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NTNU
4. Semester
Computer Vision
Øving 1 forelesning
Softmax
Statistics
Central Moments
Correlation
Covariance
Expected Value
Moments
Probability Density Function
Probability Distrubution Function (Cummulative)
Variance
5. Semester
Algoritmer og Datastrukturer
Oversikt
Oversikt AlgDat
Øvinger
Øving 6
Sortering i lineær tid
Counting Sort
Stable matching algorithms
Unstable Matching Algorithms
==Pasted== image 20231128122428.png
3-CNF-SAT
Abstrakte problemer
Aktivitetsproblemet
Algorithms
Algoritme - Algorithms
Antiparallelle kanter
Asymptotic Notation
Balansert binærtre
Banesammenligning - Path Compression
Bellman-Ford
Beslutningsproblem
Bevis - Masterteoremet
BFS-LABELING
Binær tre
Binære søketrær
Binærrelasjon
Bipartitt graf
Bredde-først søk (BFS)
Bucket Sort
Build-Max-Heap
Change base logarithm
CIRCUIT-SAT
CLIQUE
Co-NP definert ved formelle språk
CONNECTED-COMPONENTS
Dag-Shortest-Paths
Datastruktur
Del 3 - Data Structures
Del 5 - Advanced Data Structures
Del 6 - Graph Algorithms
Delsekvens (Subsequence)
Dens graf - Dense Graph
Dijkstras algoritme
Disjunkt-mengde skoger
Disjunkte mengder
Divisjonsmetoden - Division Method
Dybde-først søk (DFS)
Dynamisk programmering
Edmonds-Karps algoritme
Enkel sti
Enkoding
EXTENDED-SHORTEST-PATHS
FASTER-APSP
FIND-SET
Flere kilder eller flere sluk
Floyd-Warshalls algoritme
Flyt
Flytnett
Flytverdi
Ford-Fulkerson
Formelle språk
Forøkende sti - Augmenting Path
Fritt trær
Fullt binærtre
Gale-Shapley
GENERIC-MST
Glissen graf - Sparse Graph
Grådige algoritmer
Grådighetsegenskapen
Graf
Grafrepresentasjon
Hamiltonian cycle problemet (HAM-CYCLE)
Hashtabell
Hauger
Hauger - Heaps
Heap-Extract-Max
Heap-Increase-Key
Heap-Max
Heapsort
Heltallsteoremet
Hersk og splitt - Divide-and-Conquer
Heuristic programming
Heuristikk for å forbedre kjøretid
Huffmankoding
Hvordan bevise NPC generelt
Hvordan lage en hashfunksjon
Hvordan unngå kollisjoner - Collision Resolution Technique
In-Place Algorithm
Independent uniform hash function
INITIALIZE-SINGLE-SOURCE
Inorder Tree Walk
Insertion Sort
Iteration Method
Johnsons algoritme
Kant
Kantklassifisering
Kapasitet
Kapittel 1 - The Role of Algorithms in Computing
Kapittel 2 - Getting Started
Kapittel 3 - Characterizing Running Times
Kapittel 4 - Divide-and-Conquer
Kapittel 6 - Heapsort
Kapittel 7 - Quicksort
Kapittel 8 - Sorting in Linear Time
Kapittel 10 - Elementary Data Structures
Kapittel 11 - Hash Tables
Kapittel 12 - Binary Search Trees
Kapittel 15 - Greedy Algorithms
Kapittel 16 - Amortized Analysis
Kapittel 19 - Data Structures for Disjoint Sets
Kapittel 20 - Elementary Graph Algorithms
Kapittel 21 - Minimum Spanning Trees
Kapittel 22 - Single-Source Shortest Paths
Kapittel 23 - All-Pairs Shortest Paths
Kapittel 24 - Maximum Flow
Kapittel 25 - Matchings in Bipartite Graphs
Kjeding - Chaining
Kø - Queue
Kolisjon - Collision
Kompleksitetsklassene
Korteste vei fra alle til alle
Korteste vei fra en til alle
Left
Lenket liste - Linked List
Lett kant
LINK
LIST-PREPEND
LIST-SEARCH
MAKE-SET
Maksimal flyt - Maximum flow
Maksimum bipartitt matching
Master-theorem
Max-flow min-cut Teoremet
Max-Heap-Extract-Max
Max-Heap-Increase-Key
Max-Heap-Insert
Max-Heap-Maximum
max-heap-property
Max-Heapify
max-heaps
max-priority queues
Merge
Merge-Sort
Metoder for å finne minimale spenntrær
min-heap property
min-heaps
Minimale spenntrær
Minimalt snitt og heltallsteoremet
MST-KRUSKAL
MST-PRIM
Multiplikasjon,shift metoden - Multiply-shift method
Multiplikasjonsmetoden - Multiplication Method
Nabolister
Nabomatrise
Nabomatriser
Naiv Algoritme
Nettoflyt
NP definert ved formelle språk
NP problem
NP-harde problemer (NPH)
NP-komplett problem (NPC)
NP-komplett ved formelle språk
NP-Kompletthet
NPC problemer
Optimal Delstruktur
Optimeringsproblemer
Optimum
Overlappende Delinstanser
Oversikt AlgDat
P problem
P=NP
Parent
Partition
Pensumoversikt AlgDat
Perfekt binærtre
Polynomisk tid
PRINT-ALL-PAIRS-SHORTEST-PATH
Prioritetskøer - Priority queues
Problemer og Algoritmer
Quicksort
Radix-Sort
Random-access machine (RAM)
Randomized Partition
Randomized Quicksort
Randomized Select
Rang
Recurrence Equations
Recursion tree
Reduksjon ved formelle språk
Redusibilitet
Regularity Condition
RELAX
Representant-node
Restnett - Residual Network
RIGHT
Rotfast tre
Rotfaste trestrukturer
Ryggsekkproblemet
SAME-COMPOMENT
Sammenligningsalgoritme
SAT
Sentinels
SLOW-APSP
Sorteringsgrensa
Spenntrær
Spenntrær, kruskal og prim
Språkteori for NPC problemer
Stakk - Stack
Stirlings approximation
SUBSET-SUM
Substitution Method
Substring
Superpolynomisk tid
Topologisk sortering
Trær
Transitive Closure
Traveling Salesman Problem (TSP)
Traversering av grafer
Tree-Insert
Tree-Search
Trygg kant
UNION
Union etter rang - Union By Rank
Untitled
Urettet trær
Vektede grafer
Verifikasjonsalgoritme
VERTEX-COVER
Digital Signalbehandling
Assignments
Assigment 4
Assignment 3
Assignment 5
Assignment 6
Assignment 7
Assignment 9
Assignments
Untitled
Aliasing
Aliasing - Folding
Autocorrelation
Autocorrelation estimators
Autoregressive (AR) Process
Autoregressive, Moving Average (ARMA) process
Bandlimited Signal
Chapter 5 - Frequency-Domain Analysis of LTI Systems
Chapter 12 - Linear Prediction and Optimum Linear Filters
Circular Convolution
Crosscorrelation
Decimation
Design of Digital Filters - FIR, IIR
DFT - Discrete Fourier Transform
DFT - Discrete Fourier Transform for Filtering and Frequency Analysis
Direct form 1 - DF1
Direct form 2 - DF2
Discrete Random Signals
DTFT - Discrete Time Fourier Transform
Energy Density Spectrum
Energy spectral density
Ensemble Average
Ergodic process
Estimation Basics and Periodogram
FFT- Fast Fourier Transform
Filter Structures
Finite Geometric Series
Finite-Precision Effects
Forward Linear Prediction
Frequency-Domain Analysis of LTI Systems
Gauss-Markov Process
Geometric series
Input-output correlations
Interpolation
Inverse Z-transform and Residues
Linear Phase
Linear Prediction
Minimum-Phase
Modeling of Stochastic Processes
Moving Average (MA) process
Multirate signal processing
Noise-Whitening Filter
Nyquist Sampling Theorem
Power Density Spectrum (PDS)
Quantization in digital filters
Radix-2 FFT
Rate
Region of Convergence
Relationship between DTFT and DFT
Sampling and Reconstruction Of Signals
Sampling Theorem
Signals
Stochastic Processes
Stochastic signal
Time Domain Aliasing
Untitled
Wagon Wheel Effect
Wide-sense stationary process
Wiener Filter
Wold representation
Yule-Walker equations
Yule-Walker Method for AR Model Parameters
Zero Padding
Lecture Plans
TDT4120_forelesningsplan_h2023
Lineær Systemteori
Assignments
Assignment 4
Assignment 5
LinSys Assignment 2
LinSys Assignment 3
Lab
Forbredelser
Linsys lab forberedelser dag 2
Linsys lab forberedelser dag 3
Linsys lab forberedelser dag 4
På labben
Linsys lab dag 2
Linsys lab dag 3
Linsys lab dag 4
Report
Addivity
Algebraic Equivalence
Algebraic Ricatti Equation
Asymptotic Stabillity
Autocovariance
Band-limited Differentiation
Basis
BIBO Stability
Canonical Decompositions
Canonical Forms
Causality
Cayley Hamilton Theorem
Certainty Equivalence
Chapter 1 - Introduction
Chapter 2 -Mathematical Descriptions of Systems
Closed-loop Estimator - Luenberger Observer
Colored Noise
Colored Noise in Kalman Filter
Comprime
Controllability
Controllability Gramian
Controllability Index
Controllability Matrix
Controllable Canonical Form
Controllable Form
Conveyor Belt Case Study
Covariance Matrix
Covariance Matrix of the Estimation Error
Deterministic process
Diagonal Form
Discrete Time Covariance Matrix of the Estimation Error
Discrete Time Kalman Filter
Discrete Time Kalman Gain Matrix
Discretization
Dual System
Duality
Equivalent Representations of Systems and Matrices
Equivalent Systems
Estimate Update Law
Estimation Error
Euler Discretization
Exact discretization
Exponential Stability
Functions of a Square Matrix
Functions of Finite Polynomials
Homogeneity
Improper Transfer Function
Integral Effect
Internal Stability
Jordan Block
Jordan Form
Kalman Filter
Kalman Filter for Time Varying Models
Kalman Filter in Continuous Time
Kalman Gain
Leading Principle Minors
Lecture 2 - Equivalent representations, canonical forms, functions of square matrices
Lecture 3 - Discretization, Controllability, State feedback
Lecture 4 - State feedback, Optimal control
Lecture 5 - Realizations, Observability
Lecture 6 - State estimation and the separation principle
Lecture 7 - Stability
Lecture 8 - Canonical Decompositions and Minimal Realizations
Lecture 9 - Random Processes
Lecture 10 - Random State Space Systems
Lecture 11 and 12 - The Kalman Filter
Linear System
LinSys; Linear Quadratic Regulator - LQR
LQR and Kalman Filter Duality
Lyapunov Test for Controllability
Lyapunov Test for Stability
Lyapunov Theorem
Marginal Stability
Matrix rules and tricks
Measurement Equation Inversion
Memoryless system
Minimal Realization
Minimal realizations
Modal Form
Multi-input Controllability
Noise Supression
Nonsingular matrix
Null Space
Nullity
Observability
Observability Gramian
Observability Matrix
Open-loop Estimator
Output feedback controller
Overview LinSys
Popov-Belevitch-Hautus Test
Positive-Definite Matrix
Proper
Proper Transfer Function
Random processes
Random State Space Systems
Real constant matrix
Realizations
Reference Feed-Forward
Separation Principle
Similarity Transform
Stability
Stability in Discrete Time
Stabilizability
State Estimation
State Feedback, and Optimal Control
State Space Representation
State-feedback
Stationary Random Processes
Strictly Proper Transfer Functions
Superposition
Symmetric Matrix
Time Invariance
Transformation Matrix
TTK4115-Pensum Oversikt
Zero-state Equivalence
Teknologiledelse
Oversikt
TekLed Økonomi Eksamen
Øvinger
Øving 1
Øving 1
Øving 2
Øving 2
Balansen
Bedriftssimulator
Beslutningstrær
Beta - enkeltaksjers bidrag til porteføljerisiko
Bruk av kapitalverdimodellen
Budsjettering
Capital Asset Pricing Model - CAPM SLASH Kapitalverdimodellen - KVM
Debet og kredit
Dekningsbidrag
Dekningsgrad
Diskonteringsrente
Dobbel bokføring
Dødvektstap
Egenkapitalrentabilitet R_EK
Ekstraordinære kostnader
Erfaringsgoder
Etterspørsel (Q(P))
Etterspørselselastisitet (ε)
Finansieringsanalyse-gjeldsgrad og soliditet
Finansregnskapet
Følsomhetsanalyse
Forelesningsplan
Fortjeneste
Frikonkurranse
Giring - bruk av lån til å øke egenkapitalrentabiliteten
Gjeldsgrad
Gjennomsnittsinntekt (AR)
Gjennomsnittskostnad (AC)
Gjennomsnittsproduktiviteten til arbeid (GPL)
Grensen for diversifisering
Grunnleggende regnskapsbegreper
Grunnleggends regnskapsanalyse av finansregnskapet
Historisk risiko og avkastning
Indirekte kostnader
Inntakskost (Direkte kostnad)
Inntekt og marginalinntekt (TR, MR)
Internregnskapet
Internrenteberegning og bruk av internrenten
Investeringer
Isokostnadskurven
Isokvanter
Kalkyle etter bidragsmetoden
Kalkyle etter selvkostmetoden
Kalkyler
Kapittel 6 - Organisasjonsstruktur
Kapittel 13 - Mikroøkonomi og markedsformer
Kapittel 14 - Regnskapsanalyse og økonomistyring
Kapittel 15 - Investeringsanalyse
Konsumentoverskudd KO
Kortsiktige produksjonsfunksjoner
Kostnad
Kostnader (TC, VC, FC, AC)
Kostnadsbegreper
Kostnadsbudsjett
Kostnadsstruktur
Kredittrisiko
Læringskurveeffekt
Langsiktige produksjonsfunksjoner
Likviditetsanalyse
Likviditetsbudsjett
Likviditetsgrad 1
Likviditetsgrad 2
Lønnsomhetsanalyse-total-og egenkapitalrentabilitet
Loven om avtakende marginalprodukt
Mål på risiko
Marginalkostnad (MC)
Marginalproduktiviteten til arbeid (MPL)
Markeder med frikonkurranse
Markeder med to eller få produsenter (duopol,oligopol)
Markedsportefølje
Mikroøkonomisk teori som modellapparat
Modigliani og Millers andre proposisjon - MnM II
Modigliani og Millers første proposisjon - MnM I
Monopol og dødvektstap
Monopol, duopol og oligo poler
Monte Carlo simulering
Nash-likevekt
Naturlige monopoler
Nåverdi NV
Nåverdiberegninger
Negative skalaeffekter - Skalaulemper
Nettonåverdi NNV
Nullpunkt
Nullpunktsanalyser
Pareto-optimal
Pekuniære skalaeffekter
Portefølje
Porteføljerisiko
Positive skalaeffekter
Pristilpassere
Produsentoverskudd PO
Profitt (Ⲡ) og analyse av profitt
Profitt og analyse av profitt
Reelle skalaeffekter
Rentedekningsgrad
Resultatbudsjett
Resultatregnskapet
Risiko og avkastning
Risikoanalyse
Samfunnsøkonomisk dødvektstap
Samfunnsøkonomisk lønnsomhet SO
Sammenheng etterspørselselastisitet og marginalinntekt
Scenarioanalyse
Selskapets kapitalkostnad og verdipapirmarkedslinjen
Selvkost
Selvkostkalkyle i handelsbedrifter
Sharpe Ratio
Sikkerhetsmargin
Skalaavkastning
Søkegoder
Soliditetsgrad
T-konto
Teknologi, substitusjon og isokvanter
Tidsverdi
Tilbud og markedslikevekt
Tillitsgoder
Totalrentabilitet R_TK
Ulike markedesformer
Utbetaling
Utgift
Leseplan - 5. Semester
6. Semester
Estimering, deteksjon og klassifisering
Assignments
Estimation 1 - MVU and CRLB
Estimation 2 - Linear models, BLUE, MLE, and Bayesian Estimators
Exam
Overview Estimation
Project
README for classification project
The Iris Task
The MNIST Task
Asymptotic Properties of the MLE
Bayes Decision Rule
Bayes Risk
Bayes Theorem
Bayes' Law
Bayesian Detection
Bayesian Estimation
Bayesian Estimators
Bayesian MSE Estimators
Best Linear Unbiased Estimator (BLUE)
Binary Hypothesis
Central Moments of a Random Variable
Classical Estimation
Classifiers based on Bayes Decision Theory
Clustering
Confusion Matrix for Hypothesis Testing
Cramer-Rao Lower Bound
Decision Regions
Density Estimation
Detector
Efficient Estimator
Estimation Theory
Fisher Matrix
General Bayesian Estimators
General Linear Model
Independent and Identically Distributed Random Variables
Invariance Property of the MLE
K-means Clustering
Lecture 1 - Course Description
Lecture 2 - Review of Probability Theory, Random Variables and Stochastic Processes
Lecture 3 - Minimum-Variance Unbiased Estimation
Lecture 9 - Hypothesis Testing
Lecture 10 - Neyman-Pearson and Bayesian Detectors
Lecture 11&12 - Matched Filter and Replica Correlator
Lecture 14&15 - Classification and Classification Systems
Lectures Estimering
Likelihood Function
Likelihood-Ratio Test
Linear Classifier
Linear Discriminant Function
Linear Mapping
Linear Model
Linear Model with Non-White Noise
Linear Separable Problem
Mean Square Error
Minimum Mean Square Error
Minimum Probability of Error
Minimum Variance Unbiased Estimator
Moments of a Random Variable
Multiple-Hypotheses Testing
Neyman-Pearson Theorem
Noise Covariance Matrix
Non-Separable Problem
Nonlinear Separable Problem
Nonparametric Techniques
Observation Space
On-off Keyed Communication
Parzen-Window
Power of Test
Receiver Operating Characteristics
Reciever
Reinforcement Learning
Semi-Supervised Learning
Sigmoid
Size of Test
Statistical Independence
Sufficient Statistic
Supervised Learning
The Best Linear Unbiased Estimator
The Estimator for the Linear Model
The Maximum Likelihood Estimator
The Maximum-a-posteriori Estimator
The Minimum Variance Criterion
TTT4275 - Estimering, deteksjon og klassifisering
Unbiased Estimator
Unsupervised Learning
Untitled
With Regards To
Modellering og simulering
Assignments
Assignment 1 - Introduction to modsim
Assignment 2 - Kinematics 1
Assignment 3 - Kinematics 2
Assignment 4 - Newton-Euler
Assignment 5 - Lagrange 1
Assignment 6 - Lagrange 2
Assignment 7 - Basic Bond Graphs 1
Assignment 8 - Basic Bond Graphs 2
Exam
Overview Exam ModSim
Acceleration Form
Accuracy of the Forward (Explicit) Euler Method
Angle Axis Representation
Angular Acceleration
Angular Momentum
Angular Velocities
Basic 1-port Elements
Basic 2-port Elements
Basic Elements
Basic Hydraulic Components
Butcher Tableau
Causal Stroke
Cheat sheet for exam
Composite Rotation Transformation
Configuration Space
Consistency Condition
Constrained Lagrange
Course Recap
Differential Algebraic Equations
Differential Index
Effort
Energy Flow Direction
Euler Angles
Euler Half-Step
Euler-Lagrange Equation
Flow
From a Vector to a Skew Symmetric Matrix
Generalized Coordinates
Generalized Forces
Generalized Momentum Force
Global Error
Hydraulic system modelling procedure
Kinematics Module
Kinetic Energy
Kinetics in Linear Motion
Kinetics in Rotation
Lagrange Equation
Lagrange Mechanics
Lecture 01 - Introduction
Lecture 02 - Modelling and Simulation
Lecture 03 - Kinematics 1
Lecture 04 - Kinematics 2
Lecture 05 - Kinematics 3
Lecture 06 - Kinematics 4
Lecture 07&08 - Rigid Body Dynamics 1&2
Lecture 09&10 - Rigid Body Dynamics 3&4
Lecture 11&12 - Rigid Body Dynamics 5&6
Lecture 13&14 - System Dynamics
Lecture 15 - Basic System Modelling 3
Lecture 16 - Basic System Modelling 4
Lecture 17&18 - Basic System Modelling 5&6
Lectures ModSim
Linear Acceleration
Linear Momentum
Linear Velocity
Linear Velocity of a Point That Is Fixed Within a Rotating Reference Frame
Mid-Point Method
New Lagrangian
Newton-Euler Equations of motion
Notation ModSim
Numerical Simulation
Ordinary Differential Equation
Parallel Axis Theorem
Potential Energy
Power Bonds
Power Variables and Energy Variables
Principal Rotation Matrix
Procedure for Modelling Electrical Systems
Procedure for Modelling Mechanical Systems
Properties of Rotation Transform Matrices
Punctual Mass
Recap Rigid Body Dynamics
Reference Frame
Reynolds Number
Rotation Matrix
Rotation Transformation
Skew-Symmetric Cross Product Matrix Formula
Skew-Symmetric Matrix
Stability of the Forward Euler Method
State Space Formulations
Subsystems
Tetrahedron of State
Transformer Element
TTK4130 - Modellering og simulering
Optimalisering og regulering
AssignmentsAndLab
Lab
General Information
Helicopter Lab Day 1
Helicopter Lab Day 2
Helicopter Lab Day 3
Helicopter Lab Day 4
Helicopter Lab Report
MatlabAssignments
Matlab Assignment 1
Matlab Assignment 2
Matlab Assignment 3
Matlab Assignment 4
Matlab Assignment 5
Matlab Assignment 6
Exercise 0 - Matrix Calculus
Exercise 1 - The KKT Conditions
Exercise 2 - LP and KKT Conditions
Exercise 3 - LP, QP, and KKT Conditions
Exercise 4 - Quadratic Programming
Exercise 5 - Open-Loop Optimal Control and MPC
Exercise 6 - MPC and LQR
Exercise 7 - Riccati Equation and State Estimation
Exercise 8 - Unconstrained Optimization
Exam
Overview OptReg Exam Period
Exercises
Exercise 4 - Quadratic Programming
Exercise 5 - Open-Loop Optimal Control and MPC
Exercise 6 - MPC and LQR
Exercise 7 - Riccati Equation and State Estimation
Exercise 8 - Unconstrained Optimization
Lab
General Information
Helicopter Lab Day 2
Helicopter Lab Day 3
Helicopter Lab Day 4
Helicopter Lab Report
MatlabAssignments
Matlab Assignment 2
Matlab Assignment 3
Matlab Assignment 4
Matlab Assignment 5
Matlab Assignment 6
Oversikt
Lectures OptReg
TTK4135 - Optimalisering og regulering
∞-norm
1-norm
2-norm
Active Set
Active-Set Method for Convex QP
Basic Feasible Point (BFP)
BFGS update formula
Bias Update
Common Vector Norms (p-norms)
Condition Number
Constrained Problem
Constraint Qualification
Convex Objective Function
Convex Optimization Problem
Critical Cone
Degeneracy
Descent Directions
Detectability
Duality for Nonlinear Programming
Dynamic Optimization
Equality-Constrained Quadratic Programs
Example of Simplex Method
Feasible
Feasible Set
Fundamental Theorem of Linear Algebra
General Algorithm for Solving Unconstrained Optimization
General Dynamic Optimization Problem
General Optimization Problem
Gradient
Gradient With Respect to a Variable
Hessian
Horizon
Ill conditioned Matrix
Inequality Constraint
Infimum
Jacobian
KKT (Karush–Kuhn–Tucker) Conditions
KKT Matrix for Equality-Constrained QPs
Lagrangian
Lecture 1 - Optimization, What and Why?
Lecture 2 - Optimality Conditions
Lecture 3 - Optimality conditions, constraint qualifications, 2nd order optimality conditions
Lecture 4&5 - Linear Programming (LP)
Lecture 6&7 - Quadratic Programming
Lecture 8 - Open-Loop Dynamic Optimization
Lecture 9 - Linear Quadratic Control
Lecture 10 - Model Predictive Control
Lecture 11 - Practical use of MPC
Lecture 12 - Summing up MPC and LQ
Lecture 13 - Unconstrained Optimization
Lecture 15 - Quasi-Newton
Lecture 16 - Calculating Derivatives and Derivative-Free Optimization
Line Search Methods
Linear Independence Constraint Qualification
Linear Programming
Lipschitz Continuity
LU Decomposition
Matrix Factorization
Matrix Inversion Lemma
Mean Value Theorem
Necessary Condition
Newton Direction
Newton's Method for Nonlinear Equations
Nominal Stability
Nonlinear Equations
One step of Simplex-Algorithm
Optimality Conditions
Optimization
Positive-Semidefinite Matrix
Properties of Linear Programming Problems
Quadratic Programming Problem on Standard Form
Quasi-Dynamic Optimization
Quasi-Newton
Range Space
Robust Stability
Rosenbrock Function
s
Scaling
Secant Equation
Second-Order Necessary Conditions (SONC)
Second-Order Sufficient Condition (SOSC)
Sensitivity
Sensitivity Analysis
Sequential quadratic programming
Simplex Method
Slack Variable
Standard LP
Static Optimization
Steady State
Steepest Descent
Step Length
Strong Duality
Sufficient Condition
Taylors Theorem
Termination Criteria
The Dual LP Problem
The Feasibility Problem
The Set of Linearized Feasible Directions
Transforming a Linear Programming Problem Into Standard Form
Types of Constrained Optimization Problems
Weak Duality
Well-Conditioned Matrix
Wolfe Condition
Sanntidsprogrammering
Exam
Code Quality Trial Exam
Conditional Critical Region
Critical Region
Fixed-Priority Scheduling
Introducing Shared Variable Synchronization Trial Exam
Monitors
Mutual Exclusion
Overview Exam Sanntid
Producer-Consumer
Response Time Analysis for FPS
Scheduling
Scheduling Test
Shared Variable Synchronization Trial Exam
Shared Variable-Based Synchronization and Communication
Static and Dynamic Scheduling Scheme
Task-based Scheduling
The Cyclic Executive Approach
Utilization-Based Schedulability Test for FPS
V2023
Exercise preparation
2 - Concurrency
ExercisesAndLab
Exercise 1
Exercise1
questions
result
Exercise 2
README
resources
working-from-home
Exercise 3
README
Exercise 4
README Ex4
Exercise preparation
2 - Concurrency
3 - Networking
6 - Designing for Crashability
Success Detection
Supervisor
Lab
Peer Review
README for elevator project
Untitled
Untitled 1
Work-log Project
Lab
Peer Review
README for elevator project
Untitled
Untitled 1
Work-log Project
Acceptance Test
ACID properties
Atomic Actions
Atomic Actions, Concurrent Tasks and Reliability
Atomic Operation
Atomic Transactions
Atomicity
Backward Error Recovery
Basic Fault Tolerance
Checkpoint-Restart
Concurrent Events
Consistency
Context Switch
Cooperative Scheduling
Coordinator
Domino Effect
Durability
Dynamic Redundancy
Encapsulation
Execution Model
Failure Modes
Fault Model and Software Fault Masking
Fault Prevention
Fault Tolerance
Forward Error Recovery
How Sleep Works
Intelligent Design
Isolation
Lecture 2&3 - Code Quality
Lectures sanntid
Lightswitch
Maintainable System
Module
N-Version Programming
Preemptive Scheduling
Process Pair
pthread
pthread_create()
Race Conditions
Redundancy
Reliable Calculations
Reliable Communication
Reliable Data Storage
Scheduler
Semaphores
Shared Variable Synchronization
Sockets
Source D-Compiler
Starting a Thread
Synchronization
TCP (Transmission Control Protocol)
The Resumption Model of Asynchronous Notification Handling
Thread
Thread Swapping
Transactions
TTK4145 - Sanntidsprogrammering
Turnstile
Two-Phase Atomic Actions
UDP (User Datagram Protocol)
Week 9 - Atomic Actions, Leaving backward error recovery
Timeplan 6.semester
7. Semester
Grunnleggende Visuell Databehandling
Assignments
CG Assignment 1
CG Assignment 2
IP Assignment 2
2D manifold
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Digital Signalbehandling
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Noise-Whitening Filter
Noise-Whitening Filter
Jan 06, 2025, 1 min read
The noise whitening filter is defined as
H
I
(
z
)
=
H
(
z
)
1
Graph View
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Autoregressive (AR) Process
Autoregressive, Moving Average (ARMA) process
Moving Average (MA) process
