Effects of Interface Roughness Scattering on Radio Frequency Performance of Silicon Nanowire Transistors

The effects of an atomistic interface roughness in n-type silicon nanowire transistors (SiNWT) on the radio frequency performance are analyzed. Interface roughness scattering (IRS) is statistically investigated through a three dimensional full-band quantum transport simulation based on the sp3d5s?* tight-binding model. As the diameter of the SiNWT is scaled down below 3 nm, IRS causes a significant reduction of the cut-off frequency. The fluctuations of the conduction band edge due to the rough surface lead to a reflection of electrons through mode-mismatch. This effect reduces the velocity of electrons and hence the transconductance considerably causing a cut-off frequency reduction

A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding

This paper introduces a new approach to orthonormal wavelet image denoising. Instead of postulating a statistical model for the wavelet coefficients, we directly parametrize the denoising process as a sum of elementary nonlinear processes with unknown weights. We then minimize an estimate of the mean square error between the clean image and the denoised one. The key point is that we have at our disposal a very accurate, statistically unbiased, MSE estimate—Stein's unbiased risk estimate—that depends on the noisy image alone, not on the clean one. Like the MSE, this estimate is quadratic in the unknown weights, and its minimization amounts to solving a linear system of equations. The existence of this a priori estimate makes it unnecessary to devise a specific statistical model for the wavelet coefficients. Instead, and contrary to the custom in the literature, these coefficients are not considered random anymore. We describe an interscale orthonormal wavelet thresholding algorithm based on this new approach and show its near-optimal performance—both regarding quality and CPU requirement—by comparing with the results of three state-of-the-art nonredundant denoising algorithms on a large set of test images. An interesting fallout of this study is the development of a new, group-delay-based, parent-child prediction in a wavelet dyadic tree

Image Denoising in Mixed Poisson-Gaussian Noise

We propose a general methodology (PURE-LET) to design and optimize a wide class of transform-domain thresholding algorithms for denoising images corrupted by mixed Poisson-Gaussian noise. We express the denoising process as a linear expansion of thresholds (LET) that we optimize by relying on a purely data-adaptive unbiased estimate of the mean-squared error (MSE), derived in a non-Bayesian framework (PURE: Poisson-Gaussian unbiased risk estimate). We provide a practical approximation of this theoretical MSE estimate for the tractable optimization of arbitrary transform-domain thresholding. We then propose a pointwise estimator for undecimated filterbank transforms, which consists of subband-adaptive thresholding functions with signal-dependent thresholds that are globally optimized in the image domain. We finally demonstrate the potential of the proposed approach through extensive comparisons with state-of-the-art techniques that are specifically tailored to the estimation of Poisson intensities. We also present denoising results obtained on real images of low-count fluorescence microscopy

SURE-LET for Orthonormal Wavelet-Domain Video Denoising

We propose an efficient orthonormal wavelet-domain video denoising algorithm based on an appropriate integration of motion compensation into an adapted version of our recently devised Stein's unbiased risk estimator-linear expansion of thresholds (SURE-LET) approach. To take full advantage of the strong spatio-temporal correlations of neighboring frames, a global motion compensation followed by a selective block-matching is first applied to adjacent frames, which increases their temporal correlations without distorting the interframe noise statistics. Then, a multiframe interscale wavelet thresholding is performed to denoise the current central frame. The simulations we made on standard grayscale video sequences for various noise levels demonstrate the efficiency of the proposed solution in reducing additive white Gaussian noise. Obtained at a lighter computational load, our results are even competitive with most state-of-the-art redundant wavelet-based techniques. By using a cycle-spinning strategy, our algorithm is in fact able to outperform these methods

Investigation of ripple-limited low-field mobility in large-scale graphene nanoribbons

Combining molecular dynamics and quantum transport simulations, we study the degradation of mobility in graphene nanoribbons caused by substrate-induced ripples. First, the atom coordinates of large-scale structures are relaxed such that surface properties are consistent with those of graphene on a substrate. Then, the electron current and low-field mobility of the resulting non-flat nanoribbons are calculated within the Non-equilibrium Green\u27s Function formalism in the coherent transport limit. An accurate tight-binding basis coupling the sigma- and pi-bands of graphene is used for this purpose. It is found that the presence of ripples decreases the mobility of graphene nanoribbons on SiO2 below 3000 cm(2)/Vs, which is comparable to experimentally reported values. (C) 2013 AIP Publishing LLC

Fast Interscale Wavelet Denoising of Poisson-Corrupted Images

We present a fast algorithm for image restoration in the presence of Poisson noise. Our approach is based on (1) the minimization of an unbiased estimate of the MSE for Poisson noise, (2) a linear parametrization of the denoising process and (3) the preservation of Poisson statistics across scales within the Haar DWT. The minimization of the MSE estimate is performed independently in each wavelet subband, but this is equivalent to a global image-domain MSE minimization, thanks to the orthogonality of Haar wavelets. This is an important difference with standard Poisson noise-removal methods, in particular those that rely on a non-linear preprocessing of the data to stabilize the variance. Our non-redundant interscale wavelet thresholding outperforms standard variance-stabilizing schemes, even when the latter are applied in a translation-invariant setting (cycle-spinning). It also achieves a quality similar to a state-of-the-art multiscale method that was specially developed for Poisson data. Considering that the computational complexity of our method is orders of magnitude lower, it is a very competitive alternative. The proposed approach is particularly promising in the context of low signal intensities and/or large data sets. This is illustrated experimentally with the denoising of low-count fluorescence micrographs of a biological sample

Improving Blind Spot Denoising for Microscopy

Many microscopy applications are limited by the total amount of usable light and are consequently challenged by the resulting levels of noise in the acquired images. This problem is often addressed via (supervised) deep learning based denoising. Recently, by making assumptions about the noise statistics, self-supervised methods have emerged. Such methods are trained directly on the images that are to be denoised and do not require additional paired training data. While achieving remarkable results, self-supervised methods can produce high-frequency artifacts and achieve inferior results compared to supervised approaches. Here we present a novel way to improve the quality of self-supervised denoising. Considering that light microscopy images are usually diffraction-limited, we propose to include this knowledge in the denoising process. We assume the clean image to be the result of a convolution with a point spread function (PSF) and explicitly include this operation at the end of our neural network. As a consequence, we are able to eliminate high-frequency artifacts and achieve self-supervised results that are very close to the ones achieved with traditional supervised methods.Comment: 15 pages, 4 figure

Cytoplasmic cleavage of IMPA1 3' UTR is necessary for maintaining axon integrity

The 3′ untranslated regions (3′ UTRs) of messenger RNAs (mRNAs) are non-coding sequences involved in many aspects of mRNA metabolism, including intracellular localization and translation. Incorrect processing and delivery of mRNA cause severe developmental defects and have been implicated in many neurological disorders. Here, we use deep sequencing to show that in sympathetic neuron axons, the 3′ UTRs of many transcripts undergo cleavage, generating isoforms that express the coding sequence with a short 3′ UTR and stable 3′ UTR-derived fragments of unknown function. Cleavage of the long 3′ UTR of Inositol Monophosphatase 1 (IMPA1) mediated by a protein complex containing the endonuclease argonaute 2 (Ago2) generates a translatable isoform that is necessary for maintaining the integrity of sympathetic neuron axons. Thus, our study provides a mechanism of mRNA metabolism that simultaneously regulates local protein synthesis and generates an additional class of 3′ UTR-derived RNAs

Theory and simulation of quantum photovoltaic devices based on the non-equilibrium Green's function formalism

This article reviews the application of the non-equilibrium Green's function formalism to the simulation of novel photovoltaic devices utilizing quantum confinement effects in low dimensional absorber structures. It covers well-known aspects of the fundamental NEGF theory for a system of interacting electrons, photons and phonons with relevance for the simulation of optoelectronic devices and introduces at the same time new approaches to the theoretical description of the elementary processes of photovoltaic device operation, such as photogeneration via coherent excitonic absorption, phonon-mediated indirect optical transitions or non-radiative recombination via defect states. While the description of the theoretical framework is kept as general as possible, two specific prototypical quantum photovoltaic devices, a single quantum well photodiode and a silicon-oxide based superlattice absorber, are used to illustrated the kind of unique insight that numerical simulations based on the theory are able to provide.Comment: 20 pages, 10 figures; invited review pape

Widespread FUS mislocalization is a molecular hallmark of amyotrophic lateral sclerosis

Mutations causing amyotrophic lateral sclerosis (ALS) clearly implicate ubiquitously expressed and predominantly nuclear RNA binding proteins, which form pathological cytoplasmic inclusions in this context. However, the possibility that wild-type RNA binding proteins mislocalize without necessarily becoming constituents of cytoplasmic inclusions themselves remains relatively unexplored. We hypothesized that nuclear-to-cytoplasmic mislocalization of the RNA binding protein fused in sarcoma (FUS), in an unaggregated state, may occur more widely in ALS than previously recognized. To address this hypothesis, we analysed motor neurons from a human ALS induced-pluripotent stem cell model caused by the VCP mutation. Additionally, we examined mouse transgenic models and post-mortem tissue from human sporadic ALS cases. We report nuclear-to-cytoplasmic mislocalization of FUS in both VCP-mutation related ALS and, crucially, in sporadic ALS spinal cord tissue from multiple cases. Furthermore, we provide evidence that FUS protein binds to an aberrantly retained intron within the SFPQ transcript, which is exported from the nucleus into the cytoplasm. Collectively, these data support a model for ALS pathogenesis whereby aberrant intron retention in SFPQ transcripts contributes to FUS mislocalization through their direct interaction and nuclear export. In summary, we report widespread mislocalization of the FUS protein in ALS and propose a putative underlying mechanism for this process