Publications
736 results found
Schnupp T, Holzbrecher-Morys M, Mandic D, et al., 2009, Sensitivity of posturography to elimination of visual feedback, 4th European Conference of the International Federation for Medical and Biological Engineering (ECIFMBE), Publisher: SPRINGER, Pages: 2077-2080, ISSN: 1680-0737
Douglas SC, Mandic DP, 2009, MEAN AND MEAN-SQUARE ANALYSIS OF THE COMPLEX LMS ALGORITHM FOR NON-CIRCULAR GAUSSIAN SIGNALS, 13th IEEE Digital Signal Processing Workshop/5th IEEE Signal Processing Education Workshop, Publisher: IEEE, Pages: 101-+
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- Citations: 18
Kuh A, Mandic D, 2009, APPLICATIONS OF COMPLEX AUGMENTED KERNELS TO WIND PROFILE PREDICTION, IEEE International Conference on Acoustics, Speech and Signal Processing, Publisher: IEEE, Pages: 3581-+, ISSN: 1520-6149
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- Citations: 13
Took CC, Mandic D, Benesty J, 2009, STUDY OF THE QUATERNION LMS AND FOUR-CHANNEL LMS ALGORITHMS, IEEE International Conference on Acoustics, Speech and Signal Processing, Publisher: IEEE, Pages: 3109-+, ISSN: 1520-6149
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- Citations: 9
Huang X, Kavcic A, Ma X, et al., 2009, Upper Bounds on the Capacities of Non-Controllable Finite-State Channels Using Dynamic Programming Methods, IEEE International Symposium on Information Theory (ISIT 2009), Publisher: IEEE, Pages: 2346-+
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- Citations: 7
Rutkowski TM, Cichocki A, Tanaka T, et al., 2009, MULTICHANNEL SPECTRAL PATTERN SEPARATION - AN EEG PROCESSING APPLICATION, IEEE International Conference on Acoustics, Speech and Signal Processing, Publisher: IEEE, Pages: 373-+, ISSN: 1520-6149
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- Citations: 17
Jelfs B, Vayanos P, Javidi S, et al., 2008, Collaborative adaptive filters for online knowledge extraction and information fusion, Signal Processing Techniques for Knowledge Extraction and Information Fusion, Pages: 3-21, ISBN: 9780387743660
We present a method for extracting information (or knowledge) about the nature of a signal. This is achieved by employing recent developments in signal characterisation for online analysis of the changes in signal modality. We show that it is possible to use the fusion of the outputs of adaptive filters to produce a single collaborative hybrid filter and that by tracking the dynamics of the mixing parameter of this filter rather than the actual filter performance, a clear indication as to the nature of the signal is given. Implementations of the proposed hybrid filter in both the real R and the complex C domains are analysed and the potential of such a scheme for tracking signal nonlinearity in both domains is highlighted. Simulations on linear and nonlinear signals in a prediction configuration support the analysis; real world applications of the approach have been illustrated on electroencephalogram (EEG), radar and wind data. © 2008 Springer US.
Rutkowski TM, Cichocki A, Mandic D, 2008, Information fusion for perceptual feedback: A brain activity sonification approach, Signal Processing Techniques for Knowledge Extraction and Information Fusion, Pages: 261-273, ISBN: 9780387743660
When analysing multichannel processes, it is often convenient to use some sort of visualisation to help understand and interpret spatio-temporal dependencies between the channels, and to perform input variable selection. This is particularly advantageous when the levels of noise are high, the active channel changes its spatial location with time, and also for spatio-temporal processes where several channels contain meaningful information, such as in the case of electroencephalogram (EEG)-based brain activity monitoring. To provide insight into the dynamics of brain electrical responses, spatial sonification of multichannel EEG is performed, whereby the information from active channels is fused into music-like audio. Owing to its data fusion via fission mode of operation, empirical mode decomposition (EMD) is employed as a time-frequency analyser, and the brain responses to visual stimuli are sonified to provide audio feedback. Such perceptual feedback has enormous potential in multimodal brain computer and brain machine interfaces (BCI/BMI). © 2008 Springer US.
Mandic D, Souretis G, Leong WY, et al., 2008, Complex empirical mode decomposition for multichannel information fusion, Signal Processing Techniques for Knowledge Extraction and Information Fusion, Pages: 243-260, ISBN: 9780387743660
Information fusion via signal fission is addressed in the framework of empirical mode decomposition (EMD). In this way, a general nonlinear and non-stationary signal is first decomposed into its oscillatory components (fission); the components of interest are then combined in an ad hoc or automated fashion to provide greater knowledge about a process in hand (fusion). The extension to the field of complex numbers C is particularly important for the analysis of phase-dependent processes, such as those coming from sensor arrays. This allows us to combine the data driven nature of EMD with the power of complex algebra to model amplitude-phase relationships within multichannel data. The analysis shows that the extensions of EMD to C are not straightforward and that they critically depend on the criterion for finding local extrema within a complex signal. For rigour, convergence of EMD is addressed within the framework of fixed point theory. Simulation examples on information fusion for brain computer interface (BCI) support the analysis. © 2008 Springer US.
Liu W, Mandic DP, Cichocki A, 2008, A dual-linear predictor approach to blind source extraction for noisy mixtures, Pages: 515-519
A second-order statistics based dual-linear predictor structure is proposed for blind source extraction from noisy instantaneous mixtures. The noise component is assumed to be spatially and temporally white, but the variance information of noise is not required. A detailed proof of the proposed approach is provided and an adaptive algorithm is developed. Simulation results show that it can extract the source signals successfully. © 2008 IEEE.
Leong WY, Mandic DP, 2008, Post-Nonlinear Blind Extraction in the Presence of Ill-Conditioned Mixing, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, Vol: 55, Pages: 2631-2638, ISSN: 1549-8328
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- Citations: 12
Mandic D, Duch W, 2008, 2008 Special Issue: Computational and biological inspired neural networks, selected papers from ICANN 2007 - Preface, NEURAL NETWORKS, Vol: 21, Pages: 797-798, ISSN: 0893-6080
Yuan Y, Li Y, Mandic DP, 2008, Comparison analysis of embedding dimensions between normal and epileptic EEG the series, JOURNAL OF PHYSIOLOGICAL SCIENCES, Vol: 58, Pages: 239-247, ISSN: 1880-6546
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- Citations: 18
Erdogmus D, Mandic D, Tanaka T, 2008, Advances in blind signal processing, NEUROCOMPUTING, Vol: 71, Pages: 2069-2070, ISSN: 0925-2312
Leong WY, Liu W, Mandic DP, 2008, Blind source extraction: Standard approaches and extensions to noisy and post-nonlinear mixing, NEUROCOMPUTING, Vol: 71, Pages: 2344-2355, ISSN: 0925-2312
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- Citations: 14
Mandic DP, Vayanos P, Chen M, et al., 2008, Online detection of the modality of complex-valued real world signals, INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, Vol: 18, Pages: 67-74, ISSN: 0129-0657
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- Citations: 9
Pedzisz M, Mandic DP, 2008, A Homomorphic Neural Network for Modelling and Prediction, Neural Computation, Vol: 20, Pages: 1042-1064
Mandic DP, Vayanos P, Chen G, et al., 2008, Online Detection of the Modality of Complex Valued Real World Signals, International Journal of Neural Systems, Vol: 18, Pages: 67-74
Hirata Y, Mandic DP, Suzuki H, et al., 2008, A Collaborative Approach to the Modelling of Real World Vector Fields: Predicting the Wind Direction, Philosophical Transactions of the Royal Society A, Vol: 366, Pages: 591-607
Mandic DP, Golz M, Kuh A, et al., 2008, Signal Processing Techniques for Knowledge Extraction and Information Fusion, Publisher: Springer
Hirata Y, Mandic DP, Suzuki H, et al., 2008, Wind direction modelling using multiple observation points, 9th Experimental Chaos Conference, Publisher: ROYAL SOC, Pages: 591-607, ISSN: 1364-503X
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- Citations: 18
Mandic DP, Chen M, Gautama T, et al., 2008, On the Characterisation of the Deterministic/Stochastic and Linear/Nonlinear Nature of Time Series, Proceedings of the Royal Society A, Vol: 464, Pages: 1141-1160
Yuan Y, Li Y, Yu D, et al., 2008, Delay time-based epileptic EEG detection using artificial neural network, Pages: 502-505
The electroencephalogram (EEG) signal is very important for the diagnosis of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a time-consuming analysis of the entire length of the EEG data by an expert. A neural-network-based automated epileptic EEG detection method is proposed in this paper, which uses delay time as the input feature of an artificial neural network. Mutual information method is applied in this paper for computing the delay time parameter of EEG signals. The results indicate that the delay time values of EEG signals during an epileptic seizure become larger than those of normal EEG signals obviously, and then this phenomenon is utilized for automated epileptic EEG detection combined with probabilistic neural networks (PNN). Delay time parameter is used as the input feature of the neural network for the first time for the detection of epilepsy. It is shown that the overall accuracy as high as 100% can be achieved by using the method proposed in this paper. © 2008 IEEE.
Yuan Y, Li Y, Yu D, et al., 2008, Automated detection of epileptic seizure using artificial neural network, Pages: 1959-1962
The embedding dimension of electroencephalogram (EEG) time series is used as the input feature of artificial neural network for detecting epileptic seizure automatedly. Cao's method is applied for computing the embedding dimension of normal and epileptic EEG time series. The probabilistic neural networks (PNN) is used in this paper for the automated detection of epilepsy. The results show that the overall accuracy as high as 100% can be achieved by using the method proposed in this paper. An interesting phenomenon is also found by Cao's method that normal EEG time series is of randomness, whereas epileptic EEG time series is of some degree of determinacy, which means that epileptic EEG time series can be predicted well. © 2008 IEEE.
Chen M, Gautama T, Mandic DP, 2008, An Assessment of Qualitative Performance of Machine Learning Architectures: Modular Feedback Networks, IEEE Transactions on Neural Networks, Vol: 19, Pages: 183-189
Liu W, Mandic DP, Cichocki A, 2008, A DUAL-LINEAR PREDICTOR APPROACH TO BLIND SOURCE EXTRACTION FOR NOISY MIXTURES, IEEE Sensor Array and Multichannel Signal Processing Workshop, Publisher: IEEE, Pages: 516-+, ISSN: 1551-2282
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- Citations: 5
Liu W, Mandic DP, Cichocki A, 2008, Blind source separation based on generalised canonical correlation analysis and its adaptive realization, 1st International Congress on Image and Signal Processing, Publisher: IEEE COMPUTER SOC, Pages: 417-421
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- Citations: 5
Looney D, Mandic DP, 2008, Empirical mode decomposition for simultaneous image enhancement and fusion, IMAGE FUSION: ALGORITHMS AND APPLICATIONS, Editors: Stathaki, Publisher: ELSEVIER ACADEMIC PRESS INC, Pages: 327-341
Looney D, Mandic DP, 2008, A machine learning enhanced empirical mode decomposition, 33rd IEEE International Conference on Acoustics, Speech and Signal Processing, Publisher: IEEE, Pages: 1897-1900, ISSN: 1520-6149
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- Citations: 10
Looney D, Mandic DP, 2008, FUSION OF VISUAL AND THERMAL IMAGES USING COMPLEX EXTENSIONS OF EMD, 2nd ACM/IEEE International Conference on Distributed Smart Cameras, Publisher: IEEE, Pages: 434-441
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