The document loads libraries and plots data from the mtcars dataset. It uses gather() to reshape the data and plot mpg vs other variables like hp, cyl, and wt, facetted by each variable. It then plots mpg vs each variable with stat_smooth() added.
Jordina Vidal, Oriol Vidal Enginyeria, Barcelona, Spain.ARC research group
This document summarizes research on building simulations and monitoring of two research buildings at UAB campus. Active strategies were used to optimize energy demand including daylight analysis, comfort simulations, and CFD ventilation simulations. A holistic approach considered energy, materials, water, and waste. Results from building management systems and sensors were analyzed regarding energy and water consumption and compared to simulations. Ongoing work involves using consumption data to optimize building performance.
The document discusses modeling the probability of public transportation use based on parking rates using a logistic function. It presents the logistic function model, plots the logistic curve of probability versus parking rate, transforms the data to linearize it for regression, fits a regression line with an R-squared value of 0.9834, and extracts the alpha and C parameters from the regression equation to complete the logistic function model.
1. The document presents the results of a study modeling the probability of switching to public transportation based on parking rates. Logistic regression was used to develop a model linking the log odds of switching to parking rates.
2. A logistic regression model was also developed relating the probability of switching to public transportation to mining costs and time savings from using public transit. The model found parking rates had a positive effect while time savings had a negative effect on the log odds of switching.
3. The parameters estimated for both logistic regression models - including the coefficients for each predictor variable - are presented to allow calculation of the probability of switching to public transportation based on the developed models.
1. The document describes building a logistic regression model to predict the probability of commuters switching to public transportation based on parking rates. Data is plotted and transformed, and a logistic regression equation is fitted with parameters α=-0.9623 and C=3.5107. This model is used to predict probabilities for various parking rates.
2. A multiple logistic regression model is built to predict the probability of commuters switching to buses based on parking rates and bus subsidies. Data is transformed and separate regressions are performed on parking rates and subsidies. The final multiple logistic regression model is reported with parameters α=-0.0705, β=0.982, and C=2.4976.
3.
This document contains two MATLAB programs. The first program calculates the optimal cross-sectional area of an electrical conductor by plotting resistance, capacitance, and total cost against varying area. The second program plots fuel cost, heat rate, and incremental cost against changing power plant output to find the minimum incremental cost.
The document discusses different types of single-bit adders and multi-bit adders built from them. It describes half adders, full adders, ripple carry adders and their delay properties. It then discusses different advanced adder circuits like carry lookahead adders, carry skip adders, carry select adders and carry save adders to reduce the delay. Verilog code examples are provided for full adders, ripple carry adders, carry lookahead adders and carry skip adders.
The %HOURS function converts a number into a duration that can be added to or subtracted from a time or timestamp value. It is used to add or subtract a specified number of hours. The example shows initializing two timestamp variables, adding 2 hours to one and subtracting 2 hours from the other using %HOURS, and displaying the results.
Carry-save addition allows three n-bit numbers to be added together in O(1) time by computing the sum and carry independently and in parallel. It works by using a carry-save adder (CSA) block for each bit position. A CSA block takes in three bits and outputs their sum and carry without propagating the carry. Multiple CSA blocks can be chained or arranged in a Wallace tree to add many numbers. Using a chain takes O(m + lg(n+m)) time while a Wallace tree takes O(logm + lg(n+logm)) time to add m n-bit numbers.
Jordina Vidal, Oriol Vidal Enginyeria, Barcelona, Spain.ARC research group
This document summarizes research on building simulations and monitoring of two research buildings at UAB campus. Active strategies were used to optimize energy demand including daylight analysis, comfort simulations, and CFD ventilation simulations. A holistic approach considered energy, materials, water, and waste. Results from building management systems and sensors were analyzed regarding energy and water consumption and compared to simulations. Ongoing work involves using consumption data to optimize building performance.
The document discusses modeling the probability of public transportation use based on parking rates using a logistic function. It presents the logistic function model, plots the logistic curve of probability versus parking rate, transforms the data to linearize it for regression, fits a regression line with an R-squared value of 0.9834, and extracts the alpha and C parameters from the regression equation to complete the logistic function model.
1. The document presents the results of a study modeling the probability of switching to public transportation based on parking rates. Logistic regression was used to develop a model linking the log odds of switching to parking rates.
2. A logistic regression model was also developed relating the probability of switching to public transportation to mining costs and time savings from using public transit. The model found parking rates had a positive effect while time savings had a negative effect on the log odds of switching.
3. The parameters estimated for both logistic regression models - including the coefficients for each predictor variable - are presented to allow calculation of the probability of switching to public transportation based on the developed models.
1. The document describes building a logistic regression model to predict the probability of commuters switching to public transportation based on parking rates. Data is plotted and transformed, and a logistic regression equation is fitted with parameters α=-0.9623 and C=3.5107. This model is used to predict probabilities for various parking rates.
2. A multiple logistic regression model is built to predict the probability of commuters switching to buses based on parking rates and bus subsidies. Data is transformed and separate regressions are performed on parking rates and subsidies. The final multiple logistic regression model is reported with parameters α=-0.0705, β=0.982, and C=2.4976.
3.
This document contains two MATLAB programs. The first program calculates the optimal cross-sectional area of an electrical conductor by plotting resistance, capacitance, and total cost against varying area. The second program plots fuel cost, heat rate, and incremental cost against changing power plant output to find the minimum incremental cost.
The document discusses different types of single-bit adders and multi-bit adders built from them. It describes half adders, full adders, ripple carry adders and their delay properties. It then discusses different advanced adder circuits like carry lookahead adders, carry skip adders, carry select adders and carry save adders to reduce the delay. Verilog code examples are provided for full adders, ripple carry adders, carry lookahead adders and carry skip adders.
The %HOURS function converts a number into a duration that can be added to or subtracted from a time or timestamp value. It is used to add or subtract a specified number of hours. The example shows initializing two timestamp variables, adding 2 hours to one and subtracting 2 hours from the other using %HOURS, and displaying the results.
Carry-save addition allows three n-bit numbers to be added together in O(1) time by computing the sum and carry independently and in parallel. It works by using a carry-save adder (CSA) block for each bit position. A CSA block takes in three bits and outputs their sum and carry without propagating the carry. Multiple CSA blocks can be chained or arranged in a Wallace tree to add many numbers. Using a chain takes O(m + lg(n+m)) time while a Wallace tree takes O(logm + lg(n+logm)) time to add m n-bit numbers.
calculo de espaciamiento de pozos petroleros por el método de TozziniLuis Saavedra
This document calculates the optimal spacing between wells using the Tozzini method. It provides calculations for two scenarios:
1) With a present value of water of $170.52/m3, the optimal well spacing is 522.69 meters on a surface area of 21.4575 hectares.
2) With a present value of $629/m3, the optimal spacing is 341.44 meters on a surface area of 9.1562 hectares.
Written while studying the course Advanced Computer Networks:
Queuing theory 5
Queueing theory is the mathematical study of waiting lines, or queues.[1] A queueing model is constructed so that queue lengths and waiting time can be predicted.[1] Queueing theory is generally considered a branch of operations research because the results are often used when making business decisions about the resources needed to provide a service.
The %MINUTES function converts a number to a duration in minutes that can be added to or subtracted from a time or timestamp value. In the example, %MINUTES(2) is used to add 2 minutes to the TIME0 variable and subtract 2 minutes from the TIMESTP0 variable, demonstrating how %MINUTES can adjust time values.
Written while studying the course Advanced Computer Networks:
Queuing theory 6
Queueing theory is the mathematical study of waiting lines, or queues.[1] A queueing model is constructed so that queue lengths and waiting time can be predicted.[1] Queueing theory is generally considered a branch of operations research because the results are often used when making business decisions about the resources needed to provide a service.
The document defines a Sum class with three overloaded add() methods that take in different parameter types (two ints, three ints, and an int and float) and return the sum. It then creates a main method in an Overloading class that creates a Sum object and calls each add() method, passing different parameters to demonstrate function overloading.
This document discusses types of adders and provides details on half adders and full adders. It begins by identifying half adders and full adders as types of adders. It explains that digital computers perform arithmetic operations like addition and the basic operation is adding two binary digits. When adding more than two bits, the operation is called a full adder. Truth tables are provided for half adders and full adders. The document then shows the simplified sum of products form for a full adder using K-maps and provides the logic diagram. It concludes with assigning short notes on topics like manufacturing testing, functional testing, files and text I/O, and differentiating CPLD and FPGA architectures.
final Year Projects, Final Year Projects in Chennai, Software Projects, Embedded Projects, Microcontrollers Projects, DSP Projects, VLSI Projects, Matlab Projects, Java Projects, .NET Projects, IEEE Projects, IEEE 2009 Projects, IEEE 2009 Projects, Software, IEEE 2009 Projects, Embedded, Software IEEE 2009 Projects, Embedded IEEE 2009 Projects, Final Year Project Titles, Final Year Project Reports, Final Year Project Review, Robotics Projects, Mechanical Projects, Electrical Projects, Power Electronics Projects, Power System Projects, Model Projects, Java Projects, J2EE Projects, Engineering Projects, Student Projects, Engineering College Projects, MCA Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, Wireless Networks Projects, Network Security Projects, Networking Projects, final year projects, ieee projects, student projects, college projects, ieee projects in chennai, java projects, software ieee projects, embedded ieee projects, "ieee2009projects", "final year projects", "ieee projects", "Engineering Projects", "Final Year Projects in Chennai", "Final year Projects at Chennai", Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, Final Year Java Projects, Final Year ASP.NET Projects, Final Year VB.NET Projects, Final Year C# Projects, Final Year Visual C++ Projects, Final Year Matlab Projects, Final Year NS2 Projects, Final Year C Projects, Final Year Microcontroller Projects, Final Year ATMEL Projects, Final Year PIC Projects, Final Year ARM Projects, Final Year DSP Projects, Final Year VLSI Projects, Final Year FPGA Projects, Final Year CPLD Projects, Final Year Power Electronics Projects, Final Year Electrical Projects, Final Year Robotics Projects, Final Year Solor Projects, Final Year MEMS Projects, Final Year J2EE Projects, Final Year J2ME Projects, Final Year AJAX Projects, Final Year Structs Projects, Final Year EJB Projects, Final Year Real Time Projects, Final Year Live Projects, Final Year Student Projects, Final Year Engineering Projects, Final Year MCA Projects, Final Year MBA Projects, Final Year College Projects, Final Year BE Projects, Final Year BTech Projects, Final Year ME Projects, Final Year MTech Projects, Final Year M.Sc Projects, IEEE Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, IEEE 2009 Java Projects, IEEE 2009 ASP.NET Projects, IEEE 2009 VB.NET Projects, IEEE 2009 C# Projects, IEEE 2009 Visual C++ Projects, IEEE 2009 Matlab Projects, IEEE 2009 NS2 Projects, IEEE 2009 C Projects, IEEE 2009 Microcontroller Projects, IEEE 2009 ATMEL Projects, IEEE 2009 PIC Projects, IEEE 2009 ARM Projects, IEEE 2009 DSP Projects, IEEE 2009 VLSI Projects, IEEE 2009 FPGA Projects, IEEE 2009 CPLD Projects, IEEE 2009 Power Electronics Projects, IEEE 2009 Electrical Projects, IEEE 2009 Robotics Projects, IEEE 2009 Solor Projects, IEEE 2009 MEMS Projects, IEEE 2009 J2EE P
Peak shaving of an EV Aggregator Using Quadratic ProgrammingDaisuke Kodaira
This document provides an overview of a project in Daegu, Korea that uses an electric vehicle (EV) aggregator to reduce peak energy demand through quadratic programming algorithms. The project involves an energy storage system, photovoltaics, EV fast and slow chargers, and integrated smart metering. Simulation case studies show the algorithms can reduce energy costs by an average of 62% and peak demand by 61% compared to no scheduling. Future work will upgrade the algorithms to consider probabilistic forecasting and add new charging modes balancing grid and user needs.
1. The document presents a logistic regression model to analyze the probability of individuals switching from private transportation to public transportation based on parking rates.
2. A logistic function is fitted to the data and parameters α and C are estimated. The model shows a high goodness of fit (R2 = 0.9834).
3. The methodology is then applied to analyze switching from private to public transportation based on bus fare discounts and reduced travel time. A multiple logistic regression model is developed relating probability of switching to fare and time.
This document analyzes the technical, allocative, and economic efficiencies of farmers using the System of Rice Intensification (SRI) method versus conventional rice farming in Tamil Nadu, India. Key findings include:
- SRI farms had higher average technical (92%), allocative (76%), and economic (70%) efficiencies compared to conventional farms (73%, 35%, and 25% respectively).
- While SRI reduced some input costs like seeds, fertilizer, and water use, total production costs were only 6.4% lower for SRI farms. Increased yields of 21.7% primarily made SRI more profitable.
- More research is needed on further reducing SRI costs and
This document analyzes the link parameters for a new VSAT system used for air defense applications. It describes the transmitter and receiver parameters for the missile, satellite, and ground station. It then calculates the uplink and downlink carrier-to-noise ratios and Eb/No through link budget calculations using the given parameters. The analysis finds that the overall downlink C/N is 87.5 dB and the available Eb/No is 30.5 dB, meeting requirements for the system.
1) The document contains mathematical equations involving variables like x, y, z, a, b.
2) The equations include addition, subtraction, multiplication, division and exponent operations between the variables.
3) Many of the equations set one mathematical expression equal to another more complex expression involving multiple variables.
ggExtra Package-ggMarginal and Example -Shiny and ShinyjsDr. Volkan OBAN
The document discusses the ggExtra package in R which adds additional geometries and statistical transformations to ggplot2. It provides functions like ggMarginal to add marginal histograms or densities to ggplot2 objects. It also discusses the shinyjs package which allows users to easily improve user interaction and experience in Shiny apps through JavaScript. Examples are provided to demonstrate adding click handlers, toggling elements, and resetting forms using shinyjs functions.
This document provides examples of code using the ggmap package in R to download maps from various online sources and manipulate them. It shows how to:
1. Download static maps from Google Maps and OpenStreetMaps for specified locations and zoom levels.
2. Overlay points on a map of Europe using coordinates from geocoded locations.
3. Extract the bounding box of coordinates and plot a region of the world map within those bounds.
BOXPLOT EXAMPLES in R And An Example for BEESWARM:Dr. Volkan OBAN
This document provides examples of using R code to create boxplots and beeswarm plots from sample datasets. It includes:
1) Code to create a basic boxplot of ozone levels from the airquality dataset and customize aspects like colors, labels, and orientation.
2) Examples demonstrating how to create multiple boxplots for comparison and boxplots using other datasets with different numbers of variables.
3) Code for a beeswarm plot showing three groups of random data with different underlying distributions, customized with colors and labels.
Produce nice outputs for graphical, tabular and textual reporting in R-Report...Dr. Volkan OBAN
REFERENCE:
http://davidgohel.github.io/ReporteRs/lists.html
ReporteRs is an R package for creating Microsoft (Word docx and Powerpoint pptx) and html documents. It does not require any Microsoft component to be used. It runs on Windows, Linux, Unix and Mac OS systems. This is the ideal tool to automate reporting generation from R.
Plot3D package in R-package-for-3d-and-4d-graph-Data visualization.Dr. Volkan OBAN
This document provides examples of using the Plot3D package in R to create 3D plots and visualizations. It includes examples of plotting 3D text labels, histograms, arrows, scatter plots and adding regression planes to visualize relationships between variables in 3D space. Functions demonstrated include text3D(), hist3D(), arrows3D(), and scatter3D(). Real data sets like iris and mtcars are used for illustrative examples.
calculo de espaciamiento de pozos petroleros por el método de TozziniLuis Saavedra
This document calculates the optimal spacing between wells using the Tozzini method. It provides calculations for two scenarios:
1) With a present value of water of $170.52/m3, the optimal well spacing is 522.69 meters on a surface area of 21.4575 hectares.
2) With a present value of $629/m3, the optimal spacing is 341.44 meters on a surface area of 9.1562 hectares.
Written while studying the course Advanced Computer Networks:
Queuing theory 5
Queueing theory is the mathematical study of waiting lines, or queues.[1] A queueing model is constructed so that queue lengths and waiting time can be predicted.[1] Queueing theory is generally considered a branch of operations research because the results are often used when making business decisions about the resources needed to provide a service.
The %MINUTES function converts a number to a duration in minutes that can be added to or subtracted from a time or timestamp value. In the example, %MINUTES(2) is used to add 2 minutes to the TIME0 variable and subtract 2 minutes from the TIMESTP0 variable, demonstrating how %MINUTES can adjust time values.
Written while studying the course Advanced Computer Networks:
Queuing theory 6
Queueing theory is the mathematical study of waiting lines, or queues.[1] A queueing model is constructed so that queue lengths and waiting time can be predicted.[1] Queueing theory is generally considered a branch of operations research because the results are often used when making business decisions about the resources needed to provide a service.
The document defines a Sum class with three overloaded add() methods that take in different parameter types (two ints, three ints, and an int and float) and return the sum. It then creates a main method in an Overloading class that creates a Sum object and calls each add() method, passing different parameters to demonstrate function overloading.
This document discusses types of adders and provides details on half adders and full adders. It begins by identifying half adders and full adders as types of adders. It explains that digital computers perform arithmetic operations like addition and the basic operation is adding two binary digits. When adding more than two bits, the operation is called a full adder. Truth tables are provided for half adders and full adders. The document then shows the simplified sum of products form for a full adder using K-maps and provides the logic diagram. It concludes with assigning short notes on topics like manufacturing testing, functional testing, files and text I/O, and differentiating CPLD and FPGA architectures.
final Year Projects, Final Year Projects in Chennai, Software Projects, Embedded Projects, Microcontrollers Projects, DSP Projects, VLSI Projects, Matlab Projects, Java Projects, .NET Projects, IEEE Projects, IEEE 2009 Projects, IEEE 2009 Projects, Software, IEEE 2009 Projects, Embedded, Software IEEE 2009 Projects, Embedded IEEE 2009 Projects, Final Year Project Titles, Final Year Project Reports, Final Year Project Review, Robotics Projects, Mechanical Projects, Electrical Projects, Power Electronics Projects, Power System Projects, Model Projects, Java Projects, J2EE Projects, Engineering Projects, Student Projects, Engineering College Projects, MCA Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, Wireless Networks Projects, Network Security Projects, Networking Projects, final year projects, ieee projects, student projects, college projects, ieee projects in chennai, java projects, software ieee projects, embedded ieee projects, "ieee2009projects", "final year projects", "ieee projects", "Engineering Projects", "Final Year Projects in Chennai", "Final year Projects at Chennai", Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, Final Year Java Projects, Final Year ASP.NET Projects, Final Year VB.NET Projects, Final Year C# Projects, Final Year Visual C++ Projects, Final Year Matlab Projects, Final Year NS2 Projects, Final Year C Projects, Final Year Microcontroller Projects, Final Year ATMEL Projects, Final Year PIC Projects, Final Year ARM Projects, Final Year DSP Projects, Final Year VLSI Projects, Final Year FPGA Projects, Final Year CPLD Projects, Final Year Power Electronics Projects, Final Year Electrical Projects, Final Year Robotics Projects, Final Year Solor Projects, Final Year MEMS Projects, Final Year J2EE Projects, Final Year J2ME Projects, Final Year AJAX Projects, Final Year Structs Projects, Final Year EJB Projects, Final Year Real Time Projects, Final Year Live Projects, Final Year Student Projects, Final Year Engineering Projects, Final Year MCA Projects, Final Year MBA Projects, Final Year College Projects, Final Year BE Projects, Final Year BTech Projects, Final Year ME Projects, Final Year MTech Projects, Final Year M.Sc Projects, IEEE Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, IEEE 2009 Java Projects, IEEE 2009 ASP.NET Projects, IEEE 2009 VB.NET Projects, IEEE 2009 C# Projects, IEEE 2009 Visual C++ Projects, IEEE 2009 Matlab Projects, IEEE 2009 NS2 Projects, IEEE 2009 C Projects, IEEE 2009 Microcontroller Projects, IEEE 2009 ATMEL Projects, IEEE 2009 PIC Projects, IEEE 2009 ARM Projects, IEEE 2009 DSP Projects, IEEE 2009 VLSI Projects, IEEE 2009 FPGA Projects, IEEE 2009 CPLD Projects, IEEE 2009 Power Electronics Projects, IEEE 2009 Electrical Projects, IEEE 2009 Robotics Projects, IEEE 2009 Solor Projects, IEEE 2009 MEMS Projects, IEEE 2009 J2EE P
Peak shaving of an EV Aggregator Using Quadratic ProgrammingDaisuke Kodaira
This document provides an overview of a project in Daegu, Korea that uses an electric vehicle (EV) aggregator to reduce peak energy demand through quadratic programming algorithms. The project involves an energy storage system, photovoltaics, EV fast and slow chargers, and integrated smart metering. Simulation case studies show the algorithms can reduce energy costs by an average of 62% and peak demand by 61% compared to no scheduling. Future work will upgrade the algorithms to consider probabilistic forecasting and add new charging modes balancing grid and user needs.
1. The document presents a logistic regression model to analyze the probability of individuals switching from private transportation to public transportation based on parking rates.
2. A logistic function is fitted to the data and parameters α and C are estimated. The model shows a high goodness of fit (R2 = 0.9834).
3. The methodology is then applied to analyze switching from private to public transportation based on bus fare discounts and reduced travel time. A multiple logistic regression model is developed relating probability of switching to fare and time.
This document analyzes the technical, allocative, and economic efficiencies of farmers using the System of Rice Intensification (SRI) method versus conventional rice farming in Tamil Nadu, India. Key findings include:
- SRI farms had higher average technical (92%), allocative (76%), and economic (70%) efficiencies compared to conventional farms (73%, 35%, and 25% respectively).
- While SRI reduced some input costs like seeds, fertilizer, and water use, total production costs were only 6.4% lower for SRI farms. Increased yields of 21.7% primarily made SRI more profitable.
- More research is needed on further reducing SRI costs and
This document analyzes the link parameters for a new VSAT system used for air defense applications. It describes the transmitter and receiver parameters for the missile, satellite, and ground station. It then calculates the uplink and downlink carrier-to-noise ratios and Eb/No through link budget calculations using the given parameters. The analysis finds that the overall downlink C/N is 87.5 dB and the available Eb/No is 30.5 dB, meeting requirements for the system.
1) The document contains mathematical equations involving variables like x, y, z, a, b.
2) The equations include addition, subtraction, multiplication, division and exponent operations between the variables.
3) Many of the equations set one mathematical expression equal to another more complex expression involving multiple variables.
ggExtra Package-ggMarginal and Example -Shiny and ShinyjsDr. Volkan OBAN
The document discusses the ggExtra package in R which adds additional geometries and statistical transformations to ggplot2. It provides functions like ggMarginal to add marginal histograms or densities to ggplot2 objects. It also discusses the shinyjs package which allows users to easily improve user interaction and experience in Shiny apps through JavaScript. Examples are provided to demonstrate adding click handlers, toggling elements, and resetting forms using shinyjs functions.
This document provides examples of code using the ggmap package in R to download maps from various online sources and manipulate them. It shows how to:
1. Download static maps from Google Maps and OpenStreetMaps for specified locations and zoom levels.
2. Overlay points on a map of Europe using coordinates from geocoded locations.
3. Extract the bounding box of coordinates and plot a region of the world map within those bounds.
BOXPLOT EXAMPLES in R And An Example for BEESWARM:Dr. Volkan OBAN
This document provides examples of using R code to create boxplots and beeswarm plots from sample datasets. It includes:
1) Code to create a basic boxplot of ozone levels from the airquality dataset and customize aspects like colors, labels, and orientation.
2) Examples demonstrating how to create multiple boxplots for comparison and boxplots using other datasets with different numbers of variables.
3) Code for a beeswarm plot showing three groups of random data with different underlying distributions, customized with colors and labels.
Produce nice outputs for graphical, tabular and textual reporting in R-Report...Dr. Volkan OBAN
REFERENCE:
http://davidgohel.github.io/ReporteRs/lists.html
ReporteRs is an R package for creating Microsoft (Word docx and Powerpoint pptx) and html documents. It does not require any Microsoft component to be used. It runs on Windows, Linux, Unix and Mac OS systems. This is the ideal tool to automate reporting generation from R.
Plot3D package in R-package-for-3d-and-4d-graph-Data visualization.Dr. Volkan OBAN
This document provides examples of using the Plot3D package in R to create 3D plots and visualizations. It includes examples of plotting 3D text labels, histograms, arrows, scatter plots and adding regression planes to visualize relationships between variables in 3D space. Functions demonstrated include text3D(), hist3D(), arrows3D(), and scatter3D(). Real data sets like iris and mtcars are used for illustrative examples.
This is an analysis of the "Auto" data set from the ISLR (An Introduction to Statistical Learning: with Applications in R) package. The analysis presented here includes the following topics: data manipulation, exploratory data analysis, simple linear regression, correlation matrix, multiple linear regression, model diagnostics, residuals, normality, variance inflation factor (vif) to test for multi collinearity, levearages and modifying the model. Packages used are: ggplot2, xtable and car.
This document presents an analysis of automobile data. It begins with data manipulation steps including removing missing data and converting variables to appropriate data types. Exploratory data analysis is conducted through scatter plots and box plots to examine relationships between variables like mileage and weight grouped by cylinders. Simple and multiple linear regression models are fit to predict mileage, and model diagnostics identify violations of assumptions like homoscedasticity. Transforming the response variable to log scale addresses these issues. The modified multiple regression model has the highest R-squared value, indicating it best fits the data.
This document provides an overview of the R programming language. It describes R as a functional programming language for statistical computing and graphics that is open source and has over 6000 packages. Key features of R discussed include matrix calculation, data visualization, statistical analysis, machine learning, and data manipulation. The document also covers using R Studio as an IDE, reading and writing different data types, programming features like flow control and functions, and examples of correlation, regression, and plotting in R.
Constructing regression models using forward selection, backward elimination, and stepwise regression I found the best model that explains the variation in miles per gallon that is predictable from other car characteristics from the dataset mtcars in R
This document analyzes data from the mtcars dataset to understand the relationship between transmission type (automatic or manual) and miles per gallon (MPG). Two models are developed: Model 1 uses only transmission type as a predictor, while Model 2 adds additional variables like number of cylinders, displacement, rear axle ratio, and weight. Model 2 explains more of the variance in MPG (83.78% vs 35.98%) and suggests that while manuals may get slightly better gas mileage (0.26 MPG), the transmission type alone has little predictive power. Including other vehicle attributes is necessary to better understand factors influencing a car's MPG.
The document discusses the dplyr package for R. It provides examples of using dplyr verbs like filter, select, mutate, and summarise to subset and transform data frames. It also demonstrates grouping data with group_by and joining data with inner_join. The key features of dplyr are its simple verbs for filtering, modifying, arranging and summarizing data, its use of piping with %>%, and its convenience for working with tabular data.
CLUTO is a software toolkit used for clustering high-dimensional datasets and analyzing cluster characteristics. It contains two main algorithms: Vcluster, which clusters based on the actual multi-dimensional data representation, and Scluster, which clusters based on a pre-computed similarity matrix. CLUTO can be run from the command line with various optional parameters to control the clustering method, analysis, and visualization of results.
Parallel computing allows breaking problems into independent pieces that can be computed simultaneously across multiple processors. The document discusses using the snow package in R to set up a simple parallel cluster on a single machine and perform operations like bootstrapping in parallel. It also mentions more advanced high performance computing techniques for large memory, compiled code, profiling, and batch scheduling.
As part of the GSP’s capacity development and improvement programme, FAO/GSP have organised a one week training in Izmir, Turkey. The main goal of the training was to increase the capacity of Turkey on digital soil mapping, new approaches on data collection, data processing and modelling of soil organic carbon. This 5 day training is titled ‘’Training on Digital Soil Organic Carbon Mapping’’ was held in IARTC - International Agricultural Research and Education Center in Menemen, Izmir on 20-25 August, 2017.
Simple linear regression uses a single independent variable to predict the value of a dependent variable. Multiple linear regression extends this to use multiple independent variables to predict the dependent variable. The document demonstrates multiple linear regression in R by regressing soil organic carbon (SOC) on elevation, precipitation, and slope using the lm() function. This produces a model object that contains coefficients, residuals, fitted values and other details about the regression model.
Easy HTML Tables in RStudio with Tabyl and kableExtraBarry DeCicco
This document loads libraries, displays the mtcars dataset header, extracts a subset of the data into a new dataframe, and performs several tabulations and summaries of variables in the mtcars dataset using the tidyverse suite of packages. Key operations include tabulating gear and cyl variables, adding row and column totals, calculating percentages, and formatting outputs for presentation.
- The document discusses working with GDP data from an Excel file in R.
- It shows how to use the read_excel() function to import the Excel file and create a dataframe called gdp.
- Various data cleaning and manipulation steps are applied to the gdp dataframe, such as removing rows with missing values, renaming columns, and adding row names.
- The cleaned gdp dataframe is then saved to an RData file using save() for later use.
The document discusses the Julia programming language. It provides information on Julia's popularity compared to other languages like Python and R. It highlights several use cases for Julia in fields like finance, science, and engineering. It also demonstrates basic Julia code for tasks like data analysis, plotting, and numerical computing. Overall, the document serves as an introduction to the Julia language and provides examples of its capabilities.
Optimizer features in recent releases of other databasesSergey Petrunya
The document summarizes several recent optimizer features introduced in MySQL 8.0 and PostgreSQL versions:
- MySQL 8.0 introduced an iterator-based executor, hash joins, EXPLAIN ANALYZE, and optimizations for anti-joins, semi-joins, and subqueries.
- PostgreSQL improved query parallelism, added multi-column statistics, parallel index creation, and optimized non-recursive common table expressions.
- Both databases have focused on join algorithms, statistics gathering, and parallel query processing to improve performance. MySQL continues to adopt features from other databases in recent releases.
This document provides information about the Julia programming language. It discusses Julia's performance, use cases in different industries, available packages and tools, and ongoing development work. Key highlights include Julia's speed, its use in fields like robotics, quantitative finance, and science, and recent improvements to its machine learning and quantum computing capabilities.
The document summarizes the design of an electrically assisted human powered vehicle. It includes sections on the center of gravity calculation, bill of materials and mass table, cost analysis, performance analysis, assembly specifications, CAD assembly files, and project timeline. The key details are the vehicle will have a total mass of 184.7kg, cost $9,450 to produce, and be able to reach speeds of up to 40kph with electric assist while accommodating a single rider weighing up to 120kg.
Latin America Tour 2019 - 10 great sql featuresConnor McDonald
By expanding our knowledge of SQL facilities, we can let all the boring work be handled via SQL rather than a lot of middle-tier code, and we can get performance benefits as an added bonus. Here are some SQL techniques to solve problems that would otherwise require a lot of complex coding, freeing up your time to focus on the delivery of great applications.
Similar to library(tidyr) and library(ggplot2) (20)
Conference Paper:IMAGE PROCESSING AND OBJECT DETECTION APPLICATION: INSURANCE...Dr. Volkan OBAN
1) The document discusses using image processing and object detection techniques for insurance claims processing and underwriting. It aims to allow insurers to realistically assess images of damaged objects and claims.
2) Artificial intelligence, including computer vision, has been widely adopted in the insurance industry to analyze data like images, extract relevant information, detect fraud, and predict costs. Computer vision can recognize objects in images and help route insurance inquiries.
3) The document examines several computer vision applications for insurance - image similarity, facial recognition, object detection, and damage detection from images. It asserts that computer vision can expedite claims processing and improve key performance metrics for insurers.
Covid19py by Konstantinos Kamaropoulos
A tiny Python package for easy access to up-to-date Coronavirus (COVID-19, SARS-CoV-2) cases data.
ref:https://github.com/Kamaropoulos/COVID19Py
https://pypi.org/project/COVID19Py/?fbclid=IwAR0zFKe_1Y6Nm0ak1n0W1ucFZcVT4VBWEP4LOFHJP-DgoL32kx3JCCxkGLQ
This document provides examples of object detection output from a deep learning model. The examples detect objects like cars, trucks, people, and horses along with confidence scores. The document also mentions using Python and TensorFlow for object detection with deep learning. It is authored by Volkan Oban, a senior data scientist.
The document discusses using the lpSolveAPI package in R to solve linear programming problems. It provides three examples:
1) A farmer's profit maximization problem is modeled and solved using functions from lpSolveAPI like make.lp(), add.constraint(), and solve().
2) A simple minimization problem is created and solved to illustrate setting up the objective function and constraints.
3) A more complex problem is modeled to demonstrate setting sparse matrices, integer/binary variables, and customizing variable and constraint names.
"optrees" package in R and examples.(optrees:finds optimal trees in weighted ...Dr. Volkan OBAN
Finds optimal trees in weighted graphs. In
particular, this package provides solving tools for minimum cost spanning
tree problems, minimum cost arborescence problems, shortest path tree
problems and minimum cut tree problem.
by Volkan OBAN
k-means Clustering in Python
scikit-learn--Machine Learning in Python
from sklearn.cluster import KMeans
k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
The problem is computationally difficult (NP-hard); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data; however, k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes.[wikipedia]
ref: http://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_iris.html
This document describes using time series analysis in R to model and forecast tractor sales data. The sales data is transformed using logarithms and differencing to make it stationary. An ARIMA(0,1,1)(0,1,1)[12] model is fitted to the data and produces forecasts for 36 months ahead. The forecasts are plotted along with the original sales data and 95% prediction intervals.
k-means Clustering and Custergram with R.
K Means Clustering is an unsupervised learning algorithm that tries to cluster data based on their similarity. Unsupervised learning means that there is no outcome to be predicted, and the algorithm just tries to find patterns in the data. In k means clustering, we have the specify the number of clusters we want the data to be grouped into. The algorithm randomly assigns each observation to a cluster, and finds the centroid of each cluster.
ref:https://www.r-bloggers.com/k-means-clustering-in-r/
ref:https://rpubs.com/FelipeRego/K-Means-Clustering
ref:https://www.r-bloggers.com/clustergram-visualization-and-diagnostics-for-cluster-analysis-r-code/
Data Science and its Relationship to Big Data and Data-Driven Decision MakingDr. Volkan OBAN
Data Science and its Relationship to Big Data and Data-Driven Decision Making
To cite this article:
Foster Provost and Tom Fawcett. Big Data. February 2013, 1(1): 51-59. doi:10.1089/big.2013.1508.
Foster Provost and Tom Fawcett
Published in Volume: 1 Issue 1: February 13, 2013
ref:http://online.liebertpub.com/doi/full/10.1089/big.2013.1508
https://www.researchgate.net/publication/256439081_Data_Science_and_Its_Relationship_to_Big_Data_and_Data-Driven_Decision_Making
The Pandas library provides easy-to-use data structures and analysis tools for Python. It uses NumPy and allows import of data into Series (one-dimensional arrays) and DataFrames (two-dimensional labeled data structures). Data can be accessed, filtered, and manipulated using indexing, booleans, and arithmetic operations. Pandas supports reading and writing data to common formats like CSV, Excel, SQL, and can help with data cleaning, manipulation, and analysis tasks.
ReporteRs package in R. forming powerpoint documents-an exampleDr. Volkan OBAN
This document contains examples of plots, FlexTables, and text generated with the ReporteRs package in R to create a PowerPoint presentation. A line plot is generated showing ozone levels over time. A FlexTable is created from the iris dataset with styled cells and borders. Sections of formatted text are added describing topics in data science, analytics, and machine learning.
ReporteRs package in R. forming powerpoint documents-an exampleDr. Volkan OBAN
This document contains examples of plots, FlexTables, and text generated with the ReporteRs package in R to create a PowerPoint presentation. A line plot is generated showing ozone levels over time. A FlexTable is created from the iris dataset with styled cells and borders. Sections of formatted text are added describing topics in data science, analytics, and machine learning.
R Machine Learning packages( generally used)
prepared by Volkan OBAN
reference:
https://github.com/josephmisiti/awesome-machine-learning#r-general-purpose
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found