All Notes
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An algorithm is a set of steps for a computer program to accomplish a task. Key topics in algorithms include sorting, searching, and notation.
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Asymptotic notation is used to compare the efficiency of algorithms. It includes notations like Big-Theta, Big-O, and Big-Omega to describe running times.
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Binary search is an efficient algorithm for finding an item in a sorted list. It works by repeatedly dividing the list in half until the items location is found.
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Divide and conquer algorithms solve problems by breaking them into smaller sub-problems. This approach is used in various algorithms, including merge sort.
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Insertion sort is a sorting algorithm that iteratively inserts elements into a sorted array. It runs in O(n^2) time, with a best-case time complexity of Θ(n).
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Recursive algorithms solve problems by breaking them down into smaller instances of the same problem. This technique, called recursion, repeats until the problem is small enough to be solved directly.
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Selection sort is an algorithm that sorts arrays by repeatedly selecting the next-smallest element and swapping it into place. It has a running time of Θ(n^2) in all cases.
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Ciphers are mechanical operations that perform algorithms on individual letters or small chunks of letters.
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Cryptography involves encrypting messages to secure communication. It uses ciphers to protect information from unauthorized access.
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Cryptography involves techniques like the Caesar cipher, polyalphabetic cipher, and one-time pad to secure messages.
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Modern cryptography relies on mathematical concepts like prime factorization and modular arithmetic.
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Modular arithmetic is a system where numbers wrap around after reaching a certain value. It is used in cryptography and computer science.
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A primality test determines whether a given number is prime or composite. Primality tests include trial division, the Sieve Algorithm, and probabilistic methods like the Fermat primality test.
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Reinforcement learning involves learning to maximize a reward signal through trial and error.
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The multi-arm bandit problem is a classic example of reinforcement learning. It involves choosing among multiple options to maximize the expected total reward over time.
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Reinforcement Learning (RL) involves learning to maximize a numerical reward signal. It is a field of study that encompasses related problems and solution methods.
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Steganography hides information in plain sight by making it invisible. It differs from cryptography, which encrypts information using secret codes.
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Adaptive control addresses systems with time-varying parameters, seeking to mitigate uncertainties in system models.
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Adaptive control is a methodology used to achieve control objectives, such as stability and disturbance rejection, in systems with incomplete or uncertain models.
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This paper presents a novel data-driven control approach for discrete-time nonlinear systems, introducing a constant control input length constant of linearization and an equivalent PFDL description
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This paper presents a data-driven, model-free adaptive control method for a class of MIMO nonlinear discrete-time systems, enabling online estimation and control input calculation.
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This survey paper compares model-based control with data-driven control, highlighting the challenges of deriving accurate models and proposing data-driven control as a solution for systems with diffic
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Model-free adaptive control (MFAC) is a data-driven approach that uses pseudo-partial derivatives to create a dynamic linearization model of a plant, eliminating the need for identifying a nonlinear m
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This paper proposes a model-free adaptive resilient control method for nonlinear Cyber-Physical Systems (CPSs) to counter aperiodic jamming attacks.
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MRAC (Model Reference Adaptive Control) is a type of adaptive control system that adjusts its behavior in response to changes in the controlled system.
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Data-driven control designs controllers using only input/output data from the controlled system, eliminating the need for a mathematical plant model.
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Research areas include robotics, reinforcement learning, and algorithms. Focus is on robotics and reinforcement learning.
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2DoF robot control involves using various methods, including Active Disturbance Rejection Control. This approach has been compared to traditional PID control in existing literature.
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The linear complementarity problem seeks vectors that satisfy certain constraints. It involves a matrix and vector, with conditions including non-negativity and complementarity.
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The problem involves controlling a single leg robot by determining inputs to achieve desired height and force.
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Robot leg motion is governed by equations such as Newtons and Eulers.
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The Robotic Project involves controlling a 2DoF robot single leg. It consists of multiple parts, including modeling, control methods, and testing.
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Robotics projects involve single leg control. Robotics encompasses various technical fields.
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Single leg robots require advanced controllers for stable movement. ADAMS-Simulink is used for implementation.
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A single leg robot controller is a system that manages the movement of a robot with one leg. It requires precise balance and stabilization algorithms to maintain stability.
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This model simulates a single leg robots dynamics. It predicts motion and balance.
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Single leg robot system identification involves analyzing a robots single leg dynamics. It helps understand the relationship between the legs movement and forces acting upon it.
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Stability of legged robots is analyzed through concepts like fixed points and limit cycles. These concepts examine the robots ability to maintain balance and withstand disturbances.
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