Epistemology A Review on Knowledge

In this draft I investigated epistemology briefly. The concerns of this draft was to have an introduction to epistemology for later researches leading to the boundaries of science and physics in specific

Perineal Mass in One-Year-Old Boy: Rare Presentation of Fibrous Hamartoma of Infancy

Fibrous hamartoma of infancy is a rare benign tumor that’s mainly detected in the upper trunk. In this study authors report a 1-year-old case of perianal fibrous hamartoma which was successfully managed without need to orchidectomy or urethral manipulation

Classification of Arrhythmias Using Linear Predictive Coefficients and Probabilistic Neural Network

Cardiac arrhythmia, which means abnormality of heart rhythm, in fact refers to disorder in electrical conduction system of the heart. The aim of this paper is to present a classifier system based on Probabilistic Neural Networks in order to detect and classify abnormal heart rates, where besides its simplicity, has high resolution capability. The proposed algorithm has three stages. At first, the electrocardiogram signals impose into preprocessing block. After preprocessing and noise elimination, the exact position of R peak is detected by multi resolution wavelet analysis. In the next step, the extracted linear predictive coefficients (LPC) of QRS complex will enter in to the classification block as an input. A Support Vector Machine classifier is developed in parallel to verify and measure the PNN classifier’s success. The experiments were conducted on the ECG data from the MIT-BIH database to classify four kinds of abnormal waveforms and normal beats such as Normal sinus rhythm, Atrial premature contraction (APC), Right bundle branch block (RBBB) and Left bundle branch block (LBBB). The results show 92.9% accuracy and 93.17% sensitivit

Integrated region-based segmentation using color components and texture features with prior shape knowledge

Segmentation is the art of partitioning an image into different regions where each one has some degree of uniformity in its feature space. A number of methods have been proposed and blind segmentation is one of them. It uses intrinsic image features, such as pixel intensity, color components and texture. However, some virtues, like poor contrast, noise and occlusion, can weaken the procedure. To overcome them, prior knowledge of the object of interest has to be incorporated in a top-down procedure for segmentation. Consequently, in this work, a novel integrated algorithm is proposed combining bottom-up (blind) and top-down (including shape prior) techniques. First, a color space transformation is performed. Then, an energy function (based on nonlinear diffusion of color components and directional derivatives) is defined. Next, signeddistance functions are generated from different shapes of the object of interest. Finally, a variational framework (based on the level set) is employed to minimize the energy function. The experimental results demonstrate a good performance of the proposed method compared with others and show its robustness in the presence of noise and occlusion. The proposed algorithm is applicable in outdoor and medical image segmentation and also in optical character recognition (OCR)

Pupil Detection for Automatic Diagnosis of Eye Diseases Using Optimized Color Mapping

Introduction: Pupil and iris disorders form an important category of eye diseases. Accurate segmentation of the pupil is the first and most important step in the automatic diagnosis of diseases related to the pupil and iris. Most of the existing methods do not have enough accuracy and are sensitive to the effects of noise and specular spot reflection. In addition, the images used in these methods usually have limitations, such as the viewing angle. Method: In the proposed algorithm, a stable method is offered to remove the effects of specular spot reflection in the pupil, and necessary preprocessing is done to detect the exact location of the pupil. An optimized color mapping algorithm is proposed and the mapping is calculated with the help of the LM algorithm to accurately determine the pupil boundary. This method does not impose any restrictions on the eye image and shape, and the angle of the pupil in the image can be in any shape and direction. Results: The proposed method does not assume any specific model as the final pupil boundary (circle or oval) and is robust to noise and specular reflection factors as well. This method has been able to accurately detect the pupil boundary with the accuracy of 98.8% using UBIRIS dataset and 98% using the collected data by authors. Conclusion: The method presented in this paper can be used to increase the accuracy in determining the internal and external border of the iris to diagnose diseases related to the pupil and iris, as well as identity identification based on iris tissue

Classification of EEG-P300 Signals Extracted from Brain Activities in BCI Systems Using ν-SVM and BLDA Algorithms

In this paper, a linear predictive coding (LPC) model is used to improve classification accuracy, convergent speed to maximum accuracy, and maximum bitrates in brain computer interface (BCI) system based on extracting EEG-P300 signals. First, EEG signal is filtered in order to eliminate high frequency noise. Then, the parameters of filtered EEG signal are extracted using LPC model. Finally, the samples are reconstructed by LPC coefficients and two classifiers, a) Bayesian Linear discriminant analysis (BLDA), and b) the υ-support vector machine (υ-SVM) are applied in order to classify. The proposed algorithm performance is compared with fisher linear discriminant analysis (FLDA). Results show that the efficiency of our algorithm in improving classification accuracy and convergent speed to maximum accuracy are much better. As example at the proposed algorithms, respectively BLDA with LPC model and υ-SVM with LPC model with8 electrode configuration for subject S1 the total classification accuracy is improved as 9.4% and 1.7%. And also, subject 7 at BLDA and υ-SVM with LPC model algorithms (LPC+BLDA and LPC+ υ-SVM) after block 11th converged to maximum accuracy but Fisher Linear Discriminant Analysis (FLDA) algorithm did not converge to maximum accuracy (with the same configuration). So, it can be used as a promising tool in designing BCI systems

Pose‐invariant face recognition based on matching the occlusion free regions aligned by 3D generic model

Face recognition systems perform accurately in a controlled environment, but an unconstrained environment dramatically degrades their performance. In this study, a novel pose‐invariant face recognition system is proposed based on the occlusion free regions. This method utilises a gallery set of frontal face images and can handle large pose variations. For a 2D probe face image with an arbitrary pose, the head pose is first obtained using a robust head pose estimation method. Then, this 2D face image is normalised by a novel 3D modelling method from a single input image. In consequence, pose invariant face recognition is converted to a frontal face recognition problem. The 3D structure is reconstructed using a new method based on the estimated head pose and only one facial feature point, which is significantly reduced in comparison with the number of landmarks used in previous methods. According to the estimated poses, occlusion free regions are extracted from normalised images as feature extraction. Finally, face matching and recognition is performed using these regions from normalised test images and the corresponding regions of gallery images. Experimental results on FERET and CAS‐PEAL‐R1 databases demonstrate that the proposed method outperforms other methods, and it is robust and efficient

The Immunomodulatory Effect of Recombinant Exotoxin A of Pseudomonas Aeruginosa on Dendritic Cells Extracted from Mice Spleen

Background & Objective: Dendritic cell (DC) is as a key cell in activation of immune response against microbes and disease. Therefore, the effect of recombinant exotoxin A of Pseudomonas aeruginosa on the maturity and the activation of DCs was evaluated in this study. Materials & Methods: Recombinant exotoxin A was produced from Pseudomonas aeruginosa DNA. MTT assay was used to evaluate the cytotoxicity of this protein on DCs. The expression of co-stimulatory molecules CD40, CD86, and MHCΠ was evaluated by flow cytometry. Moreover, the effect of this antigen (Ag) on T-cell proliferation was evaluated using Mixed Lymphocyte Reaction (MLR) assay and the secretion of IL-4 and IFN- γ. Secretion of IL-12 by DCs was measured with Enzyme-Linked Immunosorbent Assay (ELISA) method. The data were collected and analyzed with one way ANOVA test. Results: Recombinant exotoxin A had no effect on DCs viability. In addition, expression of CD40, CD86, and MHCΠ did not change significantly compared to the negative control cells. Moreover, T-cells proliferation was decreased significantly at the concentration of 0.1µg/ml of this Ag. The secretion of IL-12 was increased by DCs, in contrast the secretion of IL-4 and IFN-γ in MLR supernatant did not decrease significantly. Conclusion: Exotoxin A decreases the proliferation of T-cells and also leads to a change in the pattern of cytokine secretion of immune cells