Derive the state table and the state diagram of the sequential circuit shown below . Explain the function that the circuit performs.
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Derive the state table and the state diagram of the sequential circuit shown below . Explain the function that the circuit performs.
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- writr a code to draw this picture in OpenGIImplement ApproximatePatternCount. Pattern= CGCGGC Text=GAGAAGAATTCTCCGTCGTCGTATTCTGACGTCGTAGAGACGGTCCAAAAGT CTCGACTATGAACCGAGGGATTCCCCTAGCCGTCCGCTAGCGACCTCATTGCGGC CGGCCTTAGACTCGAACTGACACGTTTTATAGCCCCAGAGAAGAGATATTCTAGTT TGAGGGTTGTGTACAAACCGCCCGGTTGTGCTCCATTACCAGCCGGGCTGGAGCT ACCTCGGGGTACAATGGAGGACGGTCCGAATGCATAGTCCAAGCTGTCTCACCCG CTTGTGGTAAAAGCGGCGAGGTGGTCAGAGCGTACAGCGGAGGTACTAGGCACC GCAGTCGGCGCGGCCG d= 2RStudio How to filter these data so that 1 and 2 are the headers in RStudio? BORN EDUC1 161 141 161 122 122 101 131 121 111 121 142 121 121 132 12
- zay7 Jump to level 1 p q r-rv-q)A-p T TT TTF TFT T T LL F TT FTF FFT FFF Check Next Fill in (-rv-q)^-p.Open LinearSolver.ipynb with Jupyter Notebook. You are given a text file, eq.txt, containing a system of linear equations: x,y,z 2,1, -2,3 2x + 1y-2z=3 1x-ly-1z = 0 1,-1,-1,0 which is equivalent to 1,1,3,12 1x + 1y +3z = 12 Part A Read the content of eq.txt and compute the following: o A Python list (not ndarray) called var containing the names of the unknowns. Example: [x, y, z) (Your solution should be general, i.e., it should work for any number of unknowns with arbitrary names.) o The ndarray A that will be used in solving the system via Numpy, as in: At = b (do not solve the system yet!) o The ndarray b that will be used in solving the system via Numpy, as in: At = b Your output should be as follows: VAF ['x', 'y', '2'1 arrayti 2., 1., -2.1, 11.-1., 1.1. [ 1., 1.2 3.11) I b array([[ 3.1, 10.1. 112-113 Part B Solve the system At= b using the A and b that you computed in Part A. Your output should be as follows: t array([[3.5], 11. 1. [2.511)pogle.com/forms/d/e/1FAlpQLSdQck13Hoxybctc1k_NHXyv5JlhwRp_MSI12JT الأخبار Goo • The Range of f(x) = v1-x is 1. [-1, 0) 2. (-00, 1] 3. (-0, 0) 4. [0, 0) 1 0 13 • If u = x+1t-yy= then. dy 1. dy 2. dx dy 3. dx dy 4. dx
- Create an RLE class that uses fixed-length encoding to reduce ASCII bytestreams with a limited number of characters, including the code as part of the compressed bitstream. Add code to compress() to generate a string alpha containing all of the unique letters in the message and use it to generate an Alphabet for use in compress(), prepend alpha (8-bit encoding plus its length) to the compressed bitstream, and then add code to expand() to read the alphabet before expansion.A fragment of code is given to compare text in the field alpha with a text in the field beta: .data alpha db 12 dup(?) beta db ’abrakadabra’, 0 ; ::: .code ; ::: mov esi, 0 mov ecx, 12 ns: mov dl, alpha[esi] cmp dl, beta[esi] jne wrong ;=====MISSING==== loop ns ; ::: wrong: Choose missing instruction to insert in the code line marked by the comment “MISSING”.2 Gaussian blurring is used to define an image scale to work in, for interpolation, for computing interest points, and in many more applications. SciPy comes with a module for filtering called scipy.ndimage.filters that can be used to compute these convolutions using a fast 1D separation. All you need to do is: from PIL import Image from numpy import * numpy from scipy.ndimage import filters im = array(Image.open('empire.jpg').convert('L')) = im2 filters.gaussian_filter(im,5) 001-08 T التعليل Here the last parameter of gaussian_filter() is the standard deviation. sagino O 80 (a) q Langla (b) Bia oltes (c) gooliage (d) toteu Figure 1.9: An example of Gaussian blurring using the scipy.ndimage.filters module. (a) original image in grayscale, (b) Gaussian filter with o=2, (c) with σ = 5, (d) with σ = 10. Figure 1.9 shows examples of an image blurred with increasing segma. Larger values gives less details. To blur color images, simply apply Gaussian blurring to each color channel. Exercises…
- TODO 9 Now let's wrap our one-hot code in a class that can be used with Sklearn's pipeline functionality. Finish the OneHotEncoding class which produces one-hot encodings and stores the columns names of the newly generated one-hot for reference later. In the transform() method, convert the input X into one-hot encodings using pd.get_dummies(). Store the output into the variable one_hot. It's similar to what you did in TODO 8. Store the names of the columns for our one-hot encoding one_hot so we can access them later if needed. Store the output into the class variable self.feature_names Think about how you access the columns of a DataFrame! class OneHotEncoding(BaseEstimator, TransformerMixin): def __init__(self): self.feature_names = None def fit(self, X: pd.DataFrame, y: pd.DataFrame = None): # We don't need to set/learn any variables so # we just need to return a reference to the object with 'self' return self def transform(self, X:…a) Suppose you have dld2d3d4d5d6d7d8d9d10d11BTREE. a set of search keys are encoded like as Here D1-D11= total 11 digits of id. A=1, B=2, C=3------------Y=25, Z=26 If your id like as 13142102020 then search key: 1 31 4 2 1 0 2 0 2 0 2 20 18 5 5 1314 21 02 0 2 0 = id and 2 20 18 55 = B TREE Search key= id+ BTREE Now construct a B+ tree for the given search key with maximum degree 4 then remove all odd digits from the constructed B+ treeBuild a DFA that accepts the set of string over {1,2,3} the sum of digits is divisible by five. For example 11111, 221, 311, 1211, etc.