Multilayer perceptron-based DFE with lattice structure

The severely distorting channels limit the use of linear equalizers and the use of the nonlinear equalizers then becomes justifiable. Neural-network-based equalizers, especially the multilayer perceptron (MLP)-based equalizers, are computationally efficient alternative to currently used nonlinear filter realizations, e.g., the Volterra type. The drawback of the MLP-based equalizers is, however, their slow rate of convergence, which limit their use in practical systems. In this work, the effect of whitening the input data in a multilayer perceptron-based decision feedback equalizer (DFE) is evaluated. It is shown from computer simulations that whitening the received data employing adaptive lattice channel equalization algorithms improves the convergence rate and bit error rate performances of multilayer perceptron-based DFE. The adaptive lattice algorithm is a modification to the one developed by Ling and Proakis (1985). The consistency in performance is observed in both time-invariant and time-varying channels. Finally, it is found in this work that, for time-invariant channels, the MLP DFE outperforms the least mean squares (LMS)-based DFE. However, for time-varying channels comparable performance is obtained for the two configuration

Multilayer perceptron-based DFE with lattice structure

The severely distorting channels limit the use of linear equalizers and the use of the nonlinear equalizers then becomes justifiable. Neural-network-based equalizers, especially the multilayer perceptron (MLP)-based equalizers, are computationally efficient alternative to currently used nonlinear filter realizations, e.g., the Volterra type. The drawback of the MLP-based equalizers is, however, their slow rate of convergence, which limit their use in practical systems. In this work, the effect of whitening the input data in a multilayer perceptron-based decision feedback equalizer (DFE) is evaluated. It is shown from computer simulations that whitening the received data employing adaptive lattice channel equalization algorithms improves the convergence rate and bit error rate performances of multilayer perceptron-based DFE. The adaptive lattice algorithm is a modification to the one developed by Ling and Proakis (1985). The consistency in performance is observed in both time-invariant and time-varying channels. Finally, it is found in this work that, for time-invariant channels, the MLP DFE outperforms the least mean squares (LMS)-based DFE. However, for time-varying channels comparable performance is obtained for the two configuration

Neural network-based decision feedback equaliser with latticestructure

The effect of whitening the input data in a multilayer perceptron-based decision feedback equaliser (DFE) is evaluated. It is shown from computer simulations that whitening of the received data employing adaptive lattice channel equalisation algorithms improves the convergence rate and bit error rate performances of multilayer perceptron-based DFE

A compound near-far end least square-fourth error minimization foradaptive echo cancellation

This article presents a novel algorithm for echo cancellers with near-end and far-end sections. The algorithm consists of simultaneously applying the least mean square (LMS) algorithm to the near-end section of the echo canceller and the least mean fourth (LMF) algorithm to the far-end section. This combination results in a substantial improvement of the performance of the proposed scheme over the LMS algorithm in Gaussian and non-Gaussian environments (additive noise). However, the application of the LMF and the LMS algorithms to the near-end and the far-end sections, respectively, results in a poor performance. Simulation results, confirm the superior performance of the new algorith

Neural network-based decision feedback equaliser with latticestructure

The effect of whitening the input data in a multilayer perceptron-based decision feedback equaliser (DFE) is evaluated. It is shown from computer simulations that whitening of the received data employing adaptive lattice channel equalisation algorithms improves the convergence rate and bit error rate performances of multilayer perceptron-based DFE

Identifying Patterns of Breast Cancer Genetic Signatures using Unsupervised Machine Learning

Deploying machine learning to improve medical diagnosis is a promising area. The purpose of this study is to identify and analyze unique genetic signatures for breast cancer grades using publicly available gene expression microarray data. The classification of cancer types is based on unsupervised feature learning. Unsupervised clustering use matrix algebra based on similarity measures which made it suitable for analyzing gene expression. The main advantage of the proposed approach is the ability to use gene expression data from different grades of breast cancer to generate features that automatically identify and enhance the cancer diagnosis. In this paper, we tested different similarity measures in order to find the best way that identifies the sets of genes with a common function using expression microarray data

Recursive least-squares backpropagation algorithm for stop-and-go decision-directed blind equalization

Stop-and-go decision-directed (S&G-DD) equalization is the most primitive blind equalization (BE) method for the cancelling of intersymbol-interference in data communication systems. Recently, this scheme has been applied to complex-valued multilayer feedforward neural network, giving robust results with a lower mean-square error at the expense of slow convergence. To overcome this problem, in this work, a fast converging recursive least squares (RLS)-based complex-valued backpropagation learning algorithm is derived for S&G-DD blind equalization. Simulation results show the effectiveness of the proposed algorithm in terms of initial convergence

A compound near-far end least square-fourth error minimization foradaptive echo cancellation

This article presents a novel algorithm for echo cancellers with near-end and far-end sections. The algorithm consists of simultaneously applying the least mean square (LMS) algorithm to the near-end section of the echo canceller and the least mean fourth (LMF) algorithm to the far-end section. This combination results in a substantial improvement of the performance of the proposed scheme over the LMS algorithm in Gaussian and non-Gaussian environments (additive noise). However, the application of the LMF and the LMS algorithms to the near-end and the far-end sections, respectively, results in a poor performance. Simulation results, confirm the superior performance of the new algorith

Changes in the Demographic and Clinicopathological Characteristics of Thyroid Cancer : A Population-Based Investigation in Algeria, 1993-2013

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Water Conservation and Management Practices at the University of Sharjah to Achieve Sustainability Excellence

The University of Sharjah is a leading educational and research institution in the Gulf region. To stimulate the different aspects of sustainability in education and research as well as to ensure the implementation of sustainability concepts throughout the University campus operations, the concept of sustainability circles is implemented. The University being in hot-arid-zone and mostly surrounded by desert terrain relies on unconventional water conservation programs and initiatives such as the use of innovation & technology, reuse and recycling of water, and awareness campaigns. In line with such programs, the use of potable water is limited for hygiene purposes and wastewater generated within the University is reused after treatment to irrigate the vast green spaces through the most efficient irrigation water application systems. Examples of water conservation practices include use of efficient water devices, reuse of treated greywater for toilet flushing at a selected location, water quality monitoring, preservation to conserve water for its intended use, promoting waterless car wash on the campus grounds etc. On-campus water is also conserved through disseminating knowledge and awareness to the University community and beyond through various sustainability related programs and initiatives organized by Sustainability Office for water conservation and environmental protection