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Identifying the most recent heavy hitters, i.e., finding the items with the highest appearances in a high speed data stream is a fundamental problem in real-time stream processing. The requirement of real-time stream applications raises significant challenges to this problem in terms of the processing latency, the space usage and the precision. Traditional schemes leverage the sliding windows based...
In this paper, a novel model reference adaptive (MRA) based speed and rotor position estimation strategy is proposed for the surface mounted permanent magnet synchronous motor (SPMSM). In the proposed strategy, a new adaptive law is proposed by considering the MRA adaptive law as a controller. Furthermore a parameter self-turning strategy and a speed compensation strategy are proposed to improve the...
We propose an algorithm to separate simultaneously speaking persons from each other, the “cocktail party problem”, using a single microphone. Our approach involves a deep recurrent neural networks regression to a vector space that is descriptive of independent speakers. Such a vector space can embed empirically determined speaker characteristics and is optimized by distinguishing between speaker masks...
In this paper, an adaptive error concealment method is proposed to recover the motion vectors of degraded macroblocks (MBs) in a frame, based on analyzing the behavior of the MBs in the frames. In this method, the behavior of the degraded MB is first estimated considering the obtained information from motion vectors of neighboring MBs in the current frame and collocated MBs in the previous frames...
State-of-charge (SOC) estimation methods based on battery model rely heavily on the accuracy of model parameters. And these parameters could vary with environment and the types of batteries. Online battery modeling methods can improve the robustness of SOC estimation algorithms through updating model constantly with real-time data. These methods have far more profound significance on algorithm adaptability...
Omnidirectional images describe the color information at a given position from all directions. Affordable 360° cameras have recently been developed leading to an explosion of the 360° data shared on social networks. However, an omnidirectional image does not contain interesting content everywhere. Some part of the images are indeed more likely to be looked at by some users than others. Knowing these...
The objective of this paper is to estimate the compressor discharge temperature measurements on an industrial gas turbine that is undergoing commissioning at site, using a data-driven model which is built using the test bed measurements of the engine. This paper proposes a Bayesian neuro-fuzzy modelling (BNFM) approach, which combines the adaptive neuro-fuzzy inference system (ANFIS) and variational...
The scattering model based on geometrical theory of diffraction (GTD) is exactly related to the electromagnetic scattering mechanism of the targets. The parameter of GTD is of significant important to the description of the target. In this paper, we propose a novel method for the parameter estimation of GTD model. The proposed method utilizes the iterative adaptive approach to estimate model parameters...
Markov Random Fields are widely used to model lightfield stereo matching problems. However, most previous approaches used fixed parameters and did not adapt to lightfield statistics. Instead, they explored explicit vision cues to provide local adaptability and thus enhanced depth quality. But such additional assumptions could end up confining their applicability, e.g. algorithms designed for dense...
This paper presents an estimation procedure of equivalent electric circuit parameters of a lithium-ion cell using data from a pulse charge/discharge test. An extraction process of the model parameters is based on a transient voltage response of relaxation process following a current pulse. The entire procedure is straightforward and there is no restriction in a number of parallel RC-branches in the...
The article considers methods of processing uncertainties in solving dynamic planning problems. Various types of uncertainties are considered, such as stochastic uncertainties, uncertainties in the parameters and structure of models, the uncertainty of the amplitude type and the probabilistic type. Methods for processing data for reducing uncertainties are proposed.
We consider example-guided audio source separation approaches, where the audio mixture to be separated is supplied with source examples that are assumed matching the sources in the mixture both in frequency and time. These approaches were successfully applied to the tasks such as source separation by humming, score-informed music source separation, and music source separation guided by covers. Most...
The industrial demands for accurate localization systems have been rapidly increasing after the introduction of the Internet of Things (IoT) concept. Self localization and tracking transmitting sources are considered essential parts of IoT applications. In this paper we studied the possibility of applying angle of arrival (AoA) estimations to localize an IoT transceiver device in an indoor environment...
This paper presents a robustness-improving discrete-time current controller utilizing predictive current control (PCC) for permanent magnet linear synchronous motor (PMLSM). The stability and robustness of the PCC system is highly affected by the parameters mismatch between the controller and the plant, and the inherent digital control delay can limit the bandwidth. Toward this, an integrated method...
This paper deals with a discrete predictive control design for motion control of robotic systems. The design considers time-varying state-space robot model. It is assumed that used robot state has to be estimated from measured robot outputs. These outputs represent controlled quantities including a bounded noise. Considering this arrangement, the paper introduces a novel solution to the state and...
Nonlinear acoustic echo cancellation (NAEC) can be mainly addressed by solving two different sub-problems: the estimation of the acoustic impulse response and the modeling of the nonlinearities rebounding in it, mostly caused by the electroacoustic chain. Both the modeling processes share an important characteristic: the majority of the parameters to be estimated are very close to zero, with only...
When identifying a nonstationary autoregressive process, e.g. for the purpose of signal prediction or parametric spectrum estimation, two important decisions must be taken. First, one should choose the appropriate order of the autoregressive model, i.e., the number of autoregressive coefficients that will be estimated. Second, if identification is carried out using the local estimation technique,...
Estimating the initial background of a scene is a key prerequisite for several applications in video analytics. In this paper, we present a simple approach that takes into account spatio-temporal motion intensities while estimating the true background. We tested the algorithm on real video sequences from the Scene Background Initialization (SBI) benchmark dataset, and the results show that the algorithm...
Digital predistortion (DPD) is an effective power amplifier (PA) linearization technique improving the system energy efficiency. At this point, real-time DPD adaptation is still an open issue due to the high computational complexity during the coefficients estimation procedure. Online censoring approach, which is effective in reducing the redundant data samples, can be applied in the DPD coefficients...
The robust adaptive control of uncertain system with unknown time-varying control coefficient is discussed. A novel output sampled control scheme based on characteristic model with neural network estimator is proposed. The design of the control scheme includes characteristic modeling, estimation for the characteristic parameters, and characteristic model-based adaptive control. The estimation method...
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