KICACT 2016
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/15608
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Item Android smartphone operated Robot(Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2016) Thiwanka, U.S.; Weerasinghe, K.G.H.D.In the present an open-source Android platform has been widely used in smartphones. Android platform has a complete software package consisting of an operating system, middleware layer and core applications. Android-based smartphones are becoming more powerful and equipped with several accessories that are useful for Robotics. The purpose of this project is to provide a powerful, user-friendly computational Android platform with simpler robot’s hardware architecture. This project describes the way of controlling robots, using smartphone and Bluetooth communication. Bluetooth has changed how people use digital device at home or office, and has transferred traditional wired digital devices into wireless devices. The project is mainly developed by using Google voice recognition feature which can be used to send commands to robot. Also motion of robot can be controlled by using the Accelerometer and the buttons in the Android app. Bluetooth communication has specifically used as a network interface controller. According to commands received from application, the robot motion can be controlled. The consistent output of a robotic system along with quality and repeatability are unmatched. This project aims at providing simple solutions to create a framework for building robots with very low cost but with high computational and sensing capabilities provided by the smartphone that is used as a control device. Using this project concept, we can help disabled people to do their work easily ex: Motorized wheelchair, remotely controlling some equipment using the smart phone. Also using this project, we can build Surveillance robot devices and reconnaissance devices can design home automation and can use to control any kind of device that can be controlled remotely. Many hardware components were used such as Arduino Uno, Adafruit Motor Shield Bluetooth module and Ultrasonic Distance Measuring Transducer Sensor. The Uno is a microcontroller board based on the ATmega328P. It contains everything needed to support the microcontroller; simply connect it to a Computer using a USB cable or power it with an AC-to-DC adapter or battery to get started. The Arduino use shield boards. These plug onto the top of the Arduino and make it easy to add functionality. This particular shield is the Adafruit Industries Motor / Stepper / Servo Shield. It has a very complete feature set, supporting servos, DC motors and stepper motors. The Bluetooth module is used to connect the smart phone with robot. It uses AT commands. The HC-SR04 ultrasonic sensor uses sonar to determine distance to an object like bats or dolphins do. It offers excellent non-contact range detection with high accuracy and stable readings in an easy-to-use package. From 2 cm to 400 cm or 1” to 13 feet. Its operation is not affected by sunlight or black materials. It comes with an ultrasonic transmitter and a receiver module. This system has two major parts. One is Android application and the other is robot hardware device. When developing this Android application new Android technologies were used ex: Google Voice and motion of the phone. To improve the security of this Application a voice login is added. In addition, a program is added to change login pin and to develop robot scan program and finally to develop two control programs using buttons with accelerometer and Google voice inputs. Arduino IDE and Arduino language is used to program the robot. Arduino has a simple methodology for running the source code. It has a setup function and a loop function. We can define variables and other things inside setup function. The loop function is running always according the content of the function body. AFmotor header is used to develop the code file to get functions to control the motor shield and the motors and used SoftwareSerial header file to make connection between Arduino and Bluetooth module. Using Black Box test method, integrity, usability, reliability, and correctness of the Android application is checked. Finally, user acceptance tests are done for different kind of users. A field-test is done to test whether the robot can identify the object in front of it and the distance limit is coded to the program. Today we are in the world of robotics. Knowingly or unknowingly, we have been using different types of robots in our daily life. The aim of this project is to evaluate whether we can design robots ourselves to do our work using a low budget and simple way. Finally, we think this project will be helpful for students who are interested in these areas and this will make a good solution for human matters. This project has many applications and a very good future scope. It also allows for modification of its components and parameters to get the desired output. This project allows customizing and automating our day-to-day things in our lives.Item Object Recognition Application - Mind Game(Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2016) Senanayake, H.M.I.M.; Weerasinghe, K.G.H.D.Visualizing is one of the main methods to remember something. For students who are studying something can remember things as a story or a component of an image. This application is designed to develop this skill by giving this application as a playing game to use. How to play this game? First this app will show a sequence of images and the user should remember what he see, not only the image but also how that image is drawn. The color combinations, shape, angle and much more details are there in a single image. The more the user can remember the things in a single image can score high in this game. Now this application tests up to what average the user remember things. User will be provided a drawing canvas and a pencil tool and then he is asked to draw the first image of the sequence what he remember. And then the second canvas is given to draw the second image. Next third canvas etc. Then the application process the images and match corresponding images of the sequence and offer score to the user considering the details he remembered accurately. How this application works? Most important part of this application is object recognition part. There are many algorithms in present to recognize object and patterns such as feature based methods, appearance based methods, geometric based methods etc. Most popular and widely used techniques are edge and angle based algorithms and pixel based algorithms. Among these methods appearance and geometric based techniques are the narrowly used techniques to develop applications. So in this research I cover that area. My recognition algorithm is to identify images by converting image details into a mathematical model. First this algorithm will identify the shapes in the image and each shape will be given some sequence of values which is based on relative area, perimeter, position co-ordinates of shapes and other special characteristics which are evaluated by a standard function. Each shape in any image will have its own mathematical structure to describe the roll of it in an image. So after processing all shapes of the image as mathematical points, the image can be saved as a mathematical structure. So for each object, objects will have a unique mathematical model. When recognizing object in a new drawn image, this new image is converted into a mathematical model using the same algorithm and match with other mathematical models which are previously processed and saved. Main advantage of this method is number of values which need to be saved as image data in this mathematical model is massively low when compared with other feature based techniques. This increases the speed efficiency. So this way is considerably efficient than edge and angle based techniques to recognize images with non-discrete lines. To match the models I apply a nearest neighbor algorithm to mathematical models, then the most matching image is selected. In the developing side, previously processed mathematical models which represents the images are saved as a two dimensional matrix. Rows in the matrix represents the image identity (image name or object name) and characteristics of images. And one column in the matrix represents a single image. So the number of rows in the matrix is equal to the number of characteristics of the image plus one. And number of columns in the matrix is a variable which depends on the number of images we are saving. And the matrix is saved in a .mat (Microsoft Access Table, used by MATLAB to save data in binary data container format) file. By this method, retrieving and reading data for matching images is very easy because this single matrix represents the whole database of images. Accuracy depends on the growth of the matrix. Because if the matrix has more details about objects, then the program can identify objects accurately. To increase more the accuracy of identifying objects, simply we can increase the number of images which are drawn in different angles or different ways of same object and saving those in the matrix. For example, if the object we want to recognize is a tree, then we can save set of drawings of mango trees, coconut trees, pine tree etc. in the matrix. So any tree will be identified accurately as a tree by the program, no matter what the genre of tree is. In the gaming application these methods are used to define different gaming levels and give the user a new experience. Preliminary the objective of this research was to recognize non-discrete pencil drawing objects accurately. Secondly above techniques are used to develop the application which gives an exercise to the human brain while giving a gaming experience. Designed algorithm is flexible to process any number of images at once and convert those into mathematical models and save all those mathematical models in a single matrix. And the designed program accurately identifies the pencil drawing objects using this matrix. Later, by including more image processing techniques such as image segmentation, this method will be able to enhance more to process and recognize other complex images too.Item Two Tier Shield Unapparent Information Deliver along with the Visual Streams(Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2016) Eranga, D.M.S.; Weerasinghe, K.G.H.D.Information put out of sight for various security purposes have become highly exciting topic in the industry and also academic areas. Encryption provides the ability of data hiding. With the development of the technology, people tend to figure out a technique which is not only capable in hiding a message, but also capable in hiding the actuality of the message. Steganography was introduces as a result of those researches. The current study is conducted in order to hide a file inside a video file. Generally, steganography benefits do not use in the industry or students even though it is widely discussing topic in modern information world. The major aim of this research is the ability to hide any type of file in a video file and retrieve hidden information. There are few algorithms/systems developed to embed a file into video files. It is a great challenge to extract secret information directly from the video, which is embedded already. The existing applications require a considerable time to embed a small message and some are not freeware. Focuses areas of the research are confidentiality, authentication, increase hidden data size, integrity, assure unapparent perceptual transparency of video file (cover object) and send/receive video files. Video consists of frames called I, P and B frames. Each frame uses LSB technique to hide information. This original message can be any kind of file type and almost all popular video file formats for carrier. Identifies the type of the message and encrypts the message file using AES256 with given key. Encrypted message size stores in four bytes and type of the message file stores in another four bytes. Propose algorithm decides the number of frames require to hide the secret information according to size of both carrier video and the secret message. Firstly, reads the video header to retrieve important information and skip the header. Video file Splits in to bitmap images with having pre-defined frame gap between two images, corresponding to the secret message size. Every bitmap image consists of red, green and blue colors and bitmap image pixel has 8 bit for each color and total of 24 bits called bit depth. Writes message size followed by the message type in the bitmap images. Then, writes the message. Each encoded image adds into the original video file. In the process of retrieval, skips the header frames and fetches the images from the video according to the pre-defined gap between images. Reads first eight bytes to identify the message size and type of the message respectively. Then, decodes the encrypted message and decrypts the message with same secret key, which used to encrypt the message. Carrier video file can be watched during the both process of encode and decode. This method doesn’t increase the size of the carrier, though the existence of the message cannot be detected. AES256 key size encryption supports the dual layer security of classified information. Proposed solution supports unique feature that can delete the hidden information, which concealed inside the video without affecting the video carrier. Encoded video is guaranteed the original quality of the carrier. So, this proposed way-out emerges along with an application called SilentVideo1.0. The system was tested to assure the quality of the final product. Testing focused on the accuracy of the propose algorithm, which is ability of hiding the existence of the information as well as the ability of retrieving the information correctly using the application. Test results guarantee the success rate of the proposed algorithm goes up to 85 percent. Furthermore, the application was evaluated for exactness of the input and output information by black box tests using 200 samples from different video formats. The aim of this work was propose a strong resolution for steganography in digital media with multi-tier protection. The hidden file capacity will be increased using sound track of the video file as well. Upcoming versions of the system will be upgraded with latest cryptographic involvement and increase the conceal message capacity along with the lowest encoding and decoding time frame.