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Database And Image Processing Using Sql Server And Python

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Database And Image Processing Using Sql Server And Python

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Author : Vivian Siahaan
language : en
Publisher: SPARTA PUBLISHING
Release Date : 2019-10-31

Database And Image Processing Using Sql Server And Python written by Vivian Siahaan and has been published by SPARTA PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-31 with Computers categories.

The book details how programmers and database professionals can develop SQL Server-based Python applications that involves database and image processing. This book is SQL Server-based python programming. Microsoft SQL Server is robust relational database management system used by so many organizations of various sizes including top fortune 100 companies. SQL Server is a relational database management system (RDBMS) developed and marketed by Microsoft. As a database server, the primary function of the SQL Server is to store and retrieve data used by other applications. Deliberately designed for various levels of programming skill, this book is suitable for students, engineers, and even researchers in various disciplines. There is no need for advanced programming experience, and school-level programming skills are needed. In the first chapter, you will learn to use several widgets in PyQt5: Display a welcome message; Use the Radio Button widget; Grouping radio buttons; Displays options in the form of a check box; and Display two groups of check boxes. In chapter two, you will learn to use the following topics: Using Signal / Slot Editor; Copy and place text from one Line Edit widget to another; Convert data types and make a simple calculator; Use the Spin Box widget; Use scrollbars and sliders; Using the Widget List; Select a number of list items from one Widget List and display them on another Widget List widget; Add items to the Widget List; Perform operations on the Widget List; Use the Combo Box widget; Displays data selected by the user from the Calendar Widget; Creating a hotel reservation application; and Display tabular data using Table Widgets. In chapter three, you will learn: How to create the initial three tables project in the School database: Teacher, Class, and Subject tables; How to create database configuration files; How to create a Python GUI for inserting and editing tables; How to create a Python GUI to join and query the three tables. In chapter four, you will learn how to: Create a main form to connect all forms; Create a project will add three more tables to the school database: Student, Parent, and Tuition tables; Create a Python GUI for inserting and editing tables; Create a Python GUI to join and query over the three tables. In chapter five, you will join the six classes, Teacher, TClass, Subject, Student, Parent, and Tuition and make queries over those tables. In chapter six, you will create dan configure database. In this chapter, you will create Suspect table in crime database. This table has eleven columns: suspect_id (primary key), suspect_name, birth_date, case_date, report_date, suspect_ status, arrest_date, mother_name, address, telephone, and photo. You will also create GUI to display, edit, insert, and delete for this table. In chapter seven, you will create a table with the name Feature_Extraction, which has eight columns: feature_id (primary key), suspect_id (foreign key), feature1, feature2, feature3, feature4, feature5, and feature6. The six fields (except keys) will have VARBINARY(MAX) data type. You will also create GUI to display, edit, insert, and delete for this table. In chapter eight, you will create two tables, Police and Investigator. The Police table has six columns: police_id (primary key), province, city, address, telephone, and photo. The Investigator table has eight columns: investigator_id (primary key), investigator_name, rank, birth_date, gender, address, telephone, and photo. You will also create GUI to display, edit, insert, and delete for both tables. In the last chapter, you will create two tables, Victim and Case_File. The Victim table has nine columns: victim_id (primary key), victim_name, crime_type, birth_date, crime_date, gender, address, telephone, and photo. The Case_File table has seven columns: case_file_id (primary key), suspect_id (foreign key), police_id (foreign key), investigator_id (foreign key), victim_id (foreign key), status, and description. You will create GUI to display, edit, insert, and delete for both tables as well.

Java Gui With Mysql Database And Image Processing

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Author : Vivian Siahaan
language : en
Publisher: SPARTA PUBLISHING
Release Date : 2019-08-26

Java Gui With Mysql Database And Image Processing written by Vivian Siahaan and has been published by SPARTA PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-26 with Computers categories.

Postgresql For Java Gui Database And Image Processing

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Author : Vivian Siahaan
language : en
Publisher: SPARTA PUBLISHING
Release Date : 2019-08-27

Postgresql For Java Gui Database And Image Processing written by Vivian Siahaan and has been published by SPARTA PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-27 with Computers categories.

In this book, you will learn how to build from scratch a criminal records management database system using Java/PostgreSQL. All Java code for digital image processing in this book is Native Java. Intentionally not to rely on external libraries, so that readers know in detail the process of extracting digital images from scratch in Java. There are only three external libraries used in this book: Connector / J to facilitate Java to MySQL connections, JCalendar to display calendar controls, and JFreeChart to display graphics. Digital image techniques to extract image features used in this book are grascaling, sharpening, invertering, blurring, dilation, erosion, closing, opening, vertical prewitt, horizontal prewitt, Laplacian, horizontal sobel, and vertical sobel. For readers, you can develop it to store other advanced image features based on descriptors such as SIFT and others for developing descriptor based matching. In the first chapter, you will learn: How to install NetBeans, JDK 11, and the PostgreSQL connector; How to integrate external libraries into projects; How the basic PostgreSQL commands are used; How to query statements to create databases, create tables, fill tables, and manipulate table contents is done.In the first chapter, you will learn: How to install NetBeans, JDK 11, and the PostgreSQL connector; How to integrate external libraries into projects; How the basic PostgreSQL commands are used; How to query statements to create databases, create tables, fill tables, and manipulate table contents is done. In the second chapter, you will learn querying data from the postgresql using jdbc including establishing a database connection, creating a statement object, executing the query, processing the resultset object, querying data using a statement that returns multiple rows, querying data using a statement that has parameters, inserting data into a table using jdbc, updating data in postgresql database using jdbc, calling postgresql stored function using jdbc, deleting data from a postgresql table using jdbc, and postgresql jdbc transaction. In third chapter, you will be taught how to extract image features, utilizing BufferedImage class, in Java GUI. In the fourth chapter, you will be taught how to create Crime database and its tables. In the fifth chapter, you will be taught to create Java GUI to view, edit, insert, and delete Suspect table data. This table has eleven columns: suspect_id (primary key), suspect_name, birth_date, case_date, report_date, suspect_ status, arrest_date, mother_name, address, telephone, and photo. In the sixth chapter, you will be taught to create Java GUI to view, edit, insert, and delete Feature_Extraction table data. This table has eight columns: feature_id (primary key), suspect_id (foreign key), feature1, feature2, feature3, feature4, feature5, and feature6. All six fields (except keys) will have a BLOB data type, so that the image of the feature will be directly saved into this table. In the seventh chapter, you will add two tables: Police_Station and Investigator. These two tables will later be joined to Suspect table through another table, File_Case, which will be built in the seventh chapter. The Police_Station has six columns: police_station_id (primary key), location, city, province, telephone, and photo. The Investigator has eight columns: investigator_id (primary key), investigator_name, rank, birth_date, gender, address, telephone, and photo. Here, you will design a Java GUI to display, edit, fill, and delete data in both tables. In the eigthth chapter, you will add two tables: Victim and File_Case. The File_Case table will connect four other tables: Suspect, Police_Station, Investigator and Victim. The Victim table has nine columns: victim_id (primary key), victim_name, crime_type, birth_date, crime_date, gender, address, telephone, and photo. The File_Case has seven columns: file_case_id (primary key), suspect_id (foreign key), police_station_id (foreign key), investigator_id (foreign key), victim_id (foreign key), status, and description. Here, you will also design a Java GUI to display, edit, fill, and delete data in both tables. Finally, this book is hopefully useful for you.

Data Analysis Using Jdbc And Sql Server With Object Oriented Approach And Apache Netbeans Ide

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-05-24

Data Analysis Using Jdbc And Sql Server With Object Oriented Approach And Apache Netbeans Ide written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-24 with Computers categories.

This book is SQL SERVER version of our previous book titled “DATA ANALYSIS USING JDBC AND MYSQL WITH OBJECT-ORIENTED APPROACH AND APACHE NETBEANS IDE”. In this project, you will use the SQL VERSION version of Northwind database which is a sample database that was originally created by Microsoft and used as the basis for their tutorials in a variety of database products for decades. The Northwind database contains the sales data for a fictitious company called “Northwind Traders,” which imports and exports specialty foods from around the world. The Northwind database is an excellent tutorial schema for a small-business ERP, with customers, orders, inventory, purchasing, suppliers, shipping, employees, and single-entry accounting. You can download the sample database from https://viviansiahaan.blogspot.com/2023/05/data-analysis-using-jdbc-and-sql-server.html. In this project, you will design the form for every table and you will plot: the territory distribution by region; the employee distributions based on city, country, title, and region; the employee distributions based on birth date, hire date, and employee name; the employee distributions based on city, country, territory, and region; the three supplier distributions based on city, region, and country; the product distributions based on city, region, country, categorized unit price, categorized units in stock, and categorized units on order; the customer distributions based on city, region, and country; the order and freight distributions based on year, month, and week; the order and freight distributions based on day, quarter, and ship country; the order and freight distributions based on ship region, ship city, and ship name; the order and freight distributions based on shipper company, customer company, and customer city; the order and freight distributions based on customer country, employee name, and employee title; the sales distributions based on year, month, week, day, quarter, and ship country; the sales distributions based on ship region, ship city, ship name, shipper company, customer company, and customer city; the sales distributions based on customer region, customer country, employee name, employee title, employee city, and employee country; the sales distributions based on product name, category name, supplier company, supplier city, supplier region, and supplier country.

Mysql For Java Gui Database Cryptography And Image Processing

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Author : Vivian Siahaan
language : en
Publisher: SPARTA PUBLISHING
Release Date : 2019-08-01

Mysql For Java Gui Database Cryptography And Image Processing written by Vivian Siahaan and has been published by SPARTA PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-01 with Computers categories.

In this book, you will learn how to build from scratch a criminal records management database system using Java / MySQL. All Java code for digital image processing in this book is Native Java. Intentionally not to rely on external libraries, so that readers know in detail the process of extracting digital images from scratch in Java. There are only three external libraries used in this book: Connector / J to facilitate Java to MySQL connections, JCalendar to display calendar controls, and JFreeChart to display graphics. Digital image techniques to extract image features used in this book are grascaling, sharpening, invertering, blurring, dilation, erosion, closing, opening, vertical prewitt, horizontal prewitt, Laplacian, horizontal sobel, and vertical sobel. For readers, you can develop it to store other advanced image features based on descriptors such as SIFT and others for developing descriptor based matching. In the first chapter, you will be shown the number of devices needed to be downloaded and installed. You need to know how to add external libraries to the NetBeans environment. These tools are needed so that you can run the Java scripts. In the second chapter, you will learn the basics of cryptography using Java. Here, you will learn how to write a Java program to count Hash, MAC (Message Authentication Code), store keys in a KeyStore, generate PrivateKey and PublicKey, encrypt / decrypt data, and generate and verify digital prints. In the third chapter, you will learn how to create and store salt passwords and verify them. You will create a Login table. In this case, you will see how to create a Java GUI using NetBeans to implement it. In addition to the Login table, in this chapter you will also create a Client table. In the case of the Client table, you will learn how to generate and save public and private keys into a database. You will also learn how to encrypt / decrypt data and save the results into a database. In the fourth chapter, you will create an Account table. This account table has the following ten fields: account_id (primary key), client_id (primarykey), account_number, account_date, account_type, plain_balance, cipher_balance, decipher_balance, digital_signature, and signature_verification. In this case, you will learn how to implement generating and verifying digital prints and storing the results into a database. In the fifth chapter, You create a table with the name of the Account, which has ten columns: account_id (primary key), client_id (primarykey), account_number, account_date, account_type, plain_balance, cipher_balance, decipher_balance, digital_signature, and signature_verification. In the sixth chapter, you will create a Client_Data table, which has the following seven fields: client_data_id (primary key), account_id (primary_key), birth_date, address, mother_name, telephone, and photo_path. In the seventh chapter, you will be taught to create Java GUI to view, edit, insert, and delete Suspect table data. This table has eleven columns: suspect_id (primary key), suspect_name, birth_date, case_date, report_date, suspect_ status, arrest_date, mother_name, address, telephone, and photo. In the eighth chapter, you will be taught how to create Crime database and its tables. In nineth chapter, you will be taught how to extract image features, utilizing BufferedImage class, in Java GUI. In the tenth chapter, you will be taught to create Java GUI to view, edit, insert, and delete Feature_Extraction table data. This table has eight columns: feature_id (primary key), suspect_id (foreign key), feature1, feature2, feature3, feature4, feature5, and feature6. All six fields (except keys) will have a BLOB data type, so that the image of the feature will be directly saved into this table. In the eleventh chapter, you will add two tables: Police_Station and Investigator. These two tables will later be joined to Suspect table through another table, File_Case, which will be built in the seventh chapter. The Police_Station has six columns: police_station_id (primary key), location, city, province, telephone, and photo. The Investigator has eight columns: investigator_id (primary key), investigator_name, rank, birth_date, gender, address, telephone, and photo. Here, you will design a Java GUI to display, edit, fill, and delete data in both tables. In the twelfth chapter, you will add two tables: Victim and File_Case. The File_Case table will connect four other tables: Suspect, Police_Station, Investigator and Victim. The Victim table has nine columns: victim_id (primary key), victim_name, crime_type, birth_date, crime_date, gender, address, telephone, and photo. The File_Case has seven columns: file_case_id (primary key), suspect_id (foreign key), police_station_id (foreign key), investigator_id (foreign key), victim_id (foreign key), status, and description. Here, you will also design a Java GUI to display, edit, fill, and delete data in both tables.

Python Gui For Signal And Image Processing

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Author : Vivian Siahaan
language : en
Publisher: SPARTA PUBLISHING
Release Date : 2019-10-05

Python Gui For Signal And Image Processing written by Vivian Siahaan and has been published by SPARTA PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-05 with Computers categories.

You will learn to create GUI applications using the Qt toolkit. The Qt toolkit, also popularly known as Qt, is a cross-platform application and UI framework developed by Trolltech, which is used to develop GUI applications. You will develop an existing GUI by adding several Line Edit widgets to read input, which are used to set the range and step of the graph (signal). Next, Now, you can use a widget for each graph. Add another Widget from Containers in gui_graphics.ui using Qt Designer. Then, Now, you can use two Widgets, each of which has two canvases. The two canvases has QVBoxLayout in each Widget. Finally, you will apply those Widgets to display the results of signal and image processing techniques.

Learn From Scratch Signal And Image Processing With Python Gui

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-06-14

Learn From Scratch Signal And Image Processing With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-14 with Technology & Engineering categories.

In this book, you will learn how to use OpenCV, NumPy library and other libraries to perform signal processing, image processing, object detection, and feature extraction with Python GUI (PyQt). You will learn how to filter signals, detect edges and segments, and denoise images with PyQt. You will also learn how to detect objects (face, eye, and mouth) using Haar Cascades and how to detect features on images using Harris Corner Detection, Shi-Tomasi Corner Detector, Scale-Invariant Feature Transform (SIFT), and Features from Accelerated Segment Test (FAST). In Chapter 1, you will learn: Tutorial Steps To Create A Simple GUI Application, Tutorial Steps to Use Radio Button, Tutorial Steps to Group Radio Buttons, Tutorial Steps to Use CheckBox Widget, Tutorial Steps to Use Two CheckBox Groups, Tutorial Steps to Understand Signals and Slots, Tutorial Steps to Convert Data Types, Tutorial Steps to Use Spin Box Widget, Tutorial Steps to Use ScrollBar and Slider, Tutorial Steps to Use List Widget, Tutorial Steps to Select Multiple List Items in One List Widget and Display It in Another List Widget, Tutorial Steps to Insert Item into List Widget, Tutorial Steps to Use Operations on Widget List, Tutorial Steps to Use Combo Box, Tutorial Steps to Use Calendar Widget and Date Edit, and Tutorial Steps to Use Table Widget. In Chapter 2, you will learn: Tutorial Steps To Create A Simple Line Graph, Tutorial Steps To Create A Simple Line Graph in Python GUI, Tutorial Steps To Create A Simple Line Graph in Python GUI: Part 2, Tutorial Steps To Create Two or More Graphs in the Same Axis, Tutorial Steps To Create Two Axes in One Canvas, Tutorial Steps To Use Two Widgets, Tutorial Steps To Use Two Widgets, Each of Which Has Two Axes, Tutorial Steps To Use Axes With Certain Opacity Levels, Tutorial Steps To Choose Line Color From Combo Box, Tutorial Steps To Calculate Fast Fourier Transform, Tutorial Steps To Create GUI For FFT, Tutorial Steps To Create GUI For FFT With Some Other Input Signals, Tutorial Steps To Create GUI For Noisy Signal, Tutorial Steps To Create GUI For Noisy Signal Filtering, and Tutorial Steps To Create GUI For Wav Signal Filtering. In Chapter 3, you will learn: Tutorial Steps To Convert RGB Image Into Grayscale, Tutorial Steps To Convert RGB Image Into YUV Image, Tutorial Steps To Convert RGB Image Into HSV Image, Tutorial Steps To Filter Image, Tutorial Steps To Display Image Histogram, Tutorial Steps To Display Filtered Image Histogram, Tutorial Steps To Filter Image With CheckBoxes, Tutorial Steps To Implement Image Thresholding, and Tutorial Steps To Implement Adaptive Image Thresholding. In Chapter 4, you will learn: Tutorial Steps To Generate And Display Noisy Image, Tutorial Steps To Implement Edge Detection On Image, Tutorial Steps To Implement Image Segmentation Using Multiple Thresholding and K-Means Algorithm, and Tutorial Steps To Implement Image Denoising. In Chapter 5, you will learn: Tutorial Steps To Detect Face, Eye, and Mouth Using Haar Cascades, Tutorial Steps To Detect Face Using Haar Cascades with PyQt, Tutorial Steps To Detect Eye, and Mouth Using Haar Cascades with PyQt, and Tutorial Steps To Extract Detected Objects. In Chapter 6, you will learn: Tutorial Steps To Detect Image Features Using Harris Corner Detection, Tutorial Steps To Detect Image Features Using Shi-Tomasi Corner Detection, Tutorial Steps To Detect Features Using Scale-Invariant Feature Transform (SIFT), and Tutorial Steps To Detect Features Using Features from Accelerated Segment Test (FAST). You can download the XML files from https://viviansiahaan.blogspot.com/2023/06/learn-from-scratch-signal-and-image.html.

Data Science Using Jdbc And Sql Server With Object Oriented Approach And Apache Netbeans Ide

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-06-04

Data Science Using Jdbc And Sql Server With Object Oriented Approach And Apache Netbeans Ide written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-04 with Computers categories.

This book is SQL SERVER version of our previous book titled “DATA SCIENCE USING JDBC AND MYSQL WITH OBJECT-ORIENTED APPROACH AND APACHE NETBEANS IDE”. This book uses the SQL SERVER version of Sakila sample database which is a fictitious database designed to represent a DVD rental store. The tables of the database include film, film_category, actor, customer, rental, payment and inventory among others. The Sakila sample database is intended to provide a standard schema that can be used for examples in books, tutorials, articles, samples, and so forth. You can download the sample database from https://viviansiahaan.blogspot.com/2023/05/data-science-using-jdbc-and-sql-server.html. In this project, you will design the form for every table and you will plot: top 10 film distribution by release year; top 10 film distribution by rating; top 10 film distribution by rental duration; top 10 film distribution by language; film distribution by categorized rental rate; film distribution by categorized length; film distribution by categorized replacement cost; top 10 film distribution by actor name; top 10 actor name distribution by average rental rate; top 10 actor name distribution by average replacement cost; film distribution by rating; rating distribution by average rental rate; rating distribution by average replacement cost; top 10 film distribution by category name, category distribution by average replacement cost; category distribution by average rental rate; category distribution by length; top 10 city distribution by by country; top 10 address distribution by district, top 10 address distribution by country; top 10 address distribution by city; top 10 address distribution by district; top 10 address distribution by country; top 10 address distribution by city; top 10 inventory distribution by release year; top 10 inventory distribution by film rating; top 10 inventory distribution by film language; top 10 inventory distribution by film rental duration; top 10 inventory distribution by city; top 10 inventory distribution by country; top 10 customer distribution by country; top 10 customer distribution by city; top 10 customer distribution by district; top 10 customer distribution by store country; top 10 customer distribution by store city; top 10 customer distribution by store district; top 10 staff distribution by country; top 10 staff distribution by city; rental distribution by year of rental date; rental distribution by month of rental date; 10 rental distribution by week of rental date; rental distribution by day of rental date; rental distribution by quarter of rental date; rental distribution by film release year; rental distribution by film duration; rental distribution by film rating; top 10 rental distribution by staff name; rental distribution by film language; top 10 rental distribution by film title; rental distribution by customer active; top 10 rental distribution by film category; top 10 rental distribution by actor name; top 10 rental distribution by customer name; top 10 rental distribution by customer city; top 10 rental distribution by customer country, top 10 rental distribution by customer district; payment distribution by year of payment date; payment distribution by month of payment date; top 10 payment distribution by week of payment date; payment distribution by day of payment date; payment distribution by quarter of payment date; payment distribution by film release year; payment distribution by film duration; payment distribution by film rating; top 10 payment distribution by staff name; payment distribution by film language; top 10 payment distribution by film title; payment distribution by customer active; top 10 payment distribution by film category; top 10 payment distribution by actor name; top 10 payment distribution by customer name; top 10 payment distribution by customer city; top 10 payment distribution by customer country; and top 10 payment distribution by customer district.

Start From Scratch Digital Image Processing With Tkinter

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-10-21

Start From Scratch Digital Image Processing With Tkinter written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-21 with Computers categories.

"Start from Scratch: Digital Image Processing with Tkinter" is a beginner-friendly guide that delves into the basics of digital image processing using Python and Tkinter, a popular GUI library. The project is divided into distinct modules, each focusing on a specific aspect of image manipulation. The journey begins with an exploration of Image Color Space. Here, readers encounter the Main Form, which serves as the entry point to the application. It provides a user-friendly interface for loading images, selecting color spaces, and visualizing various color channels. The Fundamental Utilities play a crucial role by providing core functionalities like loading images, converting color spaces, and manipulating pixel data. The project also includes forms dedicated to displaying individual color channels and offering insights into the current color space through histograms. The Plotting Utilities module facilitates the creation of visual representations such as plots and graphs, enhancing the user's understanding of color spaces. Moving on, the Image Transformation section introduces readers to techniques like the Fast Fourier Transform (FFT). The Fast Fourier Transform Utilities module enables the implementation of FFT algorithms for converting images from spatial to frequency domains. A corresponding form allows users to view images in the frequency domain, with additional adjustments made to the plotting utilities for effective visualization. In the context of Discrete Cosine Transform (DCT), readers gain insights into algorithms and functions for transforming images. The Form for Discrete Cosine Transform aids in visualizing images in the DCT domain, while the plotting utilities are modified to accommodate these transformed images. The Discrete Sine Transform (DST) section introduces readers to DST algorithms and their role in image transformation. A dedicated form for visualizing images in the DST domain is provided, and the plotting utilities are further extended to handle these transformations effectively. Moving Average Smoothing is another critical aspect covered in the project. The Filter2D Utilities facilitate the application of moving average smoothing techniques. Additionally, metrics utilities enable the assessment of the smoothing process, with forms available for displaying both metrics and the smoothed images. Next, the project addresses Exponential Moving Average techniques, modifying the existing utilities to accommodate this specific approach. Similarly, forms for visualizing results and metrics are provided. Readers are then introduced to techniques like Median Filtering, Savitzky-Golay Filtering, and Wiener Filtering. The Filter2D Utilities are adapted to facilitate these filtering methods, and metrics utilities are employed to assess the effectiveness of each technique. Forms dedicated to each filtering method provide a platform for visualizing the results. The final section of the project explores techniques such as Total Variation Denoising, Non-Local Means Denoising, and PCA Denoising. The Filter2D Utilities are once again modified to support these denoising techniques. Metrics utilities are employed to evaluate the denoising process, and dedicated forms offer visualization capabilities. By breaking down the project into these modules, readers can systematically grasp the fundamentals of digital image processing, gradually building their skills from one concept to the next. Each section provides hands-on experience and practical knowledge, making it an ideal starting point for beginners in image processing.

Data Analysis Using Jdbc And Mysql With Object Oriented Approach And Apache Netbeans Ide

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-04-19

Data Analysis Using Jdbc And Mysql With Object Oriented Approach And Apache Netbeans Ide written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-19 with Computers categories.

In this project, you will use Northwind MySQL database which is a sample database that was originally created by Microsoft and used as the basis for their tutorials in a variety of database products for decades. The Northwind database contains the sales data for a fictitious company called “Northwind Traders,” which imports and exports specialty foods from around the world. The Northwind database is an excellent tutorial schema for a small-business ERP, with customers, orders, inventory, purchasing, suppliers, shipping, employees, and single-entry accounting. You can download the sample database from https://viviansiahaan.blogspot.com/2023/04/data-analysis-using-jdbc-and-mysql-with.html. In this project, you will design the form for every table and you will plot: the territory distribution by region; the employee distributions based on city, country, title, and region; the employee distributions based on birth date, hire date, and employee name; the employee distributions based on city, country, territory, and region; the three supplier distributions based on city, region, and country; the product distributions based on city, region, country, categorized unit price, categorized units in stock, and categorized units on order; the customer distributions based on city, region, and country; the order and freight distributions based on year, month, and week; the order and freight distributions based on day, quarter, and ship country; the order and freight distributions based on ship region, ship city, and ship name; the order and freight distributions based on shipper company, customer company, and customer city; the order and freight distributions based on customer country, employee name, and employee title; the sales distributions based on year, month, week, day, quarter, and ship country; the sales distributions based on ship region, ship city, ship name, shipper company, customer company, and customer city; the sales distributions based on customer region, customer country, employee name, employee title, employee city, and employee country; the sales distributions based on product name, category name, supplier company, supplier city, supplier region, and supplier country.

Database And Image Processing Using Sql Server And Python

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