera etapa, y tras comprobar el funcionamiento deseado de nuestro filtro adaptativo DSP con señales de entrada simuladas, pasamos a un laboratorio, en donde se utilizó señales provenientes del acelerómetro 4000A, y por supuesto, del generador de funciones; el cual sirvió para la formación de nuestra señal de referencia, que permitirá la eliminación de una de las frecuencias que se emitirá del acelerómetro. Por último, cabe resaltar que pudimos obtener un comportamiento del filtro adaptativo LMS adecuado, y como se esperaba. Realizamos pruebas, con señales de entrada desfasadas, y obtuvimos curiosas respuestas a la salida del sistema, como son que la frecuencia a eliminar, mientras más desfasado estén estas señales, mas se notaba. Solucionando este punto al aumentar el orden del filtro. Finalmente podemos concluir que pese a que los filtros digitales probados en la primera etapa son útiles, para tener una respuesta lo más ideal posible hay que tener en cuenta el orden del filtro, el cual debe ser muy alto para que las frecuencias próximas a la frecuencia de corte, no se atenúen. En cambio, en los filtros adaptativos LMS, si queremos por ejemplo, eliminar una señal de entre tres señales, sólo basta con introducir la frecuencia a eliminar, por una de las entradas del filtro, en concreto la señal de referencia. De esta manera, podemos eliminar una señal de entre estas tres, de manera que las otras dos, no se vean afectadas por el procedimiento. Abstract The project, "LMS adaptive filtering applications to improve the response of accelerometers" was conducted in order to remove unwanted signals from the information signal from the accelerometers for automotive applications using algorithms LMS adaptive filters. The project is comprised of three areas for implementation and execution, which were executed from the beginning until the last day. In the first area of application, we design low pass filters, high pass, band pass and band-stop, as the filters are Butterworth, Chebyshev filters, type one and type two and elliptic filters. In this first part, what we want is to know, or in our case, remember the Matlab environment, art in its various equations offered by the mentioned environment, as well as allows us to understand some of the characteristics of these filters. To further test these filters in the DSP. In the second stage, and recalling some Matlab environment, we focus on the development and design of our LMS adaptive filter; experimented first with Matlab, for as noted above, understand the behavior of the same. When it was clear this part, proceeded to "load" the code in the DSP, compile and debug, making these latest actions by the Visual DSP. Will highlight that during this second stage began to excite the system inputs, with signals from the Cool Edit Pro, and also for how he behaved the LMS adaptive filter was used signals from a function generator, to thereby obtain a gap between the two input signals, but also used Cool Edit Pro himself for phase signals, but due to phase three could not use such software, we test the function generator. Finally, in the third stage, and after checking the desired performance of our DSP adaptive filter with simulated input signals, we went to a laboratory, where we used signals from the accelerometer 4000A, and of course, the function generator, which was used for the formation of our reference signal, enabling the elimination of one of the frequencies to be emitted from the accelerometer. Note that they were able to obtain a behavior of the LMS adaptive filter suitable as expected. We test with outdated input signals, and got curious response to the output of the system, such as the frequency to remove, the more outdated are these signs, but noticeable. Solving this point with increasing the filter order. We can conclude that although proven digital filters in the first stage are useful, to have a perfect answer as possible must be taken into account the order of the filter, which should be very high for frequencies near the frequency cutting, not weakened. In contrast, in the LMS adaptive filters if we for example, remove a signal from among three signals, only enough to eliminate the frequency input on one of the inputs of the filter, namely the reference signal. Thus, we can remove a signal between these three, so that the other two, not affected by the procedure.

Empowering and assisting natural human mobility: The simbiosis walker

This paper presents the complete development of the Simbiosis Smart Walker. The device is equipped with a set of sensor subsystems to acquire user-machine interaction forces and the temporal evolution of user's feet during gait. The authors present an adaptive filtering technique used for the identification and separation of different components found on the human-machine interaction forces. This technique allowed isolating the components related with the navigational commands and developing a Fuzzy logic controller to guide the device. The Smart Walker was clinically validated at the Spinal Cord Injury Hospital of Toledo - Spain, presenting great acceptability by spinal chord injury patients and clinical staff

Cancelación activa del ruido utilizando el KIT TMS320C5515 EZDSP

El control, o cancelación activa de ruido, consiste en la atenuación del ruido presente en un entorno acústico mediante la emisión de una señal igual y en oposición de fase al ruido que se desea atenuar. La suma de ambas señales en el medio acústico produce una cancelación mutua, de forma que el nivel de ruido resultante es mucho menor al inicial. El funcionamiento de estos sistemas se basa en los principios de comportamiento de los fenómenos ondulatorios descubiertos por Augustin-Jean Fresnel, Christiaan Huygens y Thomas Young entre otros. Desde la década de 1930, se han desarrollado prototipos de sistemas de control activo de ruido, aunque estas primeras ideas eran irrealizables en la práctica o requerían de ajustes manuales cada poco tiempo que hacían inviable su uso. En la década de 1970, el investigador estadounidense Bernard Widrow desarrolla la teoría de procesado adaptativo de señales y el algoritmo de mínimos cuadrados LMS. De este modo, es posible implementar filtros digitales cuya respuesta se adapte de forma dinámica a las condiciones variables del entorno. Con la aparición de los procesadores digitales de señal en la década de 1980 y su evolución posterior, se abre la puerta para el desarrollo de sistemas de cancelación activa de ruido basados en procesado de señal digital adaptativo. Hoy en día, existen sistemas de control activo de ruido implementados en automóviles, aviones, auriculares o racks de equipamiento profesional. El control activo de ruido se basa en el algoritmo fxlms, una versión modificada del algoritmo LMS de filtrado adaptativo que permite compensar la respuesta acústica del entorno. De este modo, se puede filtrar una señal de referencia de ruido de forma dinámica para emitir la señal adecuada que produzca la cancelación. Como el espacio de cancelación acústica está limitado a unas dimensiones de la décima parte de la longitud de onda, sólo es viable la reducción de ruido en baja frecuencia. Generalmente se acepta que el límite está en torno a 500 Hz. En frecuencias medias y altas deben emplearse métodos pasivos de acondicionamiento y aislamiento, que ofrecen muy buenos resultados. Este proyecto tiene como objetivo el desarrollo de un sistema de cancelación activa de ruidos de carácter periódico, empleando para ello electrónica de consumo y un kit de desarrollo DSP basado en un procesador de muy bajo coste. Se han desarrollado una serie de módulos de código para el DSP escritos en lenguaje C, que realizan el procesado de señal adecuado a la referencia de ruido. Esta señal procesada, una vez emitida, produce la cancelación acústica. Empleando el código implementado, se han realizado pruebas que generan la señal de ruido que se desea eliminar dentro del propio DSP. Esta señal se emite mediante un altavoz que simula la fuente de ruido a cancelar, y mediante otro altavoz se emite una versión filtrada de la misma empleando el algoritmo fxlms. Se han realizado pruebas con distintas versiones del algoritmo, y se han obtenido atenuaciones de entre 20 y 35 dB medidas en márgenes de frecuencia estrechos alrededor de la frecuencia del generador, y de entre 8 y 15 dB medidas en banda ancha. ABSTRACT. Active noise control consists on attenuating the noise in an acoustic environment by emitting a signal equal but phase opposed to the undesired noise. The sum of both signals results in mutual cancellation, so that the residual noise is much lower than the original. The operation of these systems is based on the behavior principles of wave phenomena discovered by Augustin-Jean Fresnel, Christiaan Huygens and Thomas Young. Since the 1930’s, active noise control system prototypes have been developed, though these first ideas were practically unrealizable or required manual adjustments very often, therefore they were unusable. In the 1970’s, American researcher Bernard Widrow develops the adaptive signal processing theory and the Least Mean Squares algorithm (LMS). Thereby, implementing digital filters whose response adapts dynamically to the variable environment conditions, becomes possible. With the emergence of digital signal processors in the 1980’s and their later evolution, active noise cancellation systems based on adaptive signal processing are attained. Nowadays active noise control systems have been successfully implemented on automobiles, planes, headphones or racks for professional equipment. Active noise control is based on the fxlms algorithm, which is actually a modified version of the LMS adaptive filtering algorithm that allows compensation for the acoustic response of the environment. Therefore it is possible to dynamically filter a noise reference signal to obtain the appropriate cancelling signal. As the noise cancellation space is limited to approximately one tenth of the wavelength, noise attenuation is only viable for low frequencies. It is commonly accepted the limit of 500 Hz. For mid and high frequencies, conditioning and isolating passive techniques must be used, as they produce very good results. The objective of this project is to develop a noise cancellation system for periodic noise, by using consumer electronics and a DSP development kit based on a very-low-cost processor. Several C coded modules have been developed for the DSP, implementing the appropriate signal processing to the noise reference. This processed signal, once emitted, results in noise cancellation. The developed code has been tested by generating the undesired noise signal in the DSP. This signal is emitted through a speaker simulating the noise source to be removed, and another speaker emits an fxlms filtered version of the same signal. Several versions of the algorithm have been tested, obtaining attenuation levels around 20 – 35 dB measured in a tight bandwidth around the generator frequency, or around 8 – 15 dB measured in broadband.

Noise-agnostic adaptive image filtering without training references on an evolvable hardware platform

One of the main concerns of evolvable and adaptive systems is the need of a training mechanism, which is normally done by using a training reference and a test input. The fitness function to be optimized during the evolution (training) phase is obtained by comparing the output of the candidate systems against the reference. The adaptivity that this type of systems may provide by re-evolving during operation is especially important for applications with runtime variable conditions. However, fully automated self-adaptivity poses additional problems. For instance, in some cases, it is not possible to have such reference, because the changes in the environment conditions are unknown, so it becomes difficult to autonomously identify which problem requires to be solved, and hence, what conditions should be representative for an adequate re-evolution. In this paper, a solution to solve this dependency is presented and analyzed. The system consists of an image filter application mapped on an evolvable hardware platform, able to evolve using two consecutive frames from a camera as both test and reference images. The system is entirely mapped in an FPGA, and native dynamic and partial reconfiguration is used for evolution. It is also shown that using such images, both of them being noisy, as input and reference images in the evolution phase of the system is equivalent or even better than evolving the filter with offline images. The combination of both techniques results in the completely autonomous, noise type/level agnostic filtering system without reference image requirement described along the paper.

Design and implementation of an adaptive dashboard and visualization interface for the dynamic optimization and control of Data Centers

Over the last few years, the Data Center market has increased exponentially and this tendency continues today. As a direct consequence of this trend, the industry is pushing the development and implementation of different new technologies that would improve the energy consumption efficiency of data centers. An adaptive dashboard would allow the user to monitor the most important parameters of a data center in real time. For that reason, monitoring companies work with IoT big data filtering tools and cloud computing systems to handle the amounts of data obtained from the sensors placed in a data center.Analyzing the market trends in this field we can affirm that the study of predictive algorithms has become an essential area for competitive IT companies. Complex algorithms are used to forecast risk situations based on historical data and warn the user in case of danger. Considering that several different users will interact with this dashboard from IT experts or maintenance staff to accounting managers, it is vital to personalize it automatically. Following that line of though, the dashboard should only show relevant metrics to the user in different formats like overlapped maps or representative graphs among others. These maps will show all the information needed in a visual and easy-to-evaluate way. To sum up, this dashboard will allow the user to visualize and control a wide range of variables. Monitoring essential factors such as average temperature, gradients or hotspots as well as energy and power consumption and savings by rack or building would allow the client to understand how his equipment is behaving, helping him to optimize the energy consumption and efficiency of the racks. It also would help him to prevent possible damages in the equipment with predictive high-tech algorithms.