Improved transmission performance of adaptively modulated optical OFDM signals over directly modulated DFB laser-based IMDD links using adaptive cyclic prefix.

The impact of Adaptive Cyclic Prefix (ACP) on the transmission performance of Adaptively Modulated Optical OFDM (AMOOFDM) is explored thoroughly in directly modulated DFB laser-based, IMDD links involving Multimode Fibres (MMFs)/Single-Mode Fibres (SMFs). Three ACP mechanisms are identified, each of which can, depending upon the link properties, affect significantly the AMOOFDM transmission performance. In comparison with AMOOFDM having a fixed cyclic prefix duration of 25%, AMOOFDM with ACP can not only improve the transmission capacity by a factor of >2 (>1.3) for >1000 m MMFs (<80 km SMFs) with 1 dB link loss margin enhancement, but also relax considerably the requirement on the DFB bandwidth.

7dB optical power budget improvements of 11.25Gb/s optical OFDM PON systems using optical filters

Wavelength offset super Gaussian optical filters enable 7dB increases in optical power budget of 11.25Gb/s optical OFDM PON systems using directly modulated DFBs, considerably relax filter bandwidth requirement and improve performance robustness to bandwidth variation. © 2011 Optical Society of America.

Estimating and Enhancing the Yield of Tuneling SRAM Cells by Simulation

As a novel implementation of the static random access memory (SRAM), the tunneling SRAM (TSRAM) uses the negative differential resistance of tunnel diodes (TD’s) and potentially offers considerable improvements in both standby power dissipation and integration density compared to the conventional CMOS SRAM. TSRAM has not yet been realized with a useful bit capacity mainly because the level of uniformity required of the nanoscale TD’s has been demanding and difficult to achieve. In this letter, we propose a Monte Carlo approach for estimating the yield of TSRAM cells and show that by optimizing the cell’s external circuit parameters, we can relax the allowable tolerance of a key device parameter of a resonant-TD-(RTD) based cell by three times.

Gaussian Process Vine Copulas for Multivariate Dependence

Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, it is common to assume that each of these conditional bivariate copulas is independent from its conditioning variables. In this paper, we relax this assumption by discovering the latent functions that specify the shape of a conditional copula given its conditioning variables We learn these functions by following a Bayesian approach based on sparse Gaussian processes with expectation propagation for scalable, approximate inference. Experiments on real-world datasets show that, when modeling all conditional dependencies, we obtain better estimates of the underlying copula of the data.