Sharif Digital Repository

This work presents a method to increase the face recognition accuracy using a combination of Wavelet, PCA, and Neural Networks. Preprocessing, feature extraction and classification rules are three crucial issues for face recognition. This paper presents a hybrid approach to employ these issues. For preprocessing and feature extraction steps, we apply a combination of wavelet transform and PCA. During the classification stage, the Neural Network (MLP) is explored to achieve a robust decision in presence of wide facial variations. The computational load of the proposed method is greatly reduced as comparing with the original PCA based method on the Yale and ORL face databases. Moreover, the... 

In this paper, the performance of traditional variance-based method for detection of epileptic seizures in EEG signals are compared with various methods based on nonlinear time series analysis, entropies, logistic regression, discrete wavelet transform and time frequency distributions. We noted that variance-based method in compare to the mentioned methods had the best result (100%) applied on the same database. © 2006 IEEE  

Lateral spreading generated by earthquake induced liquefaction, is a major cause for significant damage to the engineered structures, during earthquakes. Knowing the amount of displacement which is likely to occur due to the lateral spreading, will lead to better construction policies, and will reduce unexpected damages. A Neuro-Fuzzy model based on subtractive clustering is developed to predict the amount of lateral spreading expected to occur due to an earthquake. A large database containing the case histories of observed lateral spreading during seven major earthquakes of the past is used for training and evaluating the models. The results of this study show that Neuro-Fuzzy method serves... 

Much efforts have been made in the area of energy security in recent years, but due to its wide scope, it is necessary to review relevant knowledge activities and to analyze the state of knowledge of the field of energy security measurement. The purpose of this article is to present a comprehensive map of knowledge in the field of energy security. For this reason, many documents and articles have been collected during the period 2002–2019 using 7 large and reputable scientific databases as well as 53 different journals, 90% white Q1 quality. There is employed meta-synthesis, scientometrics and network analysis. In the initial survey stage where 1290 articles were found, after analyzing the... 

Compared to non-cursive scripts, optical character recognition of cursive documents comprises extra challenges in layout analysis as well as recognition of the printed scripts. This paper presents a front-end OCR for Persian/Arabic cursive documents, which utilizes an adaptive layout analysis system in addition to a combined MLP-SVM recognition process. The implementation results on a comprehensive database show a high degree of accuracy which meets the requirements of commercial use. © 2005 IEEE  

This paper presents the SkipTree, a new balanced, distributed data structure for storing data with multidimensional keys in a peer-to-peer network. The SkipTree supports range queries as well as single point queries which are routed in O(log n) hops. SkipTree is fully decentralized with each node being connected to O(logn) other nodes. The memory usage for maintaining the links at each node is O(log n log log n) on average and O(log2 n) in the worst case. Load balance is also guaranteed to be within a constant factor. © Springer-Verlag Berlin Heidelberg 2005  

This study presents applying recurrence quantification analysis (RQA) on EEG recordings and their subbands: delta, theta, alpha, beta, and gamma for epileptic seizure detection. RQA is adopted since it does not require assumptions about stationarity, length of signal, and noise. The decomposition of the original EEG into its five constituent subbands helps better identification of the dynamical system of EEG signal. This leads to better classification of the database into three groups: Healthy subjects, epileptic subjects during a seizure-free interval (Interictal) and epileptic subjects during a seizure course (Ictal). The proposed algorithm is applied to an epileptic EEG dataset provided... 

Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR... 

A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple and 2) computationally efficient. In this article, a content-based image retrieval and annotation architecture is proposed. Its attitude is decreasing the semantic gap by partitioning the image to its semantic regions and using... 

Apnea bradycardia (AB) is an outcome of apnea occurrence in preterm infants and is an observable phenomenon in cardiovascular signals. Early detection of apnea in infants under monitoring is a critical challenge for the early intervention of nurses. In this paper, we introduce two switching Kalman filter (SKF) based methods for AB detection using electrocardiogram (ECG) signal. The first SKF model uses McSharry's ECG dynamical model integrated in two Kalman filter (KF) models trained for normal and AB intervals. Whereas the second SKF model is established by using only the RR sequence extracted from ECG and two AR models to be fitted in normal and AB intervals. In both SKF approaches, a... 

Searching large image databases is a time consuming process when done manually. Current CBIR methods mostly rely on training data in specific domains. When source and domain of images are unknown, unsupervised methods provide better solutions. In this work, we use a hierarchical clustering scheme to group images in an unknown and large image database. In addition, the user should provide the current class assignment of a small number of images as a feedback to the system. The proposed method uses this feedback to guess the number of required clusters, and optimizes the weight vector in an iterative manner. In each step, after modification of the weight vector, the images are reclustered. We... 

How can we optimally trade extra computing power to reduce the communication load in distributed computing? We answer this question by characterizing a fundamental tradeoff between computation and communication in distributed computing, i.e., the two are inversely proportional to each other. More specifically, a general distributed computing framework, motivated by commonly used structures like MapReduce, is considered, where the overall computation is decomposed into computing a set of “Map” and “Reduce” functions distributedly across multiple computing nodes. A coded scheme, named “Coded Distributed Computing” (CDC), is proposed to demonstrate that increasing the computation load of the... 

A Comprehensive speech database is one of the important tools for developing speech recognition systems; these tools are necessary for telephony recognition, too. Although adequate databases for direct speech recognizers exist, there is not an appropriate database for telephony speech recognizers. Most methods suggested for solving this problem are based on building new databases which tends to consume much time and many resources; or they used a filter which simulates circuit switch behavior to transform direct databases to telephony ones, in this case resulted databases have many differences with real telephony databases. In this paper we introduce a framework for creating telephony speech... 

Content-based image retrieval (CBIR) has been one of the most active areas of research. The retrieval principle of CBIR systems is based on visual features such as colour, texture and shape or the semantic meaning of the images. A CBIR system can be used to locate medical images in large databases. This paper presents a CBIR system for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the support vector machine (SVM) learning method. This system can retrieve similar images from the database in two groups: normal and tumoural. This research uses the knowledge of the CBIR approach to the application of medical decision support and discrimination...