Sharif Digital Repository

The daily increase of fuel prices, decrease of fuel resources and the need for controlling environmental pollution, reflect the importance of optimizing the heat recovery in different industries. In the past 20 years, Pinch technology has been considered as a powerful tool used for, recovering wasted energies, optimizing the energy transfer between hot and cold streams and reducing the need for additional services. In this thesis, Pinch technology is used to optimize the process of wafer and chocolate production in one of the KitKat’s factories and different retrofits for the increase of the heat recovery are proposed. urthermore, the data collected at this factory is used to investigate the... 

The human action recognition is one of the most important concepts of computer vision in recent decades. Most of the two dimensional methods in this field are facing serious challenges such as occlusion and missing the third dimension of data. Development of depth sensors has made easy access to tracking people and 3D positions of human body joints. This Thesis proposes a new method of action recognition that utilizes the position of joints obtained by Kinect sensor. The learning stage uses Fisher Linear Discriminant Analysis (LDA) to construct discriminant feature space. Two types of distances, i.e., Euclidean and Mahalanobis, are used for recognizing the states. Also, Hidden Markov Model... 

The non-stationary nature of brain activity signals and their many inter-subject differences have created many challenges in the practical applications of emotion recognition based on electroencephalogram (EEG) signals, such as brain-computer interfaces. In such a way, the use of traditional classifiers in classifying these signals leads to a significant decrease in accuracy when applying the classifier to a new subject. Domain Adaptation methods seem to be an effective way to solve this problem by minimizing the difference between the EEG signals of different subjects. But in the basic techniques for domain adaptation, looking at all subjects' data in the same look causes the loss of a part... 

In this project, a novel action recognition method, named Fisherposes, is proposed, which is improved by several space-time (spatio-temporal) methods afterwards. The proposed method utilizes skeleton data obtained from Kinect sensor. First, pre-processing is performed in which the scales of bodies are canceled and the skeletons become aligned in order to make the method robust to location, orientation, and scale of people. In Fisherposes method, every action is defined as a sequence of body poses. Using the training samples for the poses, a Fisher subspace is created which we name it Fisherposes. Moreover, a novel distance measuring function, named regularized Mahalanobis distance, is... 

Human has different emotions such as happiness, sadness, anger, etc. Recognizing these emotions plays an important role in human-machine interface. Emotion recognition can be divided into approaches, physiological and non-physiological signals. Non-physiological signals include facial expressions, body gesture, and voice, and physiological signals include electroencephalograph (EEG), electrocardiograph (ECG), and functional magnetic resonance imaging (fMRI). EEG signal has been absorbed a lot of attention in emotion recognition because recording of EEG signal is easy and it is non-invasive. Analysis of connectivity and interaction between different areas of the brain can provide useful... 

Continuous Blood Pressure monitoring can provide invaluable information about individuals’ health conditions. However, BP is conventionally measured using inconvenient cuff-based instruments, which prevents continuous BP monitoring. This work presents an efficient algorithm, based on the Pulse Arrival Time (PAT), for the continuous and cuff-less estimation of the Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Mean Arterial Pressure (MAP) values. The proposed framework estimates the BP values through processing vital signals and extracting two types of features, which are based on either physiological parameters or whole-based representation of vital signals. Finally, the... 

Brain states analysis during consciousness is emerging research in brain-computer interface(BCI). Emotion recognition can be applied to learn brain states and stages of neural activities. Therefore, emotion recognition is crucial to the analysis of brain functioning. Electrical signals such as electroencephalogram (EEG), electrocardiogram (ECG) and functional magnetic resonance imaging(fMRI) are frequently used in emotion recognition researches. Convenience in recording, non-invasive nature and high temporal resolution are the factors that have made EEG popular in brain researches. EEG can be used to identify brain region activity solely or the connectivity of various regions in time in the... 

This study examines the behaviour of credit rating agencies during the credit crunch and compares it with normal economic conditions. The rating agencies claim that when assessing the credibility of firms over time, they can distinguish between short-term and long-term factors of credit shocks. To evaluate this claim, using Moody's rating data and variables that represent the macroeconomic conditions and financial and business risks, we study whether there is a difference in the tendency of rating agencies to downgrade the rating of firms in the periods of the credit crunch. A credit crunch index is used to determine the occurrence of credit cycles, which is obtained from the time series of... 

Make a connection between brain and computer, or Brain Computer Interface (BCI) for broad applications in areas such as medical and gamming has caused the subject to one of the most important and attractive issues in recent decades. From the perspective of pattern recognition, BCI is a classification issue that should receive signals that relate to the certain decisions of the brain and then after processing, it is concluded that the person has thought to what decision. Decisions that taken by individual, is sent from the brain to the body by signals, which is called Electroencephalogram (EEG). The number of these decisions is further, classified it also becomes more difficult. That is why... 

In Iranian Sign Language (ISL), alongside the movement of fingers, the movement of the lips is also essential for to perform words completely and correctly. The purpose of current study is to provide an automated lip-reading system using deep neural networks and implement it on Rasa social robot; So that the robot can recognize a limited number of specified Persian words. To do this, we propose an automated lip-reading system based on convolutional neural networks and long short-term memories. Convolutional neural networks in extracting features from images and long short-term memories in modeling temporal dynamics have achieved good results. We have also recorded a database in Persian... 

Hypertension precvalence is 24 and 20.5 percent in men and women, respectively. Continuous Blood Pressure monitoring can provide invaluable information about individuals’ health conditions. However, BP is conventionally measured using inconvenient cuff-based instruments, which prevents continuous BP monitoring. This work presents an efficient algorithm, based on the Pulse Arrival Time (PAT), extracted from Electrocardiogram (ECG) and Photopletysmograph (PPG), for the continuous and cuff-less estimation of the Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Mean Arterial Pressure (MAP) values. Methods: The proposed framework estimates the BP values through processing vital...