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

Pumping Lemma and Non-Regular Languages

9) Define Pumping Lemma. Prove that the language L = { aⁱ b^j | i > j } is not a regular language:

Let L be a regular language. There exists a constant k such that for every string ω in L, where the length of string ω i.e |ω| ≥ k, we can break ω into 3 strings i.e ω = xyz following conditions:

• (i) |xy| ≤ k
• (ii) y ≠ ε i.e |y| > 0
• (iii) for each q ≥ 0, xy^qz ∈ L

Let L be a regular language. w = a^(k+1) b^k is a long string (given k > 0). |w| = 2k + 1, w = xyz should satisfy:

• (i) |xy| ≤ k
• (ii) y ≠ 0
• (iii) for each q ≥ 0, xy^q z ∈ L

Let k = 3, w = a⁴ b³, which is aaaaabbb.

• (i) |xy| ≤ k, 3 ≤ 3
• (ii) y ≠ 0, y = a

Minimization of DFA's

11) Minimization of DFA’s A=(Q, Σ, δ, q₀, F):

Use... Continue reading "Pumping Lemma, Table Filling, DFA Minimization, Parse Trees, Ambiguous Grammars & Context-Free Grammars" »

Counterpoint System Codes

• SVC AVINST: 1010349
• AV FREIGHT: 1012348
• INTERNATIONAL FREIGHT: 1002100
• ORDER HOLD: 1103013
• 30 DAY WARRANTY: 1010447
• 60 DAY WARRANTY: 1010450
• 90 DAY WARRANTY: 1010451
• 1 YEAR WARRANTY: 1010448
• 2 YEAR WARRANTY: 1126476
• 3 YEAR WARRANTY: 1010449
• CHANGE ORDER: 1106948
• CHANGE ORDER – CR: 1134105
• AV RESTOCK / RETURN FREIGHT: 1129446
• SVC AGMNT/MAINT: 1010346
• AV PRICE ADJUST: 1102227
• SVC PRICE ADJUST: 1102208
• COST ADJUST AV: 1102319
• COST ADJUST SVC: 1102321
• TAX ADJUST: 1104916
• 1000 OFFSET (SALE): 1103132
• 6000 OFFSET (SERVICE DEPT): 1103305
• 9000 OFFSET (AV DEPT): 1121772
• AV SVC AGMNT -1: 1103132
• AV SVC AGMNT OFFSET: 1137139
• AV SVC DIFF OFFSET -1: 1137989
• SERVICE TICKET HOLD: 1128671
• AV TECHNICAL REPAIR: 1137141
• SVC CONTRACTOR DIFF: 1121527
• INSTALLATION

English Vocabulary Acquisition

How Children Learn Word Meaning

Children are always acquiring and building up their L1. They must find out which things can be packaged together under a label.

Knowing a Word: Types of Knowledge

Knowing a word involves different types of knowledge. The importance is how it is pronounced.

Factors Affecting Vocabulary Learning

• Demonstrability: Can the meaning be easily shown? (e.g., car = transport)
• Similarity: Is it similar to known words? (e.g., equal)
• Brevity: Is it a short or long word?
• Regularity of Form / Learning Load: Does it follow typical patterns? (e.g., bedroom)
• Opportunism: Is the word relevant to the child's immediate situation?
• Centres of Interest: Are words likely to be of relevance to the child's interests?

Key Concepts in Language Acquisition and Child Development

Questions & Answers

1. According to Chomsky, language is... A (We possess an innate capacity for language.)
2. Which of the following statements is true? C (Learners need a supportive environment.)
3. Behaviorism relies on... A (Skinner's theory of operant conditioning.)
4. According to Krashen... C (Comprehensible input is important.)
5. According to constructivism... A (Children of a similar age think similarly.)
6. The term "concentration span" refers to... A (The time and capacity for focused attention.)
7. Comprehensible input means... B (The teacher uses facial expressions and gestures.)
8. Language acquisition does not require... D (All of the above.)
9. What is the zone of proximal development? A (The zone

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.

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

When past simple and while past continuous. Used to: affirmative - S+used to+v.ing, negative - S+didn't use to+inf, interrogative - Did+S+use to+inf? Yes, I did. No, I didn't. Active Voice: Subject+verb+D.C.+Complement

Passive Voice: Subject+verb+D.C. (by)+Complement

Modals

• can/can't -> puede/no puede
• could/couldn't -> podía/no podía
• should/shouldn't -> debería/no debería -> consejo
• must -> deber -> obligación
• mustn't -> no deber -> prohibición
• have to -> tener que
• don't have to -> no tener que -> no obligación
• may -> puedo que
• might -> podía ser que -> posibilidad

Reported Speech

• now -> then
• today -> that day
• tomorrow -> the next day/the following day
• yesterday -> the previous day/the day before
• next

[Ch-1] Hardware = [Physical](iPhone) - vs - Software -- (iOS) = [Programs with instructions] -- [Layers of a Computing System] - (C.A.O.P.H.I) Communications -> Applications -> Operating Systems -> Programming -> Hardware

Information -- Abstraction - [a mental model that removes complex details] - Applies to (Miller's Law) [the average person can hold in working memory about seven items] Internal View = car engine, while Abstract View = driving -- Stonehenge, Wiltshire, England 3000 BC to 2000 BC -- Abacus - [early device to record numerical values] - Blaise Pascal - [Mechanical device for addition, subtraction, multiplication, and division] -- Joseph Jacquard - [Loom, punched card], Charles Babbage - [Analytical engine] -- Ada Lovelace... Continue reading "Understanding Hardware and Software in Computing Systems" »

INGREDIENTES:

• Present Simple: pronoun + present form of verb.
• Present Continuous: pronoun + am/is/are + verb + -ing.
• Past Simple: pronoun + second column.
• Past Continuous: pronoun + was/were + verb + -ing.
• Past Perfect: pronoun + had + third column.
• Present Perfect Simple: pronoun + have/has + third column.
• Present Perfect Continuous: pronoun + have/has + been + verb + -ing.

MODO DE USO:

• Extreme Adjectives: totally, completely, and absolutely.
• Normal Adjectives: very, extremely, rather, and quite.
• Both: really.
• Vocabulary: u1 arrogant, bossy, calm, cheerful, clever, confident, friendly, funny, hard-working, impatient, lazy, nervous, nice, patient, quite, reliable, selfish, serious, shy, talkative, tidy, unfriendly, untidy. u2 break down, check in, get

Presente simple

Afirmativa: persona + verbo (3a sg -s)(He, She, It) I play Yo juego

Negativa: persona + don't/doesn't(3a sg) + verbo You don't play

Interrogación: do/does(3a sg) + persona + verbo Does she play?

Presente continuo

Afirmativa: persona + to be (am, is, are) + verbo (-ing)I am playing Yo estoy juego

Negativa: persona + am not/ isn't/ aren't + verbo (-ing) You aren't playing

Interrogación: to be + persona + verbo (-ing) Is he playing?

Pasado simple

Afirmativa: persona+ verbo (-ed/ 2ª columna irregulares) I played Yo jugué

Negativa: persona + didn't + verbo He didn't play

Interrogación: did + persona + verbo Did you play?

Pasado continuo

Afirmativa: persona + to be en pasado (was/were) + verbo (-ing) I was playing Yo estaba jugando

Negativa:... Continue reading "English Verb Tenses: Simple, Continuous, Perfect, and Future" »