A Structural Based Feature Extraction for Detecting the Relation of Hidden Substructures in Coral Reef Images

In this paper, we present an efficient approach to extract local structural color texture features for classifying coral reef images. Two local texture descriptors are derived from this approach. The first one, based on Median Robust Extended Local Binary Pattern (MRELBP), is called Color MRELBP (CMRELBP). CMRELBP is very accurate and can capture the structural information from color texture images. To reduce the dimensionality of the feature vector, the second descriptor, co-occurrence CMRELBP (CCMRELBP) is introduced. It is constructed by applying the Integrative Co-occurrence Matrix (ICM) on the Color MRELBP images. This way we can detect and extract the relative relations between structural texture patterns. Moreover, we propose a multiscale LBP based approach with these two schemes to capture microstructure and macrostructure texture information. The experimental results on coral reef (EILAT, EILAT2, RSMAS, and MLC) and four well-known texture datasets (OUTEX, KTH-TIPS, CURET, and UIUCTEX) show that the proposed scheme is quite effective in designing an accurate, robust to noise, rotation and illumination invariant texture classification system. Moreover, it makes an admissible tradeoff between accuracy and number of features

A Novel Adaptive LBP-Based Descriptor for Color Image Retrieval

In this paper, we present two approaches to extract discriminative features for color image retrieval. The proposed local texture descriptors, based on Radial Mean Local Binary Pattern (RMLBP), are called Color RMCLBP (CRMCLBP) and Prototype Data Model (PDM). RMLBP is a robust to noise descriptor which has been proposed to extract texture features of gray scale images for texture classification. For the first descriptor, the Radial Mean Completed Local Binary Pattern is applied to channels of the color space, independently. Then, the final descriptor is achieved by concatenating the histogram of the CRMCLBP_S/M/C component of each channel. Moreover, to enhance the performance of the proposed method, the Particle Swarm Optimization (PSO) algorithm is used for feature weighting. The second proposed descriptor, PDM, uses the three outputs of CRMCLBP (CRMCLBP_S, CRMCLBP_M, CRMCLBP_C) as discriminative features for each pixel of a color image. Then, a set of representative feature vectors are selected from each image by applying k-means clustering algorithm. This set of selected prototypes are compared by means of a new similarity measure to find the most relevant images. Finally, the weighted versions of PDM is constructed using PSO algorithm. Our proposed methods are tested on Wang, Corel-5k, Corel-10k and Holidays datasets. The results show that our proposed methods makes an admissible tradeoff between speed and retrieval accuracy. The first descriptor enhances the state-of-the-art color texture descriptors in both aspects. The second one is a very fast retrieval algorithm which extracts discriminative features

Intestinal parasitic infections in chronic psychiatric patients in Sina Hospital Shahre-Kord, Iran

Background: Although the number of infectious diseases has sharply decreased in last few decades, parasitic diseases persist in developing countries. On the other hand, chronic psychiatric patients tend to have low self-control, poor personal hygiene, long term institutionalization and extremely low self-care should be monitored for parasitic diseases since psychosocial conditions can contribute to an affinity for infectious diseases

Primary Central Nervous System Lymphoma Presenting with Peripheral Neuropathy; A Rare Case of Coincident PCNSL and Mononeuritis Multiplex

A 43-year-old male presented with diplopia and right sixth-nerve palsy. Brain magnetic resonance imaging (MRI) demonstrated a lesion in the right periventricular area. High-dose corticosteroid pulse therapy did not resolve the symptom. After one month, his diplopia progressed and he developed weakness of the left lower limb. Detailed examination revealed left sixth-nerve palsy, dropped foot, waddling gait, atrophy of the gluteal muscles and mild atrophy and weakness of the right upper limb. Neurological examination supported evidence of multiple cranial nerve palsies along with asymmetrical peripheral neuropathy. Electrodiagnostic studies were compatible with a mononeuritis multiplex. Rheumatologic evaluations were normal. Malignancy work-up were normal, except for some insignificant lymph nodes. Bone marrow aspiration and biopsy were normal. The second brain MRI detected multiple homogenous enhancing lesions in the right periventricular area.The result of stereotactic biopsy and immunohistochemistry staining demonstrated primary B-cell CNS lymphoma (PCNSL). Mononeuritis multiplex has not been reported as a paraneoplastic manifestation of PCNSL yet. In other words, it is not clear whether involvement of the peripheral nervous system in our patient is a paraneoplastic manifestation of PCNSL or a coincidence of PCNSL and hematologic lymphoma presenting with peripheral vasculitic neuropathy. It is recommendedthat future studies focus more on symptoms associated with PCNSL to recognize the exact relationship between PCNSL and peripheral neuropathy

Strengthening mechanisms of graphene sheets in aluminium matrix nanocomposites

Uniform dispersion of SiC nanoparticles with a high propensity to agglomerate within a thixoformed aluminium matrix was attained using a graphene encapsulating approach. The analytical model devised in this study has demonstrated the significant role of shear lag and thermally activated dislocation mechanisms in strengthening aluminium metal matrix composites due to the exceptional negative thermal expansion coefficient of graphene sheets. This, in turn, triggers the pinning capacity of nano-sized rod-liked aluminium carbide, prompting strong interface bonding for SiC nanoparticles with the matrix, thereby enhancing tensile elongation

Optimal Monetary Policy and Stock Market Fluctuations

This study investigates the monetary policy rule including money growth and optimal Ramsey policy in restraining the stock market Fluctuations. We apply a new Keynesian monetary framework with nominal wage and price rigidities within a DSGE model for Iranian economy. Bubbles in our model emerge through a positive feedback loop mechanism supported by self-fulfilling beliefs. The sentiment shock, which represents the size of current bubbles relative to newly born bubbles, causing bubbles movement and it transfers to the real economy through endogenous credit constraint. Moreover, this study investigates the impulse and response between sentiment shock and fluctuation in aggregate variables. Our empirically findings show that: first, applying Ramsey optimal monetary policy decreases the central bank’s loss function, relative to monetary policy rule with money growth. Second, the sentiment shock drives the movements of stock market fluctuations and variations in real economy, leading to explain the positive contemporaneous correlation between stock prices and the real economy and it helps explaining the business cycles in Iran

Status of soluble ST2 levels in serum of HTLV-1 infected individuals

ST2 is a member of IL-1 receptor family expressed on Th2 cells and regulates Th2 responces. The gene of ST2 encodes soluble ST2 (sST2) and the transmembrane ST2 (ST2L) isoforms through alternative mRNA splicing. The discovery of IL33/ ST2 signaling pathway, has drawn a great scientific attention to this system. sST2 has been shown to be an indacating factor in various infl ammatory conditions. This study aims to evaluate serum sST2 levels in HTLV-1 infected patients. This study included 49 HTLV-1 seropositive cases of which 14 were sympthomatic. Controls consisted of 30 healthy volunteers. sST2 level was measured using a quantitative ELISA assay and the results of the study groups were compared. Corroborating the previous reports, sST2 was lower in females (P = 0.003). The sST2 levels was slightly increased in HTLV-1 patients, though such increase was not statistically significant (P = 0.91), in addition sST2 level did not correlate significantly to the disease duration (P = 0.78). Despite some other chronic viral infection, HTLV-1 seems not to induce high serum sST2. However owing to relatively high normal variation of sST2 levels and rather small sample size, we stongly recommend further reseach with preferably larger sample size to evalute sST2 in HTLV-1 infected patients

Classification of sEMG Signals for Muscle Fatigue Detection Using Support Vector Machines

Fatigue is a multidimensional and subjective concept and is a complex phenomenon including various causes, mechanisms and forms of manifestation. Thus, it is crucial to delineate the different levels and to quantify self- perceived fatigue. The aim of this study was to discriminate between fatigue and nonfatigue stages using support vector machine (SVM) approach. Thus, electromyographic (EMG) signals collected in the department of biomedical engineering of Islamic Azad university of Mashhad, were used. 10 features in time, frequency and time- scale domains were extracted from sEMG signals and the effect of different objective functions for dimensionality reduction and different SVM were evaluated for fatigue detection. The best accuracy (89.45%) was achieved through RBF kernel with ROC criterion while the best accuracy through linear SVM was 54.42%. These results suggest that the selected features contained some information that could be used by the nonlinear SVM with RBF kernel to best discriminate between fatigue and nonfatigue stages. &nbsp; &nbsp;</p

National Conference on Electrical and Computer Engineering

The objective of the present study was to investigate the possible relationship between bispectral parameters extracted from surface EMG (sEMG) signals and muscle force and fatigue. Our hypothesis was that changes in motor unit recruitment during muscle contraction and fatigue, affect sEMG distribution and the degree of complexity and irregularity in the muscle. Thus, four features based on higher order spectra and cumulants were extracted from sEMG signal, recorded from biceps brachii muscle of a healthy female volunteer during rest, sustained (fatiguing) 50% MVC, 100% MVC and recovery. Results obtained from weighted center of bispectrum (WCOB) analysis showed that the values of f1m and f2m were higher during rest and recovery states, while they decreased during MVCs. However, when fatigue occurred, these parameters increased slightly, again. Moreover, entropy features, namely NBE and NBSE decreased with contraction compared to rest and recovery states, indicating less complexity of time series during MVCs. However, the changes were not significant during fatigue and during changes in MVC levels from 50% to 100%. On the other hand, test of non-Gaussianity based on negentropy showed the reverse pattern of WCOB, NBE and NBSE. In addition, contour maps of bispectrum enabled us to visually differentiate each trial. </p