John J. Soraghan

A new adaptive functional-link neural-network-based DFE for overcoming co-channel interference

A new approach for the decision feedback equalizer (DFE) based on the functional-link neural network is described. The structure is applied to the problem of adaptive equalization in the presence of intersymbol interference (ISI), additive white Gaussian noise, and co-channel interference (CCI). It is shown through simulation results for a severe amplitude distorted co-channel system that the decision feedback functional-link equalizer (DFFLE) provides significantly superior bit-error rate (BER) performance characteristics compared to the conventional DFE, the linear transversal equalizer (LTE), the nonlinear radial basis function (RBF) neural-network-based structures and the feed-forward functional-link equalizer (FFLE)-based structures. The DFFLE is also shown to have a significantly simpler computational requirement relative to the RBF and the FFLE.

Electrocardiogram (ECG) Biometric Authentication Using Pulse Active Ratio (PAR)

Biometric authentication is a one-to-one verification process. A successful biometric system must be capable of avoiding a fraudulent claim while at the same time ensuring the privacy of individuals. This paper presents a novel framework for using the electrocardiogram (ECG) as a biometric system for human authentication. A new feature extraction technique known as pulse active ratio (PAR) is derived and used to generate novel ECG feature vectors. The proposed method is validated by experiments on 112 subjects, performing 9800 ECG comparisons providing a 10% improvement when compared to conventional temporal and amplitude feature extraction methods.

Developments in target micro-Doppler signatures analysis: radar imaging, ultrasound and through-the-wall radar

Target motions, other than the main bulk translation of the target, induce Doppler modulations around the main Doppler shift that form what is commonly called a target micro-Doppler signature. Radar micro-Doppler signatures are generally both target and action specific and hence can be used to classify and recognise targets as well as to identify possible threats. In recent years, research into the use of micro-Doppler signatures for target classification to address many defence and security challenges has been of increasing interest. In this article, we present a review of the work published in the last 10 years on emerging applications of radar target analysis using micro-Doppler signatures. Specifically we review micro-Doppler target signatures in bistatic SAR and ISAR, through-the-wall radar and ultrasound radar. This article has been compiled to provide radar practitioners with a unique reference source covering the latest developments in micro-Doppler analysis, extraction and mitigation techniques. The article shows that this research area is highly active and fast moving and demonstrates that micro-Doppler techniques can provide important solutions to many radar target classification challenges.

Quantitative Analysis of Facial Paralysis Using Local Binary Patterns in Biomedical Videos

Facial paralysis is the loss of voluntary muscle movement of one side of the face. A quantitative, objective, and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents a novel framework for objective measurement of facial paralysis. The motion information in the horizontal and vertical directions and the appearance features on the apex frames are extracted based on the local binary patterns (LBPs) on the temporal-spatial domain in each facial region. These features are temporally and spatially enhanced by the application of novel block processing schemes. A multiresolution extension of uniform LBP is proposed to efficiently combine the micropatterns and large-scale patterns into a feature vector. The symmetry of facial movements is measured by the resistor-average distance (RAD) between LBP features extracted from the two sides of the face. Support vector machine is applied to provide quantitative evaluation of facial paralysis based on the House-Brackmann (H-B) scale. The proposed method is validated by experiments with 197 subject videos, which demonstrates its accuracy and efficiency.

EMD-Based Filtering (EMDF) of Low-Frequency Noise for Speech Enhancement

An empirical mode decomposition-based filtering (EMDF) approach is presented as a postprocessing stage for speech enhancement. This method is particularly effective in low-frequency noise environments. Unlike previous EMD-based denoising methods, this approach does not make the assumption that the contaminating noise signal is fractional Gaussian noise. An adaptive method is developed to select the IMF index for separating the noise components from the speech based on the second-order IMF statistics. The low-frequency noise components are then separated by a partial reconstruction from the IMFs. It is shown that the proposed EMDF technique is able to suppress residual noise from speech signals that were enhanced by the conventional optimally modified log-spectral amplitude approach which uses a minimum statistics-based noise estimate. A comparative performance study is included that demonstrates the effectiveness of the EMDF system in various noise environments, such as car interior noise, military vehicle noise, and babble noise. In particular, improvements up to 10 dB are obtained in car noise environments. Listening tests were performed that confirm the results.

Small-target detection in sea clutter

Sea clutter in marine surveillance radar makes the task of detecting small targets a very challenging problem. In this paper, a set of three signal processing techniques designed to suppress unwanted sea clutter radar echo and achieve target detection with no prior knowledge of the ocean and environmental conditions is presented. These include signal averaging, time-frequency representation, and morphological filtering. Datasets from real marine radar operating in staring mode are used to illustrate the performance of the new approaches.

Compression of interference patterns with application to phase-shifting digital holography

A compression method of phase-shifting digital holographic data is presented. Three interference patterns are recorded, and holographic information is extracted from them by phase-shifting interferometry. The scheme uses standard baseline Joint Photographic Experts Group (JPEG) or standard JPEG-2000 image compression techniques on the recorded interference patterns to reduce the amount of data to be stored. High compression rates are achieved for good reconstructed object image quality. The utility of the proposed method is experimentally verified with real holographic data. Results for compression rates using JPEG-2000 and JPEG of approximately 27 and 20, respectively, for a normalized root-mean-square error of approximately 0.7 are demonstrated.

Use of Fresnelets for Phase-Shifting Digital Hologram Compression

Fresnelets are wavelet-like base functions specially tailored for digital holography applications. We introduce their use in phase-shifting interferometry (PSI) digital holography for the compression of such holographic data. Two compression methods are investigated. One uses uniform quantization of the Fresnelet coefficients followed by lossless coding, and the other uses set portioning in hierarchical trees (SPIHT) coding. Quantization and lossless coding of the original data is used to compare the performance of the proposed algorithms. The comparison reveals that the Fresnelet transform of phase-shifting holograms in combination with SPIHT or uniform quantization can be used very effectively for the compression of holographic data. The performance of the new compression schemes is demonstrated on real PSI digital holographic data

Reconstruction domain compression of phase-shifting digital holograms

Phase-shifting digital hologram compression has been mainly studied in the recording domain, where data possess a rather randomlike appearance, yielding reduced compression efficiency. We carry out the compression of such data in the reconstruction domain, which benefits from the spatial correlation of the data yielding, increased efficiency. Real holographic data are used to demonstrate the performance of the new approach. It is also shown that the reconstruction is not limited to the initially obtained view, as additional views can still be obtained with appropriate postprocessing.

Robust PCA for micro-doppler classification using SVM on embedded systems

In this paper, a novel feature extraction technique for micro-Doppler classification and its real-time implementation using a support vector machine classifier on a low-cost, embedded digital signal processor are presented. The effectiveness of the proposed technique is improved through exploitation of the outlier rejection capabilities of robust principal component analysis (PCA) in place of classic PCA.