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."
標簽： single-chip developed threshold the
上傳時間： 2017-09-12
上傳用戶：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."
標簽： single-chip developed threshold the
上傳時間： 2013-12-09
上傳用戶：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.
標簽： Overvoltage Protection Solutions Driver
上傳時間： 2013-10-14
上傳用戶：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.
上傳時間： 2013-10-07
上傳用戶：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 ==================================
標簽： Part connections example prolog
上傳時間： 2015-04-24
上傳用戶：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 .
標簽： decomposition recostruct compress wavelets
上傳時間： 2016-01-22
上傳用戶：liuchee
我用matlab寫的一個corner detector, 效果比現在流行的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.
標簽： detector matlab corner harris
上傳時間： 2013-12-30
上傳用戶：569342831
學上的基本神經元，人工的神經網絡也有基本的神經元。每個神經元有特定數量的輸入，也會為每個神經元設定權重（weight）。權重是對所輸入的資料的重要性的一個指標。然后，神經元會計算出權重合計值（net value），而權重合計值就是將所有輸入乘以它們的權重的合計。每個神經元都有它們各自的臨界值（threshold），而當權重合計值大于臨
標簽：
上傳時間： 2014-06-06
上傳用戶：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.
標簽： OTSU segmentation Gray-level segmented
上傳時間： 2017-04-24
上傳用戶：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.
標簽： well-known algorithm AdaBoost Adaptive
上傳時間： 2014-01-15
上傳用戶：qiaoyue
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