11:12

🟨 03 - Design and Simulation of Rectangular Monopole Patch Antenna at 5.8 GHz using CST Software

15:04

🟨 02 - Design and Simulation of Circular Patch Antenna at 2.45 GHz using CST Software

18:28

🟨 01 - Design and Simulation of Rectangular Patch Antenna at 2.45 GHz using CST Software

12:30

☑️28b - Inductor Basics 2: Find the Voltage, Current, Power and energy stored stored in an Inductor

12:31

☑️28a - Inductor Basics 1: Find the Voltage, Current, Power and energy stored stored in an Inductor

20:28

☑️26 - Voltage across Parallel and Series Capacitors (Total Charge in Series and Parallel Capacitor)

14:40

☑️25b - Series and Parallel Capacitors Part 2

21:41

☑️25a - Series and Parallel Capacitors Part 1 (Intro + Solved Problems)

15:13

☑️24 - Energy Stored in a Capacitor under DC Conditions (Examples 1 & 2)

09:33

☑️23d - Solved Problems on Capacitor Basics 4 (Voltage Across a Capacitor)

15:02

☑️23c - Solved Problems on Capacitor Basics 3

15:15

☑️23b - Solved Problems on Capacitor Basics 2

12:01

☑️23a - Solved Problems on Capacitor Basics 1

10:03

☑️22 - Current - voltage Relationship of a Capacitor, Power and Energy

05:35

☑️21 - Capacitance Basics: An Introduction

23:03

🟢14 - Newton's Divided Difference Polynomial Method: Linear and Quadratic Interpolation

14:51

🟢13b - Lagrange Method of Interpolation: Quadratic Interpolation

12:52

🟢13a - Lagrange Method of Interpolation: Linear Interpolation

20:05

🟢12b - Direct Method of Interpolation: Quadratic Interpolation

25:20

🟢12a - Direct Method of Interpolation: Linear Interpolation

17:29

🟢11b - Newton - Raphson Method for Functions of Several Variables (Non-Linear Systems of Equ's) 2

20:10

🟢11a - Newton - Raphson Method for Functions of Several Variables (Non-Linear Systems of Equ's) 1

11:57

🟢10b - Fixed Point Iteration Method for Multivariable Functions (Jacobi and Gauss-Seidel Method) Ex2

26:25

🟢10a - Fixed Point Iteration Method for Multivariable Functions (Jacobi and Gauss-Seidel Method) Ex1

17:24

🟢09b - Fixed Point Iteration Method (Intro): Example 2 and 3

15:17

🟢09a - Fixed Point Iteration Method (Intro): Example 1

16:51

🟢08 - Successive Over - Relaxation Method 1: Example 1

14:58

🟢07 - Gauss-Seidel Iteration Method: Example 1

21:03

🟢06c - Jacobi Iteration Method in Matrix Form: Example 1

12:56

🟢06b - Jacobi Iteration Method: Example 2

18:34

🟢06a - Jacobi Iteration Method: Example 1

22:28

🟢05 - Thomas Algorithm for Solving Tri-diagonal Matrix Systems

26:44

🟢04 - Cholesky Decomposition Method (Algorithm)

16:33

🟢03b - LU Decomposition : Example 2

18:38

🟢03a - LU Decomposition : Example 1

18:47

🟢02b - Gaussian Elimination with Partial Pivoting : Example 2

13:19

🟢02a - Gaussian Elimination with Partial Pivoting : Example 1

11:15

🟢01c - Systems of Linear Equations using Naive Gaussian Elimination - Example 3

16:05

🟢01b - Systems of Linear Equations using Naive Gaussian Elimination - Example 2

18:55

🟢01a - Intro to Naive Gaussian Elimination - Example 1

10:11

🟡17b - Double Integrals over General Regions | Example 2

17:50

🟡17a - Double Integrals over General Regions | Example 1

33:12

🟡16 - Double Integrals over Rectangular Regions | Examples 1 - 5

19:41

🟡15b - Lagrange's Multipliers: Two Constraints - Find the maximum and minimum | Ex 4 & 5

24:39

🟡15a - Lagrange's Multipliers: One Constraints - Find the maximum and minimum | Ex 1 - 3

15:23

🟡14d - Absolute Minimum and Maximum of Multivariable Functions 4

12:25

🟡14c - Absolute Minimum and Maximum of Multivariable Functions 3

20:36

🟡14b - Absolute Minimum and Maximum of Multivariable Functions 2

20:02

🟡14a - Absolute Minimum and Maximum of Multivariable Functions 1

15:51

🟡13c - Relative Minimum and Maximum of Multivariable Functions | Critical and Saddle Points Ex 3

11:41

🟡13b - Relative Minimum and Maximum of Multivariable Functions | Critical and Saddle Points Ex 2

13:08

🟡13a - Relative Minimum and Maximum of Multivariable Functions | Critical and Saddle Points Ex 1

12:20

🟡12 - Gradient Vector, Tangent Plane and Normal Line to the Surface at a Point

11:09

🟡11 - Linearization (Linear Approximation) of Multivariable Functions

15:58

🟡10 - Equation of the Tangent plane to the surface at the Point

21:50

🟡09c - Maximum Rate of Change (Directional Derivatives and the Gradient Vecctor 3)

23:11

🟡09b - Find The Gradient Vector and Directional Derivative of the Function 2

25:46

🟡09a - Directional Derivatives and the Gradient Vector 1

25:13

🟡08 - Implicit Differentiation for Partial Derivatives of (Multivariable Functions)

13:44

🟡07b - Chain Rule for Partial Derivatives 2 of (Multivariable Functions)