Faisal M Kashif

Techniques to obtain good resolution and concentrated time-frequency distributions: a review

We present a review of the diversity of concepts and motivations for improving the concentration and resolution of timefrequencydistributions (TFDs) along the individual components of the multi-component signals. The central idea has been to obtain a distribution that represents the signal’s energy concentration simultaneously in time and frequency without blur andcrosscomponents so that closely spaced components can be easily distinguished. The objective is the precise description of spectralcontent of a signal with respect to time, so that first, necessary mathematical and physical principles may be developed, andsecond, accurate understanding of a time-varying spectrum may become possible. The fundamentals in this area of research havebeen found developing steadily, with significant advances in the recent past.

Monte carlo equalization for nonlinear dispersive satellite channels

Satellite channels are generally nonlinear and dispersive in nature, due to amplifiers being driven close to saturation. These effects can cause significant degradations when they are not taken into account at either the receiver (equalization) or at the transmitter (pre-distortion). State-of-the-art equalizers rely on the forward-backward algorithm and yield excellent performance. However, they have unreasonable complexity and storage requirements, especially for highly dispersive channels and/or large constellations. In this paper, we derive an equalization strategy for nonlinear channels based on Monte Carlo methods. We present a detailed performance, complexity and storage analysis. A significant performance gain compared to the linear equalizer is reported, and the proposed technique results in a significant reduction in both complexity and storage, compared to the forward-backward equalizer.

High-resolution time-frequency methods performance analysis

This work evaluates the performance of high-resolution quadratic time-frequency distributions (TFDs) including the ones obtained by the reassignment method, the optimal radially Gaussian kernel method, the t-f autoregressive moving-average spectral estimation method and the neural network-based method. The approaches are rigorously compared to each other using several objective measures. Experimental results show that the neural network-based TFDs are better in concentration and resolution performance based on various examples.

Impact of Varying Neurons and Hidden Layers in Neural Network Architecture for a Time Frequency Application

In this paper, an experimental investigation is presented, to know the effect of varying the number of neurons and hidden layers in feed forward back propagation neural network architecture, for a time frequency application. Varying the number of neurons and hidden layers has been found to greatly affect the performance of neural network (NN), trained via various blurry spectrograms as input over highly concentrated time frequency distributions (TFDs) as targets, of the same signals. Number of neurons and hidden layers are varied during training and the impact is observed over test spectrograms of unknown multi component signals. Entropy and mean square error (MSE) is the decision criteria for the most optimum solution.

Continuous Quantitative Monitoring of Cerebral Oxygen Metabolism in Neonates by Ventilator-Gated Analysis of NIRS Recordings

Oxidative stress during fetal development, delivery, or early postnatal life is a major cause of neuropathology, as both hypoxic and hyperoxic insults can significantly damage the developing brain. Despite the obvious need for reliable cerebral oxygenation monitoring, no technology currently exists to monitor cerebral oxygen metabolism continuously and noninvasively in infants at high risk for developing brain injury. Consequently, a rational approach to titrating oxygen supply to cerebral oxygen demand - and thus avoiding hyperoxic or hypoxic insults - is currently lacking. We present a promising method to close this crucial technology gap in the important case of neonates on conventional ventilators. By using cerebral near-infrared spectroscopy and signals from conventional ventilators, along with arterial oxygen saturation, we derive continuous (breath-by-breath) estimates of cerebral venous oxygen saturation, cerebral oxygen extraction fraction, cerebral blood flow, and cerebral metabolic rate of oxygen. The resultant estimates compare very favorably to previously reported data obtained by non-continuous and invasive means from preterm infants in neonatal critical care.

Model-based estimation of intracranial pressure and cerebrovascular autoregulation

Monitoring cerebrovascular state, including intracranial pressure (ICP) and the ability to regulate cerebral blood flow, is important for patient care in stroke, traumatic brain injury and other such conditions. However, current methodologies for direct measurement of ICP are highly invasive, and expose patients to the risk of infection. In addition, vascular properties such as resistance and compliance cannot be directly assessed. In this work, we employ a mathematical model-based approach to track variations in ICP and cerebrovascular properties from signals that can be acquired entirely non-invasively. The performance on simulation data indicates that the estimates track the desired quantities closely, thus suggesting that tests using clinical data are warranted.

Multiple Neural Networks over Clustered Data (MNCD) to Obtain Instantaneous Frequencies (IFs)

In this paper we present advantage of training MNCD for obtaining time localized frequencies (also called IF), which is one useful concept for describing the changing spectral structure of a time-varying signal, arising so often in time frequency distribution (TFD) theory. It has been found that training does not give the same results every time; this is because the weights are initialized to random values and high validation error may end up training early. Moreover once a network is trained with selected input, its performance improves significantly as opposed to the one that does not receive selected input data for training. The performance of MNCD can be compared by computing the entropy, mean square error (MSE) and time consumed for convergence.

Neural Network Solution for Compesating Distortions of Time Frequency Representations

A Neural network (NN) based approach to obtain energy concentration along instantaneous frequencies (IFs) of the individual components present in the signal, is proposed. Blurry spectrograms and highly concentrated Wigner distributions (WDs) of various signals constitute the training set. The input data is grouped according to the underlying feature present in time frequency representation (TFR) image to have better generalization ability of the trained NN. Blurry TFRs of multi component signals are then given as test cases to the trained NN. Effectiveness of the approach is established by comparing the information content in each input and resultant TFR.

Time Frequency Image Analysis Using Neural Networks

A realization of obtaining energy concentration along instantaneous frequency (IF) for blurry time frequency images (TFIs) is proposed using neural networks (NNs). NN captures the fundamental principle at work once trained with various spectrogram TFIs by defining various edges present in input data. Unknown spectrogram TFIs of multi component signals, are then given as test images to the trained NN measuring the information content in each input and resultant TFI thus manifest the effectiveness of NNs in TFI analysis

In-band interference suppression in time-domain algorithms for DTMF detection

The use of dual-tone multi frequency (DTMF) signaling is very common in telecommunication systems such as telephone exchanges, private branch exchanges (PBXs), and call centers etc. One of the approaches of DTMF detection is based on the cycle estimation of the frequencies present in the DTMF signal. Any interference that lies in the frequency region of the DTMF signal disturbs the cycle estimation process employed by the time-domain algorithm thus degrading the detectors performance. In this article we present an easy-to-implement solution to this problem. The approach is based on enhancing the estimated DTMF signal frequencies, thereby reducing the effect of the in-band interference. The proposed solution was tested on simulated signals as well as real data recorded from a PBX. Improvement in the detection of the DTMF signals in the presence of in-band interference was observed in both cases.