Adaptive beamforming using frequency invariant uniform concentric circular arrays

This paper proposes new adaptive beamforming algorithms for a class of uniform concentric circular arrays (UCCAs) having near-frequency invariant characteristics. The basic principle of the UCCA frequency invariant beamformer (FIB) is to transform the received signals to the phase mode representation and remove the frequency dependence of individual phase modes through the use of a digital beamforming or compensation network. As a result, the far field pattern of the array is electronic steerable and is approximately invariant over a wider range of frequencies than the uniform circular arrays (UCAs). The beampattern is governed by a small set of variable beamformer weights. Based on the minimum variance distortionless response (MVDR) and generalized sidelobe canceller (GSC) methods, new recursive adaptive beamforming algorithms for UCCA-FIB are proposed. In addition, robust versions of these adaptive beamforming algorithms for mitigating direction-of-arrival (DOA) and sensor position errors are developed. Simulation results show that the proposed adaptive UCCA-FIBs converge much faster and reach a considerable lower steady-state error than conventional broadband UCCA beamformers without using the compensation network. Since fewer variable multipliers are required in the proposed algorithms, it also leads to lower arithmetic complexity and faster tracking performance than conventional methods. © 2007 IEEE.published_or_final_versio

A semi-definite programming (SDP) method for designing IIR sharp cut-off digital filters using frequency-response masking

IEEE International Symposium on Circuits and Systems Proceedings, Vancouver, Canada, 23-26 May 2004This paper studies the design of frequency response masking (FRM) filters with infinite duration impulse response (IIR) model and masking sub-filters. They are useful in realizing sharp cutoff digital filters with low passband delays. The designs of the model and masking filters are carried out by means of semidefinite programming (SDP) and model order reduction. Design results show that low complexity FRM filters with low passband delay can be obtained.published_or_final_versio

Adaptive beamforming using uniform concentric circular arrays with frequency invariant characteristics

This paper proposes a new method for adaptive beamforming using uniform concentric circular array (UCCA) that has nearly frequency invariant (FI) characteristics. The basic principle of FI UCCA is to transform the received signals to the phase mode and compensate for the frequency dependency of the individual phase mode through the use of a digital beamforming network. The far field pattern of the array is then determined by a set of weights and it is approximately invariant over a wide range of frequencies. Therefore, the minimum variance beamforming (MVB) approach can be used to adapt the small set of weights, as if it is a narrowband array, Design examples and simulation are given to demonstrate the usefulness of the proposed FI UCCA in broadband DOA estimation and beamforming. © 2005 IEEE.published_or_final_versio

Association between diabetes, diabetes treatment and risk of developing endometrial cancer.

BackgroundA growing body of evidence suggests that diabetes is a risk factor for endometrial cancer incidence. However, most of these studies used case-control study designs and did not adjust for obesity, an established risk factor for endometrial cancer. In addition, few epidemiological studies have examined the association between diabetes treatment and endometrial cancer risk. The objective of this study was to assess the relationships among diabetes, diabetes treatment and endometrial cancer risk in postmenopausal women participating in the Women's Health Initiative (WHI).MethodsA total of 88 107 postmenopausal women aged 50-79 years who were free of cancer and had no hysterectomy at baseline were followed until date of endometrial cancer diagnosis, death, hysterectomy or loss to follow-up, whichever came first. Endometrial cancers were confirmed by central medical record and pathology report review. Multivariate Cox proportional hazards regression models were used to estimate hazard ratios (HRs) (95% confidence interval (CI)) for diagnosis of diabetes and metformin treatment as risk factors for endometrial cancer.ResultsOver a mean of 11 years of follow-up, 1241 endometrial cancers developed. In the primary analysis that focused on prevalent diabetes at enrolment, compared with women without diabetes, women with self-reported diabetes, and the subset of women with treated diabetes, had significantly higher risk of endometrial cancer without adjusting for BMI (HR=1.44, 95% CI: 1.13-1.85 for diabetes, HR=1.57, 95% CI: 1.19-2.07 for treated diabetes). However after adjusting for BMI, the associations between diabetes, diabetes treatment, diabetes duration and the risk of endometrial cancer became non-significant. Elevated risk was noted when considering combining diabetes diagnosed at baseline and during follow-up as time-dependent exposure (HR=1.31, 95% CI: 1.08-1.59) even after adjusting for BMI. No significant association was observed between metformin use and endometrial cancer risk.ConclusionsOur results suggest that the relationship observed in previous research between diabetes and endometrial cancer incidence may be largely confounded by body weight, although some modest independent elevated risk remains

Rapid phosphorylation of the CRE binding protein precedes stress-induced activation of the corticotropin releasing hormone gene in medial parvocellular hypothalamic neurons of the immature rat.

The mechanisms of the molecular and neuroendocrine responses to stress in the immature rat have been a focus of intense investigation. A principal regulator of the these responses in both mature and developing rat is the neuropeptide corticotropin releasing hormone (CRH), and levels of hypothalamic CRH mRNA are enhanced by stress. In vitro, transcription of the CRH gene is governed by binding of the phosphorylated form of cAMP responsive element binding protein (pCREB) to the promoter. Here we tested the hypothesis that rapid, stress-induced CRH transcription occurred during the first two postnatal weeks, and is associated with pCREB expression. The time-course of induction of unedited, heteronuclear CRH RNA (CRH hnRNA) was examined in hypothalamic paraventricular nucleus (PVN) of immature rats subjected to both modest and strong acute stressors using in situ hybridization; pCREB abundance was determined in individual neurons in specific PVN sub-nuclei using immunocytochemistry and unbiased quantitative analysis. CRH hnRNA signal was negligible in PVN of immature rats sacrificed under stress-free conditions, but was readily detectable within 2 min, and peaked at 15 min, in PVN of stressed animals. Enhanced pCREB immunoreactivity was evident within 2 min of stress onset, and was enhanced specifically in stress-responsive, CRH-expressing medial parvocellular neurons. These data support the notion that, already during early postnatal life, stress induces rapid CREB phosphorylation, interaction of pCREB-containing transcription complexes with the CRE element of the CRH gene promoter, and initiation of CRH hnRNA production in stress-responsive neurons of rat PVN

Generalization of hyperbolic perturbation solution for heteroclinic orbits of strongly nonlinear self-excited oscillator

A generalized hyperbolic perturbation method for heteroclinic solutions is presented for strongly nonlinear self-excited oscillators in the more general form of x⋅⋅+g(x)=ɛf(μ,x,x⋅)x··+g(x)=ɛf(μ,x,x·). The advantage of this work is that heteroclinic solutions for more complicated and strong nonlinearities can be analytically derived, and the previous hyperbolic perturbation solutions for Duffing type oscillator can be just regarded as a special case of the present method. The applications to cases with quadratic-cubic nonlinearities and with quintic-septic nonlinearities are presented. Comparisons with other methods are performed to assess the effectiveness of the present method.postprin

A Sustainable Facility Management Outsourcing Relationships System: Artificial Neural Networks

The Contingency Outsourcing Relationship (CORE) model originated from the Four Outsourcing Relationship Types (FORT) model; the CORE model is used in the globalized Facility Management (FM) industry, while the FORT model is originally used in the global information technology industry. The purpose of this paper is to thoroughly analyse the simulated case studies of the four different categories (i.e., in-house, technical expertise, commitment and common goals) of the CORE model from the perspective of the various clients. This study builds on the previous work on the outsourcing relationships between a client and a globalized FM service provider. It further explores the application of this model with the aid of artificial neural networks (ANNs) towards a sustainable future. A quantitative methodology through a survey is used to analyse eight outsourcing strategies for the four outsourcing relationships. A set of revised rules of the CORE is introduced and discussed regarding the approaches to investigate the four simulated outsourcing relationship systems. The study further reveals that an interesting understanding of the four outsourcing categories can be systematically and efficiently implemented into the FM outsourcing relationships through the methodology of scientific Artificial Intelligence (AI). It is concluded that FM outsourcing categorization may help to define the appropriate relationships. This further detailed outcome generated from the ANN can be clearly considered a strong and solid reference to define and explain the existing outsourcing relationships between the stakeholders and the service providers with the aim to assign an outsourcing category to the FM relationship between the client and service provider based on the learnt rules

The wavelet-NARMAX representation : a hybrid model structure combining polynomial models with multiresolution wavelet decompositions

A new hybrid model structure combing polynomial models with multiresolution wavelet decompositions is introduced for nonlinear system identification. Polynomial models play an important role in approximation theory, and have been extensively used in linear and nonlinear system identification. Wavelet decompositions, in which the basis functions have the property of localization in both time and frequency, outperform many other approximation schemes and offer a flexible solution for approximating arbitrary functions. Although wavelet representations can approximate even severe nonlinearities in a given signal very well, the advantage of these representations can be lost when wavelets are used to capture linear or low-order nonlinear behaviour in a signal. In order to sufficiently utilise the global property of polynomials and the local property of wavelet representations simultaneously, in this study polynomial models and wavelet decompositions are combined together in a parallel structure to represent nonlinear input-output systems. As a special form of the NARMAX model, this hybrid model structure will be referred to as the WAvelet-NARMAX model, or simply WANARMAX. Generally, such a WANARMAX representation for an input-output system might involve a large number of basis functions and therefore a great number of model terms. Experience reveals that only a small number of these model terms are significant to the system output. A new fast orthogonal least squares algorithm, called the matching pursuit orthogonal least squares (MPOLS) algorithm, is also introduced in this study to determine which terms should be included in the final model