Author: Giandomenico Toniolo Publisher: Springer ISBN: 3030146642 Category : Technology & Engineering Languages : en Pages : 375
Book Description
This textbook presents the principal methods of stress analysis for the design of frame structures, beginning with a description of the basic criteria for probabilistic safety verification used in modern codes. The Force Method and the Displacement Method are dealt with, together with their applications to more common structural situations. A special chapter is dedicated to the second order analysis required for slender structures and for the elaboration of instability problems. In turn, a thorough set of numerical examples rounds out the text. Given its scope, the book offers an ideal learning resource for students of Civil and Building Engineering and Architecture, and a valuable reference guide for practicing structural design professionals.
Author: Hillary Keeney Publisher: ISBN: 9781365356285 Category : Languages : en Pages : 308
Book Description
Recursive Frame Analysis (RFA) is a qualitative research method for mapping and analyzing change-oriented conversation. Cybernetician and therapist Bradford Keeney invented RFA over twenty years ago as a means of discerning and indicating the bare bones organization of real-time therapeutic performance. This book revisits some of Keeney's original ideas while providing a more exhaustive theoretical foundation for RFA, a thorough exploration of its practical application as a research tool, and several detailed analyses of therapy sessions.
Author: Anders Persson Publisher: Routledge ISBN: 1317133536 Category : Social Science Languages : en Pages : 180
Book Description
This book is about Erving Goffman’s frame analysis as it, on the one hand, was presented in his 1974 book Frame Analysis and, on the other, was actually conducted in a number of preceding substantial analyses of different aspects of social interaction such as face-work, impression management, fun in games, behavior in public places and stigmatization. There was, in other words, a frame analytic continuity in Goffman’s work. In an article published after his death in 1982, Goffman also maintained that he throughout his career had been studying the same object: the interaction order. In this book, the author states that Goffman also applied an overarching perspective on social interaction: the dynamic relation between ritualization, vulnerability and working consensus. However, there were also cracks in Goffman ́s work and one is shown here with reference to the leading question in Frame Analysis – what is it that’s going on here? While framed on a "microsocial" level, that question ties in with "the interaction order" and frame analysis as a method. If, however, it is framed on a societal level, it mirrors metareflective and metasocial manifestations of changes and unrest in the interaction order that, in some ways, herald the emphasis on contingency, uncertainty and risk in later sociology. Through analyses of social media as a possible new interaction order – where frame disputes are frequent – and of interactional power, the applicability of Goffman’s frame analysis is illustrated. As such, this book will appeal to scholars and students of social theory, classical sociology and social interaction.
Author: Jim A. Kuypers Publisher: Rowman & Littlefield ISBN: 1442252731 Category : Language Arts & Disciplines Languages : en Pages : 345
Book Description
Now in its second edition, Rhetorical Criticism: Perspectives in Action presents a thorough, accessible, and well-grounded introduction to contemporary rhetorical criticism. Systematic chapters contributed by noted experts introduce the fundamental aspects of a perspective, provide students with an example to model when writing their own criticism, and address the potentials and pitfalls of the approach. In addition to covering traditional modes of rhetorical criticism, the volume presents less commonly discussed rhetorical perspectives, exposing students to a wide cross-section of techniques.
Author: Brian Shannon Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
focuses on analyzing price charts across different timeframes to identify trends, key resistance and support levels, and potential trading opportunities. The book has 184 pages. Here are some key features of the book:The book emphasizes the importance of using multiple timeframes to analyze price charts and identify trading opportunities.It provides a detailed and practical approach to analyzing price charts across different timeframes, including weekly, daily, 30-minute, 15-minute, and 5-minute timeframes.The book covers a range of technical analysis tools and techniques, including volume moving averages, VWAP, and chart patterns.It provides guidance on how to anticipate price movements rather than react to them, which can help traders make more informed trading decisions.The book includes real-world examples and case studies to illustrate how the concepts and techniques discussed in the book can be applied in practice.
Author: Mieke Verloo Publisher: Central European University Press ISBN: 6155211396 Category : Social Science Languages : en Pages : 309
Book Description
This book aims to map the diversity of meanings of gender equality across Europe and reflects on the contested concept of gender equality. In its exploration of the diverse meanings of gender equality it not only takes into account the existence of different visions of gender equality, and the way in which different political and theoretical debates crosscut these visions, but also reflects upon the geographical contexts in which visions and debates over gender equality are located. The contextual locations where these visions and debates take place include the European Union and member states such as Austria, the Netherlands, Hungary, Slovenia, Greece, and Spain. In all of these settings, the different meanings of gender equality are explored comparatively in relation to the issues of family policies, domestic violence, and gender inequality in politics, while specific national contexts discuss the issues of prostitution (Austria, Slovenia), migration (the Netherlands), homosexual rights (Spain), and antidiscrimination (Hungary). The multiple meanings of gender equality are studied through Critical Frame Analysis, a methodology that builds on social movement theory and that was refined further with elements of gender and political theory within the context of the MAGEEQ research project
Author: Vivian Siahaan Publisher: BALIGE PUBLISHING ISBN: Category : Computers Languages : en Pages : 167
Book Description
The first project in chapter one which is Canny Edge Detector presented here is a graphical user interface (GUI) application built using Tkinter in Python. This application allows users to open video files (of formats like mp4, avi, or mkv) and view them along with their corresponding Canny edge detection frames. The application provides functionalities such as playing, pausing, stopping, navigating through frames, and jumping to specific times within the video. Upon opening the application, users are greeted with a clean interface comprising two main sections: the video display panel and the control panel. The video display panel consists of two canvas widgets, one for displaying the original video and another for displaying the Canny edge detection result. These canvases allow users to visualize the video and its corresponding edge detection in real-time. The control panel houses various buttons and widgets for controlling the video playback and interaction. Users can open video files using the "Open Video" button, select a zoom scale for viewing convenience, jump to specific times within the video, play/pause the video, stop the video, navigate through frames, and even open another instance of the application for simultaneous use. The core functionality lies in the methods responsible for displaying frames and performing Canny edge detection. The show_frame() method retrieves frames from the video, resizes them based on the selected zoom scale, and displays them on the original video canvas. Similarly, the show_canny_frame() method applies the Canny edge detection algorithm to the frames, enhances the edges using dilation, and displays the resulting edge detection frames on the corresponding canvas. The application also supports mouse interactions such as dragging to pan the video frames within the canvas and scrolling to navigate through frames. These interactions are facilitated by event handling methods like on_press(), on_drag(), and on_scroll(), ensuring smooth user experience and intuitive control over video playback and exploration. Overall, this project provides a user-friendly platform for visualizing video content and exploring Canny edge detection results, making it valuable for educational purposes, research, or practical applications involving image processing and computer vision. This second project in chapter one implements a graphical user interface (GUI) application for performing edge detection using the Prewitt operator on videos. The purpose of the code is to provide users with a tool to visualize videos, apply the Prewitt edge detection algorithm, and interactively control playback and visualization parameters. The third project in chapter one which is "Sobel Edge Detector" is implemented in Python using Tkinter and OpenCV serves as a graphical user interface (GUI) for viewing and analyzing videos with real-time Sobel edge detection capabilities. The "Frei-Chen Edge Detection" project as fourth project in chapter one is a graphical user interface (GUI) application built using Python and the Tkinter library. The application is designed to process and visualize video files by detecting edges using the Frei-Chen edge detection algorithm. The core functionality of the application lies in the implementation of the Frei-Chen edge detection algorithm. This algorithm involves convolving the video frames with predefined kernels to compute the gradient magnitude, which represents the strength of edges in the image. The resulting edge-detected frames are thresholded to convert grayscale values to binary values, enhancing the visibility of edges. The application also includes features for user interaction, such as mouse wheel scrolling to zoom in and out, click-and-drag functionality to pan across the video frames, and input fields for jumping to specific times within the video. Additionally, users have the option to open multiple instances of the application simultaneously to analyze different videos concurrently, providing flexibility and convenience in video processing tasks. Overall, the "Frei-Chen Edge Detection" project offers a user-friendly interface for edge detection in videos, empowering users to explore and analyze visual data effectively. The "KIRSCH EDGE DETECTOR" project as the fifth project in chapter one is a Python application built using Tkinter, OpenCV, and NumPy libraries for performing edge detection on video files. It handles the visualization of the edge-detected frames in real-time. It retrieves the current frame from the video, applies Gaussian blur for noise reduction, performs Kirsch edge detection, and applies thresholding to obtain the binary edge image. The processed frame is then displayed on the canvas alongside the original video. This "SCHARR EDGE DETECTOR" as the sixth project in chapter one is creating a graphical user interface (GUI) to visualize edge detection in videos using the Scharr algorithm. It allows users to open video files, play/pause video playback, navigate frame by frame, and apply Scharr edge detection in real-time. The GUI consists of multiple components organized into panels. The main panel displays the original video on the left side and the edge-detected video using the Scharr algorithm on the right side. Both panels utilize Tkinter Canvas widgets for efficient rendering and manipulation of video frames. Users can interact with the application using control buttons located in the control panel. These buttons include options to open a video file, adjust the zoom scale, jump to a specific time in the video, play/pause video playback, stop the video, navigate to the previous or next frame, and open another instance of the application for parallel video analysis. The core functionality of the application lies in the VideoScharr class, which encapsulates methods for video loading, playback control, frame processing, and edge detection using the Scharr algorithm. The apply_scharr method implements the Scharr edge detection algorithm, applying a pair of 3x3 convolution kernels to compute horizontal and vertical derivatives of the image and then combining them to calculate the edge magnitude. Overall, the "SCHARR EDGE DETECTOR" project provides users with an intuitive interface to explore edge detection techniques in videos using the Scharr algorithm. It combines the power of image processing libraries like OpenCV and the flexibility of Tkinter for creating interactive and responsive GUI applications in Python. The first project in chapter two is designed to provide a user-friendly interface for processing video frames using Gaussian filtering techniques. It encompasses various components and functionalities tailored towards efficient video analysis and processing. The GaussianFilter Class serves as the backbone of the application, managing GUI initialization and video processing functionalities. The GUI layout is constructed with Tkinter widgets, comprising two main panels for video display and control buttons. Key functionalities include opening video files, controlling playback, adjusting zoom levels, navigating frames, and interacting with video frames via mouse events. Additionally, users can process frames using OpenCV for Gaussian filtering to enhance video quality and reduce noise. Time navigation functionality allows users to jump to specific time points in the video. Moreover, the application supports multiple instances for simultaneous video analysis in independent windows. Overall, this project offers a comprehensive toolset for video analysis and processing, empowering users with an intuitive interface and diverse functionalities. The second project in chapter two presents a Tkinter application tailored for video frame filtering utilizing a mean filter. It offers comprehensive functionalities including opening, playing/pausing, and stopping video playback, alongside options to navigate to previous and next frames, jump to specified times, and adjust zoom scale. Displayed on separate canvases, the original and filtered video frames are showcased distinctly. Upon video file opening, the application utilizes imageio.get_reader() for video reading, while play_video() and play_filtered_video() methods handle frame display. Individual frame rendering is managed by show_frame() and show_mean_frame(), incorporating noise addition through the add_noise() method. Mouse wheel scrolling, canvas dragging, and scrollbar scrolling are facilitated through event handlers, enhancing user interaction. Supplementary functionalities include time navigation, frame navigation, and the ability to open multiple instances using open_another_player(). The main() function initializes the Tkinter application and executes the event loop for GUI display. The third project in chapter two aims to develop a user-friendly graphical interface application for filtering video frames with a median filter. Supporting various video formats like MP4, AVI, and MKV, users can seamlessly open, play, pause, stop, and navigate through video frames. The key feature lies in real-time application of the median filter to enhance frame quality by noise reduction. Upon video file opening, the original frames are displayed alongside filtered frames, with users empowered to control zoom levels and frame navigation. Leveraging libraries such as tkinter, imageio, PIL, and OpenCV, the application facilitates efficient video analysis and processing, catering to diverse domains like surveillance, medical imaging, and scientific research. The fourth project in chapter two exemplifies the utilization of a bilateral filter within a Tkinter-based graphical user interface (GUI) for real-time video frame filtering. The script showcases the application of bilateral filtering, renowned for its ability to smooth images while preserving edges, to enhance video frames. The GUI integrates two main components: canvas panels for displaying original and filtered frames, facilitating interactive viewing and manipulation. Upon video file opening, original frames are displayed on the left panel, while bilateral-filtered frames appear on the right. Adjustable parameters within the bilateral filter method enable fine-tuning for noise reduction and edge preservation based on specific video characteristics. Control functionalities for playback, frame navigation, zoom scaling, and time jumping enhance user interaction, providing flexibility in exploring diverse video filtering techniques. Overall, the script offers a practical demonstration of bilateral filtering in real-time video processing within a Tkinter GUI, enabling efficient exploration of filtering methodologies. The fifth project in chapter two integrates a video player application with non-local means denoising functionality, utilizing tkinter for GUI design, PIL for image processing, imageio for video file reading, and OpenCV for denoising. The GUI, set up by the NonLocalMeansDenoising class, includes controls for playback, zoom, time navigation, and frame browsing, alongside features like mouse wheel scrolling and dragging for user interaction. Video loading and display are managed through methods like open_video and play_video(), which iterate through frames, resize them, and add noise for display on the canvas. Non-local means denoising is applied using the apply_non_local_denoising() method, enhancing frames before display on the filter canvas via show_non_local_frame(). The GUI fosters user interaction, offering controls for playback, zoom, time navigation, and frame browsing, while also ensuring error handling for seamless operation during video loading, processing, and denoising. The sixth project in chapter two provides a platform for filtering video frames using anisotropic diffusion. Users can load various video formats and control playback (play, pause, stop) while adjusting zoom levels and jumping to specific timestamps. Original video frames are displayed alongside filtered versions achieved through anisotropic diffusion, aiming to denoise images while preserving critical edges and structures. Leveraging OpenCV and imageio for image processing and PIL for manipulation tasks, the application offers a user-friendly interface with intuitive control buttons and multi-video instance support, facilitating efficient analysis and enhancement of video content through anisotropic diffusion-based filtering. The seventh project in chapter two is built with Tkinter and OpenCV for filtering video frames using the Wiener filter. It offers a user-friendly interface for opening video files, controlling playback, adjusting zoom levels, and applying the Wiener filter for noise reduction. With separate panels for displaying original and filtered video frames, users can interact with the frames via zooming, scrolling, and dragging functionalities. The application handles video processing internally by adding random noise to frames and applying the Wiener filter, ensuring enhanced visual quality. Overall, it provides a convenient tool for visualizing and analyzing videos while showcasing the effectiveness of the Wiener filter in image processing tasks. The first project in chapter three showcases optical flow observation using the Lucas-Kanade method. Users can open video files, play, pause, and stop them, adjust zoom levels, and jump to specific frames. The interface comprises two panels for original video display and optical flow results. With functionalities like frame navigation, zoom adjustment, and time-based jumping, users can efficiently analyze optical flow patterns. The Lucas-Kanade algorithm computes optical flow between consecutive frames, visualized as arrows and points, allowing users to observe directional changes and flow strength. Mouse wheel scrolling facilitates zoom adjustments for detailed inspection or broader perspective viewing. Overall, the application provides intuitive navigation and robust optical flow analysis tools for effective video observation. The second project in chapter three is designed to visualize optical flow with Kalman filtering. It features controls for video file manipulation, frame navigation, zoom adjustment, and parameter specification. The application provides side-by-side canvases for displaying original video frames and optical flow results, allowing users to interact with the frames and explore flow patterns. Internally, it employs OpenCV and NumPy for optical flow computation using the Farneback method, enhancing stability and accuracy with Kalman filtering. Overall, it offers a user-friendly interface for analyzing video data, benefiting fields like computer vision and motion tracking. The third project in chapter three is for optical flow analysis in videos using Gaussian pyramid techniques. Users can open video files and visualize optical flow between consecutive frames. The interface presents two panels: one for original video frames and the other for computed optical flow. Users can adjust zoom levels and specify optical flow parameters. Control buttons enable common video playback actions, and multiple instances can be opened for simultaneous analysis. Internally, OpenCV, Tkinter, and imageio libraries are used for video processing, GUI development, and image manipulation, respectively. Optical flow computation relies on the Farneback method, with resulting vectors visualized on the frames to reveal motion patterns.
Author: Giandomenico Toniolo
Author: Erving Goffman
Author: Hillary Keeney
Author: Anders Persson
Author: Jim A. Kuypers
Author: Brian Shannon
Author: Mieke Verloo
Author: Stephen Gerard Sawyer
Author: Vivian Siahaan
Author: Dr. Deepa Viswam