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GateKeeperA Proposal
presented to the Faculty of the
College of Computer Studies, University of Cebu
In Partial Fulfillment of the Requirements for the
degree of Bachelor of Science in Computer Science
By
Degala, Virly B.

Sabac, Cristy G.

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Subelario, Joseph Earl V.

Ybañez, James Dominic J.

Japeth D. LahaylahayAdviser
March 2019
ACKNOWLEDGEMENTThe researchers would like to express their gratitude of thanks and appreciation to those who helped make this thesis possible.

To the Head of CSU of the University of Cebu Lapu-Lapu and Mandaue, for allowing them to conduct a research in their school.
To their mentor, for helping them develops our system, and Mr. Jennel D. Barzo, their grammarian, for editing and for helping them in order to publish their paper.
To Mr. Japeth D. Lahaylahay, their adviser, they thank him for the untiring updates to their system and ensuring that their project is making progress.
To their parents, for always inspiring and supporting them. And also, their friends that gives them motivation and courage to go on in this journey.
To all the people not mentioned who helped them, and in some ways served as an inspiration, they extend their heartfelt appreciation for their kindness. And above all the Almighty God, who never failed them and for giving them courage and determination.

The Researchers
DEDICATIONThis Thesis Project is dedicated to:
Our beloved parents who never stop giving of themselves in countless ways. For their endless love, prayers, unconditional support and encouragement. For the big trust that they gave us and a chance to prove and improve our self through all our walks of life. They are a great source of motivation and inspiration.
Our dear adviser Mr. Japeth D. Lahaylahay, for guiding and supporting them in all the way. By imparting his knowledge, ideas and techniques which greatly helped us for the completion of the project.
Above all Almighty God, for giving us the grace courage and strength to complete this capstone project.

The Researchers
Table of Contents TOC o “3-3” h z “Header1,1,header 2 style,2” ACKNOWLEDGEMENT PAGEREF _Toc527117529 h iiDEDICATION PAGEREF _Toc527117530 h iiiTable of Contents PAGEREF _Toc527117531 h ivList of Table PAGEREF _Toc527117532 h viList of Figures PAGEREF _Toc527117533 h viiChapter I: Introduction
Rationale of the Study PAGEREF _Toc527117536 h 1 Objectives of the Study PAGEREF _Toc527117537 h 2 Scope and Limitations PAGEREF _Toc527117538 h 3 Significance of the Study PAGEREF _Toc527117539 h 4 Definition of Terms PAGEREF _Toc527117540 h 5Chapter II: Review of Related Literature and Studies
Theoretical Background PAGEREF _Toc527117542 h 6 Related Literature PAGEREF _Toc527117543 h 9 Related Studies PAGEREF _Toc527117544 h 11 Comparative Matrix PAGEREF _Toc527117545 h 13Chapter III: Research Methodology
Research Environment PAGEREF _Toc527117546 h 14 Software Engineering Methodology PAGEREF _Toc527117547 h 15 Planning/Conception-Initiation Phase PAGEREF _Toc527117548 h 16 Business Model Canvas PAGEREF _Toc527117549 h 16 Gantt Chart PAGEREF _Toc527117550 h 17 Functional Decomposition Diagram PAGEREF _Toc527117551 h 18 Analysis-Design Phase PAGEREF _Toc527117552 h 19 System Use Case PAGEREF _Toc527117553 h 20 Story Board PAGEREF _Toc527117554 h 25 Database Design PAGEREF _Toc527117555 h 31 Entity Relationship Diagram PAGEREF _Toc527117556 h 32 Data Dictionary PAGEREF _Toc527117557 h 33 Network Design PAGEREF _Toc527117558 h 37 Network Model PAGEREF _Toc527117559 h 38 Network Topology PAGEREF _Toc527117560 h 39 Development/Construction/Build Phase PAGEREF _Toc527117561 h 40 Technology Stack PAGEREF _Toc527117562 h 40 List of Module PAGEREF _Toc527117563 h 41 Curriculum Vitae PAGEREF _Toc527117564 h 44
List of Table TOC f T h z “tablestyle” c Table 1 Comparative Matrix PAGEREF _Toc527117761 h 13Table 2 Business Mode PAGEREF _Toc527117762 h 16Table 3 Driver_info Data Dictionary PAGEREF _Toc527117763 h 33Table 4 Staff Data Dictionary PAGEREF _Toc527117764 h 34Table 5 Vehicle_info Data Dictionary PAGEREF _Toc527117765 h 35Table 6 Time log Data Dictionary PAGEREF _Toc527117766 h 36Table 7 List of Modules PAGEREF _Toc527117767 h 41

List of Figures TOC h z “figurestyle” c Figure 1 Research Environment PAGEREF _Toc527117799 h 14Figure 2 Agile Development Flow Chart PAGEREF _Toc527117800 h 15Figure 3 Gantt Chart PAGEREF _Toc527117801 h 17Figure 4 Functional Decomposition Diagram PAGEREF _Toc527117802 h 18Figure 5 Business Use Case Diagram PAGEREF _Toc527117803 h 19Figure 6 Process Registration PAGEREF _Toc527117804 h 20Figure 7 Process Detection PAGEREF _Toc527117805 h 21Figure 8 Process Login PAGEREF _Toc527117806 h 22Figure 9 Manage Account PAGEREF _Toc527117807 h 23Figure 10 Generate Report PAGEREF _Toc527117808 h 24Figure 11 Login Interface PAGEREF _Toc527117809 h 25Figure 12 Landing Page for Admin PAGEREF _Toc527117810 h 26Figure 13 Landing Page for Staff PAGEREF _Toc527117811 h 27Figure 14 Database Access Interface PAGEREF _Toc527117812 h 28Figure 15 Add Info Interface PAGEREF _Toc527117813 h 29Figure 16 Generate Report Interfaces PAGEREF _Toc527117814 h 30Figure 17 Gate Keeper Database Design PAGEREF _Toc527117815 h 31Figure 18 Gate Keeper Entity Relationship Diagram (ERD) PAGEREF _Toc527117816 h 32Figure 19 Network Design PAGEREF _Toc527117817 h 37Figure 20 Network Model PAGEREF _Toc527117818 h 38Figure 21 Network Topology PAGEREF _Toc527117819 h 39
Chapter IIntroductionRationale of the StudyTechnology nowadays changes rapidly.  Mostly, people are using modern technology to do various activities. Lives have become more convenient and enjoyable. The recent development of technology has made it possible for us to lead more comfortable lives. Today, computers are one of the important things we are using. They have helped a lot in compiling a lot data and they are able to provide and retrieve information so quickly, so they have increased the pace of our work. Through the computer technology, we can cater for the need of quantity and quality of products required in daily life. Moreover, it has brought a lot of flexibility. Truly, technology had made life easier.

In this modern and fast paced world, security is one mostly important than ever. It is now one of the fastest growing industries in the world today. Almost every day one hears about damages or losses occurring due to security lapses or a lack of security on the news. Even the term security is not just limited to physical security these days. It includes things that are unseen but tremendously valuable things. Eventually, the schools have been deeply affected by the economic, political, and social conditions of our time, and have been expose to many undesirable events and behaviors such as violence, sabotage, kidnapping, carnapping, hijacking and the like.

In a traditional checking of vehicles that will enter at University of Cebu – Lapu-Lapu and Mandaue (UCLM), the Civil Security Unit (CSU) will confirm if the vehicle or the driver has the vehicle pass sticker which will indicate that the vehicle is registered and will grant passage to the driver. On the other hand, administrators’ vehicles will automatically be granted passage. In the case of visitors, the CSU will ask them which department they have an appointment with and will the said department for confirmation. This system of vehicle checking has its flaws as observed by the proponents. First, vehicle pass stickers can be duplicated and attach to unregistered vehicles for illegal entrance. Second, the vehicle logs are inputted on a paper log book, which is inefficient and not secure since CSU will have to manually input every vehicle that goes inside the premises; the said log books can be damaged easily by natural elements and have tendencies to be lost.

GateKeeper: “A License Plate Number Recognition (LPR) and Optical Character Recognition (OCR)” provides the solution with these problems. It is a system that can extract information regarding vehicle by analyzing the images of the vehicle automatically and providing more detailed information about it. It may remove the vehicle pass sticker, but it will improve the monitoring of registered vehicles in entering the school premises. Vehicle logs will be more organize and easy to access when needed. It also contributes and plays as a great role for a reliable, accurate, accessible, and effective system that cater of the needs of the clients as well as the valid customers
Objectives of the StudyThe main objective of this study is to organize, save, design and develop an Automatic Gatekeeper application. The system will do the following specific objectives:
1.To provide a specific Desktop application that automatically detects a vehicle wanting to enter the premise
1.1.Detect plate number
1.2.Detect vehicle manufacturer
1.3.Identify if the vehicle is registered
2.To be able to automatically allow passage for registered vehicles
3.To be able to alert the operator if the vehicle is unregistered
Scope and LimitationsThe researchers formulated the scope and limitations of this project to identify the boundaries of the study.
It has the following functionalities:
Process vehicle registration
Detect vehicles near the spot of the camera
Open the gate if the vehicle gets recognized as ‘registered’
Provide vehicle login/ logout time sheet.

Generate daily/weekly/monthly report
Hence, the proposed study is limited to the following:
May have difficulty identifying customized vehicles
The system will not accept a handwritten/computerize plate number
The system cannot recognize if the plate number is duplicated
The system will not detect the vehicle owner
The posted staff will call the respective department in case of a visiting vehicle/driver
Significance of the StudyThe monitoring system as one of the most useful services in the parking area. This study contributes that much and plays as a great role for a reliable, accurate, accessible, and effective system that cater of the needs of the clients as well as the valid customers. This system will monitor to all vehicle that will be given to the customers.
Admin of UCLM. Administrators will be able to easily access the system for approval and monitoring purposes.

CSU of UCLM. CSU will be able to maximize the security and safeness of the campus from unwanted threats.
Researchers. Yet this system is not guaranteed to be perfect to be implemented but it may help the researches for it is the major way to fulfill their dream to graduate
Future Researchers. This will be used as a reference, guide for future work and inquiries.

In this study there is a great significance to the people working in the company especially the customers as well. Aside of giving effective and accessible system, the management would maintain good relationship among their customers because of their satisfaction with regards to this system.

Definition of TermsDatabasea structured set of data held in a computer, especially one that is accessible in various ways
Digital Image Processingis the use of computer algorithms to perform image processing on digital images and allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processingGateKeeperis a system that can extract information regarding vehicle by analyzing the images of the vehicle automatically and providing more detailed information about it
QR Codea type of barcode that contains a matrix of dots that can be scanned using a QR scanner
Systemset of principles or procedures according to which something is done an organized scheme or method
Technologythe study or use of systems especially computers and telecommunications for storing, retrieving, and sending information
Vehicles they are classified according to their types and being monitored upon entering the campus.

Chapter IIReview of Related Literature and Studies
Theoretical Background
Optical Character Recognition (OCR) involves a computer system designed to translate images of typewritten or handwritten text (usually captured by a scanner) into machine readable and editable text. OCR could be applied to many fields like vehicle license plate recognition, information retrieval, document digitization, and in text-to-speech applications. Over the years, OCR has attracted a great deal of researches and has developed various successful methods of recognition. In this project, the researchers implemented OpenCV KNN Character Recognition to translate images of letters and digits into computer readable texts. Methodically, character recognition is a subset of the pattern recognition area. However, it was character recognition that gave the incentives for making pattern recognition and image analysis matured fields of science.

According to Veronica Ong and Derwin Suhartono (2011) in their research paper ‘Using K-Nearest Neighbor in Optical Character Recognition’. “The growth in computer vision technology has aided society with various kinds of tasks. One of these tasks is the ability of recognizing text contained in an image, or usually referred to as Optical Character Recognition (OCR). There are many kinds of algorithms that can be implemented into an OCR. The K-Nearest Neighbor is one such algorithm. This research aims to find out the process behind the OCR mechanism by using K-Nearest Neighbor algorithm; one of the most influential machine learning algorithms. It also aims to find out how precise the algorithm is in an OCR program. To do that, a simple OCR program to classify alphabets of capital letters is made to produce and compare real results. The result of this research yielded a maximum of 76.9% accuracy with 200 training samples per alphabet. A set of reasons are also given as to why the program is able to reach said level of accuracy.”
The researchers chose the KNN Character Recognition algorithm because of its consideration as the ‘top 10 most influential data mining algorithm in the research community (Wu et al., 2007).’ They have created an OCR program to detect characters present in a vehicle’s license plate.

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Components of an OCR system
A typical OCR system consists of several components. The first step in the process is to digitize the analog document using an optical scanner. When the regions containing text are located, each symbol is extracted through a segmentation process. The extracted symbols may then be preprocessed, eliminating noise, to facilitate the extraction of features in the next step.

The identity of each symbol is found by comparing the extracted features with descriptions of the symbol classes obtained through a previous learning phase. Finally, contextual information is used to reconstruct the words and numbers of the original text. In the next sections these steps and some of the methods involved are described in more detail.
Through the scanning process a digital image of the original document is captured. In OCR optical scanners are used, which generally consist of a transport mechanism plus a sensing device that converts light intensity into gray-levels. Printed documents usually consist of black print on a white background. Hence, when performing OCR, it is common practice to convert the multilevel image into a bilevel image of black and white. Often this process, known as thresholding, is performed on the scanner to save memory space and computational effort. The thresholding process is important as the results of the following recognition is totally dependent of the quality of the bilevel image. Still, the thresholding performed on the scanner is usually very simple. A fixed threshold is used, where gray-levels below this threshold is said to be black and levels above are said to be white. For a high-contrast document with uniform background, a prechosen fixed threshold can be sufficient. However, a lot of documents encountered in practice have a rather large range in contrast. In these cases, more sophisticated methods for thresholding are required to obtain a good result.
Segmentation is a process that determines the constituents of an image. It is necessary to locate the regions of the document where data have been printed and distinguish them from figures and graphics. For instance, when performing automatic mail-sorting, the address must be located and separated from other print on the envelope like stamps and company logos, prior to recognition. Applied to text, segmentation is the isolation of characters or words. The majority of optical character recognition algorithms segment the words into isolated characters which are recognized individually. Usually this segmentation is performed by isolating each connected component, that is each connected black area. This technique is easy to implement, but problems occur if characters touch or if characters are fragmented and consist of several parts. The main problems in segmentation may be divided into four groups:
Extraction of touching and fragmented characters. Such distortions may lead to several joint characters being interpreted as one single character, or that a piece of a character is believed to be an entire symbol. Joints will occur if the document is a dark photocopy or if it is scanned at a low threshold. Also, joints are common if the fonts are serried. The characters may be split if the document stems from a light photocopy or is scanned at a high threshold.

Distinguishing noise from text. Dots and accents may be mistaken for noise, and vice versa.

Mistaking graphics or geometry for text. This leads to non-text being sent to recognition.

Mistaking text for graphics or geometry. In this case the text will not be passed to the recognition stage. This often happens if characters are connected to graphics.
The image resulting from the scanning process may contain a certain amount of noise. Depending on the resolution on the scanner and the success of the applied technique for thresholding, the characters may be smeared or broken. Some of these defects, which may later cause poor recognition rates, can be eliminated by using a preprocessor to smooth the digitized characters. The smoothing implies both filling and thinning. Filling eliminates small breaks, gaps and holes in the digitized characters, while thinning reduces the width of the line. The most common techniques for smoothing, moves a window across the binary image of the character, applying certain rules to the contents of the window. In addition to smoothing, preprocessing usually includes normalization. The normalization is applied to obtain characters of uniform size, slant and rotation. To be able to correct for rotation, the angle of rotation must be found. For rotated pages and lines of text, variants of Hough transform are commonly used for detecting skew. However, to find the rotation angle of a single symbol is not possible until after the symbol has been recognized.
Template-matching and correlation techniques
These techniques are different from the others in that no features are actually extracted. Instead the matrix containing the image of the input character is directly matched with a set of prototype characters representing each possible class. The distance between the pattern and each prototype is computed, and the class of the prototype giving the best match is assigned to the pattern. The technique is simple and easy to implement in hardware and has been used in many commercial OCR machines. However, this technique is sensitive to noise and style variations and has no way of handling rotated characters.

Related LiteratureFrom literature review a lot have been done and employed to improve the system performance and various researches carried out in this area. Technically, the technology is a challenging research area which has been enabled by innovation in computers and sophisticated high-resolution infrared cameras. This made easier image processing techniques applicable to analyzing and extracting important features for plate numbers detection and recognition. The literature reviews looked at Image pre-processing, number plate detection and extraction, character extraction and segmentation, and finally recognition and interpretation.
Image acquisition is performed by high end IR cameras, placed at strategic positions to avoid obstacles in order to obtain accurate images. Image acquired by camera always reflect the camera settings; among many include color and hue, saturation and value or brightness (HSV) whereby essentially an image can be in its natural form or slightly altered. It was noted that, color images are complex in space and time. Pre-processing aims at image enhancement and restoration. This process eliminates noise, highlight edges and improve the overall quality of an image. In image pre-processing an image goes through among many procedures grey-scaling, dilation, erosion, and filtering and edge enhancement. Converting natural color images, to HSV color space, to grey scale and then to binary is important as it reduces time and space complexities. This concept was express by Reshma and Kim et al.
Number plate extraction is a process of localization of number plate region(s) from car image. Localization select right feature that will provide best results in number plate recognition stage. This is a difficult task due to the variations of shapes, sizes, color of a car, illumination conditions, texture and orientation of number plate in car images. Therefore, in order to have reliable number plate detection algorithms, in such dynamic environments, several choices have to be considered. Several features that could be deployed to extract a rectangular shape of number plate from a car image included color feature, aspect ratio, and texture edge density and shape/size of ROI, but for better detection rate the combination of features could provide more reliable solution. HSV color space and integral image properties could also be employed to locate the coordinates and position of white number plate and non- white number plates. From Liu et tal and Kim et tal. HSV enables one to find out the four coordinates of a rectangular shape containing English like symbols or texture from which X and Y coordinates, Width and Height could be extracted.

The process of number plate area detection and extraction involved three stages of finding area of interest (ROI), filtering out background and removals of false objects, and lastly computation of connected components which provided best results of required region of interest. In the process of locating the regions of Interest the HSV method was applied. MATLAB function was used to find the coordinates o (x, y, width, height) to provide the regions of interest. The masked image was computed using MATLAB functions to obtain rows and columns of our region of interest. Summation of mask from HSV process and mask from Morphological process provided the best results for computing connected components. The connected components were computed to provide the actual plate area position or coordinates of the region of interest. The properties of ROI including bounding Box, area of object, aspect ratio, extent, coordinates and perimeter of the object were considered to find the connected components. By combining all the above properties, it is possible to provide best results at later stage of segmentation and recognition. Finally, statistical analyses were applied to find the connected components to be used to locate number plate accurately. This was performed to ensure that the global boundary was maintained, by providing upper and lower limit of a ROI Left and Right limit of a ROI. Dimensional properties of rectangle shape or objects using morphology methods of dilation, erosion and fill; were also used by other researchers to increase correct plate localization.

In these methods the region that passed through the qualifiers, and also passed as a number plate, its boundary box coordinates were used to crop the original image and obtain the number plate only. The qualifier checked if the area of a region was not less than specified threshold and length of the aspect ratio is within the limits. The number plate detection and extraction returned four entries X, Y, Width and Height which could deal with adjustment of ROI because the image may contain angle. From the above methods, it was possible to eliminate the disturbances of the fake objects whose structure and components were similar to the vehicle number plate but did not match plate fixed color and coordinates. This new idea of combining several properties enabled maximized recognition rate and efficiency. The idea assumed that the white number plate area was situated somewhere between those black rows, hence by finding the largest vertical arrays of white pixels, it was possible to detect the left and right edge of the number plates.

Related StudiesAVDR System
Detection of the vehicle has become a serious initiative and an advanced area of research, which is possible through the application of VLPDR (Vehicle License Plate Detection and Recognition). Hence, VLPDR Systems can be considered, which primarily works by capturing the vehicular images and thus, interprets the License plate’s registration number automatically. AVDR (Automatic Vehicle Detection and Recognition) scheme proves to be an effective tool which helps to mechanize the hectic, tedious and the physical procedure of the workers which they encounter and deal with in their regular day life and serves to provide efficiency in the identification of the vehicles from hundreds and thousands of vehicles observed in the regular patrolling task. AVDR combines license plate detection with the shape detection of the vehicle to make a system which is more susceptible to false intrusions.

VNPR System
Vehicle number plate recognition (VNPR) system is a digital image processing technique which broadly used in vehicle transportation system to identify the vehicle. A number plate recognition system has broad implications, for e.g. traffic maintenances, tracing stolen cars, automatic electronic toll collection system and other applications. Vehicle number plate recognition (VNPI) system is capable of identifying vehicles by extracting the number plate and reading the plate to identity which unique identification code given to each vehicle, but the main aim is to control the traffic management system. Massive integration of information technologies into all aspects of modern life caused demand for processing vehicles as conceptual resources in information systems.

ELSAG ALPR Series – Leonardo S.p.A.

ELSAG ALPR Series by the company Leonardo S.p.A. represents the generations of the mobile Plate Hunters. These automatic license plate readers (ALPRs) are the most advanced systems on the market today, claims the company. Their modern mobile ALPR systems benefit law enforcement agencies, parking authorities, toll operators and access control organizations looking to enhance patrol presence, add a force multiplier and improve efficiency.

Uses ALPR Deep Learning Systems
Integrated with High-Resolution Cameras
Can be configurated according to user specifications
VPAR SERVER
VPAR SERVER uses Neuronal technology and Deep Learning to perform Traffic Analytics or Access Control in a fast and efficient way, whatever the target scenario: police cars, fixed cameras, etc. It can recognize license plates continuously, from vehicles in movement (Free-Flow) or stopped (Stop ; Go). It allows to use cameras from different brands and protocols on the same system.
Speed calculation.

Lane detection.

Direction flow detection.

Vehicle type classification.

LPR-EcocortexLPR by Eocortex is a reliable vehicle passage monitoring, access control, and logging system based on the license plate recognition technology. Using Eocortex ANPR Software, you can arrange automatic admittance of authorized vehicles, prevent entrance of unauthorized vehicles, and control the time vehicles spend in the territory. Eocortex LPR System allows you to
Add license plates to the database, create their “black” and “white” lists.
Control the opening of a rising arm barrier automatically and manually.
Save and search for time and date of recognition, license plate, links to the corresponding video frame in the archive.

Comparative MatrixTable 1 Comparative MatrixSystem License plate detection Blacklisting Time in-out logs
Generate reports Portability
GateKeeper? ? ? ? ?
AVDR System ? ? ?
VNPR System ? ? ? ?
ELSAG ALPR Series ? ? ?
VPAR SERVER ? ? ? LPR Ecocortex? ? ? ?
Chapter III
Research Methodology
In this section covers the methodology used in the study and other technical specification that will help to strengthen the study. It also covers methods and diagrams that will show for the better understanding about the study and describe the materials used to implement Gate Keeper that will meet the requirement of objectives of the study.

Research EnvironmentThis study was conducted in UCLM located at A.C Cortes Ave., Looc, Mandaue City, Cebu Philippines. The vehicles of UCLM is slowly entering the gate. The researcher proposed the Gate Keeper of monitoring the vehicles of UCLM on their manual system to improve their services to the customer, and make transaction convenient to the customer who will go inside and outside of school. To minimize the work of the personnel at the same time the customer can easily inside and exit their car to the said establishment.

Figure 1 Research EnvironmentIn April 1, 1964 a group of young men with vision and foresight, spurred by altruistic motives to help mold the moral and intellectual life of the youth, banded themselves together to form an educational institution where they founded UCLM. After all the struggles that Atty. Augusto Go had encountered he make grow and inspires students to finish educations.

Software Engineering MethodologyThe proposed system will use a method to deliver software with flexibility and this is called Agile Software Development. It is a group of software development methods in which requirements and solutions evolve through collaboration between self-organizing, cross-functional teams. It promotes adaptive planning, evolutionary development, early delivery, continuous improvement, and encourages rapid and flexible response to change.

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Figure 2 Agile Development Flow Chart
Planning/Conception-Initiation PhasePlanning is a profession that is concerned with shaping our living environment. Responsible planning entails a solid understanding and competence of the comprehensive and complex community features, including the physical, economic, and social factors that influence a community’s future.

Business Model CanvasThe Business Model Canvas is a flexible template for capturing the nine essential parts of a business model. The “canvas” is usually a large piece of paper with sections for each of a model’s elements. Participants in the design process move handwritten Post-it notes, representing proposed components of the model, around the canvas each note conveys an idea’s individual impact on the whole picture.

A business model is a dynamic system, not a collection of independent parts, so a change to one element is likely to have an impact on one or more of the others. Alteration of any of the aspects of a model-in-development is easy because of this visual, “building block” style of paper-based strategizing.

102141126026800Table 2 Business Model
Gantt Chart18669074041000Figure 3.1 shows the project schedule: including start and finish dates of activities form what is usually called work breakdown structure of a specific construction project.

Figure 3 Gantt ChartFunctional Decomposition DiagramThe figure shows the functionalities supported by the Gate Keeper. The proposed system aims to the following functionalities:

Figure 4 Functional Decomposition DiagramAnalysis-Design PhaseIn this phase, the researchers conducted a deeper understanding about the current process. The following tools are being applied:
Use Case Diagram
The use case describes the interaction between an actor and the system. The interaction yields a tangible result for the actor and a sequence of actions as shown in Figure 3.3

Figure 5 Business Use Case DiagramSystem Use Casecenter1206500
Figure 6 Process RegistrationUse Case Name: Process Registration
Purpose: To register the owner of the vehicle and driver’s details
Actor: UCLM Staff
Pre – Condition: Drivers will fill up the registration form
Post – Condition: Information has been added to database.

Steps:
Action System’s Response
Driver fills up the registration form
Administrator collects the form and input the information to database
Database will store the inputted information

Figure 7 Process DetectionUse Case Name: Process Detection
Purpose: To detect the vehicle’s plate number and driver’s QR Image
Actor: Vehicle Driver
Pre – Condition: Detect Plate Number and QR Image
Post – Condition: Vehicle Plate Number and QR Image has been validated.

Steps:
Action System’s Response
Vehicle Driver arrived at the campus
Camera identifies the validated vehicle plate number
If vehicle plate number is valid; gate indicator turns green
Else, gate indicator turns red
QR Scanner Identifies the validated QR Image

Figure 8 Process LoginUse Case Name: Process Login
Purpose: To access the Main page
Actor: UCLM Staff and UCLM Admin
Pre – Condition: UCLM Staff and Admin login to application
Post – Condition: Registered account will proceed to Main page
Steps:
Action System’s Response
UCLM Staff and Admin login to the application
Registered account will proceed to main page
if account is registered;
proceed to main page
2.2 Else, cannot proceed to main page

Figure 9 Manage AccountUse Case Name: Manage Account
Purpose: To keep track on the registered vehicles that enters the campus
Actor: UCLM Staff and UCLM Admin
Pre – Condition: UCLM Staff and admin edit the account
Post – Condition: Edited accounts will be saved to database
Steps:
Action System’s Response
UCLM Admin and Staff edit the accounts
Database will save the latest edited accounts

Figure 10 Generate ReportUse Case Name: Generate Report
Purpose: To keep track on login/logouts of the vehicle and driver
Actor: UCLM Staff and UCLM Admin
Pre – Condition: Click the generate button
Post – Condition: Information will be loaded accordingly.

Steps:
Action System’s Response
Admin clicks the generate button
Information’s that are save every day at the database will be displayed accordingly
Story BoardThis figure shows the story board of Gate Keeper

Figure 11 Login InterfaceThe figure above shows the login interface for GateKeeper.

Figure 12 Landing Page for AdminAfter successfully logging in with an Administrator account, the Admin is redirected to this interface shown above.

Figure 13 Landing Page for Staff
The interface above shows the landing page after successfully logging in with a Staff Account.

Figure 14 Database Access InterfaceThis interface is the landing page after the Admin clicks on Database Access. Admin can manipulate driver data in this interface.

Figure 15 Add Info InterfaceThe Administrator will arrive here after pressing the Add Info from the Database Access Interface

Figure 16 Generate Report InterfacesThis interface is the landing page when the Admin clicks on Generate Report from the Admin Landing Page.

Database DesignThis illustrates the logical data model contains all the needed logical and physical choices and physical storage parameters needed to generate a design in a data definition language, which can then be used to create database for proposed system Gate Keeper.

Figure 17 Gate Keeper Database DesignEntity Relationship Diagram
It is a graphical representation of an information system that shows the relationship between people, objects, places, concepts or events within that system.

Figure 18 Gate Keeper Entity Relationship Diagram (ERD)
Data DictionaryThis list of tables is set of information describing the contents, format, and structure of a database and the relationship between its elements of the proposed system Gate Keeper.

Table 3 Driver_info Data DictionaryField Name Data Type Constraint Field Size Description Example
Driver_IDShort Text Primary Key 8 Unique ID for drivers 12345678
LastnameShort Text 10 Last name of driver Bradberry
FirstnameShort Text 15 First name of driver Anna
MiddlenameShort Text 10 Middle name of drivers Jaeger
Contact_noShort Text 11 Unique drivers contact no 09562314110
Address Short Text 20 Drivers address Tipolo Mandaue City
License_noShort Text 11 Unique License no L02-10-003332
Driver_ImageBLOB 1 Unique driver image
Field Name Data Type Constraint Field Size Description Example
User_IDShort Text Primary Key 8 Unique ID for Staff and admin 12345678
LastnameShort Text 8 Last name of the staff and admin Ainsworth
FirstnameShort Text 10 First name of the staff and admin ChiseEmail_addShort Text 10 Unique email adds for the admin and staff [email protected]
Contact_noShort Text 11 Unique contact no of admin and staff 09114459270
Address Short Text 20 Address of admin and staff Pajo Lapu-lapu City, Cebu
Username Short Text 10 Unique username for admin and staff GK_Staff01
Password Short Text 10 Unique password for admin and staff [email protected]!
Account_TypeShort Text 10 Select account type Staff
Field Name Data Type Constraint Field Size Description Example
User_IDShort Text Primary Key 8 Unique ID for Staff and admin 12345678
LastnameShort Text 8 Last name of the staff and admin Ainsworth
FirstnameShort Text 10 First name of the staff and admin ChiseEmail_addShort Text 10 Unique email adds for the admin and staff [email protected]
Contact_noShort Text 11 Unique contact no of admin and staff 09114459270
Address Short Text 20 Address of admin and staff Pajo Lapu-lapu City, Cebu
Username Short Text 10 Unique username for admin and staff GK_Staff01
Password Short Text 10 Unique password for admin and staff [email protected]!
Account_TypeShort Text 10 Select account type Staff
Table 4 Staff Data DictionaryTable 5 Vehicle_info Data DictionaryField Name Data Type Constraint Field Size Description Example
Vehicle_IDShort Text Primary Key 10 Unique ID for vehicles 1234567890
QR_CodeShort Text 8 Unique QR_Code for vehicle 12345678
Vehicle_brandShort Text 10 Brand for vehicle Ford
Vehicle_colorShort Text 10 Color for vehicle Red
Vehicle_typeShort Text 10 Type for vehicle SUV
Plate_numberShort Text 15 Unique Plate numbers for vehicle ABC 1144
Driver_IDShort Text Foreign Key 8 Unique ID for drivers 12345678
Plate_imageBLOB 1 Unique Plate image for vehicle
Table 6 Time log Data DictionaryField Name Data Type Constraint Field Size Description Example
Timelog_IDShort Text Primary Key 6 Unique Time ID for vehicles 123456
Time_inDate/Time 8 Time in for Vehicles 7:30 A.M.

Time_outDate/Time 8 Time out for vehicles 9:05 AM
Date Date/Time 10 Date of the day 11/02/2018
Vehicle_IDShort Text 10 Unique ID for vehicles 1234567890
Network DesignNetwork design involves evaluating, understanding and scoping the network to be implemented. The whole network design is usually represented as a network diagram that serves as the blueprint for implementing the network physically. It also refers to the planning of the implementation of a computer network infrastructure.

53276581724500In addition to that network design is generally performed by network designers, engineers, IT administrators and other related staff. It is done before the implementation of a network infrastructure.

Figure 19 Network Design
Network ModelThe network model is a database model that is designed as a flexible approach to representing objects and their relationships. A unique feature of the network model is its schema, which is viewed as a graph where relationship types are arcs and object types are nodes. Unlike other database models, the network model’s schema is not confined to be a lattice or hierarchy; the hierarchical tree is replaced by a graph, which allows for more basic connections with the nodes.

center37592000
Figure 20 Network ModelNetwork TopologyA network topology is the arrangement of a network, including its nodes and connecting lines. There are two ways of defining network geometry: the physical topology and the logical (or signal) topology. The physical topology of a network is the actual geometric layout of workstations.

The appropriate network topology for the proposed system is star topology, because this topology works in a rather specific manner and has good pattern. Such as the case of star topology in which all nodes are individually connected to a central connection point, like a hub or a switch. It takes more cable than the other topologies, but the benefit is that if a cable fails, only one node will be brought down.

All traffic emanates from the hub of the star. The central site is in control of all the nodes attached to it. The central hub is usually fast, self-contained computer and is responsible for routing all traffic to other nodes. The main advantages of a star network are that one malfunctioning node does not affect the rest of the network.

Figure 21 Network Topology
Development/Construction/Build PhaseTechnology StackApplication and Data
Programming Languages
Python
Database
SQLite
Firebase
Frameworks
Tensor flow
Libraries
OpenCV
jQuery
Pyqt5
Software Specification
Python 3.6
OpenCV
PycharmSublime
Operating System
Windows 7 and 10
Hardware Specification
Raspberry pi – It will be used as processing machine for open CV
Webcam
Keyboard
Computer Monitor
LED monitor
Ram:
Minimum Requirements: 2GB
Best Performance: 6GB or above
HDD:
Minimum Requirements: 20GB
Best Performance: 50GB or above
Program Specification
Scans QR Code
Scans Vehicle License Plate
Stores Vehicle/Driver Records and Logs
List of ModuleTable 7 List of ModulesProgrammer Modules Access Rights
Admin Staff
Joseph Earl V. SubelarioLogin ? ?
Admin/Staff Login Joseph Earl V. SubelarioRegistration ?  
Register Driver/Admin/Staff Joseph Earl V. SubelarioManage Details ?  
Add/Edit/Delete Admin/Staff/Driver Details Joseph Earl V. SubelarioQR Code Detection   ?
QR Code Usage for Temporary Plate Numbers Joseph Earl V. SubelarioLicense Plate Recognition   ?
Recognizes Vehicle License Plate Joseph Earl V. SubelarioGenerate Report ?  
Generates Vehicle Time in/Time out Logs Joseph Earl V. SubelarioOpen Gate   ?
Automatically Open Gate upon successful input verification References
BIBLIOGRAPHY (n.d.). Retrieved from (https://bit.ly/2QPeMZt),
(n.d.). Retrieved from Prezi.com: https://prezi.com/y55imq-qa8hw/benifits-of-using-a-computer-and-how-it-helps-us-in-our-daily-lives/
(n.d.). Retrieved from www.engadget.com: https://www.engadget.com/2016/10/17/6-ways-on-how-technology-has-made-our-life-easier/
(n.d.). Retrieved from www.sciencedirect.com: https://www.sciencedirect.com/science/article/pii/S187704281000916X
(n.d.). Retrieved from www.buildingsecurity.com: https://www.buildingsecurity.com/importance-of-the-security-industry
(n.d.). Retrieved from www.elsag.com: https://www.elsag.com/alpr-products/mobile-alpr
(n.d.). Retrieved from www.neurallabs.net: http://www.neurallabs.net/en/ocr-systems/vpar-server
(n.d.). Retrieved from cctv.eocortex.com: http://cctv.eocortex.com/lp/license-plate-recognition?cm_id=1485042920_61229839161_284116114287_kwd- 325206504951_c_1t1_g_&gclid=EAIaIQobChMI8I2r1IeN3QIVnQcqCh1CAwX0EAAYASAAEgLvx_D_BwE
BIBLIOGRAPHY (n.d.). Retrieved from www.telecomabc.com: http://www.telecomabc.com/s/star.html
(n.d.). Retrieved from www.croz.net: https://croz.net/en/services/consulting/agile/
Dr. Xinhao Wang, D. R. (2007). Research Methods in Urban and Regional Planning. In D. R. Dr. Xinhao Wang, Research Methods in Urban and Regional Planning. Co-published by Tsinghua University Press, Beijing and Springer-Verlag GmbH Berlin Heidelberg .

https://www.skipr.nl/wosmedia/10309/business_model_generation_summary.pdf. (n.d.).

Jacobson, M. C. (n.d.). ” Object-Oriented software engineering : A use case driven. Addison-Wesley 1992.

technopedia.com. (n.d.). Retrieved from www.technopedia.com: https://www.techopedia.com/definition/30613/network-model-databases
WhatIs.com. (n.d.). Retrieved from https://whatis.techtarget.com/definition/network-topology
www.technopedia.com. (n.d.). Retrieved from https://www.techopedia.com/definition/30186/network-design

Curriculum Vitaeright1079500Name: James Dominic Ybañez
Birthdate: December 10, 1998
Address: Jugan, Consolacion, Cebu
Religion: Roman Catholic
E-mail: [email protected] Data:
Hobbies: Defense of the Ancients 2
Likes: Loving
Dislikes: Toxics
Educational Background:
Primary School: Consolacion Central School
Secondary School: La Consolacion College – LiloanTertiary: University of Cebu Lapu-lapu and Mandaue

right000Name: Joseph Earl SubelarioBirthdate: Feb 8, 1999
Address: Poblacion, Lapu-Lapu City
Religion: Born Again ChristialE-mail: [email protected]
Personal Data:
Hobbies: Drumming, Videogaming
Likes: Sleeping
Dislikes: Too much attention
Educational Background:
Primary School: Buaya Elementary School
Secondary School: Asian Learning Center PajoTertiary: University of Cebu Lapu-lapu and Mandaue

right952500Name: Cristy G. SabacBirthdate: December 14, 1997
Address: Humay-Humay Road, Pajo Lapu-lapu City Cebu
Religion: Roman Catholic
E-mail: [email protected]
Personal Data:
Hobbies: Playing Badminton, watching horror movies, outdoor activities
Likes: Friendly People, Fruit Salad
Dislikes: Liars, Plastic People
Educational Background:
Primary School: Tuble Elementary School
Secondary School: Pajo National High School
Tertiary: University of Cebu Lapu-lapu and Mandaue

right000Name: Virly B. DegalaBirthdate: September18, 1998
Address: Zone Sili Pakna-an Mandaue City Cebu
Religion: Born Again
E-mail: [email protected]
Personal Data:
Hobbies: Watching anime, movies, playing games and outdoor activities
Likes: Dogs and cats
Dislikes: SnobbersEducational Background:
Primary School: Pakna-an Elementary School
Secondary School: Mandaue City Comprehensive National High School
Tertiary: University of Cebu Lapu-lapu and Mandaue

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