Subspace Leakage Analysis and Improved DOA Estimation with Small Sample Size

Classical methods of DOA estimation such as the MUSIC algorithm are based on estimating the signal and noise subspaces from the sample covariance matrix. For a small number of samples, such methods are exposed to performance breakdown, as the sample covariance matrix can largely deviate from the true covariance matrix. In this paper, the problem of DOA estimation performance breakdown is investigated. We consider the structure of the sample covariance matrix and the dynamics of the root-MUSIC algorithm. The performance breakdown in the threshold region is associated with the subspace leakage where some portion of the true signal subspace resides in the estimated noise subspace. In this paper, the subspace leakage is theoretically derived. We also propose a two-step method which improves the performance by modifying the sample covariance matrix such that the amount of the subspace leakage is reduced. Furthermore, we introduce a phenomenon named as root-swap which occurs in the root-MUSIC algorithm in the low sample size region and degrades the performance of the DOA estimation. A new method is then proposed to alleviate this problem. Numerical examples and simulation results are given for uncorrelated and correlated sources to illustrate the improvement achieved by the proposed methods. Moreover, the proposed algorithms are combined with the pseudo-noise resampling method to further improve the performance.Comment: 37 pages, 10 figures, Submitted to the IEEE Transactions on Signal Processing in July 201

Cramer-Rao Bound for Sparse Signals Fitting the Low-Rank Model with Small Number of Parameters

In this paper, we consider signals with a low-rank covariance matrix which reside in a low-dimensional subspace and can be written in terms of a finite (small) number of parameters. Although such signals do not necessarily have a sparse representation in a finite basis, they possess a sparse structure which makes it possible to recover the signal from compressed measurements. We study the statistical performance bound for parameter estimation in the low-rank signal model from compressed measurements. Specifically, we derive the Cramer-Rao bound (CRB) for a generic low-rank model and we show that the number of compressed samples needs to be larger than the number of sources for the existence of an unbiased estimator with finite estimation variance. We further consider the applications to direction-of-arrival (DOA) and spectral estimation which fit into the low-rank signal model. We also investigate the effect of compression on the CRB by considering numerical examples of the DOA estimation scenario, and show how the CRB increases by increasing the compression or equivalently reducing the number of compressed samples.Comment: 14 pages, 1 figure, Submitted to IEEE Signal Processing Letters on December 201

Failure Mode and Effects Analysis Using Generalized Mixture Operators

Failure mode and effects analysis (FMEA) is a method based on teamwork to identify potential failures and problems in a system, design, process and service in order to remove them. The important part of this method is determining the risk priorities of failure modes using the risk priority number (RPN). However, this traditional RPN method has several shortcomings. Therefore, in this paper we propose a FMEAwhich uses generalized mixture operators to determine and aggregate the risk priorities of failure modes. In a numerical example, a FMEA of the LGS gas type circuit breaker product in Zanjan Switch Industries in Iran is presented to further illustrate the proposed method. The results show that the suggested approach is simple and provides more accurate risk assessments than the traditional RPN

Relationship between entrepreneurial self-efficacy and entrepreneurial intention in medical library and information science students: an Iranian perspective

Purpose: The main purpose of this study is to investigate the relationship between entrepreneurial self-efficacy and entrepreneurial intention of students in the field of medical library and information science. Methods: This study quantitatively examined seven hypotheses through Structural Equation Modeling (SEM) techniques. Using the census method, 79 students of medical library and information science of Hamadan University of Medical Sciences, Iran, were studied. Data was collected using the Entrepreneurial Self-efficacy Questionnaire proposed by De Noble et al. (1999) and the Entrepreneurial Intention Questionnaire presented by the Linan and Chen (2011). Descriptive and inferential data analysis was performed using SPSS and SmartPLS2 software at a significance level of 0.05. Results: The results showed that the variable of entrepreneurial self-efficacy and the components of initiating investor relationships and developing human resources affect the entrepreneurial intention of students, while the components of Understanding market opportunities, building an innovative environment, defining core purpose, and coping with challenges had no effect on entrepreneurship. The entrepreneurial intention of medical library and information science students is positively affected by their entrepreneurial self-efficacy. Greater self- efficacy leads to entrepreneurship. Practical implications: Considering the positive effect of entrepreneurial self-efficacy on students\u27 entrepreneurial intention, offering training courses to strengthen entrepreneurial behavior in the academic period seems useful. Originality/value: Entrepreneurship helps communities achieve social and economic growth. Entrepreneurial intention is one of the important factors in the occurrence of entrepreneurial behavior in students. Entrepreneurial Self-efficacy is a key factor in shaping and strengthening entrepreneurial intention. Given the increasing number of library and medical information science graduates and the relatively limited job market in this field, it seems useful to examine their entrepreneurial intention and the impact of social factors affecting it. Keywords: Entrepreneurial Self-efficacy, Entrepreneurial Intention, Medical Library and Information scienc

Global, regional, and national burden of colorectal cancer and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Funding: F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia, I.P. (FCT), in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy i4HB; FCT/MCTES through the project UIDB/50006/2020. J Conde acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006.proofepub_ahead_of_prin

Low-complexity detection and decoding for LDPC-coded data storage channels

LDPC codes have shown an excellent performance over various channels for different code lengths and rates, and they are finding their way to become the dominant coding scheme for various applications. As an emerging channel coding technique, LDPC codes are widely considered for digital storage systems. In this thesis, LDPC codes based on finite geometries are selected due to their great performance and structure. We will present the details of the construction and features of these codes. In this thesis, we investigate various detection and decoding techniques that exhibit different performance and complexity trade-offs. The optimal or near optimal methods usually impose an enormous amount of computations, and therefore, they may not be suitable for practical applications. On the other hand, sub-optimal algorithms may suffer from performance degradation. Our main goal in this thesis is to design low-complexity decoders and detectors that have near optimal performance. We first start by introducing a new LDPC decoder, and later, we consider the detection problem. Particularly, we consider low-complexity detection and decoding for LDPC-coded data storage channels. We introduce a new algorithm for decoding LDPC codes that has a complexity close to low-complexity algorithms, while its performance approaches the performance of the belief propagation (BP) algorithm. The BP algorithm is a well-known method for decoding LDPC codes with a great performance, but it has a high complexity.MASTER OF ENGINEERING (EEE