Sharif Digital Repository

The study of evaporation of fuel droplet and determination of the rate of vaporization are important in designing combustion chambers. For achieving high performance of a combustor, the evaporation of fuel droplets takes place within a high pressure environment. At these conditions, the use of low-pressure models is not appropriate and many effects that are assumed negligible at low ambient pressures become very important. For example, the solubility of the ambient gas into the liquid phase is increased by increasing the ambient pressure. In addition, the ideal gas assumption is not valid for these conditions and one should use an appropriate equation of state (EOS) that can predict the... 

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,... 

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  

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... 

In today's era, metal nanoparticles play an important role in technologies emerging from different sciences, such as chemistry, physics, optics, material science, due to their unique characteristics. In the development of nanooptics science, it can be said that metal nanoparticles play an important role. The ability of conductive electrons collective oscillation causes surface charge density fluctuations in nanoparticles, this phenomenon is known as surface plasmons. Surface plasmons are surprisingly coupled with light and cause the significant increase in the intensity of optical fields induced in nanoparticles. Therefore, with the presence of localized surface plasmons or plasmon... 

Understanding of the Molecules is the main purpose of the chemistry. Ab-initio molecular dynamics (AIMD) as a branch of the computational chemistry, tries to give us a deep comprehension of the molecule, and its chemical, physical and optical activities. This comprehension, relies on the accuracy of quantum mechanics, in addition to the speed of the classical mechanics. The mixing of the quantum mechanics and the classical mechanics could simulate activities of the atoms in the time-domain, provided the mixing is done with precaution. This, in turn, helps us to forecast the response of a molecule in different situations, and also translating the macroscopic phenomena in a nanoscopic... 

To date, the plasmonic properties of many metallic and semi-conducting materials have been investigated and used in various industries. One of the plasmonic material categories that have always been considered is polycyclic aromatic hydrocarbon or PAH, whose plasmonic resonance energy depends on the charge state of the molecule. In this regard, it is easy to change the plasmonic resonance energy via changing the induced charge, which is a unique feature of the mentioned materials. In addition, plasmonic structures with excitations in the infrared region are able to enhance the vibration intensity of absorbed molecules by increasing the electric field around themselves. Therefore, they have... 

Ultrafast signal processing in time-domain with high resolution and reconfigura-bility is a challenging task. This paper, for the first time, introduces a time-varying metasurface consisting of graphene microribbon array for implementing time-lens in the terahertz domain. Given that the surface conductivity of graphene is proportional to the Fermi energy level in the THz regime, it is possible to change the phase property of the incident electromagnetic pulse by changing the Fermi level while the Fermi level itself is a function of voltage. Upon this fact, a quadratic temporal phase modulator, namely time-lens has been realized. This phase modulation is applied to the impinging signal in the... 

In this research, the problem of locating fire stations and allocating equipment has been studied and a simulation-optimization approach has been presented to solve the problem. The mathematical models of this research were developed based on the idea of the randomness of the covered demand and the maximum expected coverage model. In these models, the issue of non-availability of equipment to cover accidents, the random nature of accidents, various fire incidents and the equipment needed to cover them are considered. Two mathematical models with deterministic and non-deterministic approach with different scenarios for demand are proposed. The non-deterministic model is developed with the aim... 

One of the main concerns of industrial companies’ managers is providing an efficient logistics system. To achieve an efficient logistics system, the fleet planning problem is studied by many researchers in recent years. This problem consists of multiple sub-problems at three levels: operational, tactical, and strategic. These sub-problems are closely related to each other and need to be studied and addressed in an integrated manner. In this research, an attempt is made to provide an integrated framework to solve the vehicle routing problem (operational), outsourcing problem (tactical), and fleet composition problem (strategic). These problems have various uncertainties, including customer... 

The extraordinary ability of the human brain to solve various problems has led scientists to simulate models of the human brain. One of these simulated models is artificial neural networks. Today, the power of artificial neural networks is not overlooked. The ability of artificial neural networks to solve various types of issues led us to use the thesis to solve some of the graph-based problems. Quite accurately, this graph-based problem is a matter of identifying the source of rumor in a network. In many graph networks, whether natural networks such as the network of neurons in the human brain or synthetic ones such as the types of social networks, it is possible that a rumor spreads across... 

With the high accuracy of deep neural networks, these models are now widely used in applications such as machine vision, natural language processing, and medicine. Since implementing deep neural networks faces many challenges, such as high computational demands and the need for extensive memory accesses, the design and deployment of hardware accelerators for running these models have become increasingly attractive. With the development of prototype ReRAMs, some researchers have focused on designing ReRAM-based accelerators to execute deep neural network models with high speed and low energy consumption. In general, we categorize these accelerators into two categories, fixed-weight and...