773 resultados para Data transmission systems.
Resumo:
In this paper, we propose a training-based channel estimation scheme for large non-orthogonal space-time block coded (STBC) MIMO systems.The proposed scheme employs a block transmission strategy where an N-t x N-t pilot matrix is sent (for training purposes) followed by several N-t x N-t square data STBC matrices, where Nt is the number of transmit antennas. At the receiver, we iterate between channel estimation (using an MMSE estimator) and detection (using a low-complexity likelihood ascent search (LAS) detector) till convergence or for a fixed number of iterations. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed scheme at low complexities. The fact that we could show such good results for large STBCs (e.g., 16 x 16 STBC from cyclic division algebras) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot-based channel estimation and turbo coding) establishes the effectiveness of the proposed scheme.
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Analysis of EXAFS data of complex systems containing more than one phase and one type of coordination, has been discussed. It is shown that a modified treatment of EXAFS function as well as the amplitude ratio plots provide useful means of obtaining valuable structural information. The systems investigated are: biphasic Ni+NiO mixture, NiAl2O4 with two coordinations for Ni, NiO+NiAl2O4 mixture, CoS+CoO system and Ni dispersed on Al2O3. The results obtained with these systems have been most satisfactory and serve to illustrate the utility and the applicability of the innovations described in this paper.
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The EEG time series has been subjected to various formalisms of analysis to extract meaningful information regarding the underlying neural events. In this paper the linear prediction (LP) method has been used for analysis and presentation of spectral array data for the better visualisation of background EEG activity. It has also been used for signal generation, efficient data storage and transmission of EEG. The LP method is compared with the standard Fourier method of compressed spectral array (CSA) of the multichannel EEG data. The autocorrelation autoregressive (AR) technique is used for obtaining the LP coefficients with a model order of 15. While the Fourier method reduces the data only by half, the LP method just requires the storage of signal variance and LP coefficients. The signal generated using white Gaussian noise as the input to the LP filter has a high correlation coefficient of 0.97 with that of original signal, thus making LP as a useful tool for storage and transmission of EEG. The biological significance of Fourier method and the LP method in respect to the microstructure of neuronal events in the generation of EEG is discussed.
Resumo:
Transmission of bulk power at high voltages over very long distances has become very imperative. At present, throughout the globe, this task has been mostly performed by overhead transmission lines. The dual task of mechanically supporting and electrically isolating the live phase conductors from the support tower is performed by string insulators. Whether in clean condition or under polluted conditions, the electrical stress distribution along the insulators governs the possible flashover, which is quite detrimental to the system. However, a reliable data on stress distribution in commonly employed string insulators are rather scarce. Considering this, the present work has made an attempt to study accurately, the field distribution in 220 kV strings for six different types of porcelain/ceramic insulators (Normal and Antifog discs) used for high voltage transmission. The surface charge simulation method is employed for the required field computation. Voltage and electric stress distribution is deduced and compared across different types of discs. A comparison on normalised surface resistance, which is an indicator for the stress concentration under polluted condition, is also attempted.
Resumo:
The memory subsystem is a major contributor to the performance, power, and area of complex SoCs used in feature rich multimedia products. Hence, memory architecture of the embedded DSP is complex and usually custom designed with multiple banks of single-ported or dual ported on-chip scratch pad memory and multiple banks of off-chip memory. Building software for such large complex memories with many of the software components as individually optimized software IPs is a big challenge. In order to obtain good performance and a reduction in memory stalls, the data buffers of the application need to be placed carefully in different types of memory. In this paper we present a unified framework (MODLEX) that combines different data layout optimizations to address the complex DSP memory architectures. Our method models the data layout problem as multi-objective genetic algorithm (GA) with performance and power being the objectives and presents a set of solution points which is attractive from a platform design viewpoint. While most of the work in the literature assumes that performance and power are non-conflicting objectives, our work demonstrates that there is significant trade-off (up to 70%) that is possible between power and performance.
Resumo:
Equations for the computation of integral and partial thermodynamic properties of mixing in quarternary systems are derived using data on constituent binary systems and shortest distance composition paths to the binaries. The composition path from a quarternary composition to the i-j binary is characterized by a constant value of (Xi − Xj). The merits of this composition path over others with constant values for View the MathML source or Xi are discussed. Finally the equations are generalized for higher order systems. They are exact for regular solutions, but may be used in a semiempirical mode for non-regular solutions.
Resumo:
Information forms the basis of modern technology. To meet the ever-increasing demand for information, means have to be devised for a more efficient and better-equipped technology to intelligibly process data. Advances in photonics have made their impact on each of the four key applications in information processing, i.e., acquisition, transmission, storage and processing of information. The inherent advantages of ultrahigh bandwidth, high speed and low-loss transmission has already established fiber-optics as the backbone of communication technology. However, the optics to electronics inter-conversion at the transmitter and receiver ends severely limits both the speed and bit rate of lightwave communication systems. As the trend towards still faster and higher capacity systems continues, it has become increasingly necessary to perform more and more signal-processing operations in the optical domain itself, i.e., with all-optical components and devices that possess a high bandwidth and can perform parallel processing functions to eliminate the electronic bottleneck.
Resumo:
This paper proposes an algorithm for joint data detection and tracking of the dominant singular mode of a time varying channel at the transmitter and receiver of a time division duplex multiple input multiple output beamforming system. The method proposed is a modified expectation maximization algorithm which utilizes an initial estimate to track the dominant modes of the channel at the transmitter and the receiver blindly; and simultaneously detects the un known data. Furthermore, the estimates are constrained to be within a confidence interval of the previous estimate in order to improve the tracking performance and mitigate the effect of error propagation. Monte-Carlo simulation results of the symbol error rate and the mean square inner product between the estimated and the true singular vector are plotted to show the performance benefits offered by the proposed method compared to existing techniques.
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This paper presents a fast and accurate relaying technique for a long 765kv UHV transmission line based on support vector machine. For a long EHV/UHV transmission line with large distributed capacitance, a traditional distance relay which uses a lumped parameter model of the transmission line can cause malfunction of the relay. With a frequency of 1kHz, 1/4th cycle of instantaneous values of currents and voltages of all phases at the relying end are fed to Support Vector Machine(SVM). The SVM detects fault type accurately using 3 milliseconds of post-fault data and reduces the fault clearing time which improves the system stability and power transfer capability. The performance of relaying scheme has been checked with a typical 765kV Indian transmission System which is simulated using the Electromagnetic Transients Program(EMTP) developed by authors in which the distributed parameter line model is used. More than 15,000 different short circuit fault cases are simulated by varying fault location, fault impedance, fault incidence angle and fault type to train the SVM for high speed accurate relaying. Simulation studies have shown that the proposed relay provides fast and accurate protection irrespective of fault location, fault impedance, incidence time of fault and fault type. And also the proposed scheme can be used as augmentation for the existing relaying, particularly for Zone-2, Zone-3 protection.
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This paper primarily intends to develop a GIS (geographical information system)-based data mining approach for optimally selecting the locations and determining installed capacities for setting up distributed biomass power generation systems in the context of decentralized energy planning for rural regions. The optimal locations within a cluster of villages are obtained by matching the installed capacity needed with the demand for power, minimizing the cost of transportation of biomass from dispersed sources to power generation system, and cost of distribution of electricity from the power generation system to demand centers or villages. The methodology was validated by using it for developing an optimal plan for implementing distributed biomass-based power systems for meeting the rural electricity needs of Tumkur district in India consisting of 2700 villages. The approach uses a k-medoid clustering algorithm to divide the total region into clusters of villages and locate biomass power generation systems at the medoids. The optimal value of k is determined iteratively by running the algorithm for the entire search space for different values of k along with demand-supply matching constraints. The optimal value of the k is chosen such that it minimizes the total cost of system installation, costs of transportation of biomass, and transmission and distribution. A smaller region, consisting of 293 villages was selected to study the sensitivity of the results to varying demand and supply parameters. The results of clustering are represented on a GIS map for the region.
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Background: Antiretroviral Therapy (ART) is currently the major therapeutic intervention in the treatment of AIDS. ART, however, is severely limited due to poor availability, high cytotoxicity, and enhanced metabolism and clearance of the drug molecules by the renal system. The use of nanocarriers encapsulating the antiretroviral drugs may provide a solution to the aforementioned problems. Importantly, the application of mildly immunogenic polymeric carrier confers the advantage of making the nanoparticles more visible to the immune system leading to their efficient uptake by the phagocytes. Methods: The saquinavir-loaded chitosan nanopartides were characterized by transmission electron microscopy and differential scanning calorimetry and analyzed for the encapsulation efficiency, swelling characteristics, particle size properties, and the zeta potential. Furthermore, cellular uptake of the chitosan nanocarriers was evaluated using confocal microscopy and Flow cytometry. The antiviral efficacy was quantified using viral infection of the target cells. Results: Using novel chitosan carriers loaded with saquinavir, a protease inhibitor, we demonstrate a drug encapsulation efficiency of 75% and cell targeting efficiency greater than 92%. As compared to the soluble drug control, the saquinavir-loaded chitosan carriers caused superior control of the viral proliferation as measured by using two different viral strains, NL4-3 and Indie-C1, and two different target T-cells, Jurkat and CEM-CCR5. Conclusion: Chitosan nanoparticles loaded with saquinavir were characterized and they demonstrated superior drug loading potential with greater cell targeting efficiency leading to efficient control of the viral proliferation in target T-cells. General significance: Our data ascertain the potential of chitosan nanocarriers as novel vehicles for HIV-1 therapeutics. (C) 2013 Elsevier B.V. All rights reserved.
Resumo:
It is well known that the impulse response of a wide-band wireless channel is approximately sparse, in the sense that it has a small number of significant components relative to the channel delay spread. In this paper, we consider the estimation of the unknown channel coefficients and its support in OFDM systems using a sparse Bayesian learning (SBL) framework for exact inference. In a quasi-static, block-fading scenario, we employ the SBL algorithm for channel estimation and propose a joint SBL (J-SBL) and a low-complexity recursive J-SBL algorithm for joint channel estimation and data detection. In a time-varying scenario, we use a first-order autoregressive model for the wireless channel and propose a novel, recursive, low-complexity Kalman filtering-based SBL (KSBL) algorithm for channel estimation. We generalize the KSBL algorithm to obtain the recursive joint KSBL algorithm that performs joint channel estimation and data detection. Our algorithms can efficiently recover a group of approximately sparse vectors even when the measurement matrix is partially unknown due to the presence of unknown data symbols. Moreover, the algorithms can fully exploit the correlation structure in the multiple measurements. Monte Carlo simulations illustrate the efficacy of the proposed techniques in terms of the mean-square error and bit error rate performance.