基于逻辑器件的可编程增益放大器设计

本文介绍了可编程增益放大器设计的方法。它主要由控制电路、放大电路、显示电路三大模块组成。该系统性能好、成本低、工作可靠,具有一定的工程应用价值,经测试表明,该设计基本上达到了设计的要求。     

Crack tip parameters and elastic-plastic fracture of metals

By using optical displacement-measuring techniques we obtain the crack tip fields in terms of crack tip strain, crack opening angle (COA) and crack tip opening angle (CTOA). Thin plates with different crack configurations made of six elastic-plastic materials with strain hardening exponent ranging from 3 to are studied. We find that the COA resistance curve assumes certain characteristics common to all the materials. The experimental results indicate that the critical value of COA may be used as a fracture toughness parameter for these materials.

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Résumé En utilisant une technique de mesure optique des déplacements, on obtient les champs régnant à l'estrémité d'une entaille, qui s'experiment par la déformation d'extrémité d'entaille, l'angle d'ouverture de la crique (COA et l'angle d'ouverture de la pointe de la crique (CTOA). On étudie des feuillards présentant différentes configurations de fissures et réalisés en six matériaux élasto-plastiques dont les modules d'écrouissage s'échelonnent de 3 à l'infini.On trouve que la courbe de résistance COA suppose certaines caractéristiques communes à tous les matériaux. Les résultats expérimentaux indiquent que la valeur critique du COA peut être utilisée comme paramètre de ténacité à la rupture pour les matériaux étudiés.

     

Effect of silver addition on superconductivity in the Bi1.6Pb0.4Sr1.6Ca2Cu3O10−y system

Silver/superconductor composites containing 0 to 80 vol% silver have been prepared and their properties determined. Optimum heat treatment at sintering temperatures ( 800° C) under low oxygen pressures produces material with high critical current density and improves physical properties. Magnetic susceptibility measurements have been found to be consistent with resistivity results. In order to retain a single high-T _c phase with increasing silver content, decreased oxygen partial pressures are required. Using the normal-state resistivity of these composites, a percolation threshold at a silver volume fraction of 43% was observed, while zero resistivity measurements show that a continuous superconducting network can be obtained with up to 80 vol% silver. The critical current density of 21 vol% silver-doped samples was found to be 1520 A cm^–2 at 77.3 K, compared to 260 A cm^–2 for an undoped sample.     

HAR-sEMG: A Dataset for Human Activity Recognition on Lower-Limb sEMG

Knowledge and Information Systems - In the past decade, human activity recognition (HAR) has grown in popularity due to its applications in security and entertainment. As recent years have...     

GRU-based capsule network with an improved loss for personnel performance prediction

Applied Intelligence - Personnel performance is a key factor to maintain core competitive advantages. Thus, predicting personnel future performance is a significant research domain in human...     

Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson’s disease

Neural Computing and Applications - Speech diagnosis of Parkinson’s disease (PD) as a non-invasive and simple diagnosis method is particularly worth exploring. However, the number of samples...     

基于CPLD的系统硬件看门狗设计

基于以DSP芯片TMS320F2812为核心的数字伺服控制器,以国微电子公司的SM1032国产CPLD(兼容Lattice公司的ispLSI 1032)为载体,设计了专用的系统硬件看门狗模块,具备识别DSP软件初始化时序、自由定制看门狗时序等功能。通过对伺服控制器上电及工作运行时序的分析、仿真和实验验证,确定了硬件看门狗功能模块的设计方案,并给出了实验结果。     

Probability-confidence-kernel-based localized multiple kernel learning with lp norm

Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features in terms of their discriminative power for each individual sample. However, models excessively fitting to a specific sample would obstacle the extension to unseen data, while a more general form is often insufficient for diverse locality characterization. Hence, both learning sample-specific local models for each training datum and extending the learned models to unseen test data should be equally addressed in designing LMKL algorithm. In this paper, for an integrative solution, we propose a probability confidence kernel (PCK), which measures per-sample similarity with respect to probabilistic-prediction-based class attribute: The class attribute similarity complements the spatial-similarity-based base kernels for more reasonable locality characterization, and the predefined form of involved class probability density function facilitates the extension to the whole input space and ensures its statistical meaning. Incorporating PCK into support-vectormachine-based LMKL framework, we propose a new PCK-LMKL with arbitrary l(p)-norm constraint implied in the definition of PCKs, where both the parameters in PCK and the final classifier can be efficiently optimized in a joint manner. Evaluations of PCK-LMKL on both benchmark machine learning data sets (ten University of California Irvine (UCI) data sets) and challenging computer vision data sets (15-scene data set and Caltech-101 data set) have shown to achieve state-of-the-art performances.     

Interactive image segmentation based on level sets of probabilities

In this paper, we present a robust and accurate algorithm for interactive image segmentation. The level set method is clearly advantageous for image objects with a complex topology and fragmented appearance. Our method integrates discriminative classification models and distance transforms with the level set method to avoid local minima and better snap to true object boundaries. The level set function approximates a transformed version of pixelwise posterior probabilities of being part of a target object. The evolution of its zero level set is driven by three force terms, region force, edge field force, and curvature force. These forces are based on a probabilistic classifier and an unsigned distance transform of salient edges. We further propose a technique that improves the performance of both the probabilistic classifier and the level set method over multiple passes. It makes the final object segmentation less sensitive to user interactions. Experiments and comparisons demonstrate the effectiveness of our method.