Underdetermined blind source separation based on Fuzzy C-Means and Semi-Nonnegative Matrix Factorization

Conventional blind source separation is based on over-determined with more sensors than sources but the underdetermined is a challenging case and more convenient to actual situation. Non-negative Matrix Factorization (NMF) has been widely applied to Blind Source Separation (BSS) problems. However, the separation results are sensitive to the initialization of parameters of NMF. Avoiding the subjectivity of choosing parameters, we used the Fuzzy C-Means (FCM) clustering technique to estimate the mixing matrix and to reduce the requirement for sparsity. Also, decreasing the constraints is regarded in this paper by using Semi-NMF. In this paper we propose a new two-step algorithm in order to solve the underdetermined blind source separation. We show how to combine the FCM clustering technique with the gradient-based NMF with the multi-layer technique. The simulation results show that our proposed algorithm can separate the source signals with high signal-to-noise ratio and quite low cost time compared with some algorithms

Mineral Changes in the Blood of Ovariectomized Mice after Treatment with β-Sitosterol

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Optical and structural characterization of (Mn, Fe) co-doped lead chalcogenides for optoelectronics applications

Lead chalcogenides (LCs) exhibit non-stability and lower device efficiency due to smaller bandgap (Eg) leading to poor optical properties for photovoltaic (PV) applications. In this work, optical properties of transition metals (TMs) such as (Mn and Fe) co-doped with LCs especially PbS in the framework of DFT+U (8 eV) and L/APW+lo method are theoretically investigated to predict new optical material for photovoltaic and other optoelectronics applications. The XAFS spectroscopy technique was used to analyze electronic structures and optical properties of (Mn, Fe) co-doped LCs. Midgap states of co-doped PbS reveal to improve the absorption of infrared light mainly due to slight doping with TMs. Compared to pure PbS, Mn doping in PbS induces Eg widening, blue-shift, and improve the light absorption edge. Due to co-doping, the magnetic order is translated that can lead to forming a charge compensated system which is beneficial to minimize vacancies related to defects formation

Optimized superpixel and AdaBoost classifier for human thermal face recognition

Infrared spectrum-based human recognition systems offer straightforward and robust solutions for achieving an excellent performance in uncontrolled illumination. In this paper, a human thermal face recognition model is proposed. The model consists of four main steps. Firstly, the grey wolf optimization algorithm is used to find optimal superpixel parameters of the quick-shift segmentation method. Then, segmentation-based fractal texture analysis algorithm is used for extracting features and the rough set-based methods are used to select the most discriminative features. Finally, the AdaBoost classifier is employed for the classification process. For evaluating our proposed approach, thermal images from the Terravic Facial infrared dataset were used. The experimental results showed that the proposed approach achieved (1) reasonable segmentation results for the indoor and outdoor thermal images, (2) accuracy of the segmented images better than the non-segmented ones, and (3) the entropy-based feature selection method obtained the best classification accuracy. Generally, the classification accuracy of the proposed model reached to 99% which is better than some of the related work with around 5%

Long-term drivers of broadband traffic in next-generation networks

This paper is concerned with long-term (20+ years) forecasting of broadband traffic in next-generation networks. Such long-term approach requires going beyond extrapolations of past traffic data while facing high uncertainty in predicting the future developments and facing the fact that, in 20 years, the current network technologies and architectures will be obsolete. Thus, "order of magnitude" upper bounds of upstream and downstream traffic are deemed to be good enough to facilitate such long-term forecasting. These bounds can be obtained by evaluating the limits of human sighting and assuming that these limits will be achieved by future services or, alternatively, by considering the contents transferred by bandwidth-demanding applications such as those using embedded interactive 3D video streaming. The traffic upper bounds are a good indication of the peak values and, subsequently, also of the future network capacity demands. Furthermore, the main drivers of traffic growth including multimedia as well as non-multimedia applications are identified. New disruptive applications and services are explored that can make good use of the large bandwidth provided by next-generation networks. The results can be used to identify monetization opportunities of future services and to map potential revenues for network operators