Logarithmical hopping encoding: a low computational complexity algorithm for image compression

LHE (logarithmical hopping encoding) is a computationally efficient image compression algorithm that exploits the Weber–Fechner law to encode the error between colour component predictions and the actual value of such components. More concretely, for each pixel, luminance and chrominance predictions are calculated as a function of the surrounding pixels and then the error between the predictions and the actual values are logarithmically quantised. The main advantage of LHE is that although it is capable of achieving a low-bit rate encoding with high quality results in terms of peak signal-to-noise ratio (PSNR) and image quality metrics with full-reference (FSIM) and non-reference (blind/referenceless image spatial quality evaluator), its time complexity is O( n) and its memory complexity is O(1). Furthermore, an enhanced version of the algorithm is proposed, where the output codes provided by the logarithmical quantiser are used in a pre-processing stage to estimate the perceptual relevance of the image blocks. This allows the algorithm to downsample the blocks with low perceptual relevance, thus improving the compression rate. The performance of LHE is especially remarkable when the bit per pixel rate is low, showing much better quality, in terms of PSNR and FSIM, than JPEG and slightly lower quality than JPEG-2000 but being more computationally efficient.

Computational Schemes for Optimizing Domains of Attraction in Dynamical Systems

In this paper, several computational schemes are presented for the optimal tuning of the global behavior of nonlinear dynamical sys- tems. Specifically, the maximization of the size of domains of attraction associated with invariants in parametrized dynamical sys- tems is addressed. Cell Mapping (CM) tech- niques are used to estimate the size of the domains, and such size is then maximized via different optimization tools. First, a ge- netic algorithm is tested whose performance shows to be good for determining global maxima at the expense of high computa- tional cost. Secondly, an iterative scheme based on a Stochastic Approximation proce- dure (the Kiefer-Wolfowitz algorithm) is eval- uated showing acceptable performance at low cost. Finally, several schemes combining neu- ral network based estimations and optimiza- tion procedures are addressed with promising results. The performance of the methods is illus- trated with two applications: first on the well-known van der Pol equation with stan- dard parametrization, and second the tuning of a controller for saturated systems.

A visual object counter based on a multimodal data association for embedded systems with limited computational power

A low complex but highly-efficient object counter algorithm is presented that can be embedded in hardware with a low computational power. This is achieved by a novel soft-data association strategy that can handle multimodal distributions.

A New Fast Motion Estimation and Mode Decision algorithm for H.264 Depth Maps encoding in Free Viewpoint TV

In this paper, we consider a scenario where 3D scenes are modeled through a View+Depth representation. This representation is to be used at the rendering side to generate synthetic views for free viewpoint video. The encoding of both type of data (view and depth) is carried out using two H.264/AVC encoders. In this scenario we address the reduction of the encoding complexity of depth data. Firstly, an analysis of the Mode Decision and Motion Estimation processes has been conducted for both view and depth sequences, in order to capture the correlation between them. Taking advantage of this correlation, we propose a fast mode decision and motion estimation algorithm for the depth encoding. Results show that the proposed algorithm reduces the computational burden with a negligible loss in terms of quality of the rendered synthetic views. Quality measurements have been conducted using the Video Quality Metric.

An approach to automatic learning assessment based on the computational theory of perceptions

E-learning systems output a huge quantity of data on a learning process. However, it takes a lot of specialist human resources to manually process these data and generate an assessment report. Additionally, for formative assessment, the report should state the attainment level of the learning goals defined by the instructor. This paper describes the use of the granular linguistic model of a phenomenon (GLMP) to model the assessment of the learning process and implement the automated generation of an assessment report. GLMP is based on fuzzy logic and the computational theory of perceptions. This technique is useful for implementing complex assessment criteria using inference systems based on linguistic rules. Apart from the grade, the model also generates a detailed natural language progress report on the achieved proficiency level, based exclusively on the objective data gathered from correct and incorrect responses. This is illustrated by applying the model to the assessment of Dijkstra’s algorithm learning using a visual simulation-based graph algorithm learning environment, called GRAPHs

Maximum Likelihood Estimation of Modal Parameters in Structures Using the Expectation Maximization Algorithm

This paper presents a time-domain stochastic system identification method based on maximum likelihood estimation (MLE) with the expectation maximization (EM) algorithm. The effectiveness of this structural identification method is evaluated through numerical simulation in the context of the ASCE benchmark problem on structural health monitoring. The benchmark structure is a four-story, two-bay by two-bay steel-frame scale model structure built in the Earthquake Engineering Research Laboratory at the University of British Columbia, Canada. This paper focuses on Phase I of the analytical benchmark studies. A MATLAB-based finite element analysis code obtained from the IASC-ASCE SHM Task Group web site is used to calculate the dynamic response of the prototype structure. A number of 100 simulations have been made using this MATLAB-based finite element analysis code in order to evaluate the proposed identification method. There are several techniques to realize system identification. In this work, stochastic subspace identification (SSI)method has been used for comparison. SSI identification method is a well known method and computes accurate estimates of the modal parameters. The principles of the SSI identification method has been introduced in the paper and next the proposed MLE with EM algorithm has been explained in detail. The advantages of the proposed structural identification method can be summarized as follows: (i) the method is based on maximum likelihood, that implies minimum variance estimates; (ii) EM is a computational simpler estimation procedure than other optimization algorithms; (iii) estimate more parameters than SSI, and these estimates are accurate. On the contrary, the main disadvantages of the method are: (i) EM algorithm is an iterative procedure and it consumes time until convergence is reached; and (ii) this method needs starting values for the parameters. Modal parameters (eigenfrequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using both the SSI method and the proposed MLE + EM method. The numerical results show that the proposed method identifies eigenfrequencies, damping ratios and mode shapes reasonably well even in the presence of 10% measurement noises. These modal parameters are more accurate than the SSI estimated modal parameters.

Aerodynamic optimization of the ICE2 high-speed train nose using a genetic algorithm and metamodels

An aerodynamic optimization of the ICE 2 high-speed train nose in term of front wind action sensitivity is carried out in this paper. The nose is parametrically defined by Be?zier Curves, and a three-dimensional representation of the nose is obtained using thirty one design variables. This implies a more complete parametrization, allowing the representation of a real model. In order to perform this study a genetic algorithm (GA) is used. Using a GA involves a large number of evaluations before finding such optimal. Hence it is proposed the use of metamodels or surrogate models to replace Navier-Stokes solver and speed up the optimization process. Adaptive sampling is considered to optimize surrogate model fitting and minimize computational cost when dealing with a very large number of design parameters. The paper introduces the feasi- bility of using GA in combination with metamodels for real high-speed train geometry optimization.

Computational Methods for Identification of Vibrating Structures

System identification deals with the problem of building mathematical models of dynamical systems based on observed data from the system" [1]. In the context of civil engineering, the system refers to a large scale structure such as a building, bridge, or an offshore structure, and identification mostly involves the determination of modal parameters (the natural frequencies, damping ratios, and mode shapes). This paper presents some modal identification results obtained using a state-of-the-art time domain system identification method (data-driven stochastic subspace algorithms [2]) applied to the output-only data measured in a steel arch bridge. First, a three dimensional finite element model was developed for the numerical analysis of the structure using ANSYS. Modal analysis was carried out and modal parameters were extracted in the frequency range of interest, 0-10 Hz. The results obtained from the finite element modal analysis were used to determine the location of the sensors. After that, ambient vibration tests were conducted during April 23-24, 2009. The response of the structure was measured using eight accelerometers. Two stations of three sensors were formed (triaxial stations). These sensors were held stationary for reference during the test. The two remaining sensors were placed at the different measurement points along the bridge deck, in which only vertical and transversal measurements were conducted (biaxial stations). Point estimate and interval estimate have been carried out in the state space model using these ambient vibration measurements. In the case of parametric models (like state space), the dynamic behaviour of a system is described using mathematical models. Then, mathematical relationships can be established between modal parameters and estimated point parameters (thus, it is common to use experimental modal analysis as a synonym for system identification). Stable modal parameters are found using a stabilization diagram. Furthermore, this paper proposes a method for assessing the precision of estimates of the parameters of state-space models (confidence interval). This approach employs the nonparametric bootstrap procedure [3] and is applied to subspace parameter estimation algorithm. Using bootstrap results, a plot similar to a stabilization diagram is developed. These graphics differentiate system modes from spurious noise modes for a given order system. Additionally, using the modal assurance criterion, the experimental modes obtained have been compared with those evaluated from a finite element analysis. A quite good agreement between numerical and experimental results is observed.

An algorithm for graphical computer results in shells

A Finite Element technique to interpolate general data (function values and its derivatives) has been developped. The technique can be considered as a generalized solution of the classical polynomial interpolation, because the condition for the interpolating function to be a polynomial is replaced by a minimizing condition of a given “smoothing” functional. In this way it is possible to find interpolating functions with a given level of continuity according to the class of finite elements used. Examples have been presented in order to assess the accuracy and efficiency of the procedure.

An optimal yet fast pruning algorithm to reduce latency in multiview prediction structures

We propose a new algorithm for the design of prediction structures with low delay and limited penalty in the rate-distortion performance for multiview video coding schemes. This algorithm constitutes one of the elements of a framework for the analysis and optimization of delay in multiview coding schemes that is based in graph theory. The objective of the algorithm is to find the best combination of prediction dependencies to prune from a multiview prediction structure, given a number of cuts. Taking into account the properties of the graph-based analysis of the encoding delay, the algorithm is able to find the best prediction dependencies to eliminate from an original prediction structure, while limiting the number of cut combinations to evaluate. We show that this algorithm obtains optimum results in the reduction of the encoding latency with a lower computational complexity than exhaustive search alternatives.

Development of a sensorimotor algorithm able to deal with unforeseen pushes and its implementation based on VHDL

Development of a Sensorimotor Algorithm Able to Deal with Unforeseen Pushes and Its Implementation Based on VHDL is the title of my thesis which concludes my Bachelor Degree in the Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación of the Universidad Politécnica de Madrid. It encloses the overall work I did in the Neurorobotics Research Laboratory from the Beuth Hochschule für Technik Berlin during my ERASMUS year in 2015. This thesis is focused on the field of robotics, specifically an electronic circuit called Cognitive Sensorimotor Loop (CSL) and its control algorithm based on VHDL hardware description language. The reason that makes the CSL special resides in its ability to operate a motor both as a sensor and an actuator. This way, it is possible to achieve a balanced position in any of the robot joints (e.g. the robot manages to stand) without needing any conventional sensor. In other words, the back electromotive force (EMF) induced by the motor coils is measured and the control algorithm responds depending on its magnitude. The CSL circuit contains mainly an analog-to-digital converter (ADC) and a driver. The ADC consists on a delta-sigma modulation which generates a series of bits with a certain percentage of 1's and 0's, proportional to the back EMF. The control algorithm, running in a FPGA, processes the bit frame and outputs a signal for the driver. This driver, which has an H bridge topology, gives the motor the ability to rotate in both directions while it's supplied with the power needed. The objective of this thesis is to document the experiments and overall work done on push ignoring contractive sensorimotor algorithms, meaning sensorimotor algorithms that ignore large magnitude forces (compared to gravity) applied in a short time interval on a pendulum system. This main objective is divided in two sub-objectives: (1) developing a system based on parameterized thresholds and (2) developing a system based on a push bypassing filter. System (1) contains a module that outputs a signal which blocks the main Sensorimotor algorithm when a push is detected. This module has several different parameters as inputs e.g. the back EMF increment to consider a force as a push or the time interval between samples. System (2) consists on a low-pass Infinite Impulse Response digital filter. It cuts any frequency considered faster than a certain push oscillation. This filter required an intensive study on how to implement some functions and data types (fixed or floating point data) not supported by standard VHDL packages. Once this was achieved, the next challenge was to simplify the solution as much as possible, without using non-official user made packages. Both systems behaved with a series of interesting advantages and disadvantages for the elaboration of the document. Stability, reaction time, simplicity or computational load are one of the many factors to be studied in the designed systems. RESUMEN. Development of a Sensorimotor Algorithm Able to Deal with Unforeseen Pushes and Its Implementation Based on VHDL es un Proyecto de Fin de Grado (PFG) que concluye mis estudios en la Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación de la Universidad Politécnica de Madrid. En él se documenta el trabajo de investigación que realicé en el Neurorobotics Research Laboratory de la Beuth Hochschule für Technik Berlin durante el año 2015 mediante el programa de intercambio ERASMUS. Este PFG se centra en el campo de la robótica y en concreto en un circuito electrónico llamado Cognitive Sensorimotor Loop (CSL) y su algoritmo de control basado en lenguaje de modelado hardware VHDL. La particularidad del CSL reside en que se consigue que un motor haga las veces tanto de sensor como de actuador. De esta manera es posible que las articulaciones de un robot alcancen una posición de equilibrio (p.ej. el robot se coloca erguido) sin la necesidad de sensores en el sentido estricto de la palabra. Es decir, se mide la propia fuerza electromotriz (FEM) inducida sobre el motor y el algoritmo responde de acuerdo a su magnitud. El circuito CSL se compone de un convertidor analógico-digital (ADC) y un driver. El ADC consiste en un modulador sigma-delta, que genera una serie de bits con un porcentaje de 1's y 0's determinado, en proporción a la magnitud de la FEM inducida. El algoritmo de control, que se ejecuta en una FPGA, procesa esta cadena de bits y genera una señal para el driver. El driver, que posee una topología en puente H, provee al motor de la potencia necesaria y le otorga la capacidad de rotar en cualquiera de las dos direcciones. El objetivo de este PFG es documentar los experimentos y en general el trabajo realizado en algoritmos Sensorimotor que puedan ignorar fuerzas de gran magnitud (en comparación con la gravedad) y aplicadas en una corta ventana de tiempo. En otras palabras, ignorar empujones conservando el comportamiento original frente a la gravedad. Para ello se han desarrollado dos sistemas: uno basado en umbrales parametrizados (1) y otro basado en un filtro de corte ajustable (2). El sistema (1) contiene un módulo que, en el caso de detectar un empujón, genera una señal que bloquea el algoritmo Sensorimotor. Este módulo recibe diferentes parámetros como el incremento necesario de la FEM para que se considere un empujón o la ventana de tiempo para que se considere la existencia de un empujón. El sistema (2) consiste en un filtro digital paso-bajo de respuesta infinita que corta cualquier variación que considere un empujón. Para crear este filtro se requirió un estudio sobre como implementar ciertas funciones y tipos de datos (coma fija o flotante) no soportados por las librerías básicas de VHDL. Tras esto, el objetivo fue simplificar al máximo la solución del problema, sin utilizar paquetes de librerías añadidos. En ambos sistemas aparecen una serie de ventajas e inconvenientes de interés para el documento. La estabilidad, el tiempo de reacción, la simplicidad o la carga computacional son algunas de las muchos factores a estudiar en los sistemas diseñados. Para concluir, también han sido documentadas algunas incorporaciones a los sistemas: una interfaz visual en VGA, un módulo que compensa el offset del ADC o la implementación de una batería de faders MIDI entre otras.

Bivariate Krawtchouk polynomials: Inversion and connection problems with the NAVIMA algorithm

In this paper we present a recurrent procedure to solve an inversion problem for monic bivariate Krawtchouk polynomials written in vector column form, giving its solution explicitly. As a by-product, a general connection problem between two vector column of monic bivariate Krawtchouk families is also explicitly solved. Moreover, in the non monic case and also for Krawtchouk families, several expansion formulas are given, but for polynomials written in scalar form.