Sharif Digital Repository

In the first part of this research, for the first time, we introduced a flexible high performance graphene-based supercapacitor using silver fiber fabric as the current collector. The silver fiber fabric offers remarkable advantages such as light weight, mechanical flexibility and ease of integration with electronic textiles, which well-suited for wearable energy storage devices. A new hybrid material of graphene-silver fiber fabric (rGO/SFF) was prepared through a facile electrophoretic deposition of graphene and being used as a binder-free flexible supercapacitor electrode. In order to obtain the optimum condition, the effect of deposition time was investigated and a duration time of 10... 

Epoxy resins are relatively brittle and have low resistance to crack propagation. It is common to add rubber particles to improve crack resistance in epoxies, although these particles may deteriorate the strength of the matrix. Different nanomaterials are used to overcome the inherent brittleness of epoxies and to keep the mechanical properties. Among them, carbon nanotubes has been attracted many attentions. Fractue behavior and mechanical properties of two kind of novel nanocomposites, i.e, epoxy/carbon nanotube and epoxy/rubber/carbon nanotube are investigated in current study. Electron microscopy investigations were done to assure the CNTs distribution and fracture mechanisms as well.... 

Wildfires are complex phenomena with many indeterminate and highly unpredictable driving factors that have remained unresolved. During the last decade, machine learning methods have successfully excelled in wildfire prediction as an alternative to traditional field research methods by elucidating the relationship between historical wildfire events and various important variables. The main purpose of this research is to evaluate the random forest machine learning approach and the logistic regression statistical approach to prepare a wildfire susceptibility map using data related to historical wildfires and effective variables in the Okanogan region in Washington province of the United States... 

Minimizing energy consumption is the most important concern in wireless sensor networks (WSNs). Cooperative multiple-input multiple-output (MIMO) and data aggregation are two promising techniques to conserve energy in network nodes by coordinating their transmissions through cooperative communications along with eliminating the inherent redundancy of raw data for transmissions. In this thesis, with the goal of reducing energy consumption, we present a dynamic clustering framework for combined used of cooperative MIMO and data aggregation in cluster-based WSNs. We derive a new and more general energy model for computing energy consumption of cooperative data aggregation in wireless sensor... 

In this thesis, design and analyses of honeycomb structures are investigated. Primary goal is to develop an equivalent orthotropic material model that is a good substitute for the actual honeycomb core. By replacing the actual honeycomb structure with the orthotropic model, during the finite element analyses, substantial advantages can be obtained with regard to ease of modeling and model modification, solution time and hardware resources . To figure out the best equivalent model among the approximate analytical models that can be found in the literature, a comparison is made. First sandwich beams with four different honeycomb cores are modeled in detail and these are accepted as reference... 

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 thesis, we investigate two subjects in gravitational microlensing. In first part we study wave optics effect in light curve of a binary system. Gravitational lensing occurs when light deflects in a gravitational field. This deflection produces multi-images from real one. In microlensing regime, images can not be detected separately and gravitational lensing appears as magnifying source’s flux . Microlensing has been studied mostly in geometric optics regime. At larger wavelengths, mostly at radio wavelengths, we expect to see the effects of wave optics in microlensing. Stars at radio wave are faint and to detect them we need a powerful radio telescope. In the near future, SKA (Square... 

In this investigation Magneto Mercury Reciprocating Micrpump (MMRM) the combination of Magneto-Hydrodynamic and Reciprocating micropumps has been analyzed. To achieve the analytical and numerical solution of one-tank and three-tank MMRM, the momentum, continuity, volume fraction and magnetohydrodynamic equations have been presented. The dimensionless analytical solution of one-phase and two-phase three dimensional MHD flow in the condition of using constant current and potential electrical source, has been offered.
The boundary between mercury and air has been tracked via VOF method in OpenFOAM software. VOF equation has been solved by explicit method with variable time step and maximum...