826 resultados para Blind-deaf


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Background Acute bacterial meningitis (BM) continues to be an important cause of childhood mortality and morbidity, especially in developing countries. Prognostic scales and the identification of risk factors for adverse outcome both aid in assessing disease severity. New antimicrobial agents or adjunctive treatments - except for oral glycerol - have essentially failed to improve BM prognosis. A retrospective observational analysis found paracetamol beneficial in adult bacteraemic patients, and some experts recommend slow β-lactam infusion. We examined these treatments in a prospective, double-blind, placebo-controlled clinical trial. Patients and methods A retrospective analysis included 555 children treated for BM in 2004 in the infectious disease ward of the Paediatric Hospital of Luanda, Angola. Our prospective study randomised 723 children into four groups, to receive a combination of cefotaxime infusion or boluses every 6 hours for the first 24 hours and oral paracetamol or placebo for 48 hours. The primary endpoints were 1) death or severe neurological sequelae (SeNeSe), and 2) deafness. Results In the retrospective study, the mortality of children with blood transfusion was 23% (30 of 128) vs. without blood transfusion 39% (109 of 282; p=0.004). In the prospective study, 272 (38%) of the children died. Of those 451 surviving, 68 (15%) showed SeNeSe, and 12% (45 of 374) were deaf. Whereas no difference between treatment groups was observable in primary endpoints, the early mortality in the infusion-paracetamol group was lower, with the difference (Fisher s exact test) from the other groups at 24, 48, and 72 hours being significant (p=0.041, 0.0005, and 0.005, respectively). Prognostic factors for adverse outcomes were impaired consciousness, dyspnoea, seizures, delayed presentation, and absence of electricity at home (Simple Luanda Scale, SLS); the Bayesian Luanda Scale (BLS) also included abnormally low or high blood glucose. Conclusions New studies concerning the possible beneficial effect of blood transfusion, and concerning longer treatment with cefotaxime infusion and oral paracetamol, and a study to validate our simple prognostic scales are warranted.

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In this paper, we present robust semi-blind (SB) algorithms for the estimation of beamforming vectors for multiple-input multiple-output wireless communication. The transmitted symbol block is assumed to comprise of a known sequence of training (pilot) symbols followed by information bearing blind (unknown) data symbols. Analytical expressions are derived for the robust SB estimators of the MIMO receive and transmit beamforming vectors. These robust SB estimators employ a preliminary estimate obtained from the pilot symbol sequence and leverage the second-order statistical information from the blind data symbols. We employ the theory of Lagrangian duality to derive the robust estimate of the receive beamforming vector by maximizing an inner product, while constraining the channel estimate to lie in a confidence sphere centered at the initial pilot estimate. Two different schemes are then proposed for computing the robust estimate of the MIMO transmit beamforming vector. Simulation results presented in the end illustrate the superior performance of the robust SB estimators.

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In this letter, we propose a method for blind separation of d co-channel BPSK signals arriving at an antenna array. Our method involves two steps. In the first step, the received data vectors at the output of the array is grouped into 2d clusters. In the second step, we assign the 2d d-tuples with ±1 elements to these clusters in a consistent fashion. From the knowledge of the cluster to which a data vector belongs, we estimate the bits transmitted at that instant. Computer simulations are used to study the performance of our method

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The problem of estimating multiple Carrier Frequency Offsets (CFOs) in the uplink of MIMO-OFDM systems with Co-Channel (CC) and OFDMA based carrier allocation is considered. The tri-linear data model for generalized, multiuser OFDM system is formulated. Novel blind subspace based estimation of multiple CFOs in the case of arbitrary carrier allocation scheme in OFDMA systems and CC users in OFDM systems based on the Khatri-Rao product is proposed. The method works where the conventional subspace method fails. The performance of the proposed methods is compared with pilot based Least-Squares method.

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Any waterway with one end closed and the other open is generally called a blind channel. The main flow tends to expand, separate, and cause circulation at the mouth of blind channels. The main flow continuously transfers momentum and sediment into the circulation region through the turbulent mixing region (TMR) between them, thus leading to a large amount of sediment deposition in the blind channels. This paper experimentally investigated the properties of the water flow and sediment diffusion in TMR, demonstrating that both water flow and sediment motion in TMR approximately coincide with a similar structure as in the free mixing layer induced by a jet. The similarity functions of flow velocity and sediment concentration are then assumed, based on observation, and the resulting calculation of these functions is substantially facilitated. For the kind of low velocity flow system of blind channels with a finite width, a simple formula for the sediment deposition rate in blind channels is established by analyzing the gradient of crosswise velocity and sediment concentration in TMR.

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We consider the problem of blind multiuser detection. We adopt a Bayesian approach where unknown parameters are considered random and integrated out. Computing the maximum a posteriori estimate of the input data sequence requires solving a combinatorial optimization problem. We propose here to apply the Cross-Entropy method recently introduced by Rubinstein. The performance of cross-entropy is compared to Markov chain Monte Carlo. For similar Bit Error Rate performance, we demonstrate that Cross-Entropy outperforms a generic Markov chain Monte Carlo method in terms of operation time.

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We present methods for fixed-lag smoothing using Sequential Importance sampling (SIS) on a discrete non-linear, non-Gaussian state space system with unknown parameters. Our particular application is in the field of digital communication systems. Each input data point is taken from a finite set of symbols. We represent transmission media as a fixed filter with a finite impulse response (FIR), hence a discrete state-space system is formed. Conventional Markov chain Monte Carlo (MCMC) techniques such as the Gibbs sampler are unsuitable for this task because they can only perform processing on a batch of data. Data arrives sequentially, so it would seem sensible to process it in this way. In addition, many communication systems are interactive, so there is a maximum level of latency that can be tolerated before a symbol is decoded. We will demonstrate this method by simulation and compare its performance to existing techniques.