Adaptive weighted least squares algorithm for Volterra signal modeling

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Applying prediction techniques to reduce uplink transmission and energy requirements in mobile free-viewpoint video applications

The increased efficiency of video compression algorithms, the improvements registered in reliability, data rates and quality of service of wireless transmission technologies, and the development of mobile multimedia terminals have made possible the implementation of Free-Viewpoint Video (FVV) technology on mobile platforms. The mobile environment however presents several restrictions. Two of these limiting factors being bandwidth constraints and energy availability in battery-operated mobile terminals. This paper looks at the possibility of employing prediction algorithms at the FVV server to anticipate the next viewpoint expected by the user. In doing so, the number of uplink requests is reduced to situations where the estimated view defers from the requested one and the associated transmissions required in retraining the algorithm once this occurs. Simulation results on two different prediction techniques demonstrate that the uplink transmission rate is reduced by up to 96.7% when emulating a conventional FVV usage scenario. Both prediction algorithms infer a substantial decrease in the mobile terminal’s power consumption and reduce the network’s uplink bandwidth utilization.peer-reviewe

A comparison of the performance of prediction techniques in curtailing uplink transmission and energy requirements in mobile free-viewpoint video applications

The rapid deployment of multimedia services on mobile networks together with the increase in consumer demand for immersive entertainment have paved the way for innovative video representations. Amongst these new applications is free-viewpoint video (FVV), whereby a scene is captured by an array of cameras distributed around a site to allow the user to alter the viewing perspective on demand, creating a three-dimensional (3D) effect. The implementation on mobile infrastructures is however still hindered by intrinsic wireless limitations, such as bandwidth constraints and limited battery power. To this effect, this paper presents a solution that reduces the number of uplink requests performed by the mobile terminal through view prediction techniques. The implementation and performance of four distinct prediction algorithms in anticipating the next viewpoint request by a mobile user in a typical FVV system are compared and contrasted. Additionally, each solution removes the jitter experienced by the user whilst moving from a view pattern to another by allowing some hysterisis in the convergence signal. Thus, this technique enhances the performance of all the algorithms by taking into consideration the fact that the user adapts to the presented views and will react accordingly. Simulation results illustrate that an uplink transmission reduction of up to 96.7% can be achieved in a conventional FVV simulation scenario. Therefore, the application of prediction schemes can drastically reduce the mobile terminal’s power consumption and bandwidth resource requirements on the uplink channel.peer-reviewe

Fast recursive least squares adaptive second-order volterra filter and its performance analysis

Journal ArticleAbstract-This paper presents a fast, recursive least squares (RLS) adaptive nonlinear filter. The nonlinearity is modeled using a second-order Volterra series expansion. The structure presented in the paper makes use of the ideas of fast RLS multichannel filters and has a computational complexity of 0 (N') multiplications per time instant where N - 1 represents the memory span in number of samples of the nonlinear system model. This compares with 0 (N 6) multiplications required for direct implementation. A theoretical performance analysis of the steady-state behavior of the adaptive filter operating in both stationary and nonstationary environments is presented in the paper. The analysis shows that, when the input is zero mean, Gaussian distributed, and the adaptive filter is operating in a stationary environment, the steady-state excess mean-squared error due to the coefficient noise vector is independent of the statistics of the input signal. The results of several simulation experiments are included in the paper. These results show that the adaptive Volterra filter performs well in a variety of situations. Furthermore, the steady-state behavior predicted by the analysis is in very good agreement with the experimental results

Distributed acoustic sensing for seismic activity monitoring

Continuous, real-time monitoring of surface seismic activity around the globe is of great interest for acquiring new insight into global tomography analyses and for recognition of seismic patterns leading to potentially hazardous situations. The already-existing telecommunication fiber optic network arises as an ideal solution for this application, owing to its ubiquity and the capacity of optical fibers to perform distributed, highly sensitive monitoring of vibrations at relatively low cost (ultra-high density of point sensors available with minimal deployment of new equipment). This perspective article discusses early approaches on the application of fiber-optic distributed acoustic sensors (DASs) for seismic activity monitoring. The benefits and potential impact of DAS technology in these kinds of applications are here illustrated with new experimental results on teleseism monitoring based on a specific approach: the so-called chirped-pulse DAS. This technology offers promising prospects for the field of seismic tomography due to its appealing properties in terms of simplicity, consistent sensitivity across sensing channels, and robustness. Furthermore, we also report on several signal processing techniques readily applicable to chirped-pulse DAS recordings for extracting relevant seismic information from ambient acoustic noise. The outcome presented here may serve as a foundation for a novel conception for ubiquitous seismic monitoring with minimal investment

Distributed acoustic sensing for seismic activity monitoring

Continuous, real-time monitoring of surface seismic activity around the globe is of great interest for acquiring new insight into global tomography analyses and for recognition of seismic patterns leading to potentially hazardous situations. The already-existing telecommunication fiber optic network arises as an ideal solution for this application, owing to its ubiquity and the capacity of optical fibers to perform distributed, highly sensitive monitoring of vibrations at relatively low cost (ultra-high density of point sensors available with minimal deployment of new equipment). This perspective article discusses early approaches on the application of fiber-optic distributed acoustic sensors (DASs) for seismic activity monitoring. The benefits and potential impact of DAS technology in these kinds of applications are here illustrated with new experimental results on teleseism monitoring based on a specific approach: the so-called chirped-pulse DAS. This technology offers promising prospects for the field of seismic tomography due to its appealing properties in terms of simplicity, consistent sensitivity across sensing channels, and robustness. Furthermore, we also report on several signal processing techniques readily applicable to chirped-pulse DAS recordings for extracting relevant seismic information from ambient acoustic noise. The outcome presented here may serve as a foundation for a novel conception for ubiquitous seismic monitoring with minimal investment

Adaptive estimation and equalisation of the high frequency communications channel

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Detection and classification of non-stationary signals using sparse representations in adaptive dictionaries

Automatic classification of non-stationary radio frequency (RF) signals is of particular interest in persistent surveillance and remote sensing applications. Such signals are often acquired in noisy, cluttered environments, and may be characterized by complex or unknown analytical models, making feature extraction and classification difficult. This thesis proposes an adaptive classification approach for poorly characterized targets and backgrounds based on sparse representations in non-analytical dictionaries learned from data. Conventional analytical orthogonal dictionaries, e.g., Short Time Fourier and Wavelet Transforms, can be suboptimal for classification of non-stationary signals, as they provide a rigid tiling of the time-frequency space, and are not specifically designed for a particular signal class. They generally do not lead to sparse decompositions (i.e., with very few non-zero coefficients), and use in classification requires separate feature selection algorithms. Pursuit-type decompositions in analytical overcomplete (non-orthogonal) dictionaries yield sparse representations, by design, and work well for signals that are similar to the dictionary elements. The pursuit search, however, has a high computational cost, and the method can perform poorly in the presence of realistic noise and clutter. One such overcomplete analytical dictionary method is also analyzed in this thesis for comparative purposes. The main thrust of the thesis is learning discriminative RF dictionaries directly from data, without relying on analytical constraints or additional knowledge about the signal characteristics. A pursuit search is used over the learned dictionaries to generate sparse classification features in order to identify time windows that contain a target pulse. Two state-of-the-art dictionary learning methods are compared, the K-SVD algorithm and Hebbian learning, in terms of their classification performance as a function of dictionary training parameters. Additionally, a novel hybrid dictionary algorithm is introduced, demonstrating better performance and higher robustness to noise. The issue of dictionary dimensionality is explored and this thesis demonstrates that undercomplete learned dictionaries are suitable for non-stationary RF classification. Results on simulated data sets with varying background clutter and noise levels are presented. Lastly, unsupervised classification with undercomplete learned dictionaries is also demonstrated in satellite imagery analysis