Continuous time delta sigma modulators with reduced clock jitter sensitivity

In this paper, a technique and method is presented to suppress the effect of clock-jitter in continuous-time delta-sigma modulators with switched-current (current-steering) digital to analogue converters. A behavioural, transistor-level and noise analysis are presented followed by circuit-level simulations. The proposed approach which is a switched-current type of digital to analogue conversion is fully compatible with CMOS processes and multi-bit operations which are widely used in high speed applications. Moreover, having a pulse-shaped output signal does not introduce extra demands on the modulator and hence does not increase the modulator's power consumption. A third-order continuous-time /spl Delta//spl Sigma/ modulator with the proposed digital-to-analogue converter in its feedback was used for circuit-level simulations. Results proved the robustness of the technique in suppressing the clock-jitter effects

Modification of Born impurity scattering near the surface of d-wave superconductors and influence of external magnetic field

We study the influence of Born impurity scattering on the zero-energy Andreev bound states near the surface of a d-wave superconductor with and without an externally applied magnetic field. Without an external magnetic field we show that the effect of Born impurity scattering is stronger at the surface than in the bulk. In the presence of an external magnetic field the splitting of the zero-energy Andreev bound states is shown to have a nonmonotonous temperature dependence. Born impurity scattering does not wash out the peak splitting, but instead the peak splitting is shown to be quite robust against impurities. We also show that a nonzero gap renormalization appears near the surface.Comment: 9 pages, 17 figures; minor changes; new figure 11; accepted for publication in Phys. Rev.

A new structure for capacitor-mismatch-insensitive multiply-by-two amplification

A new approach to achieve a switched-capacitor multiply-by-two gain-stage with reduced sensitivity to capacitors' mismatches is presented in this paper. It is based on sampling fully differential input signals onto both plates of the input capacitors rather than sampling onto one plate of the capacitors with the other tied to a reference. It uses one operational amplifier (op-amp) in two phases to produce the gain of two (/spl times/2). Comparing to the conventional multiply-by-two gain-stage, the mismatches between the capacitors has a much smaller influence on the accuracy of the gain of two (/spl times/2). Analytical and circuit-level analysis of the architecture and the conventional structure are presented using a generic 0.35/spl mu/m CMOS technology

Modeling of switched-capacitor delta-sigma Modulators in SIMULINK

Precise behavioral modeling of switched-capacitor /spl Delta//spl Sigma/ modulators is presented. Considering noise (switches' and op-amps' thermal noise), clock jitter, nonidealities of integrators and op-amps including finite dc-gain (DCG) and unity gain bandwidth, slew-limiting, DCG nonlinearities and the input parasitic capacitance, quantizer hysteresis, switches' clock-feedthrough, and charge injection, exhaustive behavioral simulations that are close models of the transistor-level ones can be performed. The DCG nonlinearity of the integrators, which is not considered in many /spl Delta//spl Sigma/ modulators' modeling attempts, is analyzed, estimated, and modeled. It is shown that neglecting this parameter would lead to a significant underestimation of the modulators' behavior and increase the noise floor as well as the harmonic distortion at the output of the modulator. Evaluation and validation of the models were done via behavioral and transistor-level simulations for a second-order modulator using SIMULINK and HSPICE with a generic 0.35-/spl mu/m CMOS technology. The effects of the nonidealities and nonlinearities are clearly seen when compared to the ideal modulator in the behavioral and actual modulator in the circuit-level environment

An analytic model of rotationally inelastic collisions of polar molecules in electric fields

We present an analytic model of thermal state-to-state rotationally inelastic collisions of polar molecules in electric fields. The model is based on the Fraunhofer scattering of matter waves and requires Legendre moments characterizing the "shape" of the target in the body-fixed frame as its input. The electric field orients the target in the space-fixed frame and thereby effects a striking alteration of the dynamical observables: both the phase and amplitude of the oscillations in the partial differential cross sections undergo characteristic field-dependent changes that transgress into the partial integral cross sections. As the cross sections can be evaluated for a field applied parallel or perpendicular to the relative velocity, the model also offers predictions about steric asymmetry. We exemplify the field-dependent quantum collision dynamics with the behavior of the Ne-OCS($^{1}\Sigma$) and Ar-NO($^2\Pi$) systems. A comparison with the close-coupling calculations available for the latter system [Chem. Phys. Lett. \textbf{313}, 491 (1999)] demonstrates the model's ability to qualitatively explain the field dependence of all the scattering features observed

Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition

© 2017 Elsevier Inc. Background Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text “feature engineering” and conventional machine learning algorithms such as conditional random fields and support vector machines. However, developing good features is inherently heavily time-consuming. Conversely, more modern machine learning approaches such as recurrent neural networks (RNNs) have proved capable of automatically learning effective features from either random assignments or automated word “embeddings”. Objectives (i) To create a highly accurate DNR and CCE system that avoids conventional, time-consuming feature engineering. (ii) To create richer, more specialized word embeddings by using health domain datasets such as MIMIC-III. (iii) To evaluate our systems over three contemporary datasets. Methods Two deep learning methods, namely the Bidirectional LSTM and the Bidirectional LSTM-CRF, are evaluated. A CRF model is set as the baseline to compare the deep learning systems to a traditional machine learning approach. The same features are used for all the models. Results We have obtained the best results with the Bidirectional LSTM-CRF model, which has outperformed all previously proposed systems. The specialized embeddings have helped to cover unusual words in DrugBank and MedLine, but not in the i2b2/VA dataset. Conclusions We present a state-of-the-art system for DNR and CCE. Automated word embeddings has allowed us to avoid costly feature engineering and achieve higher accuracy. Nevertheless, the embeddings need to be retrained over datasets that are adequate for the domain, in order to adequately cover the domain-specific vocabulary

Strong-field approximation for Coulomb explosion of H_2^+ by short intense laser pulses

We present a simple quantum mechanical model to describe Coulomb explosion of H$_2^+$ by short, intense, infrared laser pulses. The model is based on the length gauge version of the molecular strong-field approximation and is valid for pulses shorter than 50 fs where the process of dissociation prior to ionization is negligible. The results are compared with recent experimental results for the proton energy spectrum [I. Ben-Itzhak et al., Phys. Rev. Lett. 95, 073002 (2005), B. D. Esry et al., Phys. Rev. Lett. 97, 013003 (2006)]. The predictions of the model reproduce the profile of the spectrum although the peak energy is slightly lower than the observations. For comparison, we also present results obtained by two different tunneling models for this process.Comment: 8 pages, 4 figure

ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems

Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) and its word embedding (continuous value). Such a joint training allows the proposed system to learn the distributional properties represented by the word embeddings, empirically improving the generalization to unseen sentences. Experiments over three translation datasets have showed a consistent improvement over a strong baseline, ranging between 0.91 and 2.54 BLEU points, and also a marked improvement over a state-of-the-art system.Comment: Accepted at NAACL-HLT 201