Sharif Digital Repository

In the present study, the method of direct growth was used to grew bi-metalic MOFs based on cobalt and zinc, as electroactive centers, for preparing electrochemical sensors for the determination of glucose. In comparison to most of the electrochemical sensing platforms based on MOFs for determining glucose, which suffer from some disadvantages like time-consuming synthesis procedures and using hazardous organic solvents, the proposed in-situ growth method is much faster and without any need to toxic organic solvents. Herein, cobalt and zinc-based MOFs were grown on the surface of glassy carbon electrode by the direct and rapid conversion of layered double hydroxide nanosheets intermediates.... 

In this thesis the graphene-based magnetic nanocatalyst was synthesized. The surface of this catalyst was cavered by silica and AgNPs. In addithion of simple sepration by magnetbar, yeild of reaction was promoted by surface modification. Catalyst structure was verified by transmission electron microscopy , scanning electron microscope, fourier transform infrared spectroscopy, X-ray diffraction and Energy dispersive spectroscopy.At the end catalytic activity was examined by oxidation reaction in persense of different alcohols. The oxidation of benzylic alcohol with electron withdrawing substituents was more difficult and the yield was lower, but electron donor substituents effect positively... 

Infrastructure maintenance is one of the important tasks that require the detection and classification of the condition of their components. One of the main challenges in identifying damage in concrete structures is the speed and accuracy of object detection methods. In this research, vision-based deep learning methods have been implemented to detect and analyze damages in concrete structures. To better evaluate this method, the dataset used in this research is a combination of the three datasets used in researches, which includes 400 pictures of cracks and spalls. This dataset is first augmented using different techniques, then with YOLOv7 instance segmentation and Mask R-CNN algorithms,... 

The purpose of this thesis is to study the analogy of space-time fluid flow around Earth and to investigate the hydrodynamic forces caused by this fluid on NASA's Probe B satellite. According to recent research, Einstein's and Navier-Stokes equations are the same. The properties of space-time fluid, including equation of state, compressibility, viscosity, flow field around Earth, and the hydrodynamic forces entering Probe B satellite, are unknown. There are two views about the viscosity of space-time fluid. Spherical mass of the three-dimensional spherical sink and the nature of the sphere located in the probe satellite without a spherical solid particle It are considered within this flow.... 

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