Sharif Digital Repository

Products on Research and Development (R&D) are followed by Technology development. Projects on R&D are cost intensive and considerable of financial resources should be allocated. Optimal allocation of resources could be managed through cost effective strategy. Hence, a “Model for planning and development of technology” has been developed in the present work, which lends itself to the analysis of the industrial application of planning of technological development based on minimizing costs of both development and application of a technology. Mixed Integer Programming optimization model has been developed which enables studying the structure of R&D activities. This model is based on the concept... 

As a problem, generally, energy security components do not interfere with the calculation of the optimal energy supply situation. In this paper, we are looking to find a solution to make a framework of the impact of energy security on energy supply in order to obtain comprehensive analysis of the economic optimal point. Method is based on the competition of energy costs to meet demand during the study period. Threats that have been addressed in the energy security are seen as risky and stochastic parameters in the model. The nature of these parameters is of uncertainty type, therefore, based on the probable scenarios of the stochastic model, the perspective of the energy supply system is... 

In the recent years, there has been an increasing attention toward usage of ultrasonic for enhanced oil recovery practices. But, thorough knowledge of active mechanisms and spreading and characterization of developed fingers during this practice is not available. From the other hand, due to limited amount of light oil fields, more attentions towards recovery from heavy oils is the need of the hour. Documents show that the big issues during the enhanced oil recovery from heavy crudes are high density of reservoir oil as well as poor development of fingers during injection due to high mobility ratios. In this study, results of ultrasound effect on the heavy oil recovery and its effect on the... 

Nowadays, the world is full of multi criteria decision problems and all the people should make decisions in different situations. Sometimes, the result of a decision problem is so important as a wrong decision may result in a loss that cannot be compensated. Therefore a decision making tool seems as a must. On the other hand, in this era of rapid social and technological change leading to increasing life complexity, sensitivity analysis is an important issue in all decision making processes. Analytic Hierarchy Process and Analytic Network Process, as two common tools for multi criteria decision making, are widely used throughout the past decade in different fields. Designing the model and... 

The aim of this thesis was to find the optimum retention time for treatment of synthetic petroleum wastewater by a wastewater treatment system made with a bucket, mineral material and earthworms residing in a vermibed. In order to make the desired flow happen, height difference was utilized. The polluting substance used for making wastewater was BTEX. As the wastewater was synthetic, the concentration was also a variable. The ultimate soluble concentration was 3200 mg/l and more concentrations were not tested. Parameters such as number of worms, worm’s growth rate and their distribution pattern, humidity amount and distribution and worm’s acclimatization time during organic loading shock... 

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  

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... 

There have been many works on generative networks and image generation in the past few years, but the problem with this work is that there is no control over the generated images. The goal of disentangled image synthesis is to generate new images with specific detail and have control over the generated images. Image-based virtual try-on aims to synthesize the customer image with an in-shop clothes image to acquire seamless and natural try-on results, which have attracted increasing attention. The main procedures of image-based virtual try-on usually consist of clothes image generation and try-on image synthesis. In contrast, prior arts cannot guarantee satisfying clothes results when facing... 

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.... 

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  

In the current era, artificial intelligence and machine learning have become vital and widely used technologies across various industries. These technologies enable companies and organizations to optimize processes, predict trends, and uncover hidden patterns in data with high accuracy and speed. However, fully leveraging the capabilities of AI and ML requires the effective and efficient deployment of ML models in production environments. MLOps, a combination of DevOps and ML concepts, aids in managing the lifecycle of ML models from development to deployment and maintenance. In this research, due to international sanctions and limited access to external services such as Google Vertex AI,... 

Productivity is the effective usage of input or resources for production or output. Input (including Energy, raw materials, capital and work force), which is being used for output (including final products or services performed by an organization). Productivity, increases gross domestic product and competitiveness, and improves level of living. Firms and related management levels play the main role in improving productivity. Using productivity cycle management brings the productivity to improve as a permanent process, specifies the rout for productivity and prepares the ground. The most important step in productivity cycle (PDCA) is the measurement of productivity. In this research several... 

In this project, a method is emoloyed to implement a Viterbi decoder on GPGPU. This method is based on combining all steps of the algorithm. This combination has some challenges that are related to differences between different steps of the algorithm. So in this project, some solutions are found to handle these challenges and a high-throughput Viterbi decoder is acheived  

In the present study, the factors that affect the IPO underpricing in the Tehran Stock Exchange are investigated. We define the variables of IPO percentage, age and size of companies, dummy variable for rounded IPO prices, market return in a month before the initial public offering, average return of IPOs that had been done during one month before the IPO of each ticker and the dummy variable for industrial group in which the company is located. We had to calculate the initial return of 246 companies that were offered in the years 1380 to 1395. In this study, due to the existence of a daily stock price volatility constraint, a new algorithm was introduced to calculate the dependent... 

Cardiovascular diseases are the leading cause of death globally so early diagnosis of them is important. Many researchers focused on this field. First signs of cardiac diseases appear in the electrocardiogram signal. This signal represents the electrical activity of the heart so it’s primarily used for the detection and classification of cardiac arrhythmias. Permanent monitoring of this signal is not possible for specialists so we should do this by means of Artificial Intelligence. In this thesis, we use recurrent neural networks to classify electrocardiogram’s arrhythmias. This deep learning method, use two sources of data to learn from. The first part of data is global for everyone and the...