Adaptive Non-uniform Compressive Sampling for Time-varying Signals

In this paper, adaptive non-uniform compressive sampling (ANCS) of time-varying signals, which are sparse in a proper basis, is introduced. ANCS employs the measurements of previous time steps to distribute the sensing energy among coefficients more intelligently. To this aim, a Bayesian inference method is proposed that does not require any prior knowledge of importance levels of coefficients or sparsity of the signal. Our numerical simulations show that ANCS is able to achieve the desired non-uniform recovery of the signal. Moreover, if the signal is sparse in canonical basis, ANCS can reduce the number of required measurements significantly.Comment: 6 pages, 8 figures, Conference on Information Sciences and Systems (CISS 2017) Baltimore, Marylan

Missing Spectrum-Data Recovery in Cognitive Radio Networks Using Piecewise Constant Nonnegative Matrix Factorization

In this paper, we propose a missing spectrum data recovery technique for cognitive radio (CR) networks using Nonnegative Matrix Factorization (NMF). It is shown that the spectrum measurements collected from secondary users (SUs) can be factorized as product of a channel gain matrix times an activation matrix. Then, an NMF method with piecewise constant activation coefficients is introduced to analyze the measurements and estimate the missing spectrum data. The proposed optimization problem is solved by a Majorization-Minimization technique. The numerical simulation verifies that the proposed technique is able to accurately estimate the missing spectrum data in the presence of noise and fading.Comment: 6 pages, 6 figures, Accepted for presentation in MILCOM'15 Conferenc

Robust and Scalable Data Representation and Analysis Leveraging Isometric Transformations and Sparsity

The main focus of this doctoral thesis is to study the problem of robust and scalable data representation and analysis. The success of any machine learning and signal processing framework relies on how the data is represented and analyzed. Thus, in this work, we focus on three closely related problems: (i) supervised representation learning, (ii) unsupervised representation learning, and (iii) fault tolerant data analysis. For the first task, we put forward new theoretical results on why a certain family of neural networks can become extremely deep and how we can improve this scalability property in a mathematically sound manner. We further investigate how we can employ them to generate data representations that are robust to outliers and to retrieve representative subsets of huge datasets. For the second task, we will discuss two different methods, namely compressive sensing (CS) and nonnegative matrix factorization (NMF). We show that we can employ prior knowledge, such as slow variation in time, to introduce an unsupervised learning component to the traditional CS framework and to learn better compressed representations. Furthermore, we show that prior knowledge and sparsity constraint can be used in the context of NMF, not to find sparse hidden factors, but to enforce other structures, such as piece-wise continuity. Finally, for the third task, we investigate how a data analysis framework can become robust to faulty data and faulty data processors. We employ Bayesian inference and propose a scheme that can solve the CS recovery problem in an asynchronous parallel manner. Furthermore, we show how sparsity can be used to make an optimization problem robust to faulty data measurements. The methods investigated in this work have applications in different practical problems such as resource allocation in wireless networks, source localization, image/video classification, and search engines. A detailed discussion of these practical applications will be presented for each method

Robust Target Localization Based on Squared Range Iterative Reweighted Least Squares

In this paper, the problem of target localization in the presence of outlying sensors is tackled. This problem is important in practice because in many real-world applications the sensors might report irrelevant data unintentionally or maliciously. The problem is formulated by applying robust statistics techniques on squared range measurements and two different approaches to solve the problem are proposed. The first approach is computationally efficient; however, only the objective convergence is guaranteed theoretically. On the other hand, the whole-sequence convergence of the second approach is established. To enjoy the benefit of both approaches, they are integrated to develop a hybrid algorithm that offers computational efficiency and theoretical guarantees. The algorithms are evaluated for different simulated and real-world scenarios. The numerical results show that the proposed methods meet the Cr'amer-Rao lower bound (CRLB) for a sufficiently large number of measurements. When the number of the measurements is small, the proposed position estimator does not achieve CRLB though it still outperforms several existing localization methods.Comment: 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS): http://ieeexplore.ieee.org/document/8108770

Prevalence of metabolic syndrome in four phenotypes of PCOS and its relationship with androgenic components among Iranian women: A cross-sectional study

Background: Polycystic ovary syndrome (PCOS) increases the risk of metabolic syndrome (MetS). Insulin resistance (IR) plays a major role in the pathophysiology of both PCOS and MetS. Objective: This study was designed to compare the prevalence of MetS among different phenotypes of PCOS and its relationship with androgenic components. Materials and Methods: 182 participants eligible for this five-group comparative study were selected by convenience sampling method. They were classified according to the Rotterdam criteria: clinical and/or biochemical hyperandrogenism (H) + PCOS on ultrasound (P) + ovulation disorders (O) (n = 41), clinical and/or biochemical H + PCOS on P (n = 33), PCOS on P + O (n = 40), clinical and/or biochemical H + O (n = 37), and control (without PCOS) (n = 31). MetS was measured based on the National Cholesterol Education Program Adult Treatment Panel III criteria. Androgenic components included free androgen- index (FAI), total-testosterone (TT) level and sex-hormone-binding-globulin (SHBG). Results: A significant difference was observed between the study groups in terms of MetS prevalence (p = 0.01). In phenotype H+P+O, there was a statistically significant positive association between TG and TT, and a significant negative association between SBP and DBP with SHBG. In phenotype O+P, WC was inversely associated with SHBG. In phenotype H+O, FBS and TG were positively associated with FAI but HDL was inversely associated with FAI. Moreover, WC and DBP were positively associated with TT in phenotype H+O. No associations were detected between MetS parameters and androgenic components in other PCOS subjects (phenotype H+P) and in the control group. TT was significantly higher in the PCOS group suffering from MetS (p = 0.04). Conclusion: According to the research results, hyperandrogenic components are potent predictors of metabolic disorders. Thus, we suggest that MetS screening is required for the prevention of MetS and its related complications in PCOS women. Key words: Polycystic ovary syndrome, Metabolic syndrome, Hyperandrogenism

Comparison of dietary micronutrient intake in PCOS patients with and without metabolic syndrome

Background: Polycystic ovarian syndrome (PCOS) is the most common endocrine disorder in reproductive-age women. It is one of the risk factors of metabolic syndrome (MetS). These two syndromes have an inflammatory etiologic foundation along with oxidative stress. The present study aimed to compare the dietary intake of antioxidant micronutrients in PCOS women with and without MetS. Materials and methods: Overall, 42 participants eligible for this nested case control study were selected by the convenience sampling method. The case group included 14 PCOS patients with MetS and the control group included 28 PCOS patients without MetS. The dietary intake assessment of selenium, chromium, zinc, carotenoids, vitamin D and vitamin E was carried out by a 147-item Food Frequency Questionnaire (FFQ). PCOS and MetS were diagnosed using the Rotterdam criteria and NCEP ATP III, respectively. Statistical analysis was performed using SPSS16 software, T-test and Mann Whitney. Significant P-value was considered 0.05. Results: Dietary intake of antioxidant micronutrients (selenium, zinc, chromium, carotenoids and vitamin E) was significantly lower in the PCOS women with MetS than in the control group (P < 0.05). Conclusion: Since the PCOS patients without MetS had more intake of the aforementioned micronutrients than those with MetS, it is assumed that the dietary intake of these nutrients could probably have a protective effect on MetS. © 2021, The Author(s)

Feasibility study on ornamental fish production in Tabriz great park

Study was conducted during 2007 to 2010 for feasibility study and planning of ornamental fish bisines farms in Tabriz great park . All aspects of study support of 16 production stors in the plane. Each store has 450 squer neter and can support more than 168000 fry and mature fish for sailing . some extended supporting systems were suggested