Notes, summaries, assignments, exams, and problems for Electronics

In ground wave propagation, radio waves travel along the surface of the Earth, following the curvature of the planet. Freq Range: 30 kHz to 300 kHz (Low Freq). Key Points: Travels along the ground. Strongly attenuated by terrain, trees, buildings, etc. Limited to short distances (few hundred km). APPS: AM radio (MW band). Navigation systems & military communication.In sky wave propagation, radio waves R transmitted towards the ionosphere, which reflects or refracts them back to Earth. Freq Range: 3 MHz to 30 MHz (High Freq). Key Points: Enables long-distance communication, even intercontinental. Depends on time of day, solar activity, & ionospheric conditions. Reflected by ionospheric layers (F-layer, E-layer, etc.). APPS: International... Continue reading "Understanding Radio Wave Propagation and Signal Processing" »

Phonetic vs. Phonological Perspectives

Looking at syllables from a phonetic point of view is different from the phonological perspective. Phonetically, syllables are described according to how they are produced and how they sound.

A syllable normally has a central part, which has little or no obstruction to the airflow and sounds louder. This central part is usually a vowel. Before and after this centre (that is, at the beginning and end of the syllable), there is more obstruction to the airflow and the sounds are usually less loud.

The Articulatory Approach

One phonetic approach tries to define syllables according to the articulatory effort needed to produce them. The psychologist R. H. Stetson suggested that syllables are related to chest pulses,... Continue reading "Phonetic Analysis of Syllable Structure and Division" »

1. Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_breast_cancer TE LO DA
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.feature_selection import RFE
from sklearn.preprocessing import StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.preprocessing import OneHotEncoder

2. Load Data

data = load_breast_cancer()
X = pd.DataFrame(data.data, columns=data.feature_names)
y = pd.Series(data.target)

3. Clean Data

datos.isnull().sum()

datos.replace("?", np.nan, inplace=True)
datos.dropna(inplace=True)

4.

Ejercicios de Inglés Técnico Resueltos

1. Ubicación y Preposiciones de Lugar

3. Reading: Corrige esta descripción. Hay seis errores de ubicación.

Texto Corregido: Here is one way to set up your computer station. Put your screen in the centre of the system. Then put one speaker in the centre on the left, and put the other speaker at the bottom on the right. Put the scanner at the top on the right, and put the computer at the top on the left. Then put the printer at the top in the middle and put the DVD drive at the bottom on the left. Finally, put the keyboard at the bottom on the left, and put the mouse at the bottom on the right.

4. Language: Mira de nuevo la estación de computación en 3. ¿Son estas afirmaciones verdaderas (T) o falsas

Polo: Reading Comprehension

1. Choose the Correct Answer

• 1. b. A team usually consists of four players.
• 2. a. Has fewer people in a team than original bicycle polo.

2. True or False

• 1. True - Evidence: "Goals are scored by hitting the ball between two goalposts."
• 2. False - Evidence: "Getting control of the ball isn't easy because ball speeds can reach nearly 180 kph."
• 3. True - Evidence: "Invented in 1891 in Ireland by Richard Mecredy."

3. Complete the Sentences

• 1. A polo horse's abilities greatly influence a player's performance.
• 2. Elephant polo is controversial due to accusations of elephant mistreatment.
• 3. Seattle, Washington, is where the new version of the game, "Hardcourt" bike polo, began.

4. Vocabulary: Find Words in the Text

• 1. Spans
• 2. Basics
• 3.

To control by PWM requires 2 factors:

1-crossing detection signal by 0.

2-? determine the time, every time you walk by 0.

to detect the zero crossing can be performed
through an operational amplifier connected
comparator.

TCA = 785

Pin Symbol Function

1 GND Earth

2 Q2 *Output 2 Inverted

3 Output U QU

4 Q1 * Output 1 Inverted

5 5Synchronizing voltage Vsync (to detect

zero crossing)

6 Inhibitor

7 QZ Z Output

8 8 Vref voltage stabilized (continuous signal for the

integrator)

9 9 ramp resistor R9 (for the integrator)

10 C10 ramp Capacitance (for the integrator)

11 11 V11 Control Voltage (for comparator)

12 12 C12 pulse Extension

13 LLong pulse

14 Q1 Output 1

15 Q2 Output 2

16 l sSupply voltage.

Kinematics Data Set Analysis

The following tables represent experimental data for kinematics, including velocity, acceleration, and time intervals. These parameters are essential for solving complex motion equations in physics.

Motion Parameters and Calculations: Set 1

Motion Parameters and Calculations: Set 2

Motion Parameters and Calculations: Set 3

Motion Parameters and Calculations: Set 4

AVR Microcontroller Programming Examples

This collection of embedded C and AVR assembly examples covers common hardware interfacing and programming techniques for AVR microcontrollers, including LCD display control, matrix keypad scanning, 7-segment displays, bit manipulation, delays, and I/O port handling.

LCD Display

The following program initializes a 16x2 LCD in 8-bit mode and prints text on both lines.

#define F_CPU 1000000UL //1 MHz clock
#include <avr/io.h>
#include <util/delay.h>

void lcdCommand(unsigned char cmnd)
{
    PORTA = cmnd;
    PORTB &= ~(1<<0);  //RS = 0 for command
    PORTB &= ~(1<<1);  //RW = 0 for write
    PORTB |= (1<<2);    //EN = 1 for H to L pulse
    _delay_us(1);
    PORTB &

Preprocessing the Dataset

a. Normalize the Text

Python

import re
from nltk.tokenize import word_tokenize
import nltk
nltk.download('punkt')

# Preprocessing Function
def preprocess(text):
    text = re.sub(r'[^a-z\s]', '', text.lower())
    return word_tokenize(text)

# Apply Preprocessing
df['Processed'] = df['Sentence'].apply(preprocess)
print(df)

GPT-2 Text Generation

Python

from transformers import pipeline

# Load GPT-2 Model for Text Generation
generator = pipeline('text-generation', model='gpt2')

# Generate Text for a Given Prompt
prompt = "Once upon a time"
result = generator(prompt, max_length=50,
    num_return_sequences=1)
print(result[0]['generated_text'])

GPT-2 for AI Prompts

a. Prompt 1 - Future of AI

Python

prompt = "What is the future

Role of the Teacher

• Facilitator: Guides learning / Guía el aprendizaje
• Fosters autonomy: Fomenta autonomía
• Student-centered classes
• Scaffolding and positive feedback
• Avoids pressure: Evita presión y juicios negativos

Classroom Management

• Maintain order and attention
• Clear routines
• Attention-grabbers
• Change activities every 15–20 minutes
• Use of different spaces
• Positive reinforcement and autonomy

Communicative Language Teaching

• Communicative approach: Enfoque comunicativo
• Meaningful language: Lenguaje con significado
• Real-life situations
• Use of language to communicate

4 Dimensions of Communicative Competence

1. Linguistic: Vocabulario, gramática, sintaxis
2. Sociolinguistic: Normas sociales, cultura
3. Discourse: Cohesión y coherencia
4. Strategic: Compensar fallos, estrategias