Improving the Control Energy in Model Reference Adaptive Controllers Using Fractional Adaptive Laws

This paper presents the analysis of the control energy consumed in model reference adaptive control (MRAC) schemes using fractional adaptive laws, through simulation studies. The analysis is focused on the energy spent in the control signal represented by means of the integral of the squared control input (ISI). Also, the behavior of the integral of the squared control error (ISE) is included in the analysis. The orders of the adaptive laws were selected by particle swarm optimization (PSO), using an objective function including the ISI and the ISE, with different weighting factors. The results show that, when ISI index is taken into account in the optimization process to determine the orders of adaptive laws, the resulting values are fractional, indicating that control energy of the scheme might be better managed if fractional adaptive laws are used.      

High‐Order Recursive Filtering of Non‐Uniformly Sampled Signals for Image and Video Processing

We present a discrete‐time mathematical formulation for applying recursive digital filters to non‐uniformly sampled signals. Our solution presents several desirable features: it preserves the stability of the original filters; is well‐conditioned for low‐pass, high‐pass, and band‐pass filters alike; its cost is linear in the number of samples and is not affected by the size of the filter support. Our method is general and works with any non‐uniformly sampled signal and any recursive digital filter defined by a difference equation. Since our formulation directly uses the filter coefficients, it works out‐of‐the‐box with existing methodologies for digital filter design. We demonstrate the effectiveness of our approach by filtering non‐uniformly sampled signals in various image and video processing tasks including edge‐preserving color filtering, noise reduction, stylization, and detail enhancement. Our formulation enables, for the first time, edge‐aware evaluation of any recursive infinite impulse response digital filter (not only low‐pass), producing high‐quality filtering results in real time.     

Proteomics in aortic aneurysm – What have we learnt so far?

Aortic aneurysm is a deceptively indolent disease that can cause severe complications such as aortic rupture and dissection. In the normal aorta, vascular smooth muscle cells within the medial layer produce and sustain the extracellular matrix (ECM) that provides structural support but also retains soluble growth factors and regulates their distribution. Although the ECM is an obvious target to identify molecular processes leading to structural failure within the vessel wall, an in-depth proteomics analysis of this important sub-proteome has not been performed. Most proteomics analyses of the vasculature to date used homogenized tissue devoid of spatial information. In such homogenates, quantitative proteomics comparisons are hampered by the heterogeneity of clinical samples (i.e. cellular composition) and the dynamic range limitations stemming from highly abundant cellular proteins. An unbiased proteomics discovery approach targeting the ECM instead of the cellular proteome may decipher the complex, multivalent signals that are presented to cells during aortic remodelling. A better understanding of the ECM in healthy and diseased vessels will provide important pathogenic insights and has potential to reveal novel biomarkers.     

Interior-point methods for symmetric optimization based on a class of non-coercive kernel functions

In this paper, we generalize polynomial-time primal–dual interior-point methods for symmetric optimization based on a class of kernel functions, which is not coercive. The corresponding barrier functions have a finite value at the boundary of the feasible region. They are not exponentially convex and also not strongly convex like many usual barrier functions. Moreover, we analyse the accuracy of the algorithm for this class of functions and we obtain an upper bound for the accuracy which depends on a parameter of the class.     

Variable Structure Control with chattering elimination and guaranteed stability for a generalized T-S model

In this paper, a fuzzy based Variable Structure Control (VSC) with guaranteed stability is presented. The main objective is to obtain an improved performance of highly non-linear unstable systems. The main contribution of this work is that, firstly, new functions for chattering reduction and error convergence without sacrificing invariant properties are proposed, which is considered the main drawback of the VSC control. Secondly, the global stability of the controlled system is guaranteed.The well known weighting parameters approach, is used in this paper to optimize local and global approximation and modeling capability of T-S fuzzy model.A one link robot is chosen as a nonlinear unstable system to evaluate the robustness, effectiveness and remarkable performance of optimization approach and the high accuracy obtained in approximating nonlinear systems in comparison with the original T-S model. Simulation results indicate the potential and generality of the algorithm. The application of the proposed FLC-VSC shows that both alleviation of chattering and robust performance are achieved with the proposed FLC-VSC controller. The effectiveness of the proposed controller is proven infront of disturbances and noise effects.     

A document is known by the company it keeps: neighborhood consensus for short text categorization

During the last decades the Web has become the greatest repository of digital information. In order to organize all this information, several text categorization methods have been developed, achieving accurate results in most cases and in very different domains. Due to the recent usage of Internet as communication media, short texts such as news, tweets, blogs, and product reviews are more common every day. In this context, there are two main challenges; on the one hand, the length of these documents is short, and therefore, the word frequencies are not informative enough, making text categorization even more difficult than usual. On the other hand, topics are changing constantly at a fast rate, causing the lack of adequate amounts of training data. In order to deal with these two problems we consider a text classification method that is supported on the idea that similar documents may belong to the same category. Mainly, we propose a neighborhood consensus classification method that classifies documents by considering their own information as well as information about the category assigned to other similar documents from the same target collection. In particular, the short texts we used in our evaluation are news titles with an average of 8 words. Experimental results are encouraging; they indicate that leveraging information from similar documents helped to improve classification accuracy and that the proposed method is especially useful when labeled training resources are limited.     

Advance human–machine interface automatic evaluation

The need for accessibility evaluation tools is motivated by several endogenous and exogenous reasons coming from the end user (the designer and the developer) and companies releasing information systems. Existing evaluation tools mainly concentrate on examining the code of Web pages: Web pages more and more frequently contain non-HTML parts that entirely escape from being treated by existing techniques. This is the case of the advanced human–machine interface (AHMI), a piece of software programmed in C/C++, used for controlling the advanced flight management system in the aircraft cockpit. Studying this new user interface (UI) requires a structured approach to evaluate and validate AHMI designs. The goal in this work is to develop an evaluation tool to automate the process of evaluating the AHMI. The method addresses: support of multiple bases of guidelines (accessibility or usability or both) on-demand (partial or total evaluation), with different levels of details (a presentation for developers and for those responsible for certifying accessibility). The method goes a step toward the automatic evaluation of UI containing non-HTML parts.     

Forecasting binary longitudinal data by a functional PC-ARIMA model

In order to forecast time evolution of a binary response variable from a related continuous time series a functional logit model is proposed. The estimation of this model from discrete time observations of the predictor is solved by using functional principal component analysis and ARIMA modelling of the associated discrete time series of principal components. The proposed model is applied to forecast the risk of drought from El Niño phenomenon.     

Recognition of circular patterns on GPUs: Performance analysis and contributions

We develop a novel approach for computing the circle Hough transform entirely on graphics hardware (GPU). A primary role is assigned to vertex processors and the rasterizer, overshadowing the traditional foreground of pixel processors and enhancing parallel processing. Resources like the vertex cache or blending units are studied too, with our set of optimizations leading to extraordinary peak gain factors exceeding 358x over a typical CPU execution. Software optimizations, like the use of precomputed tables or gradient information and hardware improvements, like hyperthreading and multicores are explored on CPUs as well. Overall, the GPU exhibits better scalability and much greater parallel performance to become a solid alternative for computing the classical circle Hough transform versus those optimal methods run on emerging multicore architectures.