A geometrically constrained multimodal time domain approach for convolutive blind source separation

A novel time domain constrained multimodal approach for convolutive blind source separation is presented which incorporates geometrical 3-D cordinates of both the speakers and the microphones. The semi-blind separation is performed in time domain and the constraints are incorporated through an alternative least squares optimization. Orthogonal source model and gradient based optimization concepts have been used to construct and estimate the model parameters which fits the convolutive mixture signals. Moreover, the majorization concept has been used to incorporate the geometrical information for estimating the mixing channels for different time lags. The separation results show a considerable improvement over time domain convolutive blind source separation systems. Having diagonal or quasi diagonal covariance matrices for different source segments and also having independent profiles for different sources (which implies nonstationarity of the sources) are the requirements for our method. We evaluated the method using synthetically mixed real signals. The results show high capability of the method for separating speech signals. © 2011 EURASIP

A Block-Wise random sampling approach: Compressed sensing problem

The focus of this paper is to consider the compressed sensing problem. It is stated that the compressed sensing theory, under certain conditions, helps relax the Nyquist sampling theory and takes smaller samples. One of the important tasks in this theory is to carefully design measurement matrix (sampling operator). Most existing methods in the literature attempt to optimize a randomly initialized matrix with the aim of decreasing the amount of required measurements. However, these approaches mainly lead to sophisticated structure of measurement matrix which makes it very difficult to implement. In this paper we propose an intermediate structure for the measurement matrix based on random sampling. The main advantage of block-based proposed technique is simplicity and yet achieving acceptable performance obtained through using conventional techniques. The experimental results clearly confirm that in spite of simplicity of the proposed approach it can be competitive to the existing methods in terms of reconstruction quality. It also outperforms existing methods in terms of computation time

Sparse multichannel source separation using incoherent K-SVD method

In this paper the problem of sparse source separation of linear mixtures is addressed. We propose to apply K-SVD, which is a leading dictionary learning method, for this purpose. Further, a modified gradient-based K-SVD scheme for incoherent dictionary learning and source separation is proposed. The promising results on random synthetic signals reveal the ability of this technique for utilizing in source separation framework. We also suggest BOLD detection fMRI as an application for this method. The preliminary results confirm the successful separation of this type of data

Segmented compressive sensing

This paper presents an alternative way of random sampling of signals/images in the framework of compressed sensing. In spite of usual random samplers which take p measurements from the input signal, the proposed method uses M different samplers each taking pi'(i = 1, 2, 3 ... M) samples. Therefore, the overall number of samples will be q = M pmacr'. Using this method a variable sampling criterion based on the content of the segments is achievable. Following this idea, the calculated measurement (or sensing) matrix is also more incoherent in columns comparing to other conventional methods which is a desired feature. Our experiments show that the reconstructed signal using this method has a better SNR and is more robust compared to the systems using one sampler

Incoherent dictionary pair learning : application to a novel open-source database of chinese numbers

We enhance the efficacy of an existing dictionary pair learning algorithm by adding a dictionary incoherence penalty term. After presenting an alternating minimization solution, we apply the proposed incoherent dictionary pair learning (InDPL) method in classification of a novel open-source database of Chinese numbers. Benchmarking results confirm that the InDPL algorithm offers enhanced classification accuracy, especially when the number of training samples is limited

Investigating Intra-Tablet Coating Uniformity With Spectral-Domain Optical Coherence Tomography.

Spectral domain optical coherence tomography (SD-OCT) has recently attracted a lot of interest in the pharmaceutical industry as a fast and non-destructive modality for direct quantification of thin film coatings that cannot easily be resolved with other techniques. While previous studies with SD-OCT have estimated the intra-tablet coating uniformity, the estimates were based on limited number of B-scans. In order to obtain a more accurate estimate, a greater number of B-scans are required that can quickly lead to an overwhelming amount of data. To better manage the data so as to generate a more accurate representation of the intra-tablet coating uniformity without compromising on the achievable axial resolution and imaging depth, we comprehensively examine an algebraic reconstruction technique with OCT to significantly reduce the data required. Specifically, a set of coated pharmaceutical tablets with film coating thickness in the range of 60-100 μm is investigated. Results obtained from performing the reconstruction reveal that only 30% of the acquired data are actually required leading to a faster convergence time and a generally good agreement with benchmark data against the intra-tablet coating uniformity measured with terahertz pulsed imaging technology.The authors would like to acknowledge the financial support from UK EPSRC Research Grant EP/L019787/1 and EP/L019922/1. H.L. also acknowledges travel support from Joy Welch Educational Charitable Trust

Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system

Automatic number plate recognition (ANPR) systems are becoming vital for safety and security purposes. Typical ANPR systems are based on three stages: number plate localization (NPL), character segmentation (CS), and optical character recognition (OCR). Recently, high definition (HD) cameras have been used to improve their recognition rates. In this paper, four algorithms are proposed for the OCR stage of a real-time HD ANPR system. The proposed algorithms are based on feature extraction (vector crossing, zoning, combined zoning, and vector crossing) and template matching techniques. All proposed algorithms have been implemented using MATLAB as a proof of concept and the best one has been selected for hardware implementation using a heterogeneous system on chip (SoC) platform. The selected platform is the Xilinx Zynq-7000 All Programmable SoC, which consists of an ARM processor and programmable logic. Obtained hardware implementation results have shown that the proposed system can recognize one character in 0.63 ms, with an accuracy of 99.5% while utilizing around 6% of the programmable logic resources. In addition, the use of the heterogenous SoC consumes 36 W which is equivalent to saving around 80% of the energy consumed by the PC used in this work, whereas it is smaller in size by 95%

The effect of phosphate, fluorine, and soda content of the glass on the mechanical properties of the glass ionomer (polyalkenoate) cements

The authors would like to thank the Department of Trade and Industry, UK, under the project number of TP/5/REG/6/I/H0669A