24,099 research outputs found

    Multichannel direct detection of light dark matter: Target comparison

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    Direct-detection experiments for light dark matter are making enormous leaps in reaching previously unexplored model space. Several recent proposals rely on collective excitations, where the experimental sensitivity is highly dependent on detailed properties of the target material, well beyond just nucleus mass numbers as in conventional searches. It is thus important to optimize the target choice when considering which experiment to build. We carry out a comparative study of target materials across several detection channels, focusing on electron transitions and single (acoustic or optical) phonon excitations in crystals, as well as the traditional nuclear recoils. We compare materials currently in use in nuclear recoil experiments (Si, Ge, NaI, CsI, CaWO4), a few of which have been proposed for light dark matter experiments (GaAs, Al2O3, diamond), as well as 16 other promising polar crystals across all detection channels. We find that target- and dark-matter-model-dependent reach is largely determined by a small number of material parameters: speed of sound, electronic band gap, mass number, Born effective charge, high-frequency dielectric constant, and optical phonon energies. We showcase, for each of the two benchmark models, an exemplary material that has a better reach than in any currently proposed experiment

    A recursive frequency estimator using linear prediction and a Kalman-filter-based iterative algorithm

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    This paper proposes a new Kalman-filter-based recursive frequency estimator for discrete-time multicomponent sinusoidal signals whose frequencies may be time-varying. The frequency estimator is based on the linear prediction approach and it employs the Kalman filter to track the linear prediction coefficients (LPCs) recursively. Frequencies of the sinusoids can then be computed using the estimated LPCs. Due to the coloredness of the linear prediction error, an iterative algorithm is employed to estimate the covariance matrix of the prediction error and the LPCs alternately in the Kalman filter in order to improve the tracking performance. Simulation results show that the proposed Kalman-filter-based iterative frequency estimator can achieve better tracking results than the conventional recursive least-squares-based estimators. © 2008 IEEE.published_or_final_versio

    Sulodexide decreases albuminuria and regulates matrix protein accumulation in C57BL/6 mice with streptozotocin-induced type I diabetic nephropathy

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    OBJECTIVE: Sulodexide is a mixture of glycosaminoglycans that may reduce proteinuria in diabetic nephropathy (DN), but its mechanism of action and effect on renal histology is not known. We investigated the effect of sulodexide on disease manifestations in a murine model of type I DN. METHODS: Male C57BL/6 mice were rendered diabetic with streptozotocin. After the onset of proteinuria, mice were randomized to receive sulodexide (1 mg/kg/day) or saline for up to 12 weeks and renal function, histology and fibrosis were examined. The effect of sulodexide on fibrogenesis in murine mesangial cells (MMC) was also investigated. RESULTS: Mice with DN showed progressive albuminuria and renal deterioration over time, accompanied by mesangial expansion, PKC and ERK activation, increased renal expression of TGF-β1, fibronectin and collagen type I, III and IV, but decreased glomerular perlecan expression. Sulodexide treatment significantly reduced albuminuria, improved renal function, increased glomerular perlecan expression and reduced collagen type I and IV expression and ERK activation. Intra-glomerular PKC-α activation was not affected by sulodexide treatment whereas glomerular expression of fibronectin and collagen type III was increased. MMC stimulated with 30 mM D-glucose showed increased PKC and ERK mediated fibronectin and collagen type III synthesis. Sulodexide alone significantly increased fibronectin and collagen type III synthesis in a dose-dependent manner in MMC and this increase was further enhanced in the presence of 30 mM D-glucose. Sulodexide showed a dose-dependent inhibition of 30 mM D-glucose-induced PKC-βII and ERK phosphorylation, but had no effect on PKC-α or PKC-βI phosphorylation. CONCLUSIONS: Our data demonstrated that while sulodexide treatment reduced proteinuria and improved renal function, it had differential effects on signaling pathways and matrix protein synthesis in the kidney of C57BL/6 mice with DN.published_or_final_versio

    Laboratory measurement campaign of DVB-T signal with transmit delay diversity

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    The requirements for future DVB-T/H networks demand that broadcasters design and deploy networks that provide ubiquitous reception in challenging indoors and other obstructed situations. It is essential that such networks are designed cost-effectively and with minimized environmental impact. The EC funded project PLUTO has since its start in 2006 explored the use of diversity to improve coverage in these difficult situations. The purpose of this paper is to investigate the performance of Transmit Delay Diversity (DD) with two antennas to improve the reception of DVB-T/H systems operating in different realistic propagation conditions through a series of tests using a SPIRENT SR5500 dual channel emulator. The relationship between correlation coefficient between channels, receiver velocity and diversity gain is nvestigated. It is shown that transmit delay diversity significantly improves the quality of reception particularly in simulated fast fading mobile broadcasting applications. This paper documents research conducted by Brunel University and Broadreach Systems

    Star Formation in Violent and Normal Evolutionary Phases

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    Mergers of massive gas-rich galaxies trigger violent starbursts that - over timescales of >100> 100 Myr and regions >10> 10 kpc - form massive and compact star clusters comparable in mass and radii to Galactic globular clusters. The star formation efficiency is higher by 1 - 2 orders of magnitude in these bursts than in undisturbed spirals, irregulars or even BCDs. We ask the question if star formation in these extreme regimes is just a scaled-up version of the normal star formation mode of if the formation of globular clusters reveals fundamentally different conditions.Comment: 4 pages To appear in The Evolution of Galaxies. II. Basic building blocks, eds. M. Sauvage, G. Stasinska, L. Vigroux, D. Schaerer, S. Madde

    A new regularized TVAR-based algorithm for recursive detection of nonstationarity and its application to speech signals

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    This paper develops a new recursive nonstationarity detection method based on time-varying autoregressive (TVAR) modeling. A local likelihood estimation approach is introduced which gives more weights to observations near the current time instant but less to those distance apart. It thus allows the Wald test to be computed based on RLS-type algorithms with low computational cost. A reliable and efficient state regularized variable forgetting factor (VFF) QR decomposition (QRD)-based RLS (SR-VFF-QRRLS) algorithm is adopted for estimation for its asymptotically unbiased property and immunity to lacking of excitation. Advantages of the proposed approach over conventional approaches are 1) it provides continuous parameter estimates and the corresponding stationary intervals with low complexity, 2) it mitigates low excitation problems using state regularization, and 3) stationarity at different scales can be detected by appropriately choosing a certain window size. The effectiveness of the proposed algorithm is evaluated by testing vocal tract changes in real speech signals. © 2012 IEEE.published_or_final_versio

    A new visual object tracking algorithm using Bayesian Kalman filter

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    This paper proposes a new visual object tracking algorithm using a novel Bayesian Kalman filter (BKF) with simplified Gaussian mixture (BKF-SGM). The new BKF-SGM employs a GM representation of the state and noise densities and a novel direct density simplifying algorithm for avoiding the exponential complexity growth of conventional KFs using GM. Together with an improved mean shift (MS) algorithm, a new BKF-SGM with improved MS (BKF-SGM-IMS) algorithm with more robust tracking performance is also proposed. Experimental results show that our method can successfully handle complex scenarios with good performance and low arithmetic complexity. © IEEEpublished_or_final_versio

    A new recursive algorithm for time-varying autoregressive (TVAR) model estimation and its application to speech analysis

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    This paper proposes a new state-regularized (SR) and QR decomposition based recursive least squares (QRRLS) algorithm with variable forgetting factor (VFF) for recursive coefficient estimation of time-varying autoregressive (AR) models. It employs the estimated coefficients as prior information to minimize the exponentially weighted observation error, which leads to reduced variance and bias over traditional regularized RLS algorithm. It also increases the tracking speed by introducing a new measure of convergence status to control the FF. Simulations using synthetic and real speech signals show that the proposed method has improved tracking performance and reduced estimation error variance than conventional TVAR modeling methods during rapid changing of AR coefficients. © 2012 IEEE.published_or_final_versionThe 2012 IEEE International Symposium on Circuits and Systems (ISCAS), Seoul, Korea, 20-23 May 2012. In IEEE International Symposium on Circuits and Systems Proceedings, 2012, p. 1026-102
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