Sharif Digital Repository

Nowadays, too much attention has been paid to Solid Oxide Fuel Cells (SOFC). One of the key components in SOFC is interconnect which plays an important role in SOFC performance. Interconnect can be either a metallic or ceramic layer that sits between each individual cell. The reduction in operating temperature of SOFC resulted in interests in using metallic interconnectors. However, the oxidation of these metallic materials is big challenge. Chromium (Cr), Nickel (Ni) and Iron (Fe) alloys have been employed to overcome this deficiency. However, these alloys bring another difficulty which is the presence of active elements such as Cr which reduces the performance of interconnects by forming... 

Fullerenes are molecular solid materials which are composed of 3D closed shell carbon-only structure. As soon as mass production of fullerenes was performed, the synthesis and characterization of endohedral fullerenes having at least one non-carbon element in the hollow fullerene cage, was started (in which about 30 atoms inserted in it so far). Furthermore, because of low efficiency and purity of these nano-particles, nowadays characterization of fullerenes is one of the most important research fields. In this project, we firstly optimized the fullerene production parameters in an arc discharge reactor to reach convenient efficiency. Then, the synthesis of endohedral fullerene by vaporizing... 

Expert finding in bibliographic networks has received increasing attention in recent years. This task concerns with finding relevant researchers for a given topic. In this thesis, we propose a model to determine authority of authors who have participated in the Communities. This model has a little improvement over community based baseline model. However, due to the low performance of community based models, the proposed authority based model cannot improve the document based baseline models either. Therefore, we try to improve document based models, instead of community based models and have proposed two other models which are based on authors’ topic dominance for expert finding. Document... 

In the present research, model-based reasoning is introduced and explained based on Giere’s explanation of model. In other word, this research could be regarded as a reconstruction of model in Giere’s books and papers due to prpose a clear explanation of model-based reasoning. In the second chapter, after the introduction, the concept of model in Giere’s distinguished book, “Explainnig Science: A Cognitive Approach” (1988) is introduced and analyzed. In the second chapter, however, the concept of model in Giere’s recent works is traced. “An agent-based account of model” and “focusing on functionality rather than ontology” could be explained as the two important differences between his... 

The objective of OCR systems is to retrieve machine-encoded text from a raster image. Despite the abundance of powerful OCR algorithms for English, there are not many for Farsi. Our proposed algorithm is comprised of pre-processing, line detection, sub-word detection and segmentation, feature extraction and classification. Furthermore, hardware implementation and acceleration of this system on a GPGPU is presented. This algorithm was tested on 5 fonts including Titr, Lotus,Yekan, Koodak and Nazanin and an average accuracy above 90% was achieved  

With constant increase in the rate of VLSI circuits manufactured in sites separate from the designers and computer architects, global concern regarding the possibility of integration of malware by the manufacturing foundries has arisen. Particularly, one main issue that affects relability of the chips is modifications or additions with malicious intension, known as Harware Trojans, which are easily applicable during design and manufacturing phase of chip. This study intends to introduce a model based on the scan chain, a method is provided for intellectual property protection. Currently available IP protection solutions are usually limited to protect single FPGA configurations and require... 

Micro-rotating machinery with compact energy sources and high power density has been developed in the recent decade to run portable electronic devices. To achieve efficiency targets, these miniaturized turbo-machinery must spin at elevated rotational speeds which sometimes can reach up to one million revolutions per minute. At this relatively high spinning rate, the rotordynamic behavior of these systems plays an important role in the stage of design. On the other hand, the classical continuum theory has been proven not to be able to appropriately predict the mechanical behavior of the small-scale structures. To overcome overall mentioned weakness, in this thesis the rotor of micro-engines... 

Many mobile applications running on smartphones, wearable devices, tiny autonomous robots and IoT devices would potentially benefit from the accuracy and scalability of deep CNN-based machine learning algorithms. However,performance and energy consumption limitations make the execution of such computationally intensive algorithms on embedded mobile devices prohibitive.We present a GPU-accelerated engine, dubbed mCNN, for execution of trained deep CNNs on mobile platforms. The proposed solution takes the trained model as input and automatically optimizes its parallel implementation on the target mobile platform for efficient use of hardware resources such as mobile GPU threads and SIMD units.... 

Spiking Neural Networks(SNN) are networks which are consisted of layers of neurons, like other typical artificial neural networks. The main difference between SNN and other neural networks is the type of data transportation among neurons which is done by spikes. Spiking neural networks and their models are considered as the nearest networks and neurons to animals’ nervous systems. In aspects of hardware implementation, the type of data transportation in SNN causes them to be ultra-low power. So, implementation of these networks on chips like FPGA and also usage of SNN in applications with high processing load have startling germination, recently. In this work, we have tried to propose some... 

Every year, a lot of costs are incurred to dairy production sets for new product production or product improvement.cosidering to the high amount spent on this sector so if there is not any cost management and cost planning based on life cycle of projects , costs will increase .As a result today considering of life cycle in cost management is a Essential principle . In this study we consider kalleh dairy company as a case study and resuls of this study will be a complete economic model for cost management based on the life cycle of dairy projects  

Many studies point to a possible new stylized fact for financial time series: the multifractality. Several authors have already detected this characteristic in multiple time series in several countries. With that in mind and based on Multifractal Detrended Fluctuation Analysis (MFDFA) method, this thesis analyzes the multifractality in the Tehran Stock Exchange. This analysis is performed with daily data from Tepix index (Tehran stock exchange's main index) and other three highly marketable stocks in the Tehran Stock Exchange (Pharma index, Oil index and Metal index), wich making up 1782 observations for each index in the period from March 21, 2011 to Aug 22, 2018. We found that the studied... 

In order to adjust the electrons beam in microwave lamps, an accurate electromagnetic field that is produced by a solenoid is required. The precision of the magnetic field generated by this solenoid is very important, and in advanced systems this precision is less than 10 ppm. This system is used to adjust the radiation rate for producing a variety of radiopharmaceutical. The major problem of the system is that the electron beam changes by time due to a change in the magnetic field, and this event causes the operator's room to be radiated. Constant calibration of this system is also very demanding and impractical. An example of these highly accurate (several ppm) systems are used in the... 

Many chip manufacturers have recently introduced high-performance deep-learning hardware accelerators. In modern GPUs, programmable tensor cores accelerate the heavy operations involved in deep neural networks. This paper presents a novel solution to re-purpose tensor cores in modern GPUs for high-throughput implementation of turbo decoders. Turbo codes closely approach Shannon’s limit on channel capacity, and are widely used in many state-of-the-art wireless systems including satellite communications and mobile communications. Experimental evaluations show that the proposed solution achieves about 1.2 Gbps throughput, which is higher compared to previous GPU-accelerated solutions