Manasa S
Spark your engineering passion with interactive and detailed lessons!
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Manasa S
Bachelors degree
/ 55 min
About your engineering tutor
Hello, I'm Manasa S, a Masters graduate specialized in Power Electronics. My teaching philosophy revolves around making complex Engineering concepts easy to understand. I believe in engaging students through interactive learning methods, practical examples, and real-world applications. With expertise in Electronics and Electrical Engineering, I cater to students at all levels in College. Join me on a learning journey where we can explore and conquer Engineering Sciences together!
Meet Manasa
Manasa graduated from Visvesvaraya technological University
Engineering tutor specialities
Common Core State Standards - CCSS (USA)
State-Specific Standards (USA)
Upskilling
Test prep
Australian Curriculum (AU)
Research paper
International Baccalaureate (IB)
Lab work
Project help
Advanced Placement (AP) Program (USA)
A-Levels (UK)
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ADHD
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ASD
Engineering class snapshot
As an Electronics and Electrical Engineering tutor, I cater to college and all levels of students by making learning interactive, structured, and systematic. My teaching is concept-focused, detailed, and emphasizes problem-solving. I believe in a hands-on and practical approach to help students understand complex engineering concepts. I engage students in project-based assessments where they design and simulate engineering solutions, customizing simulations based on their engineering focus. Providing feedback on design projects, I use CAD models to illustrate areas for improvement. I also help students apply theoretical knowledge to practical design and innovation through CAD software.
Your engineering tutor also teaches
Electrical Engineering
Electronics Engineering
Engineering concepts taught by Manasa
The Student and Tutor worked through several practice questions from past papers covering fundamental concepts in Electrical Engineering. They analyzed voltage followers, diode characteristics using the piece-wise linear model, CMOS inverter operating regions based on input voltage and VTC graphs, and performed DC analysis for a BJT circuit to determine various node voltages and currents. This session was part of the Student's exam preparation.
Voltage Follower (Op-Amp Buffer)
Diode Piece-wise Linear Model: Off State
CMOS Inverter Operating Regions
Determining Output Voltage in CMOS Inverter Transition Region
PNP Bipolar Junction Transistor (BJT) DC Bias Analysis
The Student and Tutor reviewed challenging problems from a probabilistic methods class, focusing on topics like probability generating functions, normal distributions, linear transformations, joint and marginal distributions, and conditional expectation. They planned to continue reviewing material for the upcoming final exam.
Probability Generating Functions (PGF)
Linear Transformations of Normal Random Variables
Marginal CDF from Joint CDF
Chebyshev's Inequality
Independence of Random Variables
The student and tutor reviewed past exam questions for a Signals and Systems course, focusing on sampling theory, DTFT, and the relationship between continuous-time and discrete-time signals. They analyzed problems related to digital vs. analog systems, aliasing, and signal processing with filters. Future sessions were planned to review lecture content and work through the remaining problems.
Sampling Theory and Aliasing
Signal Processing Pipeline: Analog to Digital to Discrete
Discrete-Time Fourier Transform (DTFT) and Convolution
Advantages of Digital vs. Analog Signals
The student and tutor worked through several practice problems related to probability and random variables, covering discrete and continuous cases. They practiced calculating joint and marginal PMFs/CDFs, applied binomial modeling, and worked with geometric probability within a circular region. The session concluded with some questions on integration limits and constants in probability density functions.
Joint and Marginal Probability Mass Functions (PMFs)
Binomial Distribution and Independent Bernoulli Trials
Conditional Probability and Independence
Continuous Random Variables and Joint CDFs
The Student and Tutor reviewed modules 11 through 14, focusing on functions of random variables, probability bounds (Markov, Chebyshev, Chernoff), and joint/marginal distributions for discrete and continuous two-dimensional random variables. They practiced deriving PMFs/PDFs and calculating expectations and probabilities using these concepts, with the Student planning to review module 14 further.
Chernoff Bound
Joint and Conditional Distributions
Probability Bounds (Markov's
Chebyshev's)
Functions of Random Variables
The student reviewed their probabilistic methods midterm exam with the tutor, identifying and correcting mistakes in applying concepts like exponential and Poisson distributions, CDFs, and expected values. They discussed areas for improvement and planned future sessions to focus on binomial distributions, Poisson distributions, and expected values, as well as preparing for upcoming exams in probabilistic methods and signals and systems.
Mixed Random Variables and CDF Properties
Exponential Distribution
Poisson Distribution
Characteristic Function
Uniform Distribution and CDF
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Hands-on engineering classes
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