Low Pass Filters

Resources created at the University of Southampton for the module Remote Sensing for Earth Observation

Design of electron band pass filters for electrically biased finite superlattices

We design optimal band pass filters for electrons in semiconductor heterostructures, under a uniform applied electric field. The inner cells are chosen to provide a desired transmission window. The outer cells are then designed to transform purely incoming or outgoing waves into Bloch states of the inner cells. The transfer matrix is interpreted as a conformal mapping in the complex plane, which allows us to write constraints on the outer cell parameters, from which physically useful values can be obtained.

Design of electron band pass filters for electrically biased finite superlattices

We design optimal band pass filters for electrons in semiconductor heterostructures, under a uniform applied electric field. The inner cells are chosen to provide a desired transmission window. The outer cells are then designed to transform purely incoming or outgoing waves into Bloch states of the inner cells. The transfer matrix is interpreted as a conformal mapping in the complex plane, which allows us to write constraints on the outer cell parameters, from which physically useful values can be obtained.

Perception of speech through a 500 Hz low-pass filter: sensory aids and communication strategies

This paper reviews a study to investigate how a hearing impaired person can learn to discriminate speech distorted by a low pass filter in a sensory aid.

Perception of speech through a 500 Hz low-pass filter: sensory aids and communication strategies

This paper reviews a study to investigate how a hearing impaired person can learn to discriminate speech distorted by a low pass filter in a sensory aid.

Extraction of Tschebysheff design data for the low pass dielectric multilayer

The extraction of design data for the lowpass dielectric multilayer according to Tschebysheff performance is described. The extraction proceeds initially by analogy with electric-circuit design, and can then be given numerical refinement which is also described. Agreement with the Tschebysheff desideratum is satisfactory. The multilayers extracted by this procedure are of fractional thickness, symmetric with regard to their central layers.

Reducing the spurious oscillations in the lumped parameters model using low-pass filter

Voltages and currents in the transmission line are described by differential equations that are difficult to solve due soil and skin effect that has to be considered for accurate results, but it increases their complexity. Therefore there are some models to study the voltages and currents along in transmission line. The distributed parameters model that transforms the equations in time domain to the frequency domain and once the solutions are obtained, they are converted to time domain using the Inverse Laplace Transform using numerical methods. Another model is named lumped parameters model and it considers the transmission line represented by a pi-circuit cascade and the currents and voltages are described by state equations. In the simulations using the lumped parameters model, it can be observed the presence of spurious oscillations that are independent of the quantity of pi-circuits used and do not represent the real value of the transient. In this work will be projected a passive low-pass filter directly inserted in the lumped parameters model to reduce the spurious oscillations in the simulations, making this model more accurate and reliable for studying the electromagnetic transients in power systems.

From irradiance to output power fluctuations: the pv plant as a low pass filter

The power generated by large grid-connected photovoltaic (PV) plants depends greatly on the solar irradiance. This paper studies the effects of the solar irradiance variability analyzing experimental 1-s data collected throughout a year at six PV plants, totaling 18 MWp. Each PV plant was modeled as a first order filter function based on an analysis in the frequency domain of the irradiance data and the output power signals. An empiric expression which relates the filter parameters and the PV plant size has been proposed. This simple model has been successfully validated precisely determining the daily maximum output power fluctuation from incident irradiance measurements.

High Pass Filters

Resources created at the University of Southampton for the module Remote Sensing for Earth Observation

Neural networks training using the constructivism paradigms

The Backpropagation Algorithm (BA) is the standard method for training multilayer Artificial Neural Networks (ANN), although it converges very slowly and can stop in a local minimum. We present a new method for neural network training using the BA inspired on constructivism, an alphabetization method proposed by Emilia Ferreiro based on Piaget philosophy. Simulation results show that the proposed configuration usually obtains a lower final mean square error, when compared with the standard BA and with the BA with momentum factor.

Real-time crowd density estimation using images

This paper presents a technique for real-time crowd density estimation based on textures of crowd images. In this technique, the current image from a sequence of input images is classified into a crowd density class. Then, the classification is corrected by a low-pass filter based on the crowd density classification of the last n images of the input sequence. The technique obtained 73.89% of correct classification in a real-time application on a sequence of 9892 crowd images. Distributed processing was used in order to obtain real-time performance. © Springer-Verlag Berlin Heidelberg 2005.

Detecção de bordas em imagens digitais através de um processo de difusão anisotrópica não linear

The edges detection model by a non-linear anisotropic diffusion, consists in a mathematical model of smoothing based in Partial Differential Equation (PDE), alternative to the conventional low-pass filters. The smoothing model consists in a selective process, where homogeneous areas of the image are smoothed intensely in agreement with the temporal evolution applied to the model. The level of smoothing is related with the amount of undesired information contained in the image, i.e., the model is directly related with the optimal level of smoothing, eliminating the undesired information and keeping selectively the interest features for Cartography area. The model is primordial for cartographic applications, its function is to realize the image preprocessing without losing edges and other important details on the image, mainly airports tracks and paved roads. Experiments carried out with digital images showed that the methodology allows to obtain the features, e.g. airports tracks, with efficiency.

Utilização de equações diferenciais parciais no tratamento de imagens orbitais

Pós-graduação em Ciências Cartográficas - FCT

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.

On increasing the effective aperture of antennas by data processing /

Includes bibliographical references (p. 58-59)