AlAsSb avalanche photodiodes with a sub-mV/K temperature coefficient of breakdown voltage

The temperature dependence of dark current and avalanche gain were measured on AlAsSb p-i-n diodes with avalanche region widths of 80 and 230 nm. Measurements at temperatures ranging from 77 to 295 K showed that the dark current decreases rapidly with reducing temperature while avalanche gain exhibits a weak temperature dependence. No measurable band to band tunneling current was observed in the thinner diodes at an electric field of 1.07 MV/cm, corresponding to a bias of 95% of the breakdown voltage. Temperature coefficients of breakdown voltage of 0.95 and 1.47 mV/K were obtained from 80 and 230 nm diodes, respectively. These are significantly lower than a range of semiconductor materials with similar avalanche region widths. Our results demonstrated the potential of using thin AlAsSb avalanche regions to achieve low temperature coefficient of breakdown voltage without suffering from high band to band tunneling current

Ultra Thin Deployable Reflector Antennas

19 - 22 Apr 200

Iterative Multiuser Minimum Symbol Error Rate Beamforming Aided QAM Receiver

A novel iterative soft interference cancellation (SIC) aided beamforming receiver is developed for high-throughput quadrature amplitude modulation systems. The proposed SIC based minimum symbol error rate (MSER) multiuser detection scheme guarantees the direct and explicit minimization of the symbol error rate at the output of the detector. Adopting the extrinsic information transfer (EXIT) chart technique, we compare the EXIT characteristics of an iterative MSER multiuser detector (MUD) with those of the conventional minimum mean-squared error (MMSE) detector. As expected, the proposed SIC-MSER MUD outperforms the SIC-MMSE MUD. Index Terms—Beamforming, iterative multiuser detection, minimum symbol error rate, quadrature amplitude modulation

Permutation and sampling with maximum length CA for pseudorandom number generation

In this paper, we study the effect of dynamic permutation and sampling on the randomness quality of sequences generated by cellular automata (CA). Dynamic permutation and sampling have not been explored in previous CA work and a suitable implementation is shown using a two CA model. Three different schemes that incorporate these two operations are suggested - Weighted Permutation Vector Sampling with Controlled Multiplexing, Weighted Permutation Vector Sampling with Irregular Decimation and Permutation Programmed CA Sampling. The experiment results show that the resulting sequences have varying degrees of improvement in DIEHARD results and linear complexity compared to the CA

Periodic subvarieties of a projective variety under the action of a maximal rank abelian group of positive entropy

We determine positive-dimensional G-periodic proper subvarieties of an n-dimensional normal projective variety X under the action of an abelian group G of maximal rank n-1 and of positive entropy. The motivation of the paper is to understand the obstruction for X to be G-equivariant birational to the quotient variety of an abelian variety modulo the action of a finite group.Comment: Asian Journal of Mathematics (to appear), Special issue on the occasion of Prof N. Mok's 60th birthda

A stochastic variational framework for fitting and diagnosing generalized linear mixed models

In stochastic variational inference, the variational Bayes objective function is optimized using stochastic gradient approximation, where gradients computed on small random subsets of data are used to approximate the true gradient over the whole data set. This enables complex models to be fit to large data sets as data can be processed in mini-batches. In this article, we extend stochastic variational inference for conjugate-exponential models to nonconjugate models and present a stochastic nonconjugate variational message passing algorithm for fitting generalized linear mixed models that is scalable to large data sets. In addition, we show that diagnostics for prior-likelihood conflict, which are useful for Bayesian model criticism, can be obtained from nonconjugate variational message passing automatically, as an alternative to simulation-based Markov chain Monte Carlo methods. Finally, we demonstrate that for moderate-sized data sets, convergence can be accelerated by using the stochastic version of nonconjugate variational message passing in the initial stage of optimization before switching to the standard version.Comment: 42 pages, 13 figures, 9 table