Sharif Digital Repository

Integrated silicon photonic networks have attracted a lot of attention in the recent decade due to their potential for low-power and high-bandwidth communications. However, despite high bandwidth and low-loss data communication capability, optical NoCs are susceptible to on-chip temperature variations. In these promising networks, packets’ erroneous routing due to thermally-induced resonant wavelength shift of microring resonators are common temporary faults unless heat control mechanism is adopted. In other words, thermal drifts may paralyze wavelength-based operation of these networks. On the other hand, employing a heat control mechanism in an ONoC necessitates developing thermal fault... 

The AISC Seismic Design Provisions now include capacity design requirements for steel plate shear walls,which consist of thin web plates that infill frames of steel beams, denoted horizontal boundary elements)HBEs), and columns, denoted vertical boundary elements (VBEs). The thin unstiffened web plates are expected to buckle in shear at low load levels and develop tension field action, providing ductility and energy dissipation through tension yielding of the web plate. HBEs are designed for stiffness and strength requirements and are expected to anchor the tension field formation in the web plates. VBEs are designed for yielding of web plates and plastic hinge formation at the ends of the... 

Pyramidal and network ownership structures that provide ultimate owners with more control over companies are common in the Iranian stock market. Given the multi-layered shareholding networks and the significant changes that have occurred in the ownership structure of large Iranian companies in the last two decades, the purpose of this study is to investigate the relationship between ownership structure and performance of companies. To achieve this purpose, ownership structure is examined using four measures; cash flow right of the largest shareholder, the wedge between control right and cash flow right of the largest shareholder, difference in cash flow rights of the largest two... 

In the recent decades, because of the reduction of the fossil sources and increasing the environmental pollution, also worldwide population growth and the necessity of energy have made humanbeing to use the Renewable Energy. Wave energy is an opulent source of energy in many parts of the world, especially in Iran.
In this thesis, in the first chapter, different kinds of renewable energy are introduced, and the importance of them is discussed. In the second chapter, the mechanisms of extracting energy by the wave energy converters are investigated, and their types are discussed. Then, in the next chapter, wave equations, wave spctrums and simulation of a flap wave energy converter in... 

It is well known that data centres consume high amounts of energy, which has become a major concern in the field of cloud computing. Therefore, energy consumption could be reduced by using intelligent mechanisms work to adapt the set of network components to the total traffic volume. SDN is an efficient way to do so because it has many benefits over traditional approaches, such as centralised management, low capex, flexibility, scalability and virtualisation of network functions. In our work will we use the heuristic energy-aware routing (HEAR) model, which is composed of the proposed heuristic algorithm and the energy-aware routing algorithm. This work identifies the unused links and... 

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... 

Predicting the interaction and binding affinity of drug-target represents a crucial yet complex phase in the time-consuming and costly process of drug discovery and development. Advances in deep learning have significantly enhanced the ability to model and extract intricate relationships and patterns from diverse biological and pharmaceutical data. However, existing methodologies encounter several fundamental challenges, including the modeling of protein and drug representations, understanding molecular interactions, and overcoming data access limitations. Addressing these challenges has necessitated the use of various heterogeneous architectures, methods, and data structures. Despite these... 

The Major Histocompatibility Complex (MHC) binds to the derived peptides from pathogens to present them to killer T cells on the cell surface. Developing computational methods for accurate, fast, and explainable peptide-MHC binding prediction can facilitate immunotherapies and vaccine development. Various deep learning-based methods rely on feature extraction from the peptide and MHC sequences separately and ignore their valuable binding information. This paper develops a capsule neural network-based method to efficiently capture and model the peptide-MHC complex features to predict the peptide- MHC class I binding. Various evaluations over multiple datasets using popular performance metrics... 

Motif finding problem in biology is a search for repeated patterns to reveal information about gene expression, one of the most complex subsystems in genomics. ChIP-seq technology abled researchers to investigate location of protein-DNA interactions but analyzing downstream results of such experiments to find actual regulatory signals in genome is challenging. For many years, applications of motif finding had models based on limiting assumption as an exchange for lower computational complexity. Results: AKAGI program is build upon upgraded methods and new general models to investigate statistical and experimental evidences for accurately finding significant patterns among biological... 

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... 

As sequencing technologies have advanced in the field of single cells, it has become possible to investigate complex and rare cell populations and discover regulatory relationships between genes. The detection of rare cells has been greatly facilitated by this technology. However, due to the large volume of data and the complex and uncertain distribution of data, as well as the high rate of technical zeros, the analysis of single cell data clusters remains a computational and statistical challenge. Dimensionality reduction is a significant component of big data analysis. Machine learning methods provide the possibility of better analysis by reducing the non-linear dimensions of data. A graph... 

Predicting the binding affinity of drug molecules and proteins is one of the most important stages of drug discovery, development and screening, for which numerous laboratory, simulation and computational solutions have been provided. Laboratory and simulation methods require molecular structures, are time and financial expense, and computational methods do not provide accurate predictions. Therefore, the use of deep neural networks in extracting features from data with a simpler and more accessible structure of protein sequences and molecules solves these challenges with lower cost and higher accuracy. In this article, the use of a new molecular sequence named selfies, which has solved the... 

The use of hyperdimensional computing models in edge applications and biosignal processing has been steadily growing due to their inherent robustness against noise and the simplicity of their operations. Nevertheless, these models face a fundamental challenge: extremely high dimensionality and the associated hardware cost during inference, which lead to substantial energy consumption and reduced efficiency. In contrast, processing-in-memory (PIM) architectures have recently emerged as a promising approach to reduce data movement and accelerate computation; however, they require algorithms that can adapt to their structural constraints. To tackle this issue, we propose ADAPT, a framework that... 

The exponential growth of data across various domains of human society necessitates rapid and efficient data processing. In a number of machine learning models, an input query is searched across the trained class vectors to find the closest feature class vector based on the cosine similarity metric. However, performing cosine similarity computations between vectors on von Neumann machines involves a large number of multiplications, Euclidean normalizations, and divisions, thus incurring heavy hardware energy and latency overheads. Moreover, due to the memory-wall problem present in conventional architectures, frequent cosine similarity-based searches (CSSs) over class vectors require... 

In this thesis, effects of bending and torsional elasticity of a flapping wing on its average lift and input power are studied, using a semi-analytic method. Bending stiffness and torsional rigidity of wing spar, wing mass ratio and chordwise and spanwise center of gravity, also spar location are considered as variables for controlling elastic behavior of the wing.Governing integral equation is derived using Hamilton principle via modified quasi aerodynamic and linear Green-Lagrange strain tensor. Also an idealized flapping pattern is used to maximize average acceleration during each cycle. Next, integral equation is converted to a set of ordinary differential equations by means of Galerkin... 

Proper positioning of messenger RNA helps to diagnose diseases such as cancer, Alzheimer’s, and also to make drugs for treatment; Therefore, predicting the location of messenger RNA in the cell with the aim of determining the type of proteins formed from a messenger RNA sequence is very important. Although experimental methods such as complex laboratory processes can provide accurate information about the intracellular localization of messenger RNA, however, the computational overhead, including time, memory and power consumption, increases the need to design accurate, fast and low-cost tools more than ever. Nowday, deep neural networks have shown significant performance in the field of... 

Today, analyzing single-cell RNA sequencing data allows researchers to examine the gene profiles of individual cells and discover cellular differences. However, the rapid growth in data volume has created serious challenges for existing read mapping tools. Current tools either lack the necessary speed or require too much memory. Moreover, most of these tools were designed for bulk RNA data and don't fully consider the unique characteristics of single-cell data, such as high sparsity, dropout events, and cellular heterogeneity.This research presents a tool called FCGMap for mapping single-cell RNA sequencing reads, built on a two-stage architecture. The first stage uses Frequency Chaos Game... 

Global on chip communication becomes a critical power bottleneck in high performance many core architectures. The importance of power dissipation in networks-on-chip along with power reduction capability of on-chip nanophotonic interconnects has made optical network on chip a novel technology. Major advantages like high bandwidth, light speed latency and low power consumption, provide a promising solution for future of communications in many core architectures. However, the basic elements that are embedded in optical networks on chip are extremely temperature sensitive. This would lead to change in the physical characteristics of nanophotonic elements which may cause failure in network on... 

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,...