Partial Detection for Multiple Antenna Cooperation

Multi-antenna relays can significantly increase the speed and reliability of wireless systems. However, because of the complexity of MIMO detection, there is considerable overhead in implementing a MIMO relay if the conventional detect-and-forward strategy is used. To address this challenge, we propose a novel cooperative partial detection (CPO) strategy that partitions the detection task between the relay and the destination. CPO leverages the structure of the tree-based c1ose-toML MIMO detectors, and modifies the tree traversal so that instead of visiting all the levels of the tree, only a subset of the levels, thus a subset of the transmitted streams, are visited. This novel approach reduces the tree levels, i.e. dimensions, in both the relay and the destination. Moreover, CPO provides a flexible method to control the level of partitioning between the relay and the destination, and thus, adjust the detection computational complexity in the relay and the destination. Monte-Carlo simulation results show that, under equal transmit power and complexity constraint in the destination, CPO achieves a better BER performance compared to the non-relay scenario, with limited computational overhead in the relay.NokiaNational Science Foundatio

Evaluation and Determination of the Appropriate Method for Assessing Optimum Utilisation Rate of \u3cem\u3eEurotia Ceratoides\u3c/em\u3e in Upland Grasslands of Iran

Determination of optimum utilisation rate for different range species is an important factor in assessing range grazing capacity. The vast rangelands in Iran with diverse vegetation types require an accurate, economic and quick method to determine the optimum utilisation rate for different range species. This experiment was conducted to determine the most appropriate method to determine the optimum utilisation rate for Eurotia ceratoides, which is one of the important grass species in upland grasslands in Iran

Utility of B-type natriuretic peptide in predicting medium-term mortality in patients undergoing major non-cardiac surgery

We assessed the ability of pre-operative B-type natriuretic peptide (BNP) levels to predict medium-term mortality in patients undergoing major noncardiac surgery. During a median 654 days follow-up 33 patients from a total cohort of 204 patients (16%) died. The optimal cut-off in this cohort, determined using a receiver operating characteristic curve, was >35pg.mL-1. This was associated with a 3.47-fold increase in the hazard of death (p=0.001) and had a sensitivity of 70% and a specificity of 68% for this outcome. These findings extend recent work demonstrating that BNP levels obtained before major noncardiac surgery can be used to predict peri-operative morbidity, and indicate that they also forecast medium-term mortality.This work was supported by a grant from TENOVUS Scotland. The Health Services Research Unit is core-funded by the Chief Scientists Office of the Scottish Executive Health Department.Peer reviewedAuthor versio

The synthesis, characterization and biological evaluation of a stable phosphorus ylide and an imidazole as novel compounds

A convenient one pot synthesis of two novel compounds including a stable phosphorus ylide and an imidazole from electron-poor acetylenes in fairly good yields by the condensation of triphenylphosphine and acetylene derivatives, in the presence of dimethyl thiourea from the 1:1:1 addition reactions is described. The structures of the synthesized compounds were characterized by IR, 1H-NMR, 13C-NMR and elemental analysis. The newly synthesized compounds were screened for antimicrobial on Staphylococcus aureus, Bacillus sublitis, Escherichia coli and Pseudomonas aeruginosa and antifungal activities on Candida albicans, patient isolate Candida glabrata and Candida krusei. The results showed that these compounds have activity against all the tested bacteria and fungi with a sufficient minimum inhibitory concentration. KEY WORDS: Phosphorus ylides, Electron-poor acetylenes, Imidazole, Antifungal, Antimicrobial Bull. Chem. Soc. Ethiop. 2014, 28(1), 137-141.DOI: http://dx.doi.org/10.4314/bcse.v28i1.1

Comparison between artificial neural networks and mathematical models for estimating equilibrium moisture content in raisin

Empirical models and Artificial Neural Networks (ANNs) were utilized for the prediction of Equilibrium Moisture Content (EMC) in raisin.  Six empirical models including GAB, Smith, Henderson, Oswin, Halsey and D’Arsy-watt were applied for this estimation.  Two types of Multi Layer Perceptron (MLP) neural networks entitled Feed Forward Back Propagation (FFBP) and Cascade Forward Back Propagation (CFBP) were used.  In order to train the input patterns, two training algorithms consist of Levenberg-Marquardt (LM) and Bayesian regularization (BR) were used.  Thermal and relative humidity limits were 30-80℃ and 10.51%-83.62%, respectively.  The best result for mathematical models belonged to D’Arsy-Watt with R2 and the mean relative error of 0.9943% and 10.84%, respectively.  The best outcome for the use of ANN also appertained to FFBP network with LM training algorithm, topology of 2-3-3-1 and threshold function order of TANSIG-TANSIG-PURELIN.  With this optimized network, R2 and the mean relative error was 0.9969% and 8.32%, respectively.  These results show the supremacy of ANN, in comparison with empirical models.  In order to predict the EMC in raisins, empirical models can therefore be replaced with the ANN.Keywords: ANN, back propagation, sorption isotherm, EMC, Iran Citation: Chayjan R. Amiri, and M. Esna-Ashari.  Comparison between artificial neural networks and mathematical models for estimating equilibrium moisture content in raisin.  Agric Eng Int: CIGR Journal, 2010, 12(1): 158-166