Blind Source Separation and Localization Using Microphone Arrays

The blind source separation and localization problem for audio signals is studied using microphone arrays. Pure delay mixtures of source signals typically encountered in outdoor environments are considered. Our proposed approach utilizes the subspace methods, including multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithms, to estimate the directions of arrival (DOAs) of the sources from the collected mixtures. Since audio signals are generally considered broadband, the DOA estimates at frequencies with the large sum of squared amplitude values are combined to obtain the final DOA estimates. Using the estimated DOAs, the corresponding mixing and demixing matrices are computed, and the source signals are recovered using the inverse short time Fourier transform. Subspace methods take advantage of the spatial covariance matrix of the collected mixtures to achieve robustness to noise. While the subspace methods have been studied for localizing radio frequency signals, audio signals have their special properties. For instance, they are nonstationary, naturally broadband and analog. All of these make the separation and localization for the audio signals more challenging. Moreover, our algorithm is essentially equivalent to the beamforming technique, which suppresses the signals in unwanted directions and only recovers the signals in the estimated DOAs. Several crucial issues related to our algorithm and their solutions have been discussed, including source number estimation, spatial aliasing, artifact filtering, different ways of mixture generation, and source coordinate estimation using multiple arrays. Additionally, comprehensive simulations and experiments have been conducted to examine various aspects of the algorithm. Unlike the existing blind source separation and localization methods, which are generally time consuming, our algorithm needs signal mixtures of only a short duration and therefore supports real-time implementation.School of Materials Science & Engineerin

Feature Extraction and Pattern Identification for Anemometer Condition Diagnosis

Cup anemometers are commonly used for wind speed measurement in the wind industry. Anemometer malfunctions lead to excessive errors in measurement and directly influence the wind energy development for a proposed wind farm site. This paper is focused on feature extraction and pattern identification to solve the anemometer condition diagnosis problem of the PHM 2011 Data Challenge Competition. Since the accuracy of anemometers can be severely affected by the environmental factors such as icing and the tubular tower itself, in order to distinguish the cause due to anemometer failures from these factors, our methodologies start with eliminating irregular data (outliers) under the influence of environmental factors. For paired data, the relation between the relative wind speed difference and the wind direction is extracted as an important feature to reflect normal or abnormal behaviors of paired anemometers. Decisions regarding the condition of paired anemometers are made by comparing the features extracted from training and test data. For shear data, a power law model is fitted using the preprocessed and normalized data, and the sum of the squared residuals (SSR) is used to measure the health of an array of anemometers. Decisions are made by comparing the SSRs of training and test data. The performance of our proposed methods is evaluated through the competition website. As a final result, our team ranked the second place overall in both student and professional categories in this competition

Zinc finger myeloid Nervy DEAF-1 type (ZMYND) domain containing proteins exert molecular interactions to implicate in carcinogenesis

Morphogenesis and organogenesis in the low organisms have been found to be modulated by a number of proteins, and one of such factor, deformed epidermal auto-regulatory factor-1 (DEAF-1) has been initially identified in Drosophila. The mammalian homologue of DEAF-1 and structurally related proteins have been identified, and they formed a family with over 20 members. The factors regulate gene expression through association with co-repressors, recognition of genomic marker, to exert histone modification by catalyze addition of some chemical groups to certain amino acid residues on histone and non-histone proteins, and degradation host proteins, so as to regulate cell cycle progression and execution of cell death. The formation of fused genes during chromosomal translocation, exemplified with myeloid transforming gene on chromosome 8 (MTG8)/eight-to-twenty one translocation (ETO) /ZMYND2, MTG receptor 1 (MTGR1)/ZMYND3, MTG on chromosome 16/MTGR2/ZMYND4 and BS69/ZMYND11 contributes to malignant transformation. Other anomaly like copy number variation (CNV) of BS69/ZMYND11 and promoter hyper methylation of BLU/ZMYND10 has been noted in malignancies. It has been reported that when fusing with Runt-related transcription factor 1 (RUNX1), the binding of MTG8/ZMYND2 with co-repressors is disturbed, and silencing of BLU/ZMYND10 abrogates its ability to inhibition of cell cycle and promotion of apoptotic death. Further characterization of the implication of ZMYND proteins in carcinogenesis would enhance understanding of the mechanisms of occurrence and early diagnosis of tumors, and effective antitumor efficacy