AVR single-chip developed by a very low threshold, as long as the computer will be able to study the development of AVR microcontroller. Only a single-chip ISP download beginners line, the editing, debugging of software programs through a direct line into the AVR microcontroller, which can develop AVR Series Single-chip package of a variety of devices. AVR single-chip microcomputer in the industry known as "front-line struggle to seize state power."
标签： single-chip developed threshold the
上传时间： 2017-09-12
上传用户：shinesyh
AVR single-chip developed by a very low threshold, as long as the computer will be able to study the development of AVR microcontroller. Only a single-chip ISP download beginners line, the editing, debugging of software programs through a direct line into the AVR microcontroller, which can develop AVR Series Single-chip package of a variety of devices. AVR single-chip microcomputer in the industry known as "front-line struggle to seize state power."
标签： single-chip developed threshold the
上传时间： 2013-12-09
上传用户：invtnewer
Boost LED drivers are often used to drive LEDs in series. If an LED fails while open,overvoltage protection (OVP) is necessary to avoid the damage to a boost integrated circuit (IC) or output capacitor. This application report presents the solutions to increase the TPS61043 LED driver OVP threshold.
标签： Overvoltage Protection Solutions Driver
上传时间： 2013-10-14
上传用户：jiangfire
The TRS232E is a dual driver/receiver that includes a capacitive voltage generator to supply TIA/RS-232-Fvoltage levels from a single 5-V supply. Each receiver converts TIA/RS-232-F inputs to 5-V TTL/CMOS levels.This receiver has a typical threshold of 1.3 V, a typical hysteresis of 0.5 V, and can accept ±30-V inputs. Eachdriver converts TTL/CMOS input levels into TIA/RS-232-F levels. The driver, receiver, and voltage-generatorfunctions are available as cells in the Texas Instruments LinASIC™ library.
上传时间： 2013-10-07
上传用户：waitingfy
prolog 找路例子程序： === === === === === === Part 1-Adding connections Part 2-Simple Path example | ?- path1(a,b,P,T). will produce the response: T = 15 P = [a,b] ? Part 3 - Non-repeating path As an example, the query: ?- path2(a,h,P,T). will succeed and may produce the bindings: P = [a,depot,b,d,e,f,h] T = 155 Part 4 - Generating a path below a cost threshold As an example, the query: ?- path_below_cost(a,[a,b,c,d,e,f,g,h],RS,300). returns: RS = [a,b,depot,c,d,e,g,f,h] ? RS = [a,c,depot,b,d,e,g,f,h] ? no ==================================
标签： Part connections example prolog
上传时间： 2015-04-24
上传用户：ljt101007
This program compress and recostruct using wavelets. We can select level of decomposition(here maximum 4 levels are given) of images using selected wavelet. For eg:-wavelets can be haar, db1, db2,dmey............... Decomposition can be viewed in figure. (Please note that select 256X256 image for better result.) Then compression can performed, PERFL2 give compression score. Then reconstruction can be performed. Each decompsition we can choose different threshold values. For each threshold value we can calculate mse,psnr,pq(picture quality), bit ratio etc. To get pq install pqs function .
标签： decomposition recostruct compress wavelets
上传时间： 2016-01-22
上传用户：liuchee
我用matlab写的一个corner detector, 效果比现在流行的harris，susan，CSS等效果要好。 Algorithm is derived from: X.C. He and N.H.C. Yung, Curvature Scale Space Corner Detector with Adaptive threshold and Dynamic Region of Support , Proceedings of the 17th International Conference on Pattern Recognition, 2:791-794, August 2004. Improved algorithm has been included in A Corner Detector based on Global and Local Curvature Properties and submitted to Optical Engineering.
标签： detector matlab corner harris
上传时间： 2013-12-30
上传用户：569342831
学上的基本神经元，人工的神经网络也有基本的神经元。每个神经元有特定数量的输入，也会为每个神经元设定权重（weight）。权重是对所输入的资料的重要性的一个指标。然后，神经元会计算出权重合计值（net value），而权重合计值就是将所有输入乘以它们的权重的合计。每个神经元都有它们各自的临界值（threshold），而当权重合计值大于临
标签：
上传时间： 2014-06-06
上传用户：luke5347
OTSU Gray-level image segmentation using Otsu s method. Iseg = OTSU(I,n) computes a segmented image (Iseg) containing n classes by means of Otsu s n-thresholding method (Otsu N, A threshold Selection Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern. 9:62-66 1979). thresholds are computed to maximize a separability criterion of the resultant classes in gray levels. OTSU(I) is equivalent to OTSU(I,2). By default, n=2 and the corresponding Iseg is therefore a binary image. The pixel values for Iseg are [0 1] if n=2, [0 0.5 1] if n=3, [0 0.333 0.666 1] if n=4, ... [Iseg,sep] = OTSU(I,n) returns the value (sep) of the separability criterion within the range [0 1]. Zero is obtained only with images having less than n gray level, whereas one (optimal value) is obtained only with n-valued images.
标签： OTSU segmentation Gray-level segmented
上传时间： 2017-04-24
上传用户：yuzsu
AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files 1. ADABOOST_tr.m 2. ADABOOST_te.m to traing and test a user-coded learning (classification) algorithm with AdaBoost. A demo file (demo.m) is provided that demonstrates how these two files can be used with a classifier (basic threshold classifier) for two class classification problem.
标签： well-known algorithm AdaBoost Adaptive
上传时间： 2014-01-15
上传用户：qiaoyue
