Sharif Digital Repository

Hardware implementation of Spiking Neural Networks (SNN) can bring about a fast and low cost neural network which has more biological support. Memristor nanodevices are most proposed devices for use as synapses to add dynamic learning to SNNs because of their nano-scale dimensions, low power consumption and memory property. One of the most important bottlenecks in the memristor crossbar based SNNs is system-level simulation of learning process due to its huge memristor equations that should be solved for each sample of time. Due to parallel computation capability of our simulation work, we simulate the circuit on single core CPU and then proposed high performance parallel platforms as... 

Nowadays, prolific usage of free air as a medium for data transmission leads interests to optimize the conditions of devices which are involved in wireless communication systems. Inter-Symbol Interference (ISI) distortion is a natural characteristic of wireless channels that occurs due to propagating electromagnetic waves in different directions. Due to different paths between a transmitter and a receiver, the received signal consists of several copies of the transmitted signal with different delays. The most common way of eliminating or mitigating the effects of ISI is channel equalization. The present work leads to implementing a supervised equalization in wireless communication. The... 

In this thesis we concentrated on the application of memristor-rossbars for the hardware implementation of neuro-fuzzy systems. At first we showed that using memristive devices for this purpose has several problems and demonstrated the availability of the positive feedback in the behavior of this passive element. Then, by proposing new learning structure we tried to overcome some of these drawbacks. In the rest of this thesis we focused on the hardware implementation of neuro-fuzzy systems based on memristor crossbar structures. At first, hardware implementation of the Active Learning Method (ALM) is presented.
Next, new learning and computing method is proposed by creating and using... 

The first physical realization of the missing fourth fundamental element of electrical circuits, namely memristor, in 2008 by HP labs triggered an immense amount of research on the capabilities of this element in implementing artificial neurons and artificial brain. In this project we will propose several reinforcement learning-based algorithms that are implemented on a specific memristor-based structure, the memristor crossbar structure. Hence we provide a learning paradigm that resembles the human learning paradigm not only because of the the algorithmic core, which is based on learning from sparse and delayed rewards and penalties, but also because of the hardware over which the... 

Research on how the human brain processes information leading to the creation of two major groups in the field of soft computing. The first group believes that information is being processed based on linguistic concepts and if-Then rules. Fuzzy logic is based on this idea and tries to avoid exact calculations in information processing tasks. Second group believes that the power of human brain in processing is because of a large network of neurons with small abilities. These studies led to the presentation of artificial neural networks algorithms. Spiking neural network is known as third generation of artificial neural networks and tries for processing information using a real model of brain... 

Designing artificial intelligence based arithmetic machines that can intelligently perform human-like task has attracted considerable interest among researchers. The ever-increasing advances in soft-computing algorithms require appropriate hardware platforms for such algorithms. One of the most important problems with these algorithms and their hardware implementation structures is the discrepancy between the hardware and the nature of the problem. It can be argued that paying attention to hardware implementation does not necessarily guarantee an optimal implementation of these algorithms. Most of the proposed hardware implementations have very small resemblance to the biological systems... 

Memristor is a modern circuit element that demonstrates resistance memory function and can have important applications in emerging memories and artificial synapses. Binary oxides and perovskite oxides are two major classes of materials that are promising for memory resistances. Among the binary oxides, TiO2, and among the perovskite oxides, SrTiO3 (STO), are particularly promising in research on electrical resistance switching. In this thesis, by using two deposition techniques of sputtering and pulsed laser deposition (PLD), we have constructed single-layer and multi-layer memristor devices based on these two materials. Two general mechanisms control the behavior of memristors: bulk-control... 

In recent decades, the science of studying complex systems has started to evolve and mature. Complex systems research is becoming ever more important in both the natural and social sciences. The study of mathematical complex system models is used for many scientific questions poorly suited to the traditional mechanistic conception provided by science. Examples of complex systems are Earth's global climate, organisms, the human brain, social organization, an ecosystem, a living cell, and ultimately the entire universe. Motivations for studying complex and self-organized systems can be somewhat divided between science, or attempts to understand such systems, and engineering, or attempts to...