FEATURES High accuracy 0.02% maximum nonlinearity,0 V to 2 Vrms input 0.10% additional error to crest factor of3 Wide bandwidth 8 MHz at 2Vrms input600 kHz at 100 mVrms ComputesTrue rms Square Mean square Absolute value dB output (60 dB range) Chip select/power-down feature allows Analog three-state operation Quiescent current reduction from 2.2 mA to 350 uA 14-lead SBDIP,14-lead low cost CERDIP, and 16-lead SOIC_W
2022-03-02 11:04:05 7.59MB AD637
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对于android的操作系统移植讲解说明,简单明了从bootload,到应用程序移植,都有很详细的讲解
2022-03-01 13:51:15 371KB 系统移植
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每一个题目都有认真讲解解题思路,以及如何验证是否正常完成 提供练习环境给大家进行练习
2022-02-28 21:32:20 146KB 红帽 考试认证 Linux认证
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整周模糊度解算之LAMBDA算法讲解.pdf
2022-02-28 21:13:19 885KB
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结构分析法 结构分析法:是分析图像纹理的结构,从中获取结构特征。 结构分析法首先将纹理看成许多纹理基元按照一定位置的规则组成,然后分两步处理如下: 该方法适用于规则和周期性纹理,实际中较少采用 纹理基元 提取纹理基元 推论纹理基元位置规律
2022-02-28 19:03:49 590KB 图像纹理分析
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超级玛丽小游戏的JAVA程序,进入游戏后首先按空格键开始,利用方向键来控制的马里奥的移动,同时检测马里奥与场景中的障碍物和敌人的碰撞,并判断马里奥的可移动性和马里奥的生命值。当马里奥通过最后一个场景后游戏结束。。 本系统拥有的角色如下: (1)马里奥 (2)障碍物 (3)敌人
2022-02-28 18:58:07 31.49MB Java 超级玛丽游戏 毕业设计 课程设计
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设计模式(Design pattern)是一套被反复使用、多数人知晓的、经过分类编目的、代码设计经验的总结。使用设计模式是为了可重用代码、让代码更容易被他人理解、保证代码可靠性。 毫无疑问,设计模式于己于他人于系统都是多赢的,设计模式使代码编制真正工程化,设计模式是软件工程的基石,如同大厦的一块块砖石一样。项目中合理的运用设计模式可以完美的解决很多问题,每种模式在现在中都有相应的原理来与之对应,每一个模式描述了一个在我们周围不断重复发生的问题,以及该问题的核心解决方案,这也是它能被广泛应用的原因。
2022-02-28 15:17:24 1.59MB 设计模式
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Spyglass进行CDC检查的介绍
2022-02-28 11:13:14 1.14MB spyglass cdc ic
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用实例讲解RSA加密算法(精)
2022-02-27 22:12:25 295KB RSA加密算法
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Abstract—Clustering face images according to their latent identity has two important applications: (i) grouping a collection of face images when no external labels are associated with images, and (ii) indexing for efficient large scale face retrieval. The clustering problem is composed of two key parts: representation and similarity metric for face images, and choice of the partition algorithm. We first propose a representation based on ResNet, which has been shown to perform very well in image classification problems. Given this representation, we design a clustering algorithm, Conditional Pairwise Clustering (ConPaC), which directly estimates the adjacency matrix only based on the similarities between face images. This allows a dynamic selection of number of clusters and retains pairwise similarities between faces. ConPaC formulates the clustering problem as a Conditional Random Field (CRF) model and uses Loopy Belief Propagation to find an approximate solution for maximizing the posterior probability of the adjacency matrix. Experimental results on two benchmark face datasets (LFW and IJB-B) show that ConPaC outperforms well known clustering algorithms such as k-means, spectral clustering and approximate Rank-order. Additionally, our algorithm can naturally incorporate pairwise constraints to work in a semi-supervised way that leads to improved clustering performance. We also propose an k-NN variant of ConPaC, which has a linear time complexity given a k-NN graph, suitable for large datasets. Index Terms—face clustering, face representation, Conditional Random Fields, pairwise constraints, semi-supervised clustering.
2022-02-27 19:55:52 15.95MB 人脸 聚类
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