A novel underdetermined source recovery algorithm based on k-sparse component analysis

Sparse component analysis (SCA) is a popular method for addressing underdetermined blind source separation in array signal processing applications. We are motivated by problems that arise in the applications where the sources are densely sparse (i.e. the number of active sources is high and very close to the number of sensors). The separation performance of current underdetermined source recovery (USR) solutions, including the relaxation and greedy families, reduces with decreasing the mixing system dimension and increasing the sparsity level (k). In this paper, we present a k-SCA-based algorithm that is suitable for USR in low-dimensional mixing systems. Assuming the sources is at most (m−1) sparse where m is the number of mixtures; the proposed method is capable of recovering the sources from the mixtures given the mixing matrix using a subspace detection framework. Simulation results show that the proposed algorithm achieves better separation performance in k-SCA conditions compared to state-of-the-art USR algorithms such as basis pursuit, minimizing norm-L1, smoothed L0, focal underdetermined system solver and orthogonal matching pursuit

Comparison of RANTES expression in Crohn's disease and ulcerative colitis: an aid in the differential diagnosis?

BACKGROUND: RANTES (regulated on activation, normal T cell expressed and secreted) expression is increased in inflammatory bowel disease (IBD). RANTES is produced at higher levels in granulomatous conditions, so increased RANTES expression can be expected in Crohn's disease compared with ulcerative colitis. AIM: To compare RANTES expression between intestinal biopsy specimens of patients with Crohn's disease and those with ulcerative colitis. MATERIALS AND METHODS: A prospective study of patients presenting with lower gastrointestinal symptoms at the Bahrain Specialist Hospital from July 2004 to April 2005 was carried out. Endoscopic colonic biopsy specimens were taken from every patient and subjected to (a) routine haematoxylin and eosin staining examination by light microscopy, (b) immunohistochemistry for examination of RANTES protein expression by light microscopy and (c) in situ hybridisation for examination of RANTES mRNA expression by light microscopy. RANTES expression was assessed and quantified. RESULTS: 58 patients were enrolled to the study. Of them, 40 had IBD (21 had Crohn's disease and 19 had ulcerative colitis), 15 were controls with normal colonic biopsy results or non‐inflammatory lesions and 3 had colonic inflammatory lesions other than IBD. RANTES expression in lymphocytes or histiocytes was significantly higher (p = 0.04) in new patients with ulcerative colitis than in those with Crohn's disease analysed by immunohistochemistry (IHC). CONCLUSION: RANTES expression in lymphocytes or histiocytes is significantly higher in patients with ulcerative colitis than in those with Crohn's disease. Hence, RANTES IHC can be an effective method for distinguishing between biopsy specimens of patients with ulcerative colitis from those of patients with Crohn's disease, where routine histological features are indeterminate. RANTES IHC may prove to be a useful technique for identifying early or equivocal granulomas

Operation and control of HVDC stations using continuous mixed p-norm-based adaptive fuzzy technique

© The Institution of Engineering and Technology. The power system is purely non-linear and there exist a lot of uncertainties in generation, transmission, and distribution sectors. This study focuses on the operation and control of a voltage source converter (VSC)-based high voltage direct current (HVDC) transmission system, which can cope up with system non-linearity and parameter uncertainties. The HVDC system connects an offshore wind farm to the onshore power grid. A new adaptive fuzzy logic controller is proposed for both onshore and offshore HVDC stations to control the real and reactive power flow in normal and undertrain parameter conditions. The adaptive technique is based on the continuous mixed p-norm algorithm, which updates the fuzzy inference system automatically at a high convergence speed. The performance of the proposed adaptive controller is tested under different parameter uncertainties and the results are compared with Taguchi optimisation-based proportional-integral controllers