Notes, abstracts, papers, exams and problems of Computers

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Developing Logical and Mathematical Thinking in Children

Classified in Computers

Written at on English with a size of 3.73 KB.

What is Mathematical Logical Thinking?

These are the skills students develop associated with logical and mathematical concepts, reasoning, comprehension, and exploration of the world through real proportions, thus strengthening more abstract aspects of thought.

Geometry with Dinosaurs

This activity involves cutting out various geometric shapes with EVA rubber. Children will then create their own dinosaurs using these shapes. Through this activity, they can learn geometric shapes, count the number of elements used in each dinosaur (like the sides of the shapes), and create new geometric shapes from the ones they already have.

Logical Reasoning with Chupa Chups

This activity consists of creating logically structured material and playing with it using... Continue reading "Developing Logical and Mathematical Thinking in Children" »

LEGv8 Architecture and Assembly Language: Key Concepts

Classified in Computers

Written at on English with a size of 239.58 KB.

Performance Metrics

  • Elapsed Time: Represents overall system performance. It is the total time taken to complete a task.
  • User CPU Time: Indicates CPU performance. It is the time the task actively runs on the CPU, excluding idle time.
  • CPU Time: The time the CPU spends executing instructions, either from the task or the operating system, excluding idle time.
  • Clock Speed: 1 MHz equals 1 million clock cycles per second. 1 GHz equals 1 billion clock cycles per second.
  • Response Time: Equivalent to execution time.
  • Throughput: Equivalent to bandwidth.
  • Performance Comparison: (PerfA) / (PerfB) = (ExecTimeB) / (ExecTimeA) = n

Impact of Processor Upgrades

  • Replacing a processor with a faster one decreases response time and increases throughput.
  • Adding an additional
... Continue reading "LEGv8 Architecture and Assembly Language: Key Concepts" »

Tensors and Variables in PyTorch and TensorFlow

Classified in Computers

Written at on English with a size of 2.4 KB.

Tensors and Variables in PyTorch and TensorFlow

Here's a brief explanation of tensors and variables in the context of deep learning frameworks like PyTorch and TensorFlow:

Tensors

  • Definition: A tensor is a multi-dimensional array used to represent data (such as scalars, vectors, matrices, or higher-dimensional data).
  • Common Operations: Tensors can be manipulated with mathematical operations (addition, multiplication, etc.), reshaped, sliced, etc.

In PyTorch, tensors are the core data structure:

import torch
# Create a tensor
a = torch.tensor([[1, 2], [3, 4]])
# Basic operations
b = a + 2         # Adds 2 to each element
c = a * a         # Element-wise multiplication
d = a @ a         # Matrix multiplication

Output:

Tensor `b`: [[3, 4], [5, 6]]
Tensor
... Continue reading "Tensors and Variables in PyTorch and TensorFlow" »

C++ Concepts: Exception Handling to Friend Functions

Classified in Computers

Written at on English with a size of 3.16 KB.

Exception Handling

#include <iostream>
#include <stdexcept>
using namespace std;
int main() {
    try {
        int numerator = 10;
        int denominator = 0;
        int res;
        if (denominator == 0) {
            throw runtime_error("Division by zero not allowed!");
        }
        res = numerator / denominator;
        cout << "Result after division: " << res << endl;
    }
    catch (const exception& e) {
        cout << "Exception " << e.what() << endl;
    }
    return 0;
}

Operator Overloading

#include <iostream>
using namespace std;
class Test {
private:
    int num;
public:
    Test(): num(8){}
    void operator ++() {
        num = num + 2;
    }
    void Print() {
... Continue reading "C++ Concepts: Exception Handling to Friend Functions" »

CUDA Matrix Multiplication: Shared Memory

Classified in Computers

Written at on English with a size of 3.23 KB.

CUDA Matrix Multiplication Using Shared Memory

This code demonstrates matrix multiplication in CUDA, leveraging shared memory for optimization. It includes two examples: a kernel using shared memory and a host-side implementation using the Thrust library.

CUDA Kernel with Shared Memory

The following CUDA kernel performs matrix multiplication using shared memory to optimize data access:


__global__ void matMulShared(int *A, int *B, int *C, int rowsA, int colsA, int colsB) {
    __shared__ int tile_A[TILE_SIZE][TILE_SIZE], tile_B[TILE_SIZE][TILE_SIZE];
    int row = blockIdx.y * TILE_SIZE + threadIdx.y, col = blockIdx.x * TILE_SIZE + threadIdx.x, temp = 0;
    for (int i = 0; i < (colsA + TILE_SIZE - 1) / TILE_SIZE; ++i) {
        if (row <
... Continue reading "CUDA Matrix Multiplication: Shared Memory" »

Nmap, Netcat, and Metasploit Commands Cheat Sheet

Classified in Computers

Written at on English with a size of 7.67 KB.

Nmap Options

-PE: Quickly check if host is up.

-sn: Disable port scanning (host discovery).

-n: Disables DNS resolution (checks IP online without looking up hostnames).

-O: OS detection.

-A: OS detection, Version detection, Script scanning, traceroute.

-sV: Service detection (banner info, version).

-vV: Provides verbose output.

-sC: Scan with default scripts for additional info gathering.

--min-rate=5000: Ensures scan sends at least 5k packets per second.

nmap --script smb-enum-shares.nse -p 445 (ip): List shares and their properties.


To see scripts starting with X: ls /path/X

To execute script with script tracing: sudo nmap -script=smb-os-discovery -script-trace target_ip

To enumerate the SMB share files: sudo nmap -script=smb-enum-shares target_ip

Vulnerability... Continue reading "Nmap, Netcat, and Metasploit Commands Cheat Sheet" »

BPSK and QPSK Modulation Techniques with Python

Classified in Computers

Written at on English with a size of 4.97 KB.

BPSK Signal Generation

This section demonstrates the generation of a Binary Phase Shift Keying (BPSK) signal using Python.

import numpy as np
import matplotlib.pyplot as plt

def bpsk_detect(modulated_signal, carrier):
    return np.sign(modulated_signal * carrier)

message_frequency = 10
carrier_frequency = 20
sampling_frequency = 30 * carrier_frequency
t = np.arange(0, 4/carrier_frequency, 1/sampling_frequency)
message = np.sign(np.cos(2 * np.pi * message_frequency * t) + np.random.normal(scale = 0.01, size = len(t)))
carrier = np.cos(2 * np.pi * sampling_frequency/carrier_frequency * t)
modulated_signal = carrier * message
detected_message = bpsk_detect(modulated_signal, carrier)

plt.figure(figsize=(12, 8))
plt.subplot(4, 1, 1)
plt.plot(t,
... Continue reading "BPSK and QPSK Modulation Techniques with Python" »

Digital Logic Circuits: Flip-Flops, Comparators, Decoders, and Registers

Classified in Computers

Written at on English with a size of 4.04 KB.

Digital Logic Circuits

Flip-Flops

Clocked RS Flip-Flop Drawbacks

Clocked RS flip-flops have some drawbacks, such as susceptibility to race conditions, where the output can become unpredictable if the inputs change too close to the clock edge. They also require careful handling of the inputs to avoid metastability issues, which can lead to incorrect output states. Additionally, they can have higher power consumption compared to other flip-flop types due to the need for a clock signal.

JK Flip-Flop Operation

Content about JK Flip-Flop operation, characteristic table, characteristics equation, circuit diagram, and timing diagram would be added here.

Magnitude Comparator

What is a Magnitude Comparator?

A magnitude comparator is a digital circuit that compares... Continue reading "Digital Logic Circuits: Flip-Flops, Comparators, Decoders, and Registers" »

Software Design Principles and Patterns

Classified in Computers

Written at on English with a size of 4.98 MB.

Lecture 2: Dynamic Dispatch and Interfaces

  • Dynamic Dispatch: The process of selecting which implementation of a polymorphic operation to call at runtime.
  • Interface: Calling a method that is not in the interface will cause a compilation error.

Lecture 3: N/A

Lecture 4: Method Contracts, Exceptions, and Unit Testing

  • Method Contract: Should define pre/post conditions and exceptional behavior. The client is to blame if the precondition is not met, and the service is to blame if the postcondition is not met. Exceptional behavior specifies what the code will do if a precondition is violated.
  • Exception: Runtime exception (unchecked) and IO exception (checked). The IO exception must be caught; otherwise, the code won't compile.
  • Unit Test: Test boundary
... Continue reading "Software Design Principles and Patterns" »

C++ Priority Queue Implementation: Code & Explanation

Classified in Computers

Written at on English with a size of 3.58 KB.

C++ Priority Queue Implementation

This document provides a C++ implementation of a priority queue using a heap data structure. The code includes the class definition, member functions, and supporting utilities.

Priority Queue Class Definition


#ifndef priority_queue_h_
#define priority_queue_h_

#include <iostream>
#include <vector>
#include <cassert>

template <class T>
class priority_queue {
private:
    std::vector<T> m_heap;

public:
    priority_queue() {}

    priority_queue(std::vector<T> const& values)
    {
        m_heap = values;
        for (int i = 0; i < m_heap.size(); i++){
            percolate_down(i);
            for (int j = i; j < m_heap.size(); j++){
                percolate_down(
... Continue reading "C++ Priority Queue Implementation: Code & Explanation" »