FPGA设计中的编程技巧

在数字系统设计中，FPGA设计已成为数字系统设计领域中的重要方式之一，在电子、通信等领域得到了广泛应用。本文以Verilog HDL为例，讨论了在进行FPGA设计中编写代码的技巧。     

基于时域算法的频域陷波算法在扩频通信中的应用

介绍了一种新颖的频域陷波算法,以消除窄带干扰对通信的影响,并讨论了这种陷波算法在直接序列扩频通信中的应用。该算法将时域自适应滤波器中复杂的卷积运算转换为频域的相乘运算,从而减少了运算量,节省了资源,使得自适应陷波器更易于工程实现。仿真结果证实,该算法能有效地消除直接序列扩频方式中的窄带干扰。     

同步数字体系中155Mbit/s净荷的误码测试

本文基于光纤传输同步数字体系(S D H)系统的开发背景,较为详细地总结了155Mbit/s速率下的净荷误码测试机理.最后给出了净荷的误码测试方法以及误码测试系统的信号流程.     

A spectral Integral method (SIM) for layered media

A spectral integral method is presented for electromagnetic scattering from dielectric and perfectly electric conducting (PEC) objects with a closed boundary embedded in a layered medium. Two-dimensional layered medium Green's functions are computed adaptively by using Gaussian quadratures. The singular terms in the Green's functions and the non-smooth terms in their derivatives are handled appropriately to achieve exponential convergence. Numerical results, compared with the ones obtained by using other methods, demonstrate the spectral accuracy and high efficiency of the proposed method. They also confirm that the spectral integral method (SIM) is applicable to concave objects.     

A 2.5-Gb/s fully-integrated,low-power clock and recovery circuit in 0.18-μm CMOS

Based on the devised system-level design methodology,a 2.5-Gb/s monolithic bang-bang phase-locked clock and data recovery(CDR) circuit has been designed and fabricated in SMIC's 0.18-μm CMOS technology.The Pottb(a|¨)cker phase frequency detector and a differential 4-stage inductorless ring VCO are adopted,where an additional current source is added to the VCO cell to improve the linearity of the VCO characteristic.The CDR has an active area of 340×440μm~2,and consumes a power of only about 60 mW from a 1...     

WDM系统基于OTN接口的互联互通

本文介绍了新一代波分复用(WDM)系统基于光传送网络(OTN)接口的互联互通技术,包括基于TCMi级联实现的跨域端到端性能监测,并对多厂商WDM系统基于OTN接口的互联互通测试及其未来发展趋势进行简单介绍.     

感应遥控器控制智能电视播放音乐的方法及实现

随着当代社会中智能电视的飞速发展,人们对电视功能需求越来越高,对电视遥控器的控制要求也越高,仅靠通过增加按键的方式并不能适应所有的用户需求.新型的感应遥控器基于3D触摸板设计,利用感应遥控器来控制智能电视轻松实现音乐的播放,真正的实现了人机交互的理念,提高用户体验的同时也满足了当前市场的需求.     

Repair the faulty TSVs with the improved FNS-CAC codec

Through-silicon via (TSV) is a key enabling technology for the emerging 3-dimension (3D) integrated circuits (ICs). However, the crosstalk between the neighboring TSVs is one of the important sources of the soft faults. To suppress the crosstalk, the Fibonacci-numeral-system-based crosstalk avoidance code ( FNS-CAC) is an effective scheme. Meanwhile, the self-repair schemes are often used to deal with the hard faults, but the repaired results may change the mapping between signals to TSVs, thus may reduce the crosstalk suppression ability of FNS-CAC. A TSV self-repair technique with an improved FNS-CAC codec is proposed in this work. The codec is designed based on the improved Fibonacci numeral system (FNS) adders, which are adaptive to the health states of TSVs. The proposed self-repair technique is able to suppress the crosstalk and repair the faulty TSVs simultaneously. The simulation and analysis results show that the proposed scheme keeps the crosstalk suppression ability of the original FNS-CAC, and it has higher reparability than the local self-repair schemes, such as the signal-switching-based and the signal-shifting-based counterparts.     

基于高职学院实训教学过程的云计算实验环境分析与搭建

本文首先针对高职学院实验教学过程局限性分析,提出采用云计算实验环境获得实验结果和可信实验过程数据收集的改进措施。接着分析高职学院实验环境探索方向和云计算实验环境的整体思想和基本原理。然后对该实验环境进行详细功能分析,最后阐释部署该实验环境的软硬件设备和部署过程。对高职学院实验环境改善具有较强的借鉴作用。     

A survey on deploying mobile deep learning applications: A systemic and technical perspective

With the rapid development of mobile devices and deep learning, mobile smart applications using deep learning technology have sprung up. It satisfies multiple needs of users, network operators and service providers, and rapidly becomes a main research focus. In recent years, deep learning has achieved tremendous success in image processing, natural language processing, language analysis and other research fields. Despite the task performance has been greatly improved, the resources required to run these models have increased significantly. This poses a major challenge for deploying such applications on resource-restricted mobile devices. Mobile intelligence needs faster mobile processors, more storage space, smaller but more accurate models, and even the assistance of other network nodes. To help the readers establish a global concept of the entire research direction concisely, we classify the latest works in this field into two categories, which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks. We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications. Finally, we conjecture what the future may hold for deploying deep learning applications on mobile devices research, which may help to stimulate new ideas.