Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military civilian fields. With the continuous enrichment extensive expansion of application scenarios, safety UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones improve drone safety. We deployed a one-class kernel extreme learning machine (OC...

Neural Independent Component Analysis (ICA) algorithms based on unimodal source distributions provide acceptable performances in the case of Blind Source Separation (BSS) of super-gaussian sources. However, their convergence profiles are significantly slower in the case of sub-gaussian sources. In some situations it is necessary to deal with sub-gaussian signals in the form of noise or others. ...

Independent component analysis (ICA) is a computational method to solve blind source separation (BSS) problem. In this paper, an improved FastICA based on Kurtosis was proposed to separate EEG signal. It was adopted from the gradient descent algorithm to improve the converge rate. Improved FastICA method was applied to separate EEG signal from a patient who had epilepsy seizure. The separation ...

We propose a preprocessing methodology for well-log geophysical data based on Fast Independent Component Analysis (FastICA) and Discrete Cosine Transform (DCT), in order to improve the success rate of the K-NN automatic classifier. The K-NN have been commonly applied to facies recognition in well-log geophysical data for hydrocarbon reservoir modeling and characterization. The preprocess was ma...

paper addresses the high dimensionality problem in blind source separation (BSS), where the number of sources is greater than two. Two pairwise iterative schemes are proposed to tackle this high dimensionality problem. The two pairwise schemesrealize non-parametric independent component analysis (ICA) algorithms based on a new high-performance Convex Cauchy–Schwarz Divergence (CCS-DIV). These t...

InfoMax and FastICA are the independent component analysis algorithms most used and apparently most effective for brain fMRI. We show that this is linked to their ability to handle effectively sparse components rather than independent components as such. The mathematical design of better analysis tools for brain fMRI should thus emphasize other mathematical characteristics than independence.

This paper proposes a novel and successful method for recognizing palmprint based on radial basis probabilistic neural network (RBPNN) proposed by us. The RBPNN is trained by the orthogonal least square (OLS) algorithm and its structure is optimized by the recursive OLS algorithm (ROLSA). The Hong Kong Polytechnic University (PolyU) palmprint database, which is pre-processed by a fast fixed-poi...

The cocktail party effect describes human's ability to follow one voice source from a mixture of conversations, and often with the addition of background noises. These conversations may happen simultaneously. However, it is tricky for computers to handle this sort of auditory source separation problems. One relatively successful approach is to use Independent Component Analysis (ICA) to separat...

Independent Component Analysis (ICA) is a statistical method for expressing an observed set of random vectors as a linear combination of statistically independent components. This paper tackles the task of comparing two ICA algorithms, in terms of their efficiency for compact representation of market securities. A recently developed sequential blind signal extraction algorithm, SmoothICA, is co...

Audio source separation is the task of isolating sound sources that are active simultaneously in a room captured by a set of microphones. Convolutive audio source separation of equal number of sources and microphones has a number of shortcomings including the complexity of frequency-domain ICA, the permutation ambiguity and the problem’s scalabity with increasing number of sensors. In this pape...