A comparative study of four novel sleep apnoea episode prediction systems

The prediction of sleep apnoea and hypopnoea episodes could allow treatment to be applied before the event be-comes detrimental to the patients sleep, and for a more spe-cific form of treatment. It is proposed that features extracted from breaths preceding an apnoea and hypopnoea could be used in neural networks for the prediction of these events. Four different predictive systems were created, processing the nasal airflow signal using epoching, the inspiratory peak and expiratory trough values, principal component analysis (PCA) and empirical mode decomposition (EMD). The neu-ral networks were validated with naïve data from six over-night polysomnographic records, resulting in 83.50% sensi-tivity and 90.50% specificity. Reliable prediction of apnoea and hypopnoea is possible using the epoched flow and EMD of breaths preceding the event

Precise motion descriptors extraction from stereoscopic footage using DaVinci DM6446

A novel approach to extract target motion descriptors in multi-camera video surveillance systems is presented. Using two static surveillance cameras with partially overlapped field of view (FOV), control points (unique points from each camera) are identified in regions of interest (ROI) from both cameras footage. The control points within the ROI are matched for correspondence and a meshed Euclidean distance based signature is computed. A depth map is estimated using disparity of each control pair and the ROI is graded into number of regions with the help of relative depth information of the control points. The graded regions of different depths will help calculate accurately the pace of the moving target and also its 3D location. The advantage of estimating a depth map for background static control points over depth map of the target itself is its accuracy and robustness to outliers. The performance of the algorithm is evaluated in the paper using several test sequences. Implementation issues of the algorithm onto the TI DaVinci DM6446 platform are considered in the paper

Blind adaptive equalizer for broadband MIMO time reversal STBC based on PDF fitting

In this paper, we propose a new blind adaptive technique used for the equalisation of space-time block coded (STBC) signals transmitted over a dispersive MIMO channel. The proposed approach is based on minimising the difference between the probability density function (PDF) of the equalizer output — estimated via the Parzen window method — and a desired PDF based on the source symbols. The cost function combines this PDF fitting with an orthogonality criterion derived from the STBC structure of the transmitted data in order to discourage the extraction of identical signals. This cost function motivates an effective and low-cost stochastic gradient descent algorithm for adapting the equaliser. The performance is demonstrated in a number of simulations and benchmarked against other blind schemes for the equalisation of STBC over broadband MIMO channels

Regularized Adaptive Notch Filters for Acoustic Howling Suppression

Publication in the conference proceedings of EUSIPCO, Glasgow, Scotland, 200

Performance Analysis of PCFICH and PDCCH LTE Control Channels

Control channels play a key role in the evaluation of mobile system performance. The purpose of our paper is to evaluate the performance of the control channels implementation in the Long Term Evolution (LTE) system. The paper deals with the simulation of the complete signal processing chain for Physical Control Format Indicator Channel (PCFICH) and Physical Downlink Control Channel (PDCCH) in the LTE system, Release 8. We implemented a complete signal processing chain for downlink control channels as an extension of the existing MATLAB LTE downlink simulator. The paper presents results of PCFICH and PDCCH control channel computer performance analysis in various channel conditions. The results can be compared with the performance of data channels

Formulating and solving broadband multichannel problems using matrices of functions

The analysis and design of broadband multichannel systems typically involves convolutive mixing, characterised by matrices of transfer functions. Further, many broadband multichannel problems can be formulated using space-time covariance matrices that include an explicit lag variable and thus cross-correlation sequences as entries. This is in contrast to narrowband challenges, where the problem formulation relies on standard (i.e. constant) matrices; a rich set of solutions that are optimal in various senses can be reached from these formulations by matrix factorisations such as the eigenvalue or singular value decompositions. In order to extend the utility of such linear algebraic techniques to the broadband case, the diagonalisation or factorisation of matrices of functions is key. In this webinar, I will show that such matrices are quite ubiquitous in multichannel signal processing, review some of the theory for their factorisations, and show how with such broadband formulations and solutions directly generalise from their narrowband counterparts. I will sketch out a number of algorithms and illustrate their use in a few example applications such as beamforming, angle or arrival estimation, and signal compaction

A new design tool for feature extraction in noisy images based on grayscale hit-or-miss transforms

The Hit-or-Miss transform (HMT) is a well known morphological transform capable of identifying features in digital images. When image features contain noise, texture or some other distortion, the HMT may fail. Various researchers have extended the HMT in different ways to make it more robust to noise. The most successful, and most recent extensions of the HMT for noise robustness, use rank order operators in place of standard morphological erosions and dilations. A major issue with the proposed methods is that no technique is provided for calculating the parameters that are introduced to generalize the HMT, and, in most cases, these parameters are determined empirically. We present here, a new conceptual interpretation of the HMT which uses a percentage occupancy (PO) function to implement the erosion and dilation operators in a single pass of the image. Further, we present a novel design tool, derived from this PO function that can be used to determine the only parameter for our routine and for other generalizations of the HMT proposed in the literature. We demonstrate the power of our technique using a set of very noisy images and draw a comparison between our method and the most recent extensions of the HMT

When the Bellman equation cannot be solved analytically

Viele Fragen der betrieblichen Entscheidungsfindung führen ---wenn Unsicherheit involviert ist--- zu stochastischen Optimierungsproblemen der Form V(S) &= \max\limits_{x}\textnormal{E} \left\{\int_0^\infty e^{-\rho t}f(S,x)\,\textnormal{d} t\right\} wobei der Zustandsübergang dS = g(S,x)dt + \sigma dz den zugrunde liegenden stochastischen Prozess S als Ito Prozess beschreibt und dz eine Brownsche Bewegung ist. Für sehr einfache Probleme kann durch Lösung der dazugehörigen Bellman Gleichung eine analytische Lösung berechnet werden. In allen anderen Fällen ist dies jedoch mittels numerischer Methoden möglich. Konventionelle numerische Lösungsmethoden sind allerdings langsam oder teilweise unbrauchbar, weil die Lösung einen Sattelpunktpfad beschreibt und alle benachbarten Pfade divergieren. Diese Magisterarbeit verwendet die in ``Applied Computational Economics and Finance'' von M.J. Miranda and P.L. Fackler entwickelten numerischen Methoden, um ausgewählte Probleme der Modellierung der betrieblichen Entscheidungsfindung, der optimalen Preisfestlegung von kurzlebigen Konsumgütern, der Modellierung von Schwankungen im Ölpreis und der Modellierung der Auswirkungen von CO2 in der Atmosphäre zu lösen.Many questions in managerial decision making imply ---if uncertainty is involved--- stochastic optimization problems of the form V(S) &= \max\limits_{x}\textnormal{E} \left\{\int_0^\infty e^{-\rho t}f(S,x)\,\textnormal{d} t\right\} where the state transition dS = g(S,x)dt + \sigma dz describes the underlying stochastic process S as an Ito process and dz is a Brownian motion. For very simple problems, results can be obtained analytically by solving the corresponding Bellman equation. In all other cases, V(S) has to be computed numerically. Unfortunately, conventional numerical methods are either slow, or completely fail as the solution is a saddlepoint path so that all neighboring solution paths diverge. This thesis applies methods developed in ``Applied Computational Economics and Finance'' by M.J. Miranda and P.L. Fackler to solve selected problems that arise in modeling managerial decision making, optimal pricing of nondurables, modeling the ups and downs in oil prices, and modeling the impact of CO2 emission into the atmosphere