Detection of cochlear hearing loss applying wavelet packets and support vector machines

The aim of this paper is to evaluate the application of the wavelet packet transform (WP) and support vector machines (SVM) to transient evoked otoacoustic emissions (TEOAE) in order to achieve a detection of frequency-specific hearing loss. We introduce a system to determine detection rates between groups of persons with normal hearing, high frequency hearing loss, and pantonal hearing loss. The validity and use of our approach is verified on a different patient group

Shortening the order of paraunitary matrices in SBR2 algorithm

The second order sequential best rotation (SBR2) algorithm has recently been proposed as a very effective tool in decomposing a para-Hermitian polynomial matrix R(z) into a diagonal polynomial matrix T(z) and a paraunitary matrix B(,z), extending the eigenvalue decomposition to polynomial matrices, R-(z) = B(z)T(z)~B(z). However, the algorithm results in polynomials of very high order, which limits its applicability. Therefore, in this paper we evaluate approaches to reduce the order of the paraunitary matrices, either within each step of SBR2, or after convergence. The paraunitary matrix B(z) is replaced by a near-paraunitary quantity BN(z), whose error will be assessed. Simulation results show that the proposed truncation can greatly reduce the polynomial order while retaining good near-paraunitariness of BN(z)

Comparison of precoding methods for broadband MIMO systems

In this paper we investigate non-linear precoding solutions for the problem of broadband multiple-input multipleoutput (MIMO) systems. Based on a broadband singular value decomposition (BSVD) we can decouple a broadband MIMO channel into independent dispersive spectrally majorised singleinput single-output (SISO) subchannels. Bit loading is proposed to optimally utilise these SISO subchannels after mitigating their individual inter-symbol-interference (ISI) using Tomlinson- Harashima precoding (THP). This method is benchmarked against recent results of both MMSE linear and THP designed for frequency-selective MIMO channels. Simulation results show that better bit-error-ratio (BER) can be achieved especially for higher throughput targets when compared to the benchmar

A new low-cost discrete bit loading using greedy power allocation

In this paper we consider a low cost bit loading based on the greedy power allocation (GPA). Compared to the standard GPA, which is optimal in terms of maximising the data throughput, three suboptimal schemes are suggested, which perform GPA on subsets of subchannels only. We demonstrate how these schemes can reduce complexity. Two of the proposed algorithms can achieve near optimal performance by including a transfer of residual power between subsets at the expense of a very small extra cost. By simulations, we show that the two near optimal schemes perform best in two separate and distinct SNR regions

Trade-off between complexity and BER performance of a polynomial SVD-based broadband MIMO transceiver

In this paper we investigate non-linear precoding solutions for the problem of broadband multiple-input multiple output(MIMO) systems. Based on a polynomial singular value decomposition (PSVD) we can decouple a broadband MIMO channel into independent dispersive spectrally majorised single-input single-output (SISO) subchannels. In this contribution, the focus of our work is to explore the influence of approximations on the PSVD, and the performance degradation that can be expected as a result

Self-propagating High temperature Synthesis (SHS) in the high activation energy regime

We derive the precise limit of SHS in the high activation energy scaling suggested by B.J. Matkowksy-G.I. Sivashinsky in 1978 and by A. Bayliss-B.J. Matkowksy-A.P. Aldushin in 2002. In the time-increasing case the limit turns out to be the Stefan problem for supercooled water with spatially inhomogeneous coefficients. Although the present paper leaves open mathematical questions concerning the convergence, our precise form of the limit problem suggest a strikingly simple explanation for the numerically observed pulsating waves

Rule-based Machine Learning Methods for Functional Prediction

We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of compact, easily interpretable solutions. This rule-based decision model can be extended to search efficiently for similar cases prior to approximating function values. Experimental results on real-world data demonstrate that the new techniques are competitive with existing machine learning and statistical methods and can sometimes yield superior regression performance.Comment: See http://www.jair.org/ for any accompanying file