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To achieve spatial diversity and improve the average bit error probability (ABEP) of Generalized Space Shift Keying (GSSK) in Multiple-Input and Multiple-Output (MIMO) channels with low feedback overhead and computational complexity, we propose a three-step adaptive precoding strategy for single-user GSSK (SU-GSSK) transmission by exploiting the channel state information at the transmitter (CSIT)...
The exploitation of social networks and collaborative systems is a phenomenon that is gradually integrated with the practice of information retrieval on the Internet. These systems of Web 2.0, allowing users to collaborate via the free content indexing using keywords or tags; creating structures represented as tripartite hypergraphs of users, tags and resources, called folksonomies. By examining different...
In large scale wireless sensor network (WSN) energy reservation is crucial, as in such an environment sensors cannot be periodically maintain. Therefore we investigate the opportunity to reduce the power consumption by reducing the data rate traffic of the network. This is done utilizing either data correlation and sparsity in one dimension or the spatial sparsity among clustered sensor nodes. We...
During the last years, more and more mechanical applications saw the introduction of active control strategies. In detail, vibration suppression is often associated to the need of increasing system performance or improving the structure health and lifetime. This goal can be achieved considering both passive and active solutions. Among the active ones, many different control strategies have been proposed...
A novel digital predistorter (DPD) for the linearization of power amplifiers (PAs) is presented. The structure is based on a doubly-symmetric Parafac tensor decomposition of Volterra kernels and takes advantage of a frequency-domain representation, what makes it specially attractive for the transmission of orthogonal frequency division multiplexing (OFDM) signals. The approach has been successfully...
In this paper we present a formation control approach based on Gaussian potential functions, each parametrized by an agent-related control parameter. This results in different characteristics of attractive and repulsive forces among agents and targets, as well as among agents themselves and dependence of the formation's potential structure on the change of agent's configurations. Furthermore, we show...
When system identification is performed online and predictions of the system response are requested often (as in model predictive control formulations), the identification model with the best performance may not be fixed with time. Besides, more accurate models may require larger training times compared to low-order linear models. This is particularly evident in thermal dynamics in buildings where...
Malicious websites are a major cyber attack vector, and effective detection of them is an important cyber defense task. The main defense paradigm in this regard is that the defender uses some kind of machine learning algorithms to train a detection model, which is then used to classify websites in question. Unlike other settings, the following issue is inherent to the problem of malicious websites...
How to make dynamic recommendations under volatile user interest drifts has been a problem of great interest in modern recommender systems, where challenges lie in accurate and efficient measurement, modeling, and prediction of the user interest drifts. This paper studies a category-based approach to the problem with the key idea that items are aggregated into categories and recommendations are made...
The paper deals with identification of a cloud based evolving system. The antecedent part of a fuzzy rule-based system is defined by clouds and density distribution as proposed by Angelov and Yager [1], [2]. But in the current paper Mahalanobis distance is used rather than the Euclidean one when calculating the density. The idea behind is that the shape of the clouds should be reflected in the density...
This paper proposes a method for adaptive identification and predictive control using an online sequential extreme learning machine based on the recursive partial least-squares method (OS-ELM-RPLS). OL-ELM-RPLS is an improvement to the online sequential extreme learning machine based on recursive least-squares (OS-ELM-RLS) introduced in [1]. Like in the batch extreme learning machine (ELM), in OS-ELM-RLS...
The paper proposes an implementation of a motion sensorless control system in a very wide speed range — from zero speed operation, based on direct and quadrature inductance components of the stator flux linkage for the interior permanent magnet synchronous motor-IPMSM, without signal injection. The proposed observer is developed using the Lyapunov design, resulting a scheme with a full observer for...
In this paper, we explore two new approaches for software defect prediction. The similarity-based approach predicts the number of latent defects of a software module from those of modules most similar to it. The rank-based approach uses machine learning models specially trained to predict the ranks of software modules based on their actual number of latent defects. In both approaches, we use technical...
In this work, we investigate the impact of social influence of a Facebook fan page on movie box offices. We aim to enhance the accuracy of predicting the box office by leveraging the social influence among users in the fan page. We develop the Global Influence Model to compute the user influence and predict the engagements between the fan page and users. In addition, we propose the Linear Box Office...
The paper presents a gain-scheduled LQR control system for a nonlinear model of permanent magnet synchronous motor (PMSM). The most popular cascade FOC (Field Oriented Control) structure including a few single-loop PI control systems is replaced with a single multivariable state feedback controller. Due to a nonlinear nature of the PMSM dynamics equations the developed gain-scheduled controller is...
This paper presents methods for improving the accuracy of vector-based position measurements through the decoupling of multiple non-ideal sensor properties using model reference adaptive system (MRAS) techniques. The non-ideal sensor properties considered are: signal scaling errors (amplitude imbalance on the vector components), signal offsets, imperfect orthogonality (quadrature error) between sensor...
The protein structure has always been under significant exploration, as this is vitally responsible for the basic functionality. Understanding the formation of these structures has gradually been called as ‘the protein folding problem’. The solution for this problem is basically concerned with the ultimate aim to attain the native state. Broadly the methods to predict the ultimate goal, are categorized...
The acoustic docking control problem is investigated for the fully-actuated autonomous underwater vehicle (AUV) equipped with the USBL transceiver, which provides the positions of the two transponders on the dock station. Similar to the image-based visual servo control technology, the dynamics of the transponders' positions is modeled with the AUV's linear and angular velocities as the control inputs...
A distributed adaptive algorithm for estimation of sparse unknown parameters in the presence of nonGaussian noise is proposed in this paper based on normalized least mean fourth (NLMF) criterion. At the first step, local adaptive NLMF algorithm is modified by zero norm in order to speed up the convergence rate and also to reduce the steady state error power in sparse conditions. Then, the proposed...
Many researches have done to develop speech recognition systems in the past decades. However, their performance in speaker variabilities lags behind that of human recognition system. In order to solve this problem, speaker adaptation methods have proposed. These methods adapt either the acoustic model parameters or the input features of the speech recognition systems to improve their performance....
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