Sharif Digital Repository

Due to the special structure of the Hessian matrix in nonlinear least squares problems,use of effective structured updating schemes for approximating the Hessian matrix in solving such problems has been considered. Mahdavi-Amiri and Bartels used a structured BFGS method for approximating the projected Hessian matrix in solving constrained nonlinear least squares (CNLLS) problems. Recently, Mahdavi-Amiri and Ansari applied other structured DFP and BFGS methods for approximating the projected Hessian matrix in solving CNLLS problems and proved both global and asymptotic two-step superlinearconvergence of the algorithms. Here, we present other methods for approximating the structured projected... 

In recent years, application of transition metal biphasic catalytic systems for different reactions is an area of intense research activity. Among these reactions, oxidation of sulfides to sulfoxides and sulfones due to their importance as an intermediate in synthesis of organic compounds is a center of interest. Also, the catalytic conversion of alcohols to the corresponding aldehydes or ketones is a fundamental transformation in both laboratory and industrial synthetic chemistry. From environmental perspectives, the development of new catalytic oxidation systems with molecular oxygen as green oxidant is particularly attractive. In recent years, ionic liquids have been extensively studied... 

With the significant advances in software engineering and developing complicated systems, it’s important to investigate the interaction between systems. Agentoriented software engineering is a new paradigm for developing distributed intelligent systems. Agent technology currently plays an important role in complex software development. The underlying paradigm offers a large repertoire of original concepts, architectures, interaction protocols, and methodologies for the analysis and the specification of complex systems built as Multi-Agent Systems (MAS). Several efforts, originating from academia, industry, and several standardisation consortium, have been made in order to provide new tools,... 

In this project, a novel action recognition method, named Fisherposes, is proposed, which is improved by several space-time (spatio-temporal) methods afterwards. The proposed method utilizes skeleton data obtained from Kinect sensor. First, pre-processing is performed in which the scales of bodies are canceled and the skeletons become aligned in order to make the method robust to location, orientation, and scale of people. In Fisherposes method, every action is defined as a sequence of body poses. Using the training samples for the poses, a Fisher subspace is created which we name it Fisherposes. Moreover, a novel distance measuring function, named regularized Mahalanobis distance, is... 

Human has different emotions such as happiness, sadness, anger, etc. Recognizing these emotions plays an important role in human-machine interface. Emotion recognition can be divided into approaches, physiological and non-physiological signals. Non-physiological signals include facial expressions, body gesture, and voice, and physiological signals include electroencephalograph (EEG), electrocardiograph (ECG), and functional magnetic resonance imaging (fMRI). EEG signal has been absorbed a lot of attention in emotion recognition because recording of EEG signal is easy and it is non-invasive. Analysis of connectivity and interaction between different areas of the brain can provide useful... 

In this thesis we study the exponential stabilization of the one dimensional compressible Navier-Stokes system, in a bounded interval locally around a constant steady state by a localized distributed control acting only in the velocity equation. In fact this is an analysis of a paper that published by Shirshendu Chowdhury, Debayan Maity, Mithily Ramaswamy and Jean-Pierre Raymond in Journal of Differential Equations. We determine a linear feedback law able to stabilize a nonlinear transformed system. Coming back to the original nonlinear system, we obtain a nonlinear feedback law able to stabilize locally this nonlinear system. The result is providing feedback control laws stabilizing... 

A software product line is a set of products with common features. The design of this set is such that the core assets that are common features between products are implemented only once. All products in the product line use the core assets to reduce development costs. The number of products that can be produced in a software product line is exponential to the number of capabilities in the core assets and the set is very large, so the cost of testing the software product line will be very high. In the software product line testing, various methods have been provided to reduce costs, among which we can mention product prioritization and regression test techniques. In prioritization, the... 

Analysis of people appearance variation in multi-camera networks for person re-identification or person retrieval is a very challenging problem due to the many intra-class variations between different cameras. Like any problem in the field of machine vision, it is generally divided into two parts. The first part is feature extraction and the second part is feature matching for person retrieval. So far, various methods have been proposed for the extraction of discriminative features, which are generally divided into three categories: stripe-based, patch-based, and body-based methods. However, methods based on stripes, although simpler, have performed better due to their greater compatibility... 

In recent years, the simultaneous analysis of image and text by artificial intelligence has gained much attention. Video description is one of the topics used to help the blind, automate video content analysis, and more. This issue is usually examined in the context of supervised learning and is very similar to image description and machine translation.The proposed solutions to this problem are mainly in the framework of encoder-decoder and attention-based neural networks. Selection of various pre-trained networks to extract 2D and 3D visual features (description of objects and actions in the image), various hierarchical structures and different teaching methods (based on reinforcement... 

There is a need from the natural gas and energy industries to seek for an economically attractive way of converting remote gas reserves into transportable products, such as high quality fuels or petrochemicals. A possible way for the conversion of natural gas to middle distillates is based on a three-step process. Firstly, production of syngas from natural gas. Secondly,catalytic conversion of syngas into hydrocarbons, mostly parafins from C5 to C100. (Fischer-Tropsch (F-T) synthesis). Thirdly, hydrocracking of the heavy paraffinic hydrocarbons to middle distillates. The F-T synthesis step is highly exothermic. In order to control the temperature within the reactor, when considering high... 

We present a deep neural network to classify dermatological disease from patient images. Using self-supervised learning method we have utilized large amount of unlabeled data. We have pre-trained our model on 27000 dermoscopic images gathered from razi hospital, the best dermatological hospital in Iran, along with 33000 images from ISIC 2020 dataset. We have evaluated our model performance in semi-supervised and transfer learning approaches. Our experiments show that using this approach can improve model accuracy and PRC up to 20 percent on semi-supervised setting. The results also show that pretraining can improve classification PRC up to 20 percent on transfer learning task on HAM10000...