Compressive Identification of Active OFDM Subcarriers in Presence of Timing Offset

In this paper we study the problem of identifying active subcarriers in an OFDM signal from compressive measurements sampled at sub-Nyquist rate. The problem is of importance in Cognitive Radio systems when secondary users (SUs) are looking for available spectrum opportunities to communicate over them while sensing at Nyquist rate sampling can be costly or even impractical in case of very wide bandwidth. We first study the effect of timing offset and derive the necessary and sufficient conditions for signal recovery in the oracle-assisted case when the true active sub-carriers are assumed known. Then we propose an Orthogonal Matching Pursuit (OMP)-based joint sparse recovery method for identifying active subcarriers when the timing offset is known. Finally we extend the problem to the case of unknown timing offset and develop a joint dictionary learning and sparse approximation algorithm, where in the dictionary learning phase the timing offset is estimated and in the sparse approximation phase active subcarriers are identified. The obtained results demonstrate that active subcarrier identification can be carried out reliably, by using the developed framework.Comment: To appear in the proceedings of the IEEE Global Communications Conference (GLOBECOM) 201

K-Means Fingerprint Clustering for Low-Complexity Floor Estimation in Indoor Mobile Localization

Indoor localization in multi-floor buildings is an important research problem. Finding the correct floor, in a fast and efficient manner, in a shopping mall or an unknown university building can save the users' search time and can enable a myriad of Location Based Services in the future. One of the most widely spread techniques for floor estimation in multi-floor buildings is the fingerprinting-based localization using Received Signal Strength (RSS) measurements coming from indoor networks, such as WLAN and BLE. The clear advantage of RSS-based floor estimation is its ease of implementation on a multitude of mobile devices at the Application Programming Interface (API) level, because RSS values are directly accessible through API interface. However, the downside of a fingerprinting approach, especially for large-scale floor estimation and positioning solutions, is their need to store and transmit a huge amount of fingerprinting data. The problem becomes more severe when the localization is intended to be done on mobile devices which have limited memory, power, and computational resources. An alternative floor estimation method, which has lower complexity and is faster than the fingerprinting is the Weighted Centroid Localization (WCL) method. The trade-off is however paid in terms of a lower accuracy than the one obtained with traditional fingerprinting with Nearest Neighbour (NN) estimates. In this paper a novel K-means-based method for floor estimation via fingerprint clustering of WiFi and various other positioning sensor outputs is introduced. Our method achieves a floor estimation accuracy close to the one with NN fingerprinting, while significantly improves the complexity and the speed of the floor detection algorithm. The decrease in the database size is achieved through storing and transmitting only the cluster heads (CH's) and their corresponding floor labels.Comment: Accepted to IEEE Globecom 2015, Workshop on Localization and Tracking: Indoors, Outdoors and Emerging Network

On the Correlation between Teachers’ Emotional Intelligence and Learners’ Motivation: The Case of Iranian EFL Learners

The aim of the current study was to investigate the possible correlation between teachers’ emotional intelligence and EFL learners’ motivation. To conduct the research, a sample of 240 EFL learners and 26 EFL teachers was selected. The instruments utilized in the current scrutiny were Bar-On's (1997) Emotional Intelligence Inventory and Gardner's (1985) Attitude Motivation Test Battery. The analysis of data was carried out through running correlation, multiple regressions and t-test analyses. Based on the gained upshots, the teachers' emotional intelligence was found to significantly correlate with EFL learners’ motivation in the study sample. Proficiency level was also reported to play a considerable part vis-à-vis the relationship between the teachers' emotional intelligence and learners' motivation. Moreover, the five sub-scales of Bar-On's emotional intelligence inventory came to have a significant correlation with learners’ motivation. Finally, a significant amount of correlation held between the twelve sub-scales of motivation and teachers’ emotional intelligence. Keywords: emotional intelligence, Iranian EFL teachers/learners, motivatio

On Statistical Modelling and Hypothesis Testing by Information Theoretic Methods

The main objective of this thesis is to study various information theoretic methods and criteria in the context of statistical model selection. The focus in this research is on Rissanen’s Minimum Description Length (MDL) principle and its variants, with a special emphasis on the Normalized Maximum Likelihood (NML).We extend the Rissanen methodology for coping with infinite parametric complexity and discuss two particular cases. This is applied for deriving four NMLcriteria and investigate their performance. Furthermore, we find the connection between Stochastic Complexity (SC), defined as minus logarithm of NML, and other model selection criteria.We also study the use of information theoretic criteria (ITC) for selecting the order of autoregressive (AR) models in the presence of nonstationarity. In particular, we give a modified version of Sequentially NML (SNML) when the model parameters are estimated by forgetting factor LS algorithm.Another contribution of the thesis is in connection with the new approach for composite hypothesis testing using Optimally Distinguishable Distributions (ODD). The ODD-detector for subspace signals in Gaussian noise is introduced and its performance is evaluated.Additionally, we exploit the Kolmogorov Structure Function (KSF) to derive a new criterion for cepstral nulling, which has been recently applied to the problem of periodogram smoothing.Finally, the problem of fairness in multiaccess communication systems is investigated and a new method is proposed. The new approach is based on partitioning the network into subnetworks and employing two different multiple-access schemes within and across subnetworks. It is also introduced an algorithm for selecting optimally the subnetworks such that to achieve the max-min fairness

An analysis of underlying factors for implementing privatization in Iranian sport

The purpose of this research was to analyze the underlying factors of privatization implementation in Iran's sport, which was developed by developmental approach. Statistical population of this research was consisted of all managers and experts involved in sports privatization in the country. Regarding that this is a qualitative research, a total of 20 people were selected using the snowball purposeful sampling technique as a statistical sample. The data collection tool was interview. Interviews continued until the theoretical saturation stage was fulfilled. The data obtained from interviews were analyzed in three stages of open, axial and selective coding. The results of the research identified 41 concepts and 5 categories including factors related to financial market, management factors, media factors, cultural factors and legal factors that provide the platform for implementation of privatization in the sport of the country. According to the results of this research, privatization in Iran's sport has been affected by various conditions, it is suggested that sport authorities are encouraged to provide a condition in which people can be trained in order to gain specialty to enter in various areas such as advertising private sector and proper culture creation in press and TV and paying attention to philosophy of sport and culture creation among the people

FBG Sensor for Contact Level Monitoring and Prediction of Perforation in Cardiac Ablation

Atrial fibrillation (AF) is the most common type of arrhythmia, and is characterized by a disordered contractile activity of the atria (top chambers of the heart). A popular treatment for AF is radiofrequency (RF) ablation. In about 2.4% of cardiac RF ablation procedures, the catheter is accidently pushed through the heart wall due to the application of excessive force. Despite the various capabilities of currently available technology, there has yet to be any data establishing how cardiac perforation can be reliably predicted. Thus, two new FBG based sensor prototypes were developed to monitor contact levels and predict perforation. Two live sheep were utilized during the study. It was observed during operation that peaks appeared in rhythm with the heart rate whenever firm contact was made between the sensor and the endocardial wall. The magnitude of these peaks varied with pressure applied by the operator. Lastly, transmural perforation of the left atrial wall was characterized by a visible loading phase and a rapid signal drop-off correlating to perforation. A possible pre-perforation signal was observed for the epoxy-based sensor in the form of a slight signal reversal (12–26% of loading phase magnitude) prior to perforation (occurring over 8 s)