New devices and device platforms are continually being released as the.pdf
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New devices and device platforms are continually being released as the IoT landscape matures. You need to understand the key characteristics that are common across most IoT devices to compare and evaluate new devices as they become available. Which of the following high-level capabilities characterize IoT devices? Select one: a. Data acquisition and control b. Data processing and storage c. Connectivity d. All of the above Clear my choice.
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#pragma once
#include <functional> // std::less
#include <iostream>
#include <queue> // std::queue
#include <utility> // std::pair
template <typename K, typename V, typename Comparator = std::less<K>>
class BinarySearchTree
{
public:
using key_type = K;
using value_type = V;
using key_compare = Comparator;
using pair = std::pair<key_type, value_type>;
using pointer = pair*;
using const_pointer = const pair*;
using reference = pair&;
using const_reference = const pair&;
using difference_type = ptrdiff_t;
using size_type = size_t;
private:
struct BinaryNode
{
pair element;
BinaryNode *left;
BinaryNode *right;
BinaryNode( const_reference theElement, BinaryNode *lt, BinaryNode *rt )
: element{ theElement }, left{ lt }, right{ rt } { }
BinaryNode( pair && theElement, BinaryNode *lt, BinaryNode *rt )
: element{ std::move( theElement ) }, left{ lt }, right{ rt } { }
};
using node = BinaryNode;
using node_ptr = node*;
using const_node_ptr = const node*;
node_ptr _root;
size_type _size;
key_compare comp;
public:
BinarySearchTree() {
// TODO
}
BinarySearchTree( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree( BinarySearchTree && rhs ) {
// TODO
}
~BinarySearchTree() {
// TODO
}
const_reference min() const { return min( _root )->element; }
const_reference max() const { return max( _root )->element; }
const_reference root() const {
// TODO
}
bool contains( const key_type & x ) const { return contains( x, _root ); }
value_type & find( const key_type & key ) { return find( key, _root )->element.second; }
const value_type & find( const key_type & key ) const { return find( key, _root )-
>element.second; }
bool empty() const {
// TODO
}
size_type size() const {
// TODO
}
void clear() {
clear( _root );
_size = 0;
}
void insert( const_reference x ) { insert( x, _root ); }
void insert( pair && x ) { insert( std::move( x ), _root ); }
void erase( const key_type & x ) { erase(x, _root); }
BinarySearchTree & operator=( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree & operator=( BinarySearchTree && rhs ) {
// TODO
}
private:
void insert( const_reference x, node_ptr & t ) {
// TODO
}
void insert( pair && x, node_ptr & t ) {
// TODO
}
void erase( const key_type & x, node_ptr & t ) {
// TODO
}
const_node_ptr min( const_node_ptr t ) const {
// TODO
}
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// TODO
}
bool contains( const key_type & x, const_node_ptr t ) const {
// TODO
}
node_ptr find( const key_type & key, node_ptr t ) {
// TODO
}
const_node_ptr find( const key_type & key, const_node_ptr t ) const {
// TODO
}
void clear( node_ptr & t ) {
// TODO
}
node_ptr clone ( const_node_ptr t ) const {
// TODO
}
public:
template <typename KK, typename VV, typename CC>
friend void printLevelByLevel( const BinarySearchTree<KK, VV, CC>& bst, std::ostream & out
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#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
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latent_dimensions = 100
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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"))
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model.add(Activation("tanh"))
#Generating the output image
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image = model(noise)
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#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 the generated images.
def display_images():
r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
#Scaling the generated images
generated_images = 0.5 * genera.
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#include<stdio.h>
#include<stdlib.h>
#include<math.h>
#include<string.h>
#define MAX_N 500000
#define MAX_S 10
struct tugboat {
int x, y, dist;
char name[21];
};
int cmp(const void* a, const void* b) {
const struct tugboat* t1 = (const struct tugboat*) a;
const struct tugboat* t2 = (const struct tugboat*) b;
if (t1->dist != t2->dist) {
return t1->dist - t2->dist;
} else {
return strcmp(t1->name, t2->name);
}
}
int main() {
int xs, ys, xn, yn;
scanf("%d%d%d%d", &xs, &ys, &xn, &yn);
int n;
scanf("%d", &n);
struct tugboat tugboats[MAX_N];
int i;
for (i = 0; i < n; i++) {
scanf("%d%d%s", &tugboats[i].x, &tugboats[i].y, tugboats[i].name);
int dx = tugboats[i].x - xs;
int dy = tugboats[i].y - ys;
tugboats[i].dist = dx * dx + dy * dy;
}
qsort(tugboats, n, sizeof(struct tugboat), cmp);
int num_tugs = 0;
char tugs[MAX_N][21];
int prev_x = xs, prev_y = ys;
for (i = 0; i < n; i++) {
if (tugboats[i].x >= prev_x && tugboats[i].y >= prev_y) {
num_tugs++;
strcpy(tugs[num_tugs - 1], tugboats[i].name);
prev_x = tugboats[i].x;
prev_y = tugboats[i].y;
}
}
printf("%d\n", num_tugs);
for (i = 0; i < num_tugs; i++) {
printf("%s\n", tugs[i]);
}
return 0;
}
Here are the instructions:
Tugboat Troubles Objective Give practice with sorting in C. Story The ship's engine has ceased,
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Need help with the GUI buttons- I have the menu and the first section.pdf
Need help with the GUI buttons. I have the menu and the first section to load media. I need help
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MovieDVD, and MusicCD classes from Week 3 -> Practice Exercise - Inheritance solution. -
Add an attribute to Media class to store indication when media object is rented versus available.
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of Media objects - has functionality to load Media objects from files - creates/updates Media
files - has functionality to add new Media object to its Media list - has functionality to find all
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file for sample look and feel. (Hint: for command-driven menu check out Week 2: Practice
Exercise - EncapsulationPlus and for GUI check out Week 8: Files in GUI example) - selection
to load Media files from a given directory (user supplies directory) - selection to find a media
object for a specific title value (user supplies title and should display to user the media
information once it finds it - should find all media with that title) - selection to rent a media
object based on its id value (user supplies id and should display rental fee value to the user) -
selection to exit program 4) Program should throw and catch Java built-in and user-defined
exceptions as appropriate 5) Your classes must be coded with correct encapsulation:
private/protected attributes, get methods, and set methods and value validation 6) There should
be appropriate polymorphism: overloading, overriding methods, and dynamic binding 7)
Program should take advantage of the inheritance properties as appropriate.
Instructions for Submissions thorugh G- Classroom.pptx
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
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#pragma once
#include <functional> // std::less
#include <iostream>
#include <queue> // std::queue
#include <utility> // std::pair
template <typename K, typename V, typename Comparator = std::less<K>>
class BinarySearchTree
{
public:
using key_type = K;
using value_type = V;
using key_compare = Comparator;
using pair = std::pair<key_type, value_type>;
using pointer = pair*;
using const_pointer = const pair*;
using reference = pair&;
using const_reference = const pair&;
using difference_type = ptrdiff_t;
using size_type = size_t;
private:
struct BinaryNode
{
pair element;
BinaryNode *left;
BinaryNode *right;
BinaryNode( const_reference theElement, BinaryNode *lt, BinaryNode *rt )
: element{ theElement }, left{ lt }, right{ rt } { }
BinaryNode( pair && theElement, BinaryNode *lt, BinaryNode *rt )
: element{ std::move( theElement ) }, left{ lt }, right{ rt } { }
};
using node = BinaryNode;
using node_ptr = node*;
using const_node_ptr = const node*;
node_ptr _root;
size_type _size;
key_compare comp;
public:
BinarySearchTree() {
// TODO
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BinarySearchTree( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree( BinarySearchTree && rhs ) {
// TODO
}
~BinarySearchTree() {
// TODO
}
const_reference min() const { return min( _root )->element; }
const_reference max() const { return max( _root )->element; }
const_reference root() const {
// TODO
}
bool contains( const key_type & x ) const { return contains( x, _root ); }
value_type & find( const key_type & key ) { return find( key, _root )->element.second; }
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bool empty() const {
// TODO
}
size_type size() const {
// TODO
}
void clear() {
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}
void insert( const_reference x ) { insert( x, _root ); }
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void erase( const key_type & x ) { erase(x, _root); }
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// TODO
}
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// TODO
}
private:
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// TODO
}
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// TODO
}
void erase( const key_type & x, node_ptr & t ) {
// TODO
}
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// TODO
}
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// TODO
}
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// TODO
}
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// TODO
}
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// TODO
}
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// TODO
}
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// TODO
}
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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
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from keras.optimizers import Adam,SGD
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#The number was randomly chosen and any number
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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"))
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model.add(Dropout(0.25))
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model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
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#Step 6: Define a utility function to display the generated images.
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r, c = 4,4
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#include<stdio.h>
#include<stdlib.h>
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#define MAX_S 10
struct tugboat {
int x, y, dist;
char name[21];
};
int cmp(const void* a, const void* b) {
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int xs, ys, xn, yn;
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scanf("%d", &n);
struct tugboat tugboats[MAX_N];
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printf("%s\n", tugs[i]);
}
return 0;
}
Here are the instructions:
Tugboat Troubles Objective Give practice with sorting in C. Story The ship's engine has ceased,
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Priya Shaw - https://www.linkedin.com/in/priya-shaw
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Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
Giridhar Meka - https://www.linkedin.com/in/giridharmeka
Priya Shaw - https://www.linkedin.com/in/priya-shaw
Shyam Raj Prasad-
https://www.linkedin.com/in/shyam-raj-prasad/
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