An efficient of estimation stages for segmentation skin lesions based optimization algorithm

Modern dermatology distinguishes premature diagnosis for example an important part in reducing the death percentage and promising less aggressive treatment for patients. The classifications comprise various stages that must be selected suitably using the characteristics of the filter pointing to get a dependable analysis. The dermoscopic images hold challenges to be faced and overcome to enhance the automatic diagnosis of hazardous lesions. It is calculated to survey a different metaheuristic and evolutionary computing working for filter design systems. Approximately general computing techniques are observed to improve features of infect design method. Nevertheless, the median filter (MF) is normally multimodal with respect to the filter factors and so, reliable approaches that can provide optimal solutions are required. The design of MF depends on modern artificial swarm intelligence technique (MASIT) optimization algorithm which has proven to be more effective than other population-based algorithms to improve of estimation stages for segmentation skin lesions. A controlled artificial bee colony (ABC) algorithm is advanced for solving factors optimization problems and, also the physical-programming-depend on ABC way is applied to proposal median filter, and the outcomes are compared to another approaches

Linear Phase FIR Low Pass Filter Design Based on Firefly Algorithm

In this paper, a linear phase Low Pass FIR filter is designed and proposed based on Firefly algorithm. We exploit the exploitation and exploration mechanism with a local search routine to improve the convergence and get higher speed computation. The optimum FIR filters are designed based on the Firefly method for which the finite word length is used to represent coefficients. Furthermore, Particle Swarm Optimization (PSO) and Differential Evolution algorithm (DE) will be used to show the solution. The results will be compared with PSO and DE methods. Firefly algorithm and Parks–McClellan (PM) algorithm are also compared in this paper thoroughly. The design goal is successfully achieved in all design examples using the Firefly algorithm. They are compared with that obtained by using the PSO and the DE algorithm. For the problem at hand, the simulation results show that the Firefly algorithm outperforms the PSO and DE methods in some of the presented design examples. It also performs well in a portion of the exhibited design examples particularly in speed and quality

Communication Subsystems for Emerging Wireless Technologies

The paper describes a multi-disciplinary design of modern communication systems. The design starts with the analysis of a system in order to define requirements on its individual components. The design exploits proper models of communication channels to adapt the systems to expected transmission conditions. Input filtering of signals both in the frequency domain and in the spatial domain is ensured by a properly designed antenna. Further signal processing (amplification and further filtering) is done by electronics circuits. Finally, signal processing techniques are applied to yield information about current properties of frequency spectrum and to distribute the transmission over free subcarrier channels

Identification of Linear / Nonlinear Systems via the Coyote Optimization Algorithm (COA)

Classical techniques used in system identification, like the basic least mean square method (LMS) and its other forms; suffer from instability problems and convergence to a locally optimal solution instead of a global solution. These problems can be reduced by applying optimization techniques inspired by nature. This paper applies the Coyote optimization algorithm (COA) to identify linear or nonlinear systems. In the case of linear systems identification, the infinite impulse response (IIR) filter is used to constitute the plants. In this work, COA algorithm is applied to identify different plants, and its performance is investigated and compared to that based on particle swarm optimization algorithm (PSOA), which is considered as one of the simplest and most popular optimization algorithms. The performance is investigated for different cases including same order and reduced-order filter models. The acquired results illustrate the ability of the COA algorithm to obtain the lowest error between the proposed IIR filter and the actual system in most cases. Also, a statistical analysis is performed for the two algorithms. Also, the COA is used to optimize the identification process of nonlinear systems based on Hammerstein models. For this purpose, COA is used to determine the parameters of the Hammerstein models of two different examples, which were identified in the literature using other algorithms. For more investigation, the fulfillment of the COA is compared to that of some other competitive heuristic algorithms. Most of the results prove the effectiveness of COA in system identification problems

Digital Filters and Signal Processing

Digital filters, together with signal processing, are being employed in the new technologies and information systems, and are implemented in different areas and applications. Digital filters and signal processing are used with no costs and they can be adapted to different cases with great flexibility and reliability. This book presents advanced developments in digital filters and signal process methods covering different cases studies. They present the main essence of the subject, with the principal approaches to the most recent mathematical models that are being employed worldwide

Digital Filter Design Using Improved Teaching-Learning-Based Optimization

Digital filters are an important part of digital signal processing systems. Digital filters are divided into finite impulse response (FIR) digital filters and infinite impulse response (IIR) digital filters according to the length of their impulse responses. An FIR digital filter is easier to implement than an IIR digital filter because of its linear phase and stability properties. In terms of the stability of an IIR digital filter, the poles generated in the denominator are subject to stability constraints. In addition, a digital filter can be categorized as one-dimensional or multi-dimensional digital filters according to the dimensions of the signal to be processed. However, for the design of IIR digital filters, traditional design methods have the disadvantages of easy to fall into a local optimum and slow convergence. The Teaching-Learning-Based optimization (TLBO) algorithm has been proven beneficial in a wide range of engineering applications. To this end, this dissertation focusses on using TLBO and its improved algorithms to design five types of digital filters, which include linear phase FIR digital filters, multiobjective general FIR digital filters, multiobjective IIR digital filters, two-dimensional (2-D) linear phase FIR digital filters, and 2-D nonlinear phase FIR digital filters. Among them, linear phase FIR digital filters, 2-D linear phase FIR digital filters, and 2-D nonlinear phase FIR digital filters use single-objective type of TLBO algorithms to optimize; multiobjective general FIR digital filters use multiobjective non-dominated TLBO (MOTLBO) algorithm to optimize; and multiobjective IIR digital filters use MOTLBO with Euclidean distance to optimize. The design results of the five types of filter designs are compared to those obtained by other state-of-the-art design methods. In this dissertation, two major improvements are proposed to enhance the performance of the standard TLBO algorithm. The first improvement is to apply a gradient-based learning to replace the TLBO learner phase to reduce approximation error(s) and CPU time without sacrificing design accuracy for linear phase FIR digital filter design. The second improvement is to incorporate Manhattan distance to simplify the procedure of the multiobjective non-dominated TLBO (MOTLBO) algorithm for general FIR digital filter design. The design results obtained by the two improvements have demonstrated their efficiency and effectiveness

A Novel Method for Acoustic Noise Cancellation

Over the last several years Acoustic Noise Cancellation (ANC) has been an active area of research and various adaptive techniques have been implemented to achieve a better online acoustic noise cancellation scheme. Here we introduce the various adaptive techniques applied to ANC viz. the LMS algorithm, the Filtered-X LMS algorithm, the Filtered-S LMS algorithm and the Volterra Filtered-X LMS algorithm and try to understand their performance through various simulations. We then take up the problem of cancellation of external acoustic feedback in hearing aid. We provide three different models to achieve the feedback cancellation. These are - the adaptive FIR Filtered-X LMS, the adaptive IIR LMS and the adaptive IIR PSO models for external feedback cancellation. Finally we come up with a comparative study of the performance of these models based on the normalized mean square error minimization provided by each of these feedback cancellation schemes

Spatio-temporal prediction of wind fields

Short-term wind and wind power forecasts are required for the reliable and economic operation of power systems with significant wind power penetration. This thesis presents new statistical techniques for producing forecasts at multiple locations using spatiotemporal information. Forecast horizons of up to 6 hours are considered for which statistical methods outperform physical models in general. Several methods for producing hourly wind speed and direction forecasts from 1 to 6 hours ahead are presented in addition to a method for producing five-minute-ahead probabilistic wind power forecasts. The former have applications in areas such as energy trading and defining reserve requirements, and the latter in power system balancing and wind farm control. Spatio-temporal information is captured by vector autoregressive (VAR) models that incorporate wind direction by modelling the wind time series using complex numbers. In a further development, the VAR coefficients are replaced with coefficient functions in order to capture the dependence of the predictor on external variables, such as the time of year or wind direction. The complex-valued approach is found to produce accurate speed predictions, and the conditional predictors offer improved performance with little additional computational cost. Two non-linear algorithms have been developed for wind forecasting. In the first, the predictor is derived from an ensemble of particle swarm optimised candidate solutions. This approach is low cost and requires very little training data but fails to capitalise on spatial information. The second approach uses kernelised forms of popular linear algorithms which are shown to produce more accurate forecasts than their linear equivalents for multi-step-ahead prediction. Finally, very-short-term wind power forecasting is considered. Five-minute-ahead parametric probabilistic forecasts are produced by modelling the predictive distribution as logit-normal and forecasting its parameters using a sparse-VAR (sVAR) approach. Development of the sVAR is motivated by the desire to produce forecasts on a large spatial scale, i.e. hundreds of locations, which is critical during periods of high instantaneous wind penetration.Short-term wind and wind power forecasts are required for the reliable and economic operation of power systems with significant wind power penetration. This thesis presents new statistical techniques for producing forecasts at multiple locations using spatiotemporal information. Forecast horizons of up to 6 hours are considered for which statistical methods outperform physical models in general. Several methods for producing hourly wind speed and direction forecasts from 1 to 6 hours ahead are presented in addition to a method for producing five-minute-ahead probabilistic wind power forecasts. The former have applications in areas such as energy trading and defining reserve requirements, and the latter in power system balancing and wind farm control. Spatio-temporal information is captured by vector autoregressive (VAR) models that incorporate wind direction by modelling the wind time series using complex numbers. In a further development, the VAR coefficients are replaced with coefficient functions in order to capture the dependence of the predictor on external variables, such as the time of year or wind direction. The complex-valued approach is found to produce accurate speed predictions, and the conditional predictors offer improved performance with little additional computational cost. Two non-linear algorithms have been developed for wind forecasting. In the first, the predictor is derived from an ensemble of particle swarm optimised candidate solutions. This approach is low cost and requires very little training data but fails to capitalise on spatial information. The second approach uses kernelised forms of popular linear algorithms which are shown to produce more accurate forecasts than their linear equivalents for multi-step-ahead prediction. Finally, very-short-term wind power forecasting is considered. Five-minute-ahead parametric probabilistic forecasts are produced by modelling the predictive distribution as logit-normal and forecasting its parameters using a sparse-VAR (sVAR) approach. Development of the sVAR is motivated by the desire to produce forecasts on a large spatial scale, i.e. hundreds of locations, which is critical during periods of high instantaneous wind penetration

Signal processing with optical delay line filters for high bit rate transmission systems

In den letzten Jahrzehnten ist das globale Kommunikationssystem in einem immer größerem Maße ein integraler Bestandteil des täglichen Lebens geworden. Optische Kommunikationssysteme sind die technologische Basis für diese Entwicklung. Nur Fasern können die riesige benötigte Bandbreite bereitstellen. Während für die ersten optischen Übertragungssysteme die Faser als "flacher" Kanal betrachtet werden konnte, machen Wellenlängenmultiplex und steigende Übertragungsraten die Einbeziehung von immer mehr physikalischen Effekten notwendig. Bei einer Erhöhung der Kanaldatenrate auf 40 Gbit/s und mehr ist die statische Kompensation von chromatischer Dispersion nicht mehr ausreichend. Die intrinsische Toleranz der Modulationsformate gegenüber Dispersion nimmt quadratisch mit der Symbolrate ab. Daher können beispielsweise durch Umwelteinflüsse hervorgerufene Dispersionsschwankungen die Dispersionstoleranz der Modulationsformate überschreiten. Dies macht eine adaptive Dispersionskompensation notwendig, was gleichzeitig auch Dispersionsmonitoring erfordert, um den adaptiven Kompensator steuern zu können. Vorhandene Links können mit Restdispersionskompensatoren ausgestattet werden, um sie für Hochgeschwindigkeitsübertragungen zu ertüchtigen. Optische Kompensationstechniken sind unabhängig von der Kanaldatenrate. Daher wird eine Erhöhung der Datenrate problemlos unterstützt. Optische Kompensatoren können WDM-fähig gebaut werden, um mehrere Kanäle auf einmal zu entzerren. Das Buch beschäftigt sich mit optischen Delay-Line-Filtern als eine Klasse von optischen Kompensatoren. Die Filtersynthese von solchen Delay-Line-Filtern wird behandelt. Der Zusammenhang zwischen optischen Filtern und digitalen FIR-Filtern mit komplexen Koeffizienten im Zusammenhang mit kohärenter Detektion wird aufgezeigt. Iterative und analytische Methoden, die die Koeffizienten für dispersions- und dispersions-slope-kompensierende Filter produzieren, werden untersucht. Genauso wichtig wie die Kompensation von Dispersion ist die Schätzung der Dispersion eines Signals. Mit Delay-Line-Filtern können die Restseitenbänder eines Signals genutzt werden, um die Dispersion zu messen. Alternativ kann nichtlineare Detektion angewandt werden, um die Pulsverbreiterung, die hauptsächlich von der Dispersion herrührt, zu schätzen. Mit gemeinsamer Dispersionskompensation und Dispersionsmonitoring können Dispersionskompensatoren auf die Signalverzerrungen eingestellt werden. Spezielle Eigenschaften der Filter zusammen mit der analytischen Beschreibung können genutzt werden, um schnelle und zuverlässige Steueralgorithmen zur Filtereinstellung bereitzustellen. Schließlich wurden Prototypen derartiger faseroptischen Kompensatoren von chromatischer Dispersion und Dispersions-Slope hergestellt und charakterisiert. Die Einheiten und ihr Systemverhalten wird gezeigt und diskutiert.Over the course of the past decades, the global communication system has become a central part of people's everyday lives. Optical communication systems are the technological basis for this development. Only fibers can provide the huge bandwidth that is required. Where the fiber could be regarded as a flat channel for the first optical transmission systems wavelength multiplexing and increasing line rates made it necessary to take more and more physical effects into account. When the line rates are increased to 40 Gbit/s and higher static chromatic dispersion compensation is not enough. The modulation format's intrinsic tolerance for dispersion decreases quadratically with the symbol rate. Thus, environmentally induced chromatic dispersion fluctuations may exceed the dispersion tolerance of the modulation formats. This makes an adaptive dispersion compensation necessary implying also the need for a monitoring scheme to steer the adaptive compensator. Legacy links that are CD-compensated by DCFs can be upgraded with residual dispersion compensators to make them ready for high speed transmission. Optical compensation is independent from the line rate. Hence, increasing the data rates is inherently supported. Optical compensators can be built WDM ready compensating multiple channels at once. The book deals with optical delay line filters as one class of optical compensators. The filter synthesis of such delay line filters is addressed. The connection between optical filters and digital FIR filters with complex coefficients that are used in conjunction with coherent detection could be shown. Iterative and analytical methods that produce the coefficients for dispersion (and also dispersion slope) compensating filters are researched. As important as the compensation of dispersion is the estimation of the dispersion of a signal. Using delay line filters, the vestigial sidebands of a signal can be used to measure the dispersion. Alternatively, nonlinear detection can be used to estimate the pulse broadening which is caused mainly by dispersion. With dispersion compensation and dispersion monitoring, dispersion compensators can be adapted to the signal's impairment. Special properties of the filter in conjunction with an analytical description can be used to provide a fast and reliable control algorithm for setting the filter to a given dispersion and centering it on a signal. Finally, prototypes of such fiber optic chromatic dispersion and dispersion slope compensation filters were manufactured and characterized. The device and system characterization of the prototypes is presented and discussed