Sharif Digital Repository

Due to the important role of surface-related properties of NPs in their biological behavior, simple and fast methods that could precisely demonstrate accurate information about NPs' surface, structure and morphology are highly desirable. In this study a set of surface morphological nano-descriptors (size, shape, surface area, agglomeration state, curvature, corner count and aspect ratio) have been defined and extracted from Transmission Electron Microscopy (TEM) images of nanoparticles (NPs) by Digital Image Processing methods. The extracted data represent a thorough description of the surface and morphologies of NPs lying beyond their TEM images and can supply the data required for a... 

It is important to distinguish overlapped cell for tracking and segmentation biological cells from images. In this research, a novel or comprehensive method is provided with morphological features of cell and minimum distance for overlapped cells separation (OCS). it's necessary to say that this algorithm is not based on type and number of Overlapped cells. In this presented method based on a distance chart. At first, every cell or rectangle of cell is separated of the original image. Also, multiplex cell is distinguished by using minimum perimeter polygon (MPP) method. Distance chart is achieved on the base of morphological procedure and Computing of cell boundary distance to Cell middle... 

Microstructural evolution of the spray atomized and powder thixoformed hyper-eutectic A390 aluminum alloy was investigated. The spray atomized powder revealed homogeneous and very fine silicon particles distribution, due to the rapid solidification of the alloy. The semi-solid powders were extruded into a closed die cavity through a hole for the plastic deformation of the powder particles. A drop forge of 45kg weight at different heights was used in this investigation. Remarkable rearrangement and growth of the silicon rich phase was revealed in the final stage  

Analyzing the features of the chromosomes can be very useful for diagnosis of many genetic disorders or prediction of the possible abnormalities that may occur in the future generations. For this purpose, karyotype is often used which to make it, there is necessary to identify each one of the 24 chromosomes from the microscopic images. Definition and extraction of the morphological and band pattern based features for each chromosome is the first step to identify them. An important class of the morphological features is the location of the chromosome's centromere. Thus, centromere localization is an initial step in designing an automatic karyotyping system. In this paper, a novel algorithm... 

Dyslipidemia is considered to be one of the main heart risk factors, affecting the endothelial vascular function, which can be non-invasively investigated by ultrasound flow-mediated dilation (US-FMD). However, US-FMD comes at a high-cost and is operator-dependent. In this paper, the effect of dyslipidemia on the photoplethysmogram (PPG) signal recorded from collateral index fingers is investigated following a previous study where it was shown that results similar to that of US-FMD can be replicated by the PPG. Two groups, consisting of 30 healthy subjects free from any risk factors and 30 subjects who have only dyslipidemia as risk factor were respectively considered. The percent change in... 

In this paper, we present a machine learning model to estimate the blood pressure (BP) of a person using only his photoplethysmogram (PPG) signal. We propose algorithms to better detect some critical points of the PPG signal, such as systolic and diastolic peaks, dicrotic notch and inflection point. These algorithms are applicable to different PPG signal morphologies and improve the precision of feature extraction. We show that the logarithm of dicrotic notch reflection index, the ratio of low-to high-frequency components of heart rate (HR) variability signal, and the product of HR multiplied by the modified Normalized Pulse Volume (mNPV) are the key features in accurately estimating the BP... 

Biodegradable acrylate-terminated polyurethane/acrylate (APUA) filled with 2-hydroxyethyl methacrylate functionalized carbon nanotube (CNT-HEMA) was prepared by in situ free radical crosslinking. CNT-HEMA enhanced crystallinity of soft domain and caused more phase separation between hard and soft domains of APUA. Tensile testing showed a considerable improvement in elastic modulus (∼160%) and tensile strength (∼30%) at 1 wt% loading. Morphological features of APUA induced by nanotubes were found to be dominant on mechanical properties of APUA/CNT-HEMA. CNT-HEMA increased water contact angle of APUA; however, wettability of APUA/CNT-HEMA maintained in acceptable range for biomedical... 

Biodegradable acrylate-terminated polyurethane/acrylate (APUA) filled with 2-hydroxyethyl methacrylate functionalized carbon nanotube (CNT-HEMA) was prepared by in situ free radical crosslinking. CNT-HEMA enhanced crystallinity of soft domain and caused more phase separation between hard and soft domains of APUA. Tensile testing showed a considerable improvement in elastic modulus (∼160%) and tensile strength (∼30%) at 1 wt% loading. Morphological features of APUA induced by nanotubes were found to be dominant on mechanical properties of APUA/CNT-HEMA. CNT-HEMA increased water contact angle of APUA; however, wettability of APUA/CNT-HEMA maintained in acceptable range for biomedical... 

Polylactic acid (PLA) and thermoplastic starch (TPS) are known as bio-based and biodegradable thermoplastic polymers that can be used in different applications owing to their inherent physical and mechanical properties. In order to reduce the higher costs of PLA and tuning its physical and mechanical properties suitable for short life packaging applications, blending of PLA with the TPS, more economical biodegradable polymer, has been considered in academic and industrial researches. However, melt blending of PLA with TPS without compatibilization process caused some drawbacks such as coarsening morphology and declining mechanical properties and ductility because of thermodynamic... 

This paper illustrates the use of a combined neural network model for classification of electrocardiogram (ECG) beats. We present a trainable neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care. We process a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising, a feature extraction and a classification. At first we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. Then...