Can you please separate the code into imagespy sharpenpy.pdf
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Can you please separate the code into images.py, sharpen.py, & smokey.gif Instructions 1 #Header file section for image module. The edge-detection function (detectEdges) described in Chapter 7 and shown below returns a 2 from images import Image black and white image. Think of a similar way to transform color values so that the new image is 3 #Function defition of still in its original colors but the outlines within it are merely sharpened. edge_detection: This function 4 #is used to identifying and def detectEdges(image, amount): marking the outline "" "Builds and returns a new image in which the edges of 5 # of an image. the argument image are highlighted and the colors are 6 def edge_detection(image, reduced to black and white."" " degree, threshold): 7 #Function defition of def average(triple): average is used to (r,g,b)= triple 8 #calculate the average of return(r+g+b)//3 given pixel blackPixel =(0,0,0) whitePixel =(255,255,255) new = image.clone () for y in range(image.getHeight () - 1): for x in range( 1 , image.getWidth ()) : oldPixel = image. getPixel(x,y) leftPixel = image.getPixel (x1,y) bottomPixel = image. getPixel(x,y+ 1) oldLum = average ( oldPixel ) leftLum = average ( left tixel ) bottomLum = average (bottomPixel ) if abs(oldLum - leftLum) > amount or 1 abs(oldLum - bottomLum) > amount: new. setPixel ( x,y, blackPixel) else:Instructions CENGAGE | MINDTAP Q Search this course Programming Exercise 7.10.
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The code in image3.cpp has the error as shown above, could you help .pdf
Select the correct answer.
6.41
5.41
5.409
5.411
Solution
1)Find the partial sumof the seriesrounded to the nearest thousandth. Question 7 options: n
an=5/4^n sum 1 1.2500 1.2500 2 0.3125 1.5625 3 0.0781 1.6406 4 0.0195 1.6602 5 0.0049
1.6650 6 0.0012 1.6663 7 0.0003 1.6666 8 0.0001 1.6666 9 0.0000 1.6667 10 0.0000 1.6667 11
0.0000 1.6667 none of answers fit 1.667 2)Given the series: and. Determine whether each
series is convergent or divergent. Question 8 options: a) converges because cos ^2(10n)is greater
than or equal to zero and less than 1 so series a is 1 so it converges and so does a b diverges
because cos is greater than or equal to -1 and less than or equal to 1, which does not converge to
0, so b diverges ans Ais convergent,Bis divergent. 3)Determine whether the series converges
or diverges. NO SERIES Select the correct answer. Question 9 options: neither diverges
converges 4)Determine whether the sequence converges or diverges. diverges because n(n-6)
goes to infinity as n goes to infinity Question 10 options: converges diverges.
Recommended
The code in image3.cpp has the error as shown above, could you help .pdf
Select the correct answer.
6.41
5.41
5.409
5.411
Solution
1)Find the partial sumof the seriesrounded to the nearest thousandth. Question 7 options: n
an=5/4^n sum 1 1.2500 1.2500 2 0.3125 1.5625 3 0.0781 1.6406 4 0.0195 1.6602 5 0.0049
1.6650 6 0.0012 1.6663 7 0.0003 1.6666 8 0.0001 1.6666 9 0.0000 1.6667 10 0.0000 1.6667 11
0.0000 1.6667 none of answers fit 1.667 2)Given the series: and. Determine whether each
series is convergent or divergent. Question 8 options: a) converges because cos ^2(10n)is greater
than or equal to zero and less than 1 so series a is 1 so it converges and so does a b diverges
because cos is greater than or equal to -1 and less than or equal to 1, which does not converge to
0, so b diverges ans Ais convergent,Bis divergent. 3)Determine whether the series converges
or diverges. NO SERIES Select the correct answer. Question 9 options: neither diverges
converges 4)Determine whether the sequence converges or diverges. diverges because n(n-6)
goes to infinity as n goes to infinity Question 10 options: converges diverges.
Digital Image Processing
Digital Image Processing project under Guidance of Prof Girisha Navada at NITK Surathkal
Images in matlab
University of Engineering and Technology, Taxila
Introduction to image processing in MATLAB
We are restricted from importing cv2 numpy stats and other.pdf
We are restricted from importing cv2, numpy, stats and other third party libraries, with the
only exception of math, importing math library is allowed (import math).
the input image contains objects of four geometric shapes: circle, square, rectangle, and ellipse.
The shapes have a brighter intensity compared to the background. The objective of the
assignment is to count the total number of each geometric shape in the image by performing
binary image processing. The overall steps are
Copmute the histogram
Compute optimal threshold
Create binary image
Perform blob-coloring
For each region, compute area, centroid, and shape (circle, square, rectangle, or ellipse)
Count the number of circles, number of squares, number of rectangles, and number of ellipses.
Mark the center of each region with a label (c for circle, r for rectangle, s for square, and e for
ellipse)
Objective 2: Perform compression using run-length encoding and decoding of a binary image.
Shape Counting:
a. Write a program to binarize a gray-level image based on the assumption that the image has a
bimodal histogram. Determine the optimal threshold required to binarize the image. Your code
should report both the binarized image and the optimal threshold value. Also assume that
background is darker than foreground objects in the input gray-level image.
Starter code available in directory region_analysis/
region_analysis/binary_image.py:
compute_histogram: write your code to compute the histogram in this function, If you return a list it
will automatically save the graph in output folder
find_threshold: Write your code to compute the optimal threshold. This should be implemented
using the iterative algorithm discussed in class (See Week 4, Lecture 7, slide 42 on teams). Do not
implment the Otsu's thresholding method. No points are awarded for Otsu's method.
binarize: write your code to threshold the input image to create a binary image here. This function
should return a binary image which will automatically be saved in output folder. For visualization
one can use intensity value of 255 instead of 1 in the binary image. That way the objects appear
white over black background
Any output images or files will be saved to "output" folder
b. Write a program to perform blob-coloring. The input to your code should be a binary image (0's,
and 255's) and the output should be a list of objects or regions in the image.
region_analysis/shape_counting.py:
blob_coloring: write your code for blob coloring here, takes as input a binary image and returns a
list/dictionary of objects or regions.
Any output images will be saved to "output" folder
c. Ignore shapes smaller than 10 pixels in area generate a report of the remaining regions (region
Number, Centroid, Area, and Shape).
region_analysis/shape_counting.py:identify_shapes: write your code for computing the statistics of
each object/region, i.e area and location (centroid) here, and the shape (c for circle, s for square, r
for rectancle, and e for .
Introduction to Image Processing with MATLAB
The slides describes the fundamentals of image processing with MATLAB for beginners with sample MATLAB codes
Using the code below- I need help with creating code for the following.pdf
Using the code below, I need help with creating code for the following:
1) Write Python code to plot the images from the first epoch. Take a screenshot of the images
from the first epoch.
2) Write Python code to plot the images from the last epoch. Take a screenshot of the images
from the last epoch.
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display th.
Information search using text and image query
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Information search using text and image query
Abstract An image retrieval and re-ranking system utilizing a visual re-ranking framework which is proposed in this paper the system retrieves a dataset from the World Wide Web based on textual query submitted by the user. These results are kept as data set for information retrieval. This dataset is then re-ranked using a visual query (multiple images selected by user from the dataset) which conveys user’s intention semantically. Visual descriptors (MPEG-7) which describe image with respect to low-level feature like color, texture, etc are used for calculating distances. These distances are a measure of similarity between query images and members of the dataset. Our proposed system has been assessed on different types of queries such as apples, Console, Paris, etc. It shows significant improvement on initial text-based search results.This system is well suitable for online shopping application. Index Terms: MPEG-7, Color Layout Descriptor (CLD), Edge Histogram Descriptor (EHD), image retrieval and re-ranking system
Cells in the embryo that give rise to skeletal muscle and ne.pdf
Cells in the embryo that give rise to skeletal muscle and nerves are functionally different
because.....
Group of answer choices
A )They have different nuclear DNA with different genes being transcribed and translated.
B) They have the same nuclear DNA with different genes being transcribed and translated.
C) They have the same nuclear DNA with the same genes being transcribed and translated.
D) They have different nuclear DNA but the same genes are being transcribed and translated..
Cells have to react upon a wide variety of signals The term.pdf
Cells have to react upon a wide variety of signals. The term signal transduction describes the
overall process of how cells convert an extracellular signal into a specific cellular response and
includes plenty of different intracellular signaling pathways. However, there are intracellular
signaling molecules which are found in almost all signal transduction pathways.
List 10 signal transduction pathways that involve protein tyrosine kinases. Give one example
diagram of the signal transduction pathway that involves a protein kinase..
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Similar to Can you please separate the code into imagespy sharpenpy.pdf
Digital Image Processing
Digital Image Processing project under Guidance of Prof Girisha Navada at NITK Surathkal
Images in matlab
University of Engineering and Technology, Taxila
Introduction to image processing in MATLAB
We are restricted from importing cv2 numpy stats and other.pdf
We are restricted from importing cv2, numpy, stats and other third party libraries, with the
only exception of math, importing math library is allowed (import math).
the input image contains objects of four geometric shapes: circle, square, rectangle, and ellipse.
The shapes have a brighter intensity compared to the background. The objective of the
assignment is to count the total number of each geometric shape in the image by performing
binary image processing. The overall steps are
Copmute the histogram
Compute optimal threshold
Create binary image
Perform blob-coloring
For each region, compute area, centroid, and shape (circle, square, rectangle, or ellipse)
Count the number of circles, number of squares, number of rectangles, and number of ellipses.
Mark the center of each region with a label (c for circle, r for rectangle, s for square, and e for
ellipse)
Objective 2: Perform compression using run-length encoding and decoding of a binary image.
Shape Counting:
a. Write a program to binarize a gray-level image based on the assumption that the image has a
bimodal histogram. Determine the optimal threshold required to binarize the image. Your code
should report both the binarized image and the optimal threshold value. Also assume that
background is darker than foreground objects in the input gray-level image.
Starter code available in directory region_analysis/
region_analysis/binary_image.py:
compute_histogram: write your code to compute the histogram in this function, If you return a list it
will automatically save the graph in output folder
find_threshold: Write your code to compute the optimal threshold. This should be implemented
using the iterative algorithm discussed in class (See Week 4, Lecture 7, slide 42 on teams). Do not
implment the Otsu's thresholding method. No points are awarded for Otsu's method.
binarize: write your code to threshold the input image to create a binary image here. This function
should return a binary image which will automatically be saved in output folder. For visualization
one can use intensity value of 255 instead of 1 in the binary image. That way the objects appear
white over black background
Any output images or files will be saved to "output" folder
b. Write a program to perform blob-coloring. The input to your code should be a binary image (0's,
and 255's) and the output should be a list of objects or regions in the image.
region_analysis/shape_counting.py:
blob_coloring: write your code for blob coloring here, takes as input a binary image and returns a
list/dictionary of objects or regions.
Any output images will be saved to "output" folder
c. Ignore shapes smaller than 10 pixels in area generate a report of the remaining regions (region
Number, Centroid, Area, and Shape).
region_analysis/shape_counting.py:identify_shapes: write your code for computing the statistics of
each object/region, i.e area and location (centroid) here, and the shape (c for circle, s for square, r
for rectancle, and e for .
Introduction to Image Processing with MATLAB
The slides describes the fundamentals of image processing with MATLAB for beginners with sample MATLAB codes
Using the code below- I need help with creating code for the following.pdf
Using the code below, I need help with creating code for the following:
1) Write Python code to plot the images from the first epoch. Take a screenshot of the images
from the first epoch.
2) Write Python code to plot the images from the last epoch. Take a screenshot of the images
from the last epoch.
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display th.
Information search using text and image query
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Information search using text and image query
Abstract An image retrieval and re-ranking system utilizing a visual re-ranking framework which is proposed in this paper the system retrieves a dataset from the World Wide Web based on textual query submitted by the user. These results are kept as data set for information retrieval. This dataset is then re-ranked using a visual query (multiple images selected by user from the dataset) which conveys user’s intention semantically. Visual descriptors (MPEG-7) which describe image with respect to low-level feature like color, texture, etc are used for calculating distances. These distances are a measure of similarity between query images and members of the dataset. Our proposed system has been assessed on different types of queries such as apples, Console, Paris, etc. It shows significant improvement on initial text-based search results.This system is well suitable for online shopping application. Index Terms: MPEG-7, Color Layout Descriptor (CLD), Edge Histogram Descriptor (EHD), image retrieval and re-ranking system
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nuestros invitados, respondi Cindy Bridgetown, vicepresidenta de Imagineering y jefa de Luke.
Cindy, no podemos controlar la conversacin si Mickey habla con los invitados. Los nios y los
padres se vern heridos en sus sentimientos de una forma u otra. Y es demasiado exigente para
nuestros empleados.
PREGUNTAS:
1.Usando como referencia Qu hacen los Gerentes de Operaciones? En el recuadro del captulo,
explique por qu estas actividades clave (diseo de procesos, diseo de trabajos, gestin de calidad y
diseo de encuentros de servicio) se relacionan con la situacin del caso.
2. Proporcione un buen y un mal ejemplo de un "momento de la verdad" si Mickey Mouse habla
con .
Caso 1 JK desarroll un dolor agudo en el costado que iba y.pdf
Caso 1
JK desarroll un dolor agudo en el costado que iba y vena en oleadas. Se volvi tan intenso que ella
estaba paseando por el suelo y gimiendo. Su esposo la llev al departamento de emergencias,
donde sus pruebas de laboratorio mostraron una pequea cantidad de hematuria y un ligero
aumento de glbulos blancos en su sangre. Su calificacin de dolor era un 9 en una escala del 1 al
10 (siendo 10 el ms alto). A JK se le administr morfina intravenosa y expuls varios clculos
pequeos en la orina durante las siguientes horas..
CASO Por qu una nueva empresa de BPO sirve cerveza gratis.pdf
CASO: Por qu una nueva empresa de BPO sirve cerveza gratis?
Una empresa australiana que dise el software que hizo funcionar el rover Curiosity en Marte abri
recientemente una oficina en Fort Bonifacio, donde se desborda cerveza gratis todos los viernes y
los empleados pueden usar pantalones cortos y trabajar en cualquier parte de la oficina.
Tratamos de hacer tantas cosas como podamos para crear una comunidad y tiempos de conexin,
y eso se ve en todas nuestras oficinas. Proponemos formas de crear puntos de conexin social.
Queremos que la oficina sea una extensin de la conexin que tienen las personas y parte de eso
es crear eventos como estos, dice Jeff Diana, director de personal de Atlassian, un proveedor lder
de software de colaboracin fundado en Sdney.
oficina elegante
Atlassian abri una nueva oficina en el segundo piso del Edificio 3 en la elegante Bonifacio High
Street en Fort Bonifacio CBD. La nueva oficina ampliar las operaciones de Atlassian en Filipinas,
ya que pretende duplicar pronto sus 40 empleados o defensores que participan en servicios
compartidos en las divisiones de recursos humanos, finanzas, soporte de sistemas y servicio al
cliente.
Atlassian, que deriv su nombre de la palabra Atlas con el objetivo de cambiar el mundo a travs del
poder del software, inaugur su oficina de Fort Bonifacio el 15 de enero de 2015 y ya proyectaba
una cultura de trabajo nueva, vibrante y dinmica en Fort Bonifacio.
Puedes venir con ropa informal. Este espacio realmente permite eso. Es una oportunidad para
encontrar un espacio que encaje con nuestra cultura y nuestros valores, dice Sandi Kochhar, jefe
de operaciones de personas y servicios compartidos de Atlassian, refirindose a la nueva oficina
de Fort Bonifacio.
Estamos creciendo muy rpidamente y esta es una gran oportunidad para nosotros de encontrar
una excelente ubicacin que est creciendo con nosotros. Pensamos que estamos optimizados aqu
en el Fuerte, dice ella.
No tradicional
Lo otro es que estamos buscando un espacio no tradicional, porque no somos una empresa
tradicional. Entonces, lo que encuentras en Makati y Ortigas son edificios de oficinas ms
tradicionales. Para nosotros tener este espacio es muy parecido a las oficinas de Atlassian. Es un
entorno nico el que tenemos aqu. Tenemos suerte de haberlo encontrado en el Fuerte, dice
Kochhar.
Atlassian, que estableci su presencia con un empleado en Filipinas en octubre de 2013, se
transfiri desde una pequea oficina en la ciudad de Makati. Consideramos la infraestructura, cmo la
gente llegar a trabajar fcilmente. Encontramos un espacio perfecto que es este que se ajusta a
nuestros requisitos, dice Bennieson Co, jefe de la divisin de servicios compartidos de Atlassian en
Filipinas.
Fundada en 2002 por Mike Cannon-Brookes y Scott Farquhar con un espritu de "no tonteras",
Atlassian, una empresa privada, fue nombrada el mejor lugar para trabajar de Australia el ao
pasado. Sus 40.000 clientes van desde pequeas empresas hasta organizaciones cientf.
Caso del mundo real 41 El especialista en mejora de la do.pdf
Caso del mundo real 4.1
El especialista en mejora de la documentacin clnica (CDI) del hospital le informa a usted, el
administrador de HIM del hospital Anywhere, que el hospital ha estado recibiendo multas por
reembolso. La documentacin del mdico no identifica adecuadamente las condiciones especficas
que CMS ha identificado como condiciones adquiridas en el hospital (HAC). En la mayora de los
casos, estas condiciones estn presentes en los pacientes antes de ser admitidos. Sin embargo,
los mdicos estn documentando estas condiciones ms adelante en las estadas de los pacientes,
haciendo parecer que los pacientes han adquirido estas condiciones del hospital. El hospital est
siendo penalizado financieramente porque estas condiciones se consideran prevenibles si el
hospital sigue las normas y directrices nacionales de tratamiento.
Cualquier hospital tiene un EHR slido. Muchas iniciativas de mejora de la documentacin han sido
aprovechadas por las capacidades tecnolgicas del EHR. Como gerente de HIM, se pregunta si se
podra hacer algo desde el punto de vista de la tecnologa que pudiera ayudar a los mdicos a
identificar, en el momento de la admisin, las condiciones que causan el problema del reembolso y
documentar adecuadamente las condiciones. Los mdicos que capturan y documentan
adecuadamente estas condiciones demostraran que el hospital est siguiendo los estndares y
pautas nacionales de tratamiento y que los pacientes no estn adquiriendo estas condiciones en el
hospital.
Rena un equipo multidisciplinario compuesto por mdicos, representantes del ciclo de ingresos,
HIM y representantes de sistemas de informacin.
Preguntas de discusin de casos del mundo real: formato de prrafo
2. Cul es el beneficio de desarrollar una solucin EMR para este problema?
3. Cmo se relaciona la adherencia a las pautas de tratamiento para la prevencin de las
condiciones identificadas como adquiridas en el hospital con otras iniciativas de pago por
desempeo?.
Caso Peditrico 4 Sabina Vsquez Compleja Cesiones de Doc.pdf
Caso Peditrico 4: Sabina Vsquez (Compleja) Cesiones de Documentacin
1. Documente su evaluacin enfocada inicial de Sabina Vsquez.
2. Identificar y documentar diagnsticos clave de enfermera para Sabina Vsquez.
3. En referencia a su registro de comentarios, documente la atencin de enfermera que brind y la
respuesta de Sabina Vsquez.
4. Documente la enseanza del paciente que le brindara a Sabina Vsquez y su familia antes del
alta..
Caso Demasiado amable con la gente John acababa de gradua.pdf
Caso
Demasiado amable con la gente
John acababa de graduarse de la Facultad de Administracin de Empresas de la Universidad
Estatal y se uni a la pequea empresa de su familia, que emplea a veinticinco trabajadores
semicalificados. La primera semana en el trabajo, su pap lo llam y le dijo: John, he tenido la
oportunidad de observarte trabajando con hombres y mujeres durante los ltimos dos das y,
aunque odio hacerlo, siento que debo decir algo. . Eres demasiado amable con la gente. S que te
ensearon eso de las relaciones humanas en la universidad, pero aqu no funciona. Recuerdo
cuando se inform por primera vez sobre los estudios de Hawthorne y todos en la universidad se
emocionaron mucho con ellos, pero cranme, hay ms en el manejo de personas que solo ser
amable con ellos.
Respuesta:
1. Cmo reaccionaras a los comentarios de tu padre si fueras John?
2. Crees que el padre de John entendi e interpret correctamente los estudios de Hawthorne?
3. Qu fases de gestin cree que ha atravesado el padre de John en este negocio familiar?.
Caso de Estudio 6a Sistema Neurolgico Esclerosis Mltiple.pdf
Caso de Estudio #6a Sistema Neurolgico: Esclerosis Mltiple
Paciente: Myrtle
Recin diagnosticado, (25 aos), Historia social: sin hogar, desempleado, soltero,
No fumador; sin ETOH (ETOH es un acrnimo de alcohol etlico) ; sin drogas ilcitas
Estudiante universitario en lnea: lucha por comprender el material
EMRR (Esclerosis Mltiple Remitente Recidivante)
Sntomas
2006: visin borrosa y doble
2013: visin doble; urgencia urinaria; mala atencion
2012: Entumecimiento en ambas piernas
Diagnstico: EMRR - Esclerosis mltiple remitente recidivante
(2 episodios de sntomas neurolgicos atribuibles al SNC, separados en el espacio y en el tiempo)
Problemas de memoria pasados los 6 meses; fatiga; intolerancia al calor; problemas con el
equilibrio
Prevalencia por Dominios Cognitivos
Memoria 40%
Procesamiento de informacin 35%
Atencin/concentracin 30%
Resolucin de problemas 20%
Habilidades Visuopastiales 20%
Fluidez verbal 10%
Mltiples dominios (promedio)22%
Apoyando el Diagnstico
Mltiples lesiones de sustancia blanca con realce
Seal T2 alta en el nervio ptico derecho
Lesin realzada en el borde en T6-7
Notas de bandas en LCR y no en suero
medicamentos
Estudio de caso de discusin #6a.
1. Cul es el efecto de la desmielizacin en los nervios de una persona que tiene esclerosis mltiple?
2. Por qu son apropiados los medicamentos?
3. Por qu cree que las mujeres tienen mayor riesgo de EM?.
Caso de estudio Aplicar el principio de gestin de recursos.pdf
Caso de estudio
Aplicar el principio de gestin de recursos humanos
Donna Moreau estuvo empleada durante nueve aos como asistente de habitacin en el hotel
Windjammer. Su trabajo y asistente durante ese perodo fueron considerados excelentes. El
hotel estaba moderadamente ocupado durante la semana y normalmente se llenaba de
turistas los fines de semana.
De acuerdo con una poltica del hotel que requiere una notificacin de dos semanas, el 1 de
mayo Donna present una solicitud de "da libre" para el sbado 15 de mayo para asistir a la
ceremonia de graduacin de la escuela secundaria de su nica hija a la 1:00 p. m. Se
esperaba que el hotel tuviera una escasez extrema de personal el fin de semana del 15 de
mayo debido a algunas renuncias y despidos del personal, as como a la previsin de que se
agotaran las habitaciones.
La supervisora de Donna, Tara Roach, rechaz la solicitud de Donna para el da libre, ya que
el departamento de limpieza necesitaba que ella trabajara todo el fin de semana. DOnna
estaba visiblemente molesta cuando se public el horario y se enter de que su solicitud
haba sido denegada. Se enfrent a Tara y dijo: Estar atendiendo a mi hija durante 17 aos, y
de ninguna manera me perder ese da!. Tara respondi que lo senta mucho, pero que todas
las solicitudes de los empleados para ese fin de semana haban sido denegadas y Donna se
present a trabajar segn lo programado.
El sbado de la graduacin, Donna, de acuerdo con la poltica escrita del hotel, llam "enferma"
cuatro horas antes de que comenzara su turno. El hotel estaba extremadamente ocupado
y, en parte debido a la ausencia de Donna, cada encargado de la habitacin que se present
en el trabajo asign una carga de trabajo superior a la media, lo que provoc una gran tensin
en el departamento.
Tara, que estaba enfadada por lo que vio como un desprecio deliberado por la autoridad
supervisora, y recordando la conversacin anterior con Donna, registr la llamada de la
empleada como una "excusa inaceptable" y complet un formulario que deca que Donna, de
hecho, la haba dejado. trabajo voluntariamente al negarse a trabajar su mierda asignada.
Tara se refiri a la parte del manual del empleado que Donna firm cuando se uni al hotel. El
manual deca, en parte:
Se considerar que el empleado ha renunciado o abandonado voluntariamente su empleo
en cualquiera de los siguientes casos;
Ausencia del trabajo por uno (1) o ms das consecutivos sin excusa aceptable para la
empresa.
tardanzas habituales
No presentarse al trabajo dentro de las 24 horas posteriores a una solicitud de informe
Donna volvi al trabajo al da siguiente y descubri que la haban eliminado del horario.
Tambin se le inform que ya no era empleada del hotel. Donna solicit compensacin por
desempleo. En su estado, los trabajadores que renunciaron voluntariamente a sus trabajos
generalmente no eran elegibles para la compensacin por desempleo. Aquellos que son
despedidos normalmente reciben ese beneficio (que finalmente es pagado por el hotel).
Revise la a.
Caso Autoridad Aeroportuaria La Autoridad Aeroportuaria .pdf
Caso: Autoridad Aeroportuaria
La Autoridad Aeroportuaria (AA) ha estado bajo una presin considerable por parte del Gobierno
Regional para aumentar la capacidad en el Aeropuerto de Changow. Construido originalmente
hace cincuenta aos como un pequeo aeropuerto regional que atiende a una poblacin de 250 000
habitantes, la pista nica de Changow est a plena capacidad durante 20 horas al da. La poblacin
de la regin ha aumentado a ms de 5 millones y gran parte de este crecimiento en los viajes areos
se debe a la rpida expansin econmica de la regin a partir de la industria y el turismo.
Las opciones que enfrenta el AA no son atractivas. Aumentar la capacidad en Changow presenta
serios problemas de ingeniera en el nico terreno disponible al norte, sobre el cual se debe
construir una nueva pista. Los problemas se deben a la geologa local y algunas inundaciones
regulares del delta del ro Hankse que fluye cerca. Una poblacin residencial de alrededor de 1
milln vive dentro de un rea de 15 kilmetros al norte del aeropuerto y creen que sus vidas se veran
gravemente afectadas por una nueva pista importante.
Adems, los desarrollos econmicos cercanos al aeropuerto se amontonan hasta el lmite sur, que
es el nico medio de acceso adecuado desde la ciudad de Changow para una carretera ms ancha.
La tierra cercana a los lmites este y oeste contiene principalmente propiedades residenciales de
altos ingresos, mezcladas con sitios verdes aislados, que contienen plantas naturales raras, aves
exticas y otros pequeos animales salvajes. El Gobierno del Estado recientemente design algunas
de estas reas como 'Sitios del Patrimonio Nacional'.
La otra opcin es construir un aeropuerto completamente nuevo en Fongow, a una distancia de
cuarenta kilmetros de Changow, en tierras agrcolas, actualmente cultivadas por miles de pequeos
propietarios, cuyas familias han trabajado la tierra all durante muchas generaciones. Se
necesitaran nuevos caminos de acceso, adems, quizs un sistema ferroviario que requerira
muchos puentes y tneles, y el aeropuerto estara cerca de altas colinas boscosas. Hay dudas
sobre la factibilidad operativa de construir un aeropuerto tan lejos de la capital regional.
Los representantes polticos locales, todos miembros de la coalicin gobernante, favorecen la
expansin del aeropuerto de Changow por motivos de 'desarrollo econmico nacional', 'progreso
social' y orgullo nacional. Lo mismo ocurre con las empresas locales, que afirman que el
aeropuerto actual est frenando la expansin econmica en la regin. Los pasajeros del aeropuerto,
tanto de negocios como de turistas, se ven afectados por largos retrasos en los vuelos y
conexiones perdidas, y atascos de trfico durante el acceso y la salida a lo largo de la nica e
inadecuada carretera sur del aeropuerto hacia la ciudad de Changow.
La inquietud/preocupacin del pblico sobre las propuestas para ampliar el aeropuerto de Changow
han surgido y estn atrayendo el inters de los medios. La principal oposicin proviene de los veci.
Caso 9A El Departamento de Poltica obtiene una renovacin.pdf
Caso 9-A: El Departamento de Poltica obtiene una renovacin de liderazgo
Lesley se gradu recientemente de la universidad con su ttulo en administracin de empresas. Pas
los ltimos dos veranos trabajando en Connecticut Mutual Insurance (CMI), como pasante en
varios trabajos diferentes mientras los empleados tomaban sus vacaciones. Recientemente recibi
y acept una oferta para unirse a CMI a tiempo completo como supervisora del departamento de
renovacin de plizas.
CMI es una gran compaa de seguros. Solo en la oficina central, donde trabajar Lesley, hay ms de
1500 empleados. La empresa cree firmemente en el desarrollo personal de sus empleados. Esta
creencia se traduce en una filosofa, que emana de las principales oficinas ejecutivas, de
confianza y respeto para todos los empleados de CMI. La compaa tambin se encuentra
regularmente en la cima de la mayora de las listas de "mejores empresas para trabajar", en gran
parte debido a sus programas progresivos de trabajo y vida y su fuerte compromiso para
minimizar los despidos.
En el nuevo trabajo de Lesley, ella dirigir las actividades de dieciocho empleados de renovacin de
plizas. Sus trabajos requieren poca capacitacin y estn altamente estandarizados. La
responsabilidad de un empleado es asegurarse de que se enven los avisos de renovacin de las
plizas actuales, tabular cualquier cambio en las primas, informar a la divisin de ventas si se
cancelar una pliza como resultado de la falta de respuesta a los avisos de renovacin y responder
preguntas y resolver problemas. relacionados con las renovaciones.
Las personas en el grupo de trabajo de Lesley tienen edades comprendidas entre los diecinueve
y los sesenta y dos aos, con una mediana de edad de veinticinco. En su mayora son graduados
de secundaria con poca experiencia laboral previa. Ganan entre $2,350 y $3,200 al mes. Lesley
reemplazar a una empleada de CMI desde hace mucho tiempo, Jan Allison. Jan se jubila despus
de treinta y siete aos en CMI, los ltimos catorce como supervisora de renovacin de plizas. Debido
a que Lesley pas algunas semanas en el grupo de Jan el verano pasado, est familiarizada con el
estilo de Jan y conoce a la mayora de los miembros del departamento. Pero la gente no conoce
muy bien a Lesley y sospecha que acaba de salir de la universidad y tiene poca experiencia en el
departamento.
La realidad es que Lesley obtuvo este trabajo porque la gerencia quera a alguien con un ttulo
universitario para supervisar el departamento. La crtica ms vocal de Lesley es Lillian Lantz. Lillian
tiene ms de cincuenta aos, ha sido empleada de renovacin de plizas durante ms de una docena
de aos y, como la "gran anciana" del departamento, tiene mucho peso entre los miembros del
grupo. Lesley sabe que ser difcil liderar este departamento sin el apoyo de Lillian.
Lesley est decidida a comenzar con el pie derecho su liderazgo en el departamento. Como
resultado, ha estado pensando mucho en las cualidades de un supervisor eficaz.
Respondiendo al caso
9-13. Qu .
Caso de debate filtracin de datos de Equifax La empresa de.pdf
Caso de debate: filtracin de datos de Equifax La empresa de informes de crdito Equifax estuvo en
el centro de una filtracin masiva de datos que afect a ms de 145 millones de clientes. En 2017, los
piratas informticos aprovecharon una vulnerabilidad en el software del sitio web de Equifax y
robaron informacin personal, incluidos nombres, direcciones y nmeros de seguro social, de hasta
145 millones de estadounidenses. Un incidente separado pero relacionado en Equifax involucr a
15 millones de ciudadanos britnicos cuyos registros fueron violados entre 2011 y 2016. El fracaso
de las medidas de control e informes internos de Equifax condujo a una violacin generalizada de
los derechos de las personas a la privacidad de su informacin personal y se convirti en un enorme
crisis de relaciones pblicas para la empresa. El principal abogado de Equifax, John Kelley, fue
investigado por la junta directiva por su posible participacin en el encubrimiento del hackeo y su
mal manejo de la situacin. Kelley fue responsable de aprobar las ventas de acciones de la
empresa por parte de los ejecutivos despus de que se descubriera la infraccin, pero antes de que
se revelara al pblico. Tras la divulgacin de la infraccin, el precio de las acciones de la empresa
cay un 14 por ciento. Los inversores vendieron aproximadamente 4500 millones de dlares (el 25
%) del valor de mercado de la empresa despus de que se hiciera pblico el ataque. Ms de 10
millones de estadounidenses vieron expuestos los datos de sus licencias de conducir durante el
hackeo. Muchas personas que haban proporcionado la informacin de su licencia de conducir a la
empresa simplemente estaban verificando su informacin para recibir informes crediticios y
calificaciones de Equifax. Algunos haban ingresado su informacin en la pgina web de la compaa
en un esfuerzo por resolver disputas de informes de crdito. La pgina web de disputas de informes
crediticios haba sido particularmente vulnerable a las brechas de seguridad. El CEO de Equifax,
Richard Smith, admiti durante las audiencias del Captulo 6 tica organizacional 133 PDF final para
imprimir 134 Segunda parte tica empresarial law43665_ch06_115-136.indd 134 11/14/18 01:42
PM del Congreso que l y otros ejecutivos estaban al tanto de las debilidades de seguridad , pero
que un solo empleado de la empresa no haba prestado atencin a las advertencias de seguridad y
no se asegur de la implementacin de las correcciones de software. Smith agreg que hubo una
falla en sus sistemas de software diseados para buscar la ausencia de "parches" necesarios para
proteger la informacin privada. Otros mecanismos de control interno en Equifax parecan haber
sido ignorados o disfuncionales. Frederick Lemieux, director del programa de posgrado en
Inteligencia Aplicada de la Universidad de Georgetown, culp de la violacin a lo que llam
"complicidad pasiva" en la cultura de la empresa. (La complicidad significa estar involucrado en
un acto indebido; la complicidad pasiva implica que los ej.
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June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
The Challenger.pdf DNHS Official Publication
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
special B.ed 2nd year old paper_20240531.pdf
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Unit 2- Research Aptitude (UGC NET Paper I).pdf
This slide describes the research aptitude of unit 2 in the UGC NET paper I.
The Roman Empire A Historical Colossus.pdf
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Thesis Statement for students diagnonsed withADHD.ppt
Presentation required for the master in Education.
The approach at University of Liverpool.pptx
How libraries can support authors with open access requirements for UKRI funded books
Wednesday 22 May 2024, 14:00-15:00.
Palestine last event orientationfvgnh .pptx
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Supporting (UKRI) OA monographs at Salford.pptx
How libraries can support authors with open access requirements for UKRI funded books
Wednesday 22 May 2024, 14:00-15:00.
Operation Blue Star - Saka Neela Tara
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The geography of Taylor Swift - some ideas
Geographical themes connected with Taylor Swift's ERAS tour - coming to the UK in June 2024
Synthetic Fiber Construction in lab .pptx
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
678020731-Sumas-y-Restas-Para-Colorear.pdf
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The French Revolution Class 9 Study Material pdf free download
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Home assignment II on Spectroscopy 2024 Answers.pdf
Answers to Home assignment on UV-Visible spectroscopy: Calculation of wavelength of UV-Visible absorption
Biological Screening of Herbal Drugs in detailed.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
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June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
Thesis Statement for students diagnonsed withADHD.ppt
Thesis Statement for students diagnonsed withADHD.ppt
The French Revolution Class 9 Study Material pdf free download
The French Revolution Class 9 Study Material pdf free download
Home assignment II on Spectroscopy 2024 Answers.pdf
Home assignment II on Spectroscopy 2024 Answers.pdf
Can you please separate the code into imagespy sharpenpy.pdf
- 1. Can you please separate the code into images.py, sharpen.py, & smokey.gif Instructions 1 #Header file section for image module. The edge-detection function (detectEdges) described in Chapter 7 and shown below returns a 2 from images import Image black and white image. Think of a similar way to transform color values so that the new image is 3 #Function defition of still in its original colors but the outlines within it are merely sharpened. edge_detection: This function 4 #is used to identifying and def detectEdges(image, amount): marking the outline "" "Builds and returns a new image in which the edges of 5 # of an image. the argument image are highlighted and the colors are 6 def edge_detection(image, reduced to black and white."" " degree, threshold): 7 #Function defition of def average(triple): average is used to (r,g,b)= triple 8 #calculate the average of return(r+g+b)//3 given pixel blackPixel =(0,0,0) whitePixel =(255,255,255) new = image.clone () for y in range(image.getHeight () - 1): for x in range( 1 , image.getWidth ()) : oldPixel = image. getPixel(x,y) leftPixel = image.getPixel (x1,y) bottomPixel = image. getPixel(x,y+ 1) oldLum = average ( oldPixel ) leftLum = average ( left tixel ) bottomLum = average (bottomPixel ) if abs(oldLum - leftLum) > amount or 1 abs(oldLum - bottomLum) > amount: new. setPixel ( x,y, blackPixel) else:Instructions CENGAGE | MINDTAP Q Search this course Programming Exercise 7.10