Sharif Digital Repository

Among different natural disasters, earthquake has always been known as one of the most effective ones on human lives, and this is only because we still cannot predict the exact time and place of its happening. So, giving special attention to the methods which can omit or minimize the damages of this natural event is needed for controlling the loss in the event of an earthquake. On the other hand schools are known as places which are full of people from younger ages for at least half of each day, and they are considered to be not well educated about how to react in an earthquake in comparison to older people and also considered vulnerable at the time of this natural event. According to what... 

Spiking neural networks (SNNs) are third generation of neural networks. Similar to traditional neural networks, SNNs are comprised neurons. However, not only structure but also information processing is inspired by animal neural systems. SNNs can be called the most similar networks to animal neural systems. In such networks, the information is processed based on propagation of spike signals through the network. The type of data flow in these networks leads to being low-power when they are implemented on hardware. Therefore,there has been a upward trend in hardware implementation of them, like their FPGA implementations, for applications such as Big Data and Machine Learning. In this thesis,... 

The application of deep learning algorithms in the processing of telecommunication signals is increasing. One of the issues in this area is the automatic recognition of radio signal modulation. Recognizing the type of signal modulation as the first stage of baseband signal processing on the receiver side is a key issue in military and civilian applications. The problem with classical algorithms for solving this problem is their strong dependence on channel characteristics and factors resulting from the transmitter and receiver being non-ideal. However, modern algorithms based on deep neural networks have been able to make the model somewhat resistant to these factors. However, the detection... 

Hand pose estimation is something that has applications in many fields, including augmented and virtual reality systems, as well as mixed reality. Hand gesture recognition and classification applications including sign language recognition and non-handheld senarios (such as storefront contactless Survey systems) that have found special cases in the Qovid-19 pandemic period Shows the highest hand pose estimation importance. In our work, we target 2D and 3D estimation at the same time and also use RGB camera as a sensor to record input data. It becomes more economical to achieve Compared with using RGBD or depth sensors . There is only one RGB image in our input. Also there is no contract to... 

Nowadays, Multi-Object Tracking (MOT) plays a crucial role in various computer vision applications such as autonomous vehicles, surveillance, and robotics. Traditional MOT methods often struggle with challenges such as high errors when dealing with complex scenarios involving occlusion, scale variations, and object interactions. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs) and Transformer models, have demonstrated significant capabilities in addressing these challenges. This thesis presents a study on the use of deep learning techniques, specifically CNNs and Transformer models, for solving the problem of Multi-Object Tracking. It begins by... 

In aerospace applications, the weight of structures is considered as a major performance parameter. One of the most effective ways for weight reduction is then attributed to the structurally continuous manufacturing of mechanical components. In this way, not only the weight, but also the stress concentration, the cost, joints and fasteners applications, and the time of manufacturing are noticeably reduced. With this method, components with complicated shapes could be formed in a single manufacturing step. Special specification of whisker reinforced Metal Matrix Composites (MMC’s) has made these materials very attractive for the aerospace applications.That is why the superplastic forming of... 

Real-time simulators are important in design and test of power systems. Real-time simulators with hardware-in-loop capabilities are used in design of new elements in order to achieve maximum compatibility between subsystems, or in diagnosis and maintenance.The purpose of this dissertation is to design a real-time simulator for electrical machines. For this purpose, a three-phase induction machine is chosen and its model is extracted and discretized. This procedure results in a system of differential equations in form of AX = B. The extracted equations needs to be solved in real-time at every time-step. The real-time algorithms are imple-mented on FPGA. The implemented real-time simulator... 

Nowadays, thanks to improvement of processing hardware and plenty of data available, artificial intelligence and specifically Deep Learning are being one of the powerful tools for solving different problems. Also graph is one of the powerful tools for modeling different data structures. Graph matching is one of the problems in the field of graph problems.In this thesis we consider the problem of graph matching in Erdos-Renyi graphs. The graph matching problem refers to recovering the node-to-node correspondence between two correlated graphs. Previous works theoretically showed that recovering is feasible in sparse Erdos-R´enyi graphs if and only if the probability of having an edge between a...