501 research outputs found
Revisiting Multi-Step Nonlinearity Compensation with Machine Learning
For the efficient compensation of fiber nonlinearity, one of the guiding
principles appears to be: fewer steps are better and more efficient. We
challenge this assumption and show that carefully designed multi-step
approaches can lead to better performance-complexity trade-offs than their
few-step counterparts.Comment: 4 pages, 3 figures, This is a preprint of a paper submitted to the
2019 European Conference on Optical Communicatio
Revisiting Efficient Multi-Step Nonlinearity Compensation with Machine Learning: An Experimental Demonstration
Efficient nonlinearity compensation in fiber-optic communication systems is
considered a key element to go beyond the "capacity crunch''. One guiding
principle for previous work on the design of practical nonlinearity
compensation schemes is that fewer steps lead to better systems. In this paper,
we challenge this assumption and show how to carefully design multi-step
approaches that provide better performance--complexity trade-offs than their
few-step counterparts. We consider the recently proposed learned digital
backpropagation (LDBP) approach, where the linear steps in the split-step
method are re-interpreted as general linear functions, similar to the weight
matrices in a deep neural network. Our main contribution lies in an
experimental demonstration of this approach for a 25 Gbaud single-channel
optical transmission system. It is shown how LDBP can be integrated into a
coherent receiver DSP chain and successfully trained in the presence of various
hardware impairments. Our results show that LDBP with limited complexity can
achieve better performance than standard DBP by using very short, but jointly
optimized, finite-impulse response filters in each step. This paper also
provides an overview of recently proposed extensions of LDBP and we comment on
potentially interesting avenues for future work.Comment: 10 pages, 5 figures. Author version of a paper published in the
Journal of Lightwave Technology. OSA/IEEE copyright may appl
Convolutive Blind Source Separation Methods
In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks
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Video content analysis for automated detection and tracking of humans in CCTV surveillance applications
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented
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