Sharif Digital Repository

ALM (Active Learning Method) is based on fuzzy concepts, striving for human-like information processing. Typical fuzzy methods for control and modeling applications rely on linguistic processing with membership functions of the kind in fuzzy rules. ALM, however, processes multiple features of a target by converting each into patterns. This simulates the human approach to the acquisition of knowledge from complex source targets in that it focuses on each feature of the target and processes that information in pattern-like images rather than utilizing numerical interpretations. In ALM, the ink drop spread (IDS) method has been proposed as a means of obtaining useful information... 

Image segmentation is a technique which divides an image into significant parts. The accuracy of this technique plays an important role when it applies on medical images. Among various image segmentation methods, clustering methods have been extensively investigated and used. Since it is an unsupervised method, the existence of a small amount of side-information which is extracted from a specific application (in this case, medical image) could improve its accuracy. Using this side-information in clustering methods introduces a new generation of clustering approaches called semi-supervised clustering. This information usually has a format of pair-wise constraints and can be prepared easily... 

Human brain is one of the most wonderful complex machine which is designed for ever. A huge complex network, composed of neurons as tiny biological and chemical processors which are distributed and work together as a super parallel system to do control and vital activities of human body. Today the main secrecies of operation mechanism in individual neurons as fundamental elements of brain are reasonably understood, but network interactions of this wonderful processors and full understanding of information coding in brain seems elusive and remains as a big challenge in many interdisciplinary fields of science, from biology to cognitive science and engineering.
Thus human brain learning... 

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... 

Wireless Sensor Networks (WSNs) attracted lots of researchers in recent years, these sensors are small and have limited computational resources and are not expensive in comparision with conventional sensors. These sensors can sense, measure, gathering information from environment and send them to user, they are typically characterized by limited communication capabilities due to tight energy and bandwidth constraints. As a result, WSNs have inspired a resurgence in research on decentralized algorithms, structure of these networks are variable and dynamic, sensors may deleted from the network or added to the network, so using models which don’t require precies specifications of environment is... 

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... 

In this thesis, the goal is movement recording by observer and learning it to Fuzzy Elastic Matching Machine (FEMM). For this purpose, first using the camera (Microsoft LifeCam HD-3000), the information specified on the man, taken during the move. After preprocessing performed on the data, Data on each node of the FEMM the classified and is given to special FEMM for that movement and the FEMM to be trained by adaptive neuro fuzzy inference system. During each iteration of training, Sensitivity of FEMM for training new movement information is reduced because the FEMM updated correctly. In test part, one movement among all movements that training in all FEMMs is selected and is done by... 

Design of a spiking neural network suitable for electronic hardware implementation has been a hot research topic for a long time, in recent years implementation of spiking neural network in real time and biological scale has become important for scientists. This is due to the proximity of these networks to human brain, and the efforts led to the creation of numerous biological models and hardware which has been proposed in this context. This research includes two main parts, the first part is effort to implement a greater number of neurons per device and the second part proposes a communication system among devices to achieve biological scale. This work investigates novel neural elements and... 

Recently, many investigations have been devoted to problems of controlling mobile-wheeled inverted pendulum (MWIP) models, which have been widely applied in the field of autonomous robotics and intelligent vehicles. The MWIP models are not only of theoretical interest but are also of practical interest. Many practical systems have been implemented that were based on the MWIP models, such as the JOE, the Segway, etc. Among these applications, the Segway PT has been a popular personal transporter, since it was invented in 2002 Such systems are characterized by the ability to balance on its two wheels and spin on the spot. This additional maneuverability allows them to easily navigate on... 

Quadrotors are vertical take-off and landing (VTOL) aircrafts with high maneuver capabilities thanks to their four blades. They are widely used for various applications more than any other unmanned aircraft. Taking photos in military zones and industrial complexes is one such application. Due to the highly noisy radio and GPS (Global Positioning System) signals that are a characteristic of these environments, the task of controlling the quadrotors using them is impossible. An alternative approach that is employed in our research is the use of the aerial images from the on-board camera for autonomous navigation. To this end, we first obtain a reference image for the operation area. These... 

The researches done on legged robots can be categorized in two main groups: based on analytical methods and intelligent methods. All researched done by intelligent methods are similar in adaptation of learning methods. Robots can improve their actions with learning and there is no need to do an exact modeling of environment and system. Also this is adaptive to changes in system and environment. There are different approaches for studying intelligence in cognitive science. Embodiment approach focuses on body, its properties and connection to environment. Controller, morphology and environment effect doing the tasks in embodiment. Morphology contains sensors, shapes of elements, actuators and... 

The inverted pendulum system has been considered as a well known nonlinear system for testing control algorithms. A two-wheeled balancing robot is a mobile inverted pendulum system whose structure is a combination of a wheeled mobile robot and an inverted pendulum system. Published article studied the Robot in different views. Some papers define it as a vehicle, some other try to model and so many use it to determine the control system. This thesis presents design of fuzzy logic controller for a two-wheeled balancing robot using fuzzy methodes. First we have designed a classic fuzzy logic controller to control both of balancing and Trajectory control of robot. The Fuzzy controller was... 

Control of powered lower limb prostheses has a locomotion mode- ependent structure which demands a pattern recognizer that can classify the current locomotion mode and also detect transitions between them in an appropriate time. In the way to achieve this goal, this project presents a locomotion mode recognition system to classify daily locomotion modes consist of level- walking, stair climbing, slope walking, standing and sitting using low-cost mechanical sensors. Since these signals have a quasi-periodic nature, using sequential pattern recognition tools, such as Hidden Markov Model(HMM) improves the recognition performance,because they use sequences of information to make a decision. On... 

Optical character recognition (OCR) method has been used in converting printed text into editable text. OCR is very usefuland popular method in various applications. Accuracy of OCRn can be dependent on text preprocessing and segmentation algorithms. Sometimes it is difficult to retrieve text from the image because of different size, style, orientation, complex background of image etc. and Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations and a deep belief network (DBN) is a probabilistic ,... 

Due to diversity in amputation possibilities, this thesis focuses on the specific case of ring finger amputation with some amount of stomp remaining. The goal is to design a robotic finger as a prosthetic, which its flexion and extension (position control) are instructed from an armband. The armband is equipped with 8 force sensors to detect volume variations in patient’s forearm. Its embedded microcontroller processes signals taken from these sensors, and using machine learning techniques (K-Nearest Regressor), decides on a specific angle for the finger. This angle value updates constantly, and using a flex sensor and a DC motor in finger, the desired flexion/extension is performed.... 

In Energy Management Systems, data analysis is done based on the information gathered from the electricity utility. The closer the information is to its real value, the better the data analysis in EMS becomes. Hence, improving the accuracy of the collected information leads to an improvement in data analysis in EMS. The information collected from the network consists of the measured values of voltage phasor at each bus, the generation or load power at each bus, and the power flow in branches. Since the total number of sensors are usually less than the total network’s parameters, it is not possible to precisely determine the values of Network’s parameters, and only an estimation of them can... 

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... 

Wall detection, as a part of scene understanding and indoor 3D modeling, can be used in robotic, architecture, and augmented reality. In robotic, scene analysis and understanding is knows as one of the main steps in robot navigation and simultaneous localization and mapping and walls need to be detected to form a 3D map of indoor environment. Ceiling and floor are also important elements of an indoor environment, therefore we can see our problem as a room layout estimation if we consider ceiling and floor. Using a deep learning structure, we estimate room layout in a semantic segmentation manner. Our approach is real-time. Our proposed method uses a deep fully-convolutional network, layout... 

In applications related to measuring and directing the emotions of an individual or a group of people to the desired state (such as monitoring the driver's condition while driving, as an individual, or suppressing inflammation around the financial markets, as a set of individuals), due to the variety of environmental conditions, in each new application, the tools used to detect emotions, or the controllers and stimuli used to steer the situation to the desired state, need to be adapted to the specific conditions of that application or to design again from the beginning. In this study, by designing and performing an experiment on 13 people, by looking at the individual (or group of people in... 

With the development of artificial intelligence as well as robotics, affective computing which is about recognizing human emotions by machines and creating artificial emotions in them in order to create a proper social reaction is becoming an important topic. Most of the research in this field has been focused on recognizing and detecting human emotions. In this study, assuming a proper understanding of emotions by artificial intelligence models, an attempt has been made to better clarify the emotional space. In more detail, the goal is to examine the changes in the human emotional state in order to gain better control of his behavior. In this study, by analyzing the feelings of users and...