AUTOMATED PAGE LAYOUT SYSTEM USING GENETIC ALGORITHMS

Abstract

The present invention discloses an automated page layout system using genetic algorithms to create aesthetic, user-defined layouts for digital albuming and web-based imaging applications. The system consists of interconnected hardware and software components, including a Page Creator Module (PCM), an Image Placement Module (IPM), a Genetic Algorithm Processor (GAP), and a Fitness Evaluation Module (FEM), all managed by a System Controller Unit (SCU) with real-time operating system support. The GAP, utilizing an FPGA, optimizes layouts through genetic algorithms by iteratively adjusting placement based on user preferences, which are configured through a User Configuration Module (UCM). A high-speed Data Storage Unit (DSU) and a touchscreen interface enable efficient data access and interactive user feedback. This architecture provides rapid, optimized layouts that align with aesthetic preferences, offering a robust solution for automated page layout generation in imaging applications.

Specification

Description:[014] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit, and scope of the present disclosure as defined by the appended claims.
[015] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[016] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.
[017] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[018] The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.
[019] Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[020] In an embodiment of the invention and referring to Figures 1, the present invention discloses an automated page layout system using genetic algorithms for creating aesthetic and user-defined layouts in digital albuming and web-based imaging applications. This system integrates a sophisticated combination of hardware and software components, allowing the generation of optimized page layouts through evolutionary algorithms. The invention comprises various modules, including a page creator module, an image placement module, a fitness evaluation module, and a user interface (UI) for user-defined customization. These components are implemented within a client-server architecture, enabling real-time interaction and processing in a networked environment.
[021] At the core of the system lies the Page Creator Module (PCM), a critical component that determines the arrangement of images across multiple album pages. This module is responsible for dividing a set of images into discrete pages according to user preferences, such as the number of images per page, layout style, and orientation. The PCM interfaces directly with a Data Storage Unit (DSU), a hardware component comprising SSD (Solid State Drive) arrays configured to provide high-speed access to image data. The DSU is connected to the PCM through a high-bandwidth network interface, ensuring rapid retrieval of images. The PCM retrieves images, parses user specifications, and distributes images across pages as initial layout instances to be further optimized by the genetic algorithm.
[022] The Image Placement Module (IPM), implemented as a separate but interconnected software component, refines the position of images on each page. After the PCM distributes images into pages, the IPM arranges these images by considering design elements such as spacing, alignment, and image size ratios. To accomplish this, the IPM leverages a Graphics Processing Unit (GPU) with parallel processing capabilities to accelerate image positioning tasks. The IPM uses CUDA (Compute Unified Device Architecture) technology, which allows for optimized image rendering calculations, and it interfaces with the PCM via an Ethernet-based LAN Switch, enabling synchronized and low-latency communication between the modules.
[023] The Genetic Algorithm Processor (GAP) is a dedicated hardware-software hybrid unit responsible for optimizing page layouts. The GAP includes a Field-Programmable Gate Array (FPGA) configured with genetic algorithm logic, enabling accelerated fitness evaluations and crossover operations. The genetic algorithm is implemented as a hardware-accelerated solution that applies mutation and selection techniques iteratively. The FPGA within the GAP generates a population of potential layouts, each evaluated based on user-defined design preferences. These preferences are encoded within the system through a User Configuration Module (UCM), a software interface allowing the user to define aesthetic parameters such as color schemes, image placement order, and spacing requirements. The UCM is integrated into the system's user interface and communicates with the GAP to ensure that user preferences guide the evolutionary process.
[024] Once potential layouts are generated, a Fitness Evaluation Module (FEM) assesses each layout's suitability. The FEM, integrated within the GAP, computes a fitness score for each layout based on alignment, color harmony, and adherence to user-defined parameters. The FEM utilizes a Digital Signal Processor (DSP), which enhances computation speed for scoring metrics related to visual aesthetics. The DSP processes layout images in real time, rapidly evaluating multiple configurations to identify those with the highest fitness scores. The FEM is directly connected to the DSU for retrieving images and uses an advanced Ethernet protocol (e.g., 10 Gigabit Ethernet) to ensure fast data transfer rates.
[025] The System Controller Unit (SCU) coordinates the interaction between PCM, IPM, GAP, FEM, and UCM. The SCU is implemented as a centralized control unit with an Advanced RISC Machine (ARM) processor, providing the processing power required to manage complex interactions between all system components. It schedules tasks, monitors data flow, and ensures that each module's output is synchronized. The SCU is configured with real-time operating system (RTOS) software, enabling deterministic responses to user inputs and efficient resource allocation.
[026] To enable user interaction, the invention includes a Touchscreen Display Unit (TDU) that serves as a user interface. The TDU, configured with an LCD screen and a capacitive touch panel, displays the layout previews and allows users to adjust layout parameters interactively. The TDU is connected to the SCU through an HDMI interface and uses USB connections for touch input detection. Users can view real-time layout updates, make adjustments to aesthetic parameters, and choose between different layout options.
[027] The system's Power Supply Unit (PSU) provides stable and isolated power to all components, supporting the GPU, FPGA, DSP, ARM processor, and other modules. The PSU is designed with high-efficiency converters to ensure continuous operation of all system modules, and it includes backup battery integration for uninterrupted operation in case of power loss.
[028] The system's inter-module communication relies on a high-speed Ethernet backbone, with data transmitted across a Gigabit LAN Switch. The PCM, IPM, and GAP modules are connected via this Ethernet backbone, allowing for seamless data flow. Additionally, the DSP and FPGA within the GAP are directly connected to the DSU via PCI Express (PCIe) links, ensuring rapid access to image data for fitness evaluation and genetic processing. Each module is configured with software protocols optimized for low-latency data transmission, enabling the system to perform real-time layout adjustments based on genetic algorithm computations.
[029] The entire layout optimization process is initiated when the SCU receives an image set and user-defined design parameters from the TDU. The SCU then instructs the PCM to create initial page distributions, which are forwarded to the IPM for preliminary image placements. The IPM configures these placements based on basic design parameters and sends them to the GAP for further refinement using genetic algorithms. After several iterations of layout adjustment and fitness evaluation, the FEM selects the best layouts, which are displayed on the TDU for user review.
[030] To demonstrate the efficacy of the invention, Table 1 compares the time taken to achieve optimized layouts with this system versus conventional template-based and rule-based systems. Additionally, Table 2 presents a comparison of user satisfaction ratings based on layout quality, as surveyed across various test users. The results indicate that this system significantly reduces layout generation time and increases user satisfaction by providing layouts that better align with aesthetic preferences.

[031] The proposed automated page layout system demonstrates clear advantages over traditional methods, enabling faster, more visually appealing layouts that align with user-defined criteria. Through the use of genetic algorithms and advanced hardware components, this invention provides an efficient and highly adaptable solution for automated page layout generation in digital albuming and imaging applications. , Claims:1. An automated page layout system for generating optimized layouts in digital albuming and web-based imaging applications, comprising:
a) a Page Creator Module (PCM) configured to distribute images across multiple album pages based on user-defined preferences;
b) an Image Placement Module (IPM) arranged to position images on individual pages by considering design elements such as spacing, alignment, and size ratios;
c) a Genetic Algorithm Processor (GAP), comprising a Field-Programmable Gate Array (FPGA), configured to optimize page layouts through genetic algorithm techniques, including mutation, crossover, and selection;
d) a Fitness Evaluation Module (FEM), integrated within the GAP, configured to evaluate each layout's fitness based on user-defined design criteria;
e) a User Configuration Module (UCM) enabling user customization of layout parameters via an interactive interface;
f) a System Controller Unit (SCU) to coordinate interactions between PCM, IPM, GAP, FEM, and UCM, with real-time operating system (RTOS) software for efficient task management;
g) a Touchscreen Display Unit (TDU) to display real-time layout previews and receive user adjustments;
h) a Data Storage Unit (DSU) comprising SSD arrays for high-speed image retrieval;
i) a Power Supply Unit (PSU) designed to power the system's components.
2. The system as claimed in claim 1, wherein the PCM is configured to access the DSU through a high-bandwidth network interface, enabling rapid image retrieval and ensuring the PCM operates with minimal latency.
3. The system as claimed in claim 1, wherein the IPM utilizes a Graphics Processing Unit (GPU) with parallel processing capabilities to accelerate image positioning tasks, leveraging CUDA technology to optimize rendering calculations.
4. The system as claimed in claim 1, wherein the GAP's FPGA is configured to perform accelerated fitness evaluations by iterating through potential layouts, applying genetic algorithm operations including mutation and crossover, and selecting layouts with the highest fitness scores.
5. The system as claimed in claim 1, wherein the FEM includes a Digital Signal Processor (DSP) configured to compute visual fitness scores in real time based on criteria such as alignment, color harmony, and user-defined aesthetic preferences.
6. The system as claimed in claim 1, wherein the SCU includes an Advanced RISC Machine (ARM) processor, providing the processing power necessary to manage interactions among PCM, IPM, GAP, FEM, and UCM, and configured to run on RTOS for deterministic response times.
7. The system as claimed in claim 1, wherein the TDU includes a capacitive touch panel and LCD display, enabling interactive adjustments by displaying layout previews and capturing user-defined parameters in real-time.
8. The system as claimed in claim 1, wherein the DSU is directly connected to the DSP and FPGA in the GAP through PCI Express (PCIe) links, facilitating high-speed data access for genetic processing and fitness evaluation.
9. The system as claimed in claim 1, further includes a Gigabit LAN Switch to connect the PCM, IPM, and GAP via a high-speed Ethernet backbone, supporting synchronized data flow and low-latency communication between modules.
10. The system as claimed in claim 1, wherein the PSU includes high-efficiency converters and a backup battery, ensuring uninterrupted operation and continuous power supply to all components of the automated page layout system.

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