光电编码器在齿轮传动误差中的应用

提出了降低传动系统转速,用光电编码器代替圆光栅测量齿轮传动误差信号的思想,对于采集的行星轮系传动误差实验数据进行了有效性的判定,用Burg算法对传动误差信号进行了功率谱密度的AR模型的参数估计,绘制出实验测得的传动误差功率谱密度曲线,对光电编码器测量传动误差结果进行了分析,证明了在低转速情况下,用光电编码器代替圆光栅测量传动误差是行之有效的。     

超宽带无线传感器网络性能分析

超宽带（UWB）无线电技术是近年来发展迅猛的新型通信方式,它无需中频和射频电路,对于减小体积和降低能耗具有特别的意义,尤其适合无线传感器网络（WSNs）。利用UWB信号的物理特性,将信道按照时间方式划分成一个个时隙作为子信道供不同的节点使用,分析了WSNs中不同子信道个数下,节点的发送概率和分组冲突概率的关系,得出了多信道方式下有利于减小分组的冲突概率的结论。     

光通信中一种有效的差分脉冲位置宽度调制

考虑脉冲位置调制(PPM)在解调时需要严格的时钟同步和符号同步,而且功率越高,时隙越窄的问题,结合差分脉冲位置调制(DPPM)和脉冲宽度调制(PWM)提出了一种有效的调制方式--差分脉冲位置宽度调制(DPPM+PWM).研究了采用DPPM+PWM的红外通信系统在加性高斯白噪声干扰下的性能,对DPPM+PWM的符号结构、发射功率、带宽需求、传输容量以及误包率等特性进行了分析,并通过仿真与开关键控(OOK),PPM,PPM等调制方式进行了比较.结果表明DP-PM+PWM频带利用率高,传输容量大,不需要符号同步,且增加了脉冲信号的长度,大大简化系统实现,是一种很有实际应用价值的调制方式.     

浅议视觉传达设计中的“空白”

空白,也称为"虚空间",指被其他要素包围的空间。空白部分运用的好,有时则会成为设计中最突出、最令人瞩目的部分。本文就针对空白在视觉设计中的一些作用做相关的探讨。     

基于CC1100的无线抄表系统

介绍了一种利用单片机和CC1100无线通讯芯片构成的无线抄表系统,叙述了工作原理和实现过程,并重点介绍了CC1100的电磁波唤醒功能和原理,给出了详细的结论.     

用图像说话

当今社会已经进入了以图像为中心的时代,电影、电视、绘画、摄影、广告、设计、建筑、动漫、网络、游戏、多媒体等互为激荡汇流,这就是人们所说的视觉文化时代。图像,是我们向大众传播的一种有效的视觉语言,在某种程度上,图像视觉的感染力、传达效应远远大于文字。在设计中,我们应该尽量使用图像去传达我们的创意,而不是依靠设计说明去阐述。因为,图像本身会说话。     

Proteomic analysis of sera of asymptomatic,early‐stage patients with Wilson's disease

Wilson's disease (WD) is characterized by excessive accumulation of intracellular copper in liver and extrahepatic tissues, leading to significant oxidative stress and tissue damage. To date, several diagnostic biomarkers for WD such as serum ceruloplasmin, serum or urine copper levels and copper content in liver have been identified. However, these biomarkers may not be convincing for the diagnosis in some WD patients. To identify additional novel diagnostic biomarkers, we compared the serum protein profiles of asymptomatic childhood WD patients (n=20), without neurologic manifestation or liver cirrhosis, with normal controls (n=13). Fourteen spots, five up‐regulated and nine down‐regulated (>2‐fold), were differentially expressed in WD patients in comparison to normal control on 2‐DE. Among them, three spots were down‐regulated in both male and female WD. MS/MS analysis revealed that the three spots were complement component C3, complement factor B and alpha‐2 macroglobulin. By comparative proteome analysis, complement component C3, complement factor B and alpha‐2 macroglobulin, which are related to oxidative stress and inflammation, turned out to be good candidates for novel diagnostic biomarkers for early stages of WD.     

基于EZW的水下作战环境应用产品压缩研究

如何有效利用海军数据链系统传输战术水声环境产品是海军作战环境保障的关键问题之一.然而,战术水声环境数据具有较强的时空变化性,其数据量相对来说较为庞大,且在实际作战环境中,传输信道存在着各种干扰,特别是在海洋战场环境中,通信传输条件较差,传输时间紧迫,致使图像信息的传输容易受到外部因素的非正常干扰甚至中断,从而影响用户端对图像信息的接收.因此.寻求一种不依赖于通信时间和其它外部情况限制町随时结束编码实现数据压缩与渐进传输的编码方法是作战环境信息处理的一项必不可少的工作.运用目前较为先进的嵌入式小波零树编码算法对战术水声环境数据进行压缩,取得了较好的压缩效果,并在此基础上实现了图像的渐进传输,能够较好的适应复杂海洋战场的需求.     

Ensemble feature selection and classification methods for machine learning-based coronary artery disease diagnosis

Coronary artery disease (CAD) is a condition in which the heart is not fed sufficiently as a result of the accumulation of fatty matter. As reported by the World Health Organization, around 32% of the total deaths in the world are caused by CAD, and it is estimated that approximately 23.6 million people will die from this disease in 2030. CAD develops over time, and the diagnosis of this disease is difficult until a blockage or a heart attack occurs. In order to bypass the side effects and high costs of the current methods, researchers have proposed to diagnose CADs with computer-aided systems, which analyze some physical and biochemical values at a lower cost. In this study, for the CAD diagnosis, (i) seven different computational feature selection (FS) methods, one domain knowledge-based FS method, and different classification algorithms have been evaluated; (ii) an exhaustive ensemble FS method and a probabilistic ensemble FS method have been proposed. The proposed approach is tested on three publicly available CAD data sets using six different classification algorithms and four different variants of voting algorithms. The performance metrics have been comparatively evaluated with numerous combinations of classifiers and FS methods. The multi-layer perceptron classifier obtained satisfactory results on three data sets. Performance evaluations show that the proposed approach resulted in 91.78%, 85.55%, and 85.47% accuracy for the Z-Alizadeh Sani, Statlog, and Cleveland data sets, respectively.     

Towards Sustainable Agricultural Systems: A Lightweight Deep Learning Model for Plant Disease Detection

A country’s economy heavily depends on agricultural development. However, due to several plant diseases, crop growth rate and quality are highly suffered. Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information. Therefore, the agricultural management system is searching for an automatic early disease detection technique. To this end, an efficient and lightweight Deep Learning (DL)-based framework (E-GreenNet) is proposed to overcome these problems and precisely classify the various diseases. In the end-to-end architecture, a MobileNetV3Small model is utilized as a backbone that generates refined, discriminative, and prominent features. Moreover, the proposed model is trained over the PlantVillage (PV), Data Repository of Leaf Images (DRLI), and a new Plant Composite (PC) dataset individually, and later on test samples, its actual performance is evaluated. After extensive experimental analysis, the proposed model obtained 1.00%, 0.96% and 0.99% accuracies on all three included datasets. Moreover, the proposed method achieves better inference speed when compared with other State-Of-The-Art (SOTA) approaches. In addition, a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pre-trained models and other Machine Learning (ML) and DL methods.