NGS Based Haplotype Assembly Using Matrix Completion

We apply matrix completion methods for haplotype assembly from NGS reads to develop the new HapSVT, HapNuc, and HapOPT algorithms. This is performed by applying a mathematical model to convert the reads to an incomplete matrix and estimating unknown components. This process is followed by quantizing and decoding the completed matrix in order to estimate haplotypes. These algorithms are compared to the state-of-the-art algorithms using simulated data as well as the real fosmid data. It is shown that the SNP missing rate and the haplotype block length of the proposed HapOPT are better than those of HapCUT2 with comparable accuracy in terms of reconstruction rate and switch error rate. A program implementing the proposed algorithms in MATLAB is freely available at https://github.com/smajidian/HapMC

Deep Learning for QoT Estimation in SMF and FMF Links

We explore deep learning-based classification and regression algorithms to estimate quality of transmission in single-mode and few-mode fiber links. Both approaches are shown to be effective and low complexity

Polynomial order reducing property of the lattice filter in the presence of quadratic FM signals

This paper presents the behaviour of reflection coefficients of FIR lattice filters for quadratic FM signals. We show that the optimal reflection coefficients form linear FM signals with a reduced order compared to that of the input polynomial phase. Similarly, by considering the linear FM signal produced by the coefficients as the input to another lattice filter, sinusoidal signals are generated. This new characteristic, which we call the Polynomial Order Reducing (FOR) property of the lattice filter, is correspondingly held for the adaptive coefficients. This property is used for estimating the Instantaneous Frequency (IF) of a linear FM signal

Polynomial order reducing property of lattice filters for FM signals with polynomial phases

The behaviour of the optimal and adaptive reflection coefficients of lattice filters for FM signals with polynomial phases of order p is investigated. It is theoretically shown that the optimal reflection coefficients form FM signals with lower order polynomial phases p - 1. This new characteristic which is correspondingly held for the adaptive reflection coefficients is introduced as the Polynomial Order Reducing (FOR) property of the reflection coefficients. Using the FOR property, by inputting the signal produced by one adaptive coefficient to the next adaptive lattice filter, the input polynomial order can frequently be reduced so that a sinusoid is obtained. More interestingly, the phases of these signals are related to each other. While, the FOR property is heuristically worthwhile, it may potentially be used in several signal processing applications. As an example, this property is applied to the Instantaneous Frequency (IF) estimation of a linear FM signal

Performance analysis of adaptive lattice filters for impulsive signals

The existence of impulsive signals with alpha-stable non-Gaussian distributions has already been reported in some applications. In this paper, a new adaptive lattice algorithm is proposed for adaptive filtering of alpha-stable AR processes. The performance of this algorithm is compared to that of other stochastic gradient-based algorithms for stable AR processes for different alpha's. It is shown that the proposed algorithm achieves a superior convergence speed with respect to the other algorithms

Rolling element bearing faults diagnosis based on autocorrelation of optimized: wavelet de-noising technique

Machinery failure diagnosis is an important component of the condition based maintenance (CBM) activities for most engineering systems. Rolling element bearings are the most common cause of rotating machinery failure. The existence of the amplitude modulation and noises in the faulty bearing vibration signal present challenges to effective fault detection method. The wavelet transform has been widely used in signal de-noising, due to its extraordinary time-frequency representation capability. In this paper, a new technique for rolling element bearing fault diagnosis based on the autocorrelation of wavelet de-noised vibration signal is applied. The wavelet base function has been derived from the bearing impulse response. To enhance the fault detection process the wavelet shape parameters (damping factor and center frequency) are optimized based on kurtosis maximization criteria. The results show the effectiveness of the proposed technique in revealing the bearing fault impulses and its periodicity for both simulated and real rolling bearing vibration signals.Khalid F. Al-Raheem, Asok Roy, K. P. Ramachandran, D. K. Harrison, Steven Grainge