Sharif Digital Repository

Digital computers, comprising universal logic gates, are tremendously versatile. This versatility associated with the increasing integrability of digital electronics due to Moore’s law has left nearly no room for analog computers in the last few decades. However, in the case of some specific problems such as modeling complex and nonlinear systems or pro-cessing large amounts of data, computation time and power is still a serious restriction of digital computing. Optical phenomena are fast enough and offer novel approaches to overcome the mentioned restrictions about the system speed and power consumption. Therefore, optical structures make possible to perform ultra-fast spatial analog... 

Diffractive deep neural network is an optical machine-learning framework that uses diffractive surfaces, optical devices, electro-optic devices and engineered matterials to optically perform computational tasks. These diffractive networks, after their desing and train phase by computers and machine learning algorithms, are physically fabricated using 3D printing or lithography, to actualize the model of trained network. Machine learning processes and alghorithms are performed through light-matter interaction and diffraction of light. This procedure is done at the speed of light and without the need of any power, except for the light illumination for the input object. In comparison with... 

In this research, an optical processing method for DNA classification is presented in order to overcome the problems in the previous methods. With improving in the operational capacity of the sequencing process, which has increased the number of genomes, comparing sequences with a complete database of genomes is a serious challenge to computational methods. Most current classification programs suffer from either slow classification speeds, large memory requirements, or both. To achieve high speed and accuracy at the same time, we suggest using optical processing methods. The performance of electronic processing-based computing, especially in the case of large data processing, is usually... 

Analog computing has emerged as a promising candidate for Neural networks' implementation due to its high Interconnectivity, high bandwidth, parallel computing, high-speed processing, and low power consumption. Artificial Neural Networks have a wide range of applications; however, the Implementation of complicated Neural Networks on traditional computers would encounter two fundamental obstacles: limited processing speed and non-optimal energy consumption. This thesis's primary focus is on designing and simulating a whole-passive planar Optical neural network(ONN) based on silicon photonics technology. Firstly, the concept of ONN is studied using some lately proposed work. The device is... 

Biological sequences, such as DNA, RNA, and proteins, contain vital information and activities of living organisms. Any changes or mutations in these sequences can alter the structure of proteins, which are the basis of all cell activities. This can cause disruptions in cellular functions. With advancements in cell data extraction technology, the amount of genetic data has increased, leading to a need for efficient methods to identify and study mutations. One of the primary tools for this work is biological sequence comparators. Despite the different types of sequence comparators available, there is still a need for an algorithm that is fast, accurate, scalable, and uses few resources.... 

In recent years, the biological evolution of molecular detection capabilities based on gene analysis has provided a reliable performance in the diagnosis of a disease before a symptom emerges. Human gene storage requires a large amount of computer memory (about 1.5 GB for each DNA) and the search for a specific pattern within it with electronic computers is time and power consuming. Optical computing uses light parallel processing capabilities to find the pattern in a digital field, which can be used to process large volumes of data in short time and low power consumption, while electronic computers process data in series with high power dissipation. According to the Optalysys report, the... 

Electronic processors have encountered Moore’s limitations and they can not meet expectations following computer science advances in new algorithms such as neural networks. These limitations have brought research to focus on light as a high-capacity carrier of information. Light’s degrees of freedom can be exploited whenever the structure has devices able to carry, keep, manipulate and detect each DoF's information but this exploitation may itself lead to either a large footprint or more power consumption. Moreover, there are some scientific and technological difficulties in matching different computational algorithms to these DoF’s requirements. As a result, one must consider all advantages... 

Optical computing is a new approach to the hardware implementation of devices that were previously implemented digitally and electronically. It has attracted a great deal of interest due to its benefits, which include high bandwidth, extensive internal connections, the possibility of parallel processing, high calculation speed, and low power consumption. Consequently, this type of implementation is regarded as an appropriate substrate for optical neural networks. Compact and low-power CMOS-compatible hardware can be used for on-chip optical neural networks (ONNs), enabling affordable and portable image classification solutions for applications like autonomous vehicles, healthcare, and... 

Due to the increasing volume of data in various fields, existing electronic processors face a major challenge. While processing power has increased, solving complex problems in a timely manner remains a major challenge for today's processors. Neuromorphic engineering offers a potential solution by looking to processors found in nature, such as the human brain. This field of research involves investigating natural processors and designing new ones based on these models. To address issues related to manufacturing and integrating transistors, increasing processor costs, and the limitations of Moore's law, it is possible to use analog signals, such as sound or light, instead of electrical... 

In this research, two novel optical methods have been proposed for DNA local sequence alignment. The proposed methods benefit from algorithms and methods in computer field and ability of parallelism in optical wave to achieve a low-cost process and propose an easy understanding output in DNA local sequence alignment procedure. The first method is built upon moiré matching technique which is extended by proposed HAPPOC scheme using amplitude, phase, and polarization of optical wave. For analyzing the extended moiré output, a novel 3D Artificial Neural Network is designed and developed by optical structure. The second structure, as named HAWPOD method, is based on DV-Curve method. The HAWPOD... 

The classification of biological sequences is an open issue for a variety of data sets, such as viral and metagenomics sequences. Therefore, many studies utilize neural network tools, as the well-known methods in this field, and focus on designing customized network structures. However, a few works focus on more effective factors, such as input encoding method or implementation technology, to address accuracy and efficiency issues in this area. Therefore, in this work, we propose an image-based encoding method, called as WalkIm, whose adoption, even in a simple neural network, provides competitive accuracy and superior efficiency, compared to the existing classification methods (e.g. VGDC,... 

Convolutional neural networks (CNNs) are at the heart of several machine learning applications, while they suffer from computational complexity due to their large number of parameters and operations. Recently, all-optical implementation of the CNNs has achieved many attentions, however, the recently proposed optical architectures for CNNs cannot fully utilize the tremendous capabilities of optical processing, due to the required electro-optical conversions in-between successive layers. Therefore, in our first study, we proposed OP-AlexNet which has five convolutional layers and three fully connected layers. Array of 4f optical correlators is considered as the optical convolutional layer,...