Learn From Scratch Signal And Image Processing With Python Gui

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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.

Learn From Scratch Signal And Image Processing With Python Gui

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Author : RISMON HASIHOLAN. SIANIPAR
language : en
Publisher:
Release Date : 2021

Learn From Scratch Signal And Image Processing With Python Gui written by RISMON HASIHOLAN. SIANIPAR and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

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.

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.

Learn From Scratch Machine Learning With Python Gui

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2021-03-03

Learn From Scratch Machine Learning 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 2021-03-03 with Computers categories.

In this book, you will learn how to use NumPy, Pandas, OpenCV, Scikit-Learn and other libraries to how to plot graph and to process digital image. Then, you will learn how to classify features using Perceptron, Adaline, Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) models. You will also learn how to extract features using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel Principal Component Analysis (KPCA) algorithms and use them in machine learning. 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. You will also 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, Tutorial Steps To Implement Image Denoising, 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, Tutorial Steps To Extract Detected Objects, 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). In Chapter 4, In this tutorial, you will learn how to use Pandas, NumPy and other libraries to perform simple classification using perceptron and Adaline (adaptive linear neuron). The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron, Tutorial Steps To Implement Perceptron with PyQt, Tutorial Steps To Implement Adaline (ADAptive LInear NEuron), and Tutorial Steps To Implement Adaline with PyQt. In Chapter 5, you will learn how to use the scikit-learn machine learning library, which provides a wide variety of machine learning algorithms via a user-friendly Python API and to perform classification using perceptron, Adaline (adaptive linear neuron), and other models. The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron Using Scikit-Learn, Tutorial Steps To Implement Perceptron Using Scikit-Learn with PyQt, Tutorial Steps To Implement Logistic Regression Model, Tutorial Steps To Implement Logistic Regression Model with PyQt, Tutorial Steps To Implement Logistic Regression Model Using Scikit-Learn with PyQt, Tutorial Steps To Implement Support Vector Machine (SVM) Using Scikit-Learn, Tutorial Steps To Implement Decision Tree (DT) Using Scikit-Learn, Tutorial Steps To Implement Random Forest (RF) Using Scikit-Learn, and Tutorial Steps To Implement K-Nearest Neighbor (KNN) Using Scikit-Learn. In Chapter 6, you will learn how to use Pandas, NumPy, Scikit-Learn, and other libraries to implement different approaches for reducing the dimensionality of a dataset using different feature selection techniques. You will learn about three fundamental techniques that will help us to summarize the information content of a dataset by transforming it onto a new feature subspace of lower dimensionality than the original one. Data compression is an important topic in machine learning, and it helps us to store and analyze the increasing amounts of data that are produced and collected in the modern age of technology. You will learn the following topics: Principal Component Analysis (PCA) for unsupervised data compression, Linear Discriminant Analysis (LDA) as a supervised dimensionality reduction technique for maximizing class separability, Nonlinear dimensionality reduction via Kernel Principal Component Analysis (KPCA). You will learn: 6.1 Tutorial Steps To Implement Principal Component Analysis (PCA), Tutorial Steps To Implement Principal Component Analysis (PCA) Using Scikit-Learn, Tutorial Steps To Implement Principal Component Analysis (PCA) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Linear Discriminant Analysis (LDA), Tutorial Steps To Implement Linear Discriminant Analysis (LDA) with Scikit-Learn, Tutorial Steps To Implement Linear Discriminant Analysis (LDA) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Kernel Principal Component Analysis (KPCA) Using Scikit-Learn, and Tutorial Steps To Implement Kernel Principal Component Analysis (KPCA) Using Scikit-Learn with PyQt. In Chapter 7, you will learn how to use Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset. You will learn: Tutorial Steps To Load MNIST Dataset, Tutorial Steps To Load MNIST Dataset with PyQt, Tutorial Steps To Implement Perceptron With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement , Tutorial Steps To Implement Support Vector Machine (SVM) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Support Vector Machine (SVM) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, and Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt.

Start From Scratch Digital Signal Processing With Tkinter

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-10-13

Start From Scratch Digital Signal 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 2023-10-13 with Computers categories.

In this project, you will create a multi-form GUI to implement digital signal processing. Creating a GUI involves designing an interface where users can input parameters and visualize the results of various signal processing techniques. Each form corresponds to a specific technique and is implemented using the tkinter library. The "Simple Sinusoidal Form" allows users to generate and visualize a basic sinusoidal signal. It includes input fields for parameters like frequency, amplitude, and time period. The utilities associated with this form provide functions to generate and plot the simple sinusoidal signal. The "Two Sinusoidals Form" extends the previous form, enabling users to generate and visualize two combined sinusoidal signals. It provides input fields for frequencies, amplitudes, and time periods of both signals. The utilities handle the generation and plotting of the combined sinusoidal signals. The "More Two Sinusoidals Form" further extends the previous form to generate and visualize additional combined sinusoidal signals. It includes input fields for frequencies, amplitudes, and time periods of three sinusoidal signals. The utilities handle the generation and plotting of these combined signals. Forms for various modulation techniques (AM, FM, PM, ASK, FSK, PSK) are available. These allow users to generate and visualize modulated signals by providing input fields for modulation indices, carrier frequencies, and time periods. The utilities in each form handle the signal generation and modulation process, as well as the plotting of the modulated signals. Forms for different filter designs (FIR, Butterworth, Chebyshev Type 1) cover lowpass, highpass, bandpass, and bandstop filters. They include input fields for filter order, cutoff frequencies, and other relevant parameters. The utilities in each form implement the filter design and frequency response plotting. Wavelet transformation forms focus on wavelet-based techniques, including scaling, decomposition, and denoising. They provide input fields for wavelet type, thresholding methods, and other wavelet-specific parameters. The utilities handle the wavelet transformations, denoising, and visualizing the results. Forms for various denoising techniques (MA, EMA, Median, SGF, Wiener, TV, NLM, PCA) cover different smoothing and denoising methods. They offer input fields for relevant denoising parameters. The utilities for each form implement the denoising process and display the denoised signals. Each form's utility methods interact with the GUI elements, taking user inputs and performing the corresponding signal processing tasks. These utilities encapsulate the underlying algorithms and ensure a seamless interaction between the user interface and the backend computations. In summary, this session involves creating a comprehensive GUI for a wide range of signal processing techniques, including signal generation, modulation, filtering, wavelet transformations, and various denoising methods. Each form and its associated utilities handle specific tasks, ensuring an intuitive and effective user experience.

Learn From Scratch Visual C Net With Mysql

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2020-10-05

Learn From Scratch Visual C Net With Mysql 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 2020-10-05 with Computers categories.

In Tutorial 1, you will start building a Visual C# interface for database management system project using MySQL. The database, named DBMS, is created. The designed interface in this tutorial will used as the main terminal in accessing other forms. This tutorial will also discuss how to create login form and login table. In Tutorial 2, you will build a project, as part of database management system, where you can store information about valuables in school. The table will have seven fields: Item (description of the item), Location (where the item was placed), Shop (where the item was purchased), DatePurchased (when the item was purchased), Cost (how much the item cost), SerialNumber (serial number of the item), PhotoFile (path of the photo file of the item), and Fragile (indicates whether a particular item is fragile or not). In Tutorial 3 up to Tutorial 4, you will perform the steps necessary to add 6 tables using phpMyAdmin into DBMS database. You will build each table and add the associated fields as needed. In this tutorials, you will create a library database project, as part of database management system, where you can store all information about library including author, title, and publisher. In Tutorial 5 up to Tutorial 7, you will perform the steps necessary to add 8 more tables using phpMyAdmin into DBMS database. You will build each table and add the associated fields as needed. In this tutorials, you will create a high school database project, as part of database management system, where you can store all information about school including parent, teacher, student, subject, and, title, and grade.

Learn From Scratch Visual Basic Net With Mysql

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2020-11-04

Learn From Scratch Visual Basic Net With Mysql 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 2020-11-04 with Computers categories.

This book will teach you with step-by-step approach to develop from scratch a MySQL-driven desktop application that readers can develop for their own purposes to implement school database project using Visual Basic .NET. In Tutorial 1, you will perform the steps necessary to add 8 tables using phpMyAdmin into School database that you will create. You will build each table and add the associated fields as needed. In this tutorial, you will also build login form and main form. In Tutorial 2, you will build such a form for Parent table. This table has thirteen fields: ParentID, FirstName, LastName, BirthDate, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, PhotoFile, and FingerFile). You need fourteen label controls, two picture boxes, six text boxes, four comboxes, one check box, one date time picker, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, six buttons for other utilities, one button for searching member’s name, one button to upload parent’s photo, and button to upload parent’s finger. Place these controls on the form. In Tutorial 3, you will build such a form for Student table. This table has fifteen fields: StudentID, ParentID, FirstName, LastName, BirthDate, YearEntry, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, PhotoFile, and FingerFile). You need sixteen label controls, two picture boxes, six text boxes, five comboxes, one check box, two date time pickers, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, seven buttons for controlling editing features, one button for searching parent’s name, one button to open parent form, one button to upload student’s photo, and one button to upload student’s finger. In Tutorial 4, you will build a form for Teacher table. This table has fifteen fields: TeacherID, RegNumber, FirstName, LastName, BirthDate, Rank, Status, Ethnicity, Nationality, Mobile, Phone, Religion, Gender, PhotoFile, and FingerFile). You need an input form so that user can edit existing records, delete records, or add new records. The form will also have the capability of navigating from one record to another. You need sixteen label controls, one picture box, seven text boxes, five comboxes, one check box, one date time picker, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, six buttons for controlling editing features, one button for searching teacher’s name, and one button to upload teacher’s photo. In Tutorial 5, you will build a form for Subject table. This table has only three fields: SubjectID, Name, and Description. You need four label controls, four text boxes, one openfiledialog, and one printpreviewdialog. You also need four buttons for navigation, secen buttons for utilities, and one button for searching subject name. Place these controls on the form. You will also build a form for Grade table. This table has seven fields: GradeID, Name, SubjectID, TeacherID, SchoolYear, TimaStart, and TimeFinish. You need to add seven label controls, one text box, four comboxes, and two date time pickers. You also need four buttons for navigation, seven buttons for controlling editing features, one button to open subject form, and one button to open teacher form. In Tutorial 6, you will build a form for Grade_Student table. This table has only three fields: Grade_StudentID, GradeID, and StudentID. You need an input form so that user can edit existing records, delete records, or add new records. The form will also have the capability of navigating from one record to another. You need two label controls and two comboxes. You also need four buttons for navigation, seven buttons for controlling editing features, one button to open grade form, and one button to open student form.

Zero To Mastery The Complete Guide To Learning Sqlite And Python Gui

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-08-17

Zero To Mastery The Complete Guide To Learning Sqlite And 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 2022-08-17 with Computers categories.

In this project, we provide you with the SQLite version of The Oracle Database Sample Schemas that provides a common platform for examples in each release of the Oracle Database. The sample database is also a good database for practicing with SQL, especially SQLite. The detailed description of the database can be found on: http://luna-ext.di.fc.ul.pt/oracle11g/server.112/e10831/diagrams.htm#insertedID0. The four schemas are a set of interlinked schemas. This set of schemas provides a layered approach to complexity: A simple schema Human Resources (HR) is useful for introducing basic topics. An extension to this schema supports Oracle Internet Directory demos; A second schema, Order Entry (OE), is useful for dealing with matters of intermediate complexity. Many data types are available in this schema, including non-scalar data types; The Online Catalog (OC) subschema is a collection of object-relational database objects built inside the OE schema; The Product Media (PM) schema is dedicated to multimedia data types; The Sales History (SH) schema is designed to allow for demos with large amounts of data. An extension to this schema provides support for advanced analytic processing. The HR schema consists of seven tables: regions, countries, locations, departments, employees, jobs, and job_histories. This book only implements HR schema, since the other schemas will be implemented in the next books.

Rsa Cryptosystem Key Generation Encryption Decryption And Digital Signatures Learn By Examples With Python And Tkinter

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2024-08-28

Rsa Cryptosystem Key Generation Encryption Decryption And Digital Signatures Learn By Examples With Python And 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 2024-08-28 with Computers categories.

Unlock the secrets of modern cryptography explored in this book, a comprehensive guide that takes you from the fundamentals to advanced applications in encryption, decryption, and digital signatures. Whether you're a beginner or an experienced developer, this book offers hands-on examples, real-world scenarios, and detailed explanations that make complex concepts accessible and engaging. Dive into the world of RSA, as you learn to build secure systems and protect sensitive information with confidence. Perfect for anyone looking to master the art of cryptography, this book is your key to the future of digital security. In chapter one, we implemented RSA key generation within a Tkinter-based GUI application. This example was designed to be user-friendly, allowing users to generate RSA keys with a simple button click. The process involved generating a private key and a corresponding public key, which were then displayed within a text widget for easy copying and saving. This example demonstrated the ease with which RSA keys can be generated programmatically, making cryptography more accessible to users who may not be familiar with command-line interfaces. In chapter two, we embarked on a journey to create a sophisticated RSA encryption and decryption project. We began by constructing a comprehensive Tkinter-based GUI application that allows users to generate RSA key pairs, create and sign transactions, verify signatures, and securely store transactions. The initial focus was on setting up the graphical user interface, with multiple tabs dedicated to different functionalities, ensuring that the application was both user-friendly and feature-rich. The core functionality of the application revolves around RSA key generation, transaction creation, and digital signing. The RSA keys are generated using the cryptography library, and users can generate private and public keys, which are then displayed in the application. This setup forms the foundation for securely signing transactions. The transaction creation process involves entering details like the sender, receiver, amount, and currency, after which the transaction data is signed using the private key, producing a digital signature. This digital signature ensures the authenticity and integrity of the transaction, preventing any tampering or forgery. Once transactions are signed, they can be stored in a secure manner. The application allows users to save these transactions, along with their digital signatures, in a JSON file, providing a permanent and verifiable record. This storage mechanism is crucial for maintaining the integrity of financial transactions or any sensitive data, as it ensures that each transaction is accompanied by a corresponding signature and public key, enabling later verification. The verification process is another key component of the project. The application retrieves stored transactions and verifies the digital signature against the stored public key. This process ensures that the transaction has not been altered since it was signed, confirming its authenticity. The verification feature is critical in real-world applications, where data integrity and authenticity are paramount, such as in financial systems, legal documents, or secure communications. Throughout the chapter, the project was designed with a strong emphasis on real-world applicability, robustness, and security. The example provided not only serves as a practical guide for implementing RSA encryption and decryption with digital signatures but also highlights the importance of secure key management, transaction integrity, and data authenticity in modern cryptographic applications. This project demonstrates the power of RSA in securing sensitive data and transactions in a user-friendly and accessible way, making it an essential tool for developers working with encryption in real-world scenarios. In chapter three, we some projects focused on RSA digital signatures, delving into the creation of synthetic datasets, key generation, data signing, and verification processes. The project’s primary objective is to demonstrate how RSA digital signatures can be applied in a real-world scenario by securely signing and verifying user data. This example uses a synthetic dataset of user information, including user IDs, names, emails, and registration dates, to illustrate the practical implementation of RSA cryptography. The project begins with generating RSA keys using the generate_rsa_keys function. This function creates a pair of keys: a private key used for signing data and a public key for verifying the signature. These keys are essential for the RSA cryptographic process, where the private key ensures that the data remains authentic and unaltered, while the public key is used to verify the authenticity of the signed data. The keys are serialized into PEM format, a widely-used encoding standard that facilitates the secure storage and transmission of cryptographic keys. Next, a synthetic user dataset is generated using the create_synthetic_user_dataset function. This dataset comprises a specified number of user records, each containing a unique user ID, name, email address, and registration date. The purpose of this synthetic data is to simulate a realistic environment where user information needs to be securely signed and verified. By using a synthetic dataset, we ensure that the example remains versatile and adaptable to various scenarios without relying on actual sensitive information. Once the dataset is generated, the sign_data function is employed to sign each user's data using the RSA private key. This process involves creating a digital signature for each record, ensuring that any alteration to the data after signing would invalidate the signature. The digital signature serves as a cryptographic proof of the data’s integrity and authenticity, providing a robust mechanism to detect tampering or unauthorized modifications. The signatures are then stored alongside the user data for subsequent verification. Finally, the project includes a mechanism for storing the signed data and public key in a JSON file, and a function for retrieving and verifying the data. The store_user_data function saves the user data, corresponding signatures, and the public key to a file, allowing for secure storage and later retrieval. The retrieve_and_verify_user_data function reads the stored data, verifies each signature using the public key, and confirms whether the data remains unaltered. This final step completes the demonstration of how RSA digital signatures can be effectively used to secure user data, making it a comprehensive example for those learning about cryptographic techniques in real-world applications.