Metrics and Grids

本文介绍了如何为不同尺寸和密度的屏幕设计应用程序界面。通过使用密度无关像素(dp)单位来确保触摸目标大小适合各种设备,并保持48dp节奏进行布局。

Metrics and Grids

Devices vary not only in physical size, but also in screen density (DPI). To simplify the way you design for multiple screens, think of each device as falling into a particular size bucket and density bucket:

  • The size buckets are handset (smaller than 600dp) and tablet (larger than or equal 600dp).
  • The density buckets are LDPIMDPIHDPIXHDPI, and XXHDPI.

Optimize your application's UI by designing alternative layouts for some of the different size buckets, and provide alternative bitmap images for different density buckets.

Because it's important that you design and implement your layouts for multiple densities, the guidelines below and throught the documentation refer to layout dimensions with dp measurements instead of pixels.

Space considerations

Devices vary in the amount of density-independent pixels (dp) they can display.

To see more, visit the Screen Sizes and Densities Device Dashboard.

48dp Rhythm


Touchable UI components are generally laid out along 48dp units.

 
Why 48dp?

On average, 48dp translate to a physical size of about 9mm (with some variability). This is comfortably in the range of recommended target sizes (7-10 mm) for touchscreen objects and users will be able to reliably and accurately target them with their fingers.

If you design your elements to be at least 48dp high and wide you can guarantee that:

  • your targets will never be smaller than the minimum recommended target size of 7mm regardless of what screen they are displayed on.
  • you strike a good compromise between overall information density on the one hand, and targetability of UI elements on the other.
 
Mind the gaps

Spacing between each UI element is 8dp.

Examples


基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究(Matlab代码实现)内容概要:本文围绕“基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究”展开,提出了一种结合数据驱动方法与Koopman算子理论的递归神经网络(RNN)模型线性化方法,旨在提升纳米定位系统的预测控制精度与动态响应能力。研究通过构建数据驱动的线性化模型,克服了传统非线性系统建模复杂、计算开销大的问题,并在Matlab平台上实现了完整的算法仿真与验证,展示了该方法在高精度定位控制中的有效性与实用性。; 适合人群:具备一定自动化、控制理论或机器学习背景的科研人员与工程技术人员,尤其是从事精密定位、智能控制、非线性系统建模与预测控制相关领域的研究生与研究人员。; 使用场景及目标:①应用于纳米级精密定位系统(如原子力显微镜、半导体制造设备)中的高性能预测控制;②为复杂非线性系统的数据驱动建模与线性化提供新思路;③结合深度学习与经典控制理论,推动智能控制算法的实际落地。; 阅读建议:建议读者结合Matlab代码实现部分,深入理解Koopman算子与RNN结合的建模范式,重点关注数据预处理、模型训练与控制系统集成等关键环节,并可通过替换实际系统数据进行迁移验证,以掌握该方法的核心思想与工程应用技巧。
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
红包 添加红包
表情包 插入表情
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值