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Cognitive radio (CR) systems need to detect the presence of a primary user (PU) signal by continuously sensing the spectrum area of interest. Radiowave propagation effects like fading and shadowing often complicate sensing of spectrum holes because the PU signal can be weak in a particular area. Cooperative spectrum sensing is seen as a prospective solution to enhance the detection of PU signals....
This paper addresses two problems of interest in the present time, namely, the characterization of price variations and the corresponding load response to them. The paper begins by defining Price Elasticity Matrices (PEMs) and shows how they can be used to model demands deviation from their scheduled levels due to price differentials. It then explains the reason as to why extending PEMs characterization...
Accurate state of charge (SOC) is critical for battery energy management system in electric vehicle (EV) application. Overcharge and over discharge will shorten battery's lifespan and induce potential safety problem, which may even permanently damage the lithium-ion battery. Thus, a data driven model is proposed for improving the accuracy of SOC estimation in this paper. A preliminary mathematic model...
In this paper various technique are describe for sensorless speed control of permanent magnet synchronous motor (PMSM). For low and medium power range PMSM play important role in motion control application in industry. Such application needs position and speed controllers with high accuracy, fast dynamical response and efficiency in the design process and implementation. In this paper various sensorless...
In cyber-physical systems, state estimation errors can be caused not just by process noise or measurement noise in sensors, but also by errors in the communication network. Jitter in packet delivery over a communication network carrying sensor measurements can result in timing errors which result in large outlier type state estimation errors, such as, for example, velocity estimation errors in vehicular...
In Direction-of-Arrival (DOA) estimation for multiple sources, removal of noisy data points from a set of local DOA estimates increases the resulting estimation accuracy, especially when there are many sources and they have small angular separation. In this work, we propose a post-processing technique for the enhancement of DOA extraction from a set of local estimates using the consistency of these...
HTTP-based adaptive streaming (HAS) has recently been widely deployed on the Internet. In the HAS approach, a video content is encoded at multiple bitrates and the encoded video content is segmented into small parts of fixed durations. The HAS client requests a video segment, and stores it in the playout buffer. Many studies have shown a robust rate adaptation algorithm is critical to ensuring quality-of-experience...
Computer simulation of stiff dynamical systems usually requires researchers and engineers to apply implicit numerical quadrature formulae. The most efficient of stiff ODE solvers use adaptive timestep technique, which reduces computational costs by varying integration step size according to the local truncation error estimation. Due to their stability, the implicit methods can handle large step sizes...
In this article, an adaptive model following control of a nonlinear multi-input multi-output (MIMO) coupled system with unknown parameters is considered. The decoupling matrix of the system is assumed to be singular, so that the system can not be decoupled by static state feedback. A dynamic state feedback with nonlinear structure algorithm is applied to design the controller. An observer is used...
In present work a modified MRAS for induction motors is presented. Associated with vector control, the speed estimation performances are improved especially at low speed. Lyapunov theory based controllers is used to design the controllers of the vector control. Robustness against the parameter variations and the low speeds mode is proven. The experimental tests are conducted using dSPACE DS1104.
Multi-task feature learning aims to identify the shared features among tasks to improve generalization. Recent works have shown that the non-convex learning model often returns a better solution than the convex alternatives. Thus a non-convex model based on the capped-1, 1 regularization was proposed in [1], and the corresponding efficient multi-stage multi-task feature learning algorithm (MSMTFL)...
Despite being an essential prerequisite at the basis of many applications ranging from surveillance to computational photography, the problem of initial background estimation seems to be marginally investigated. In this paper, we present a reliable CNN-based solution to estimate the initial background (BG) of a scene, given not necessarily a whole sequence but just a small set of frames containing...
In video background modeling, ghosting occurs when an object that belongs to the background is assigned to the foreground. In the context of Principal Component Pursuit, this usually occurs when a moving object occludes a high contrast background object, a moving object suddenly stops, or a stationary object suddenly starts moving. Based on a previously developed incremental PCP method, we propose...
In scene analysis, the availability of an initial background model that describes the scene without foreground objects is at the basis of many computer vision applications. Multi-modal models of the scene background are frequently adopted in the applications, where each mode tries to keep track of the multiple background modes observed along the sequence. In this work we specifically address the problem...
The contributions of this paper are towards the successful implementation of secondary flux based model reference adaptive system (SF-MRAS) for the speed estimation and primary field oriented control of brushless doubly-fed reluctance machine (BDFRM) in motoring and generating modes of operation. The speed estimation arrangement uses two pairs of independent secondary flux equations in reference and...
Out-of-vocabulary (OOV) words can pose a particular problem for automatic speech recognition (ASR) of broadcast news. The language models (LMs) of ASR systems are typically trained on static corpora, whereas new words (particularly new proper nouns) are continually introduced in the media. Additionally, such OOVs are often content-rich proper nouns that are vital to understanding the topic. In this...
We present a physically inspired model for the problem of redshift estimation. Typically, redshift estimation has been treated as a regression problem that takes as input magnitudes and maps them to a single target redshift. In this work we acknowledge the fact that observed magnitudes may actually admit multiple plausible redshifts, i.e. the distribution of redshifts explaining the observed magnitudes...
Evolutionary algorithms used to solve complex optimization problems usually need to perform a large number of fitness function evaluations, which often requires huge computational overhead. This paper proposes a self-adaptive similarity-based surrogate model as a fitness inheritance strategy to reduce computationally expensive fitness evaluations. Gaussian similarity measurement, which considers the...
Just noticeable difference (JND), which reveals the visibility of our human visual system (HVS), is useful for image/video coding. Due to the content complexity, it is hard to accurately estimate the JND thresholds for different image blocks (e.g., edge and texture). Research on cognitive science indicates that the HVS is adaptive to extract the visual regularities for scene perception and understanding...
To accurately estimate the reliability of highly reliable rail systems and comply with contractual obligations, rail system suppliers such as ALSTOM require efficient reliability estimation techniques. Standard Monte-Carlo methods in their crude form are inefficient in estimating static network reliability of highly reliable systems. Importance Sampling techniques are an advanced class of variance...
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