2015 1st International Conference on Next Generation Computing Technologies (NGCT-2015) Dehradun, India, 4-5 September 2015
Automatic Gadget Charger using Coin Detection Gunjan Chhabra∗ , Sunil Kumar † and Pankaj Badoni‡ ∗
CIT CoES, UPES, Dehradun, India,
[email protected] CIT CoES, UPES, Dehradun, India,
[email protected] ‡ CIT CoES, UPES, Dehradun, India,
[email protected]
†
communication n era, mobile -telephone -telephone in Abstract—In this communicatio dustry dustry has grown grown profoundl profoundly. y. The urban population population uses the latest latest mobile-p mobile-phone hone technology technology while while the rural population population buys second second hand ones, mostly mostly with degraded battery, battery, that require require frequent charging. This battery-problem becomes a giant when user doesnt have a standard charger or an electricity connection. In this this paper paper,, resea researc rcher herss intent intent is to propo propose se a pub publi licc coin coin based mobile battery charging system. By using image processing techniques, value of the coin has been detected for a limited time, it will charge the device accordingly. A suitable microcontroller is programm programmed ed for all the controllin controlling g applicatio applications. ns. The source source for charging is obtained either from a direct power grid or by solar energy. Keywords Keywords-- Image processing; processing; Charging Charging system; system; coin detecdetection; Circuit designing.
I.
INTRO INTRODUC DUCTI TION ON
Mobile phones, being an essential technological advancement, have enabled every individual to keep in contact with any niche of the globe. They have become a major source of communic communicatio ation, n, be it with regards regards to the business business aspect or personal communication. These days mobile phones have also become become a portable portable computing device that can serve multiple purposes. This increase creasess the usage usage of mobile mobile teleph telephone one sets sets and its demand demand in the community. An increase in the usage of mobile phone for various purposes consumes an equal measure of energy in effect effect requirin requiring g frequent frequent charging charging.. As every every applicati application on that works on mobiles require battery power, this results in faster discharge of the battery power. In our regul regular ar live livess (where (where our routin routines es are fixed) fixed),, the mobile phone can be charged in such a manner that the battery power power is always always avail availabl able. e. But, But, in cases cases where where the routin routinee change changess due to any any event event or incide incident, nt, there there may be vario various us situations where the battery power level becomes too low or is completely discharged. As a consequence, even emergency phone calls are not possible. Many critics have argued that a public mobile phone charging service would not be a lucrative business business because because most of the users can charge charge their phones at home, home, in their their office officess or in their their vehic vehicles les.. Life Life is neve neverr predictable, so in such unpredictability a public system would be very useful. Students, tourists and people utilizing public transportation would become the prospective customers for a public mobile phone charger service. Coin Based Charging System (CBCS) brings a very wonderful solution for commuters and travelers who need to charge their mobile phones and gadgets immediately diately.. Coin operated operated mobile phone charging charging system system could could become become a new new busin business ess milest milestone one.. The coin-b coin-base ased d mobile mobile 978-1-4673-6809-4/15/$31.00 ©2015 IEEE
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battery battery charging charging system system is designed designed to resolve resolve the problem problem of low low batt batter ery y. This This coul could d also also be usef useful ul in the the even eventt of unpredicta unpredictable ble grid power power and availa availabili bility ty of abundant abundant solar power. This proposal for a coin based universal mobile battery charger is presented in this paper. The The user user has has to plug plug the the mobi mobile le phon phonee into into one one of the the adapte adapters rs and insert insert a coin; coin; the phone phone will will then then be give given n a micromicro-pul pulse se for chargin charging. g. It does does not bring a mobile mobile from ’dead’ dead’ to fully fully charg charged ed state. state. The charg charging ing capaci capacity ty of the mobile is designed with the help of predefined values. It is, of course, possible to continue charging the mobile by inserting more coins. The solar power application to battery charging has been studied studied in the past. Solar Solar chargers chargers convert convert light energy energy into dire direct ct curr curren entt for for a rang rangee of volt voltag agee that that can can be used used for for charging the battery. In this paper, the design and development of a coin based universal mobile battery charger based on the main power and solar power has been discussed. This is of prime importance in rural areas where the mobiles are basic needs for communication and the main power is not available all the time. II. A.
LITERATURE LITERATURE SURVEY SURVEY AND RELATED RELATED WORK Theoretical Theoretical Background Background
Especially in India, one cannot imagine his/her life without out coin coins. s. A pers person on uses uses coin coinss in dail daily y life life,, be it bank banks, s, superm supermark arkets ets or grocer grocery y stores stores,, etc. etc. They They have have become become an inte integr gral al part part of tran transa sact ctio ions ns in our our day day to day day life life.. And And then then there there is a basic basic demand demand of highly highly exact and effici efficient ent automatic coin recognition system. Alternative to daily uses, the coin recognition systems can also be used for the research purpose by the institutes or organizations that deal with the ancient coins. There are three cases of coin recognition systems available in the market based on different methods: 1) 2) 3)
Mecha Mechanic nical al method method based based system systemss Electr Electroma omagne gnetic tic method method based based syst system emss Image Image proces processin sing g base based d syst system emss
The mechanical method based systems use parameters like diameter diameter or radius, radius, thickness thickness,, weight weight and magnetism magnetism of the coin to differentiate between the coins. But these parameters cannot be used to differentiate between the different materials of the coins. It implies, if we provide two coins, one original and other fake having having same diameter, diameter, thickness, thickness, weight weight and magnetism, but made up of different materials to the mechanical method based coin recognition system then it will treat both both the coins as authen authentic tic so these these system systemss can be fooled fooled easily.
2015 1st International Conference on Next Generation Computing Technologies (NGCT-2015) Dehradun, India, 4-5 September 2015
The electromagnetic method based systems can differentiate between different materials because, in these systems, the coins pass through an oscillating coil at a certain frequency and different materials bring different changes in the amplitude and direction of frequency. Thus these changes and the other parameters like diameter, thickness, weight and magnetism can be utilized to distinguish between coins. The electromagnetic method based coin recognition systems improve the accuracy of recognition but still they can be duped by some game coins. [1] B. Related Projects
R. Bremananth et al. in 2005 proposed a system that focuses only on the numerals rather than the use of other images presented on the front and rear side of the coin. For experiment they used 1-rupee, 2-rupee, and 5-rupee Indian coin. Extract numeral image from the given coin image and this image is used for character recognition process. This suggested approach can easily be carried out in whatever real time business transactions. The system resulting from this research recognizes numerals using neural pattern analysis with a 92.43% success rate of our test data. [2] Lu Zhang et al. in 2005-06 developed a program, as dissertation work, for counting coins in computer vision approach using MATLAB. The main purpose of this project is to apply computer vision techniques to develop a program which should recognize coins in an image, and enumerate their value. That is to have a computer, read the image and calculated the total value of the coins which are on the image. In that respect are various techniques involved, such as image color segmentation, image edge detection, noise filtering, and Hough transformation and hence along. The key to accomplish this project is the color segmentation of coins and edge enhancement, which separates coins with their color difference and provides the efficiency. After the computer programs are established, an experiment which applies the programs with UK coins shows that it works well and the error depends on the qualities of the coins images. A database containing a large number of images is required for the use of this method. [3] Adnan Khashman et al. in 2006 proposed an intelligent coin identification system (ICIS) that uses coin patterns for identification helps preventing confusion between different coins of similar physical dimensions. For recognition of rotated coins of various degrees, ICIS used pattern averaging and neural network. In pre-processing phase ICIS apply thresholding, cropping, compressing, trimming, pattern averaging on images. And then neural network is trained using these images. ICIS used 1 TL and 2 EURO coins in recognition. ICIS used a 3layer back propagation neural network with 400 input neurons, 25 hidden neurons and 2- output neuron. The neural network is trained using 20 images out of available 120 coin images. The Accuracy rate achieved was 96.3%. [4] C. M. Velu and P. Vivekanandan et al. in 2009 developed a system for the Indian coin recognition system of image segmentation by heuristic approach and Hough transformation method. This system is developed mainly to classify the coins offered in the Hundi by the devotees of Tirumala Tirupati Devasthanam (TTD), Tirupati, India. The objective is to count money by recognizing the coins and count the total sum based 951
on its value. The system is proposed to design coin recognition by applying heuristic approach, based on the coin table. This table stores parameters of each coin. This paper concentrates on affine transformations such as scaling, shearing and so on. This method returns 97of result in recognizing the coin icon. [5] Hussein R. Al-Zoubi et al. in 2010 suggested a coin recognition method using a statistical approach to classify Jordanian coins. There are seven different coins used in Jordan: 500fils, 250fils, 100fils, 50fils, 25fils, 10fils, and 5fils. Color and area of a coin was the central feature for sorting. First convert the colored image into grey level and then apply the threshold value to convert it into black and white image. Then the binary image is cleaned by opening and closing through erosion and dilation, after that calculate the value of each RGB color. Then on the basis of these value decisions is made that to which category the coin belongs. Total 1050 experiments, 150 for each coin were carried out to examine the proposed system. The Accuracy rate achieved was 9 C. M. Velu and P. Vivekanandan et al. in 2011 presented a methodology for Indian Coin Recognition and Sum Counting System of Image Data Mining Using Artificial Neural Networks. The objective of this paper is to classify, recognize and count the total value of newly released Indian coin s of different denomination, in terms of Indian National Rupees. The characteristics of old coins and new coins of different designations are considered for classification. In this paper, it is proposed to introduce ML-CPNN approach. This approach is then compared with other approaches. The Roberts edge detection method gives 93% of accuracy and Laplacian of Gaussian method 95% of the result, the Canny edge detection method yields 97.25% result and the ML-CPNN approach yields 99.47% of recognition rate. [7] Vaibhav Gupta et al. in 2011 presented a method based on image subtraction for recognition of Indian coins of different denomination. The Process performs 3 checks (radius, coarse and fine) on the input image. Instantly compute the radius of the input image and then based on the radius a test image is selected from the database. Then subtraction between the input image and database image is done. By plotting the resultant values we obtain a lower limit value which if less than a standard threshold provides the identification of the coin. [8] Shatrughan Modi et al. in 2011 presented an Artificial Neural Network based Automated Coin Recognition System for Indian coins. They used Indian coins of denominations ‘1, ‘2, ‘5, and ‘10. This system takes images of coins from both sides. First of all apply pre-processing for images like cropping, trimming, pattern averaging, etc. and then passed the input data set to Neural Network for training. 4536 images are used for training and 252 images are used for validation and testing each. It eased back propagation neural network with 400 input units, 30 hidden layers and 14 output units. This system gives 97.74% recognition rate. [9] Saranya das.Y.M et al. in 2013 presented a system to classify Indian coins discharged recently. This system is based on Advanced Harris -Hessian Algorithm, used the parameters such as size, weight, surface, etc. of coins and also used the concept of rotation invariance. The primary goals of this project are: Recognize the coins, count the coins and then get the total value. First, we apply preprocessing of the image and then pre-processed images are passed to the Harris -Hessian
2015 1st International Conference on Next Generation Computing Technologies (NGCT-2015) Dehradun, India, 4-5 September 2015
detector, it detects interest points. Now these features are fed to the Hough Transform, it detects circles and calculates the radius of coins . It is a low cost system having recognition rate close to 100%. [10] Deepika Mehta et al. in 2013 presented a system to detect and recognize the Overlapped coins using Otsus Algorithm based on the Hough Transform technique. This project includes three step detection, extraction and recognition. For segmentation uses an Otsus algorithm, for detecting overlapping uses the Hough transform and for recognition uses radius thresholding. The Detection rate of overlapped coins is 91% and recognition rate is 40% to 50%. [11] Fig. 1.
III.
Before implementation of overall setup, the working conditions should be set up to improve the efficiency and effectiveness of the program. Because there are hundreds of conditions about how the coins would be displayed on an image. For example, the size and shape of the same coin could also change according to the view of the camera that captures the coins. If the camera is placed just above the coin, the shape of the coin will be a circle. Otherwise, the shape of the coin will be an ellipse. And likewise, if the camera placed near the coins, the size of the coin on the image captured by the camera will be relatively larger as compared to the size of coins captured by the camera which placed far from the coins. A.
Block diagram of overall system
PLAN AND DESIGN
Potential schemes for finding coins
The appropriate computation strategy used in the project and described in this paper is a threshold range matching (upper and lower limit for coins), because of usage of the low resolution camera. According to this strategy, when the image is captured in real time, the range is being matched by the program and indicates the value of the coin to the microcontroller for the further process/working of the system. The images of the coins are captured in different lightening conditions to decide the range for each valued (Re.1 or Rs. 2) coin. B. Stages of development
The evolution of the CBCS system was split into four main stages according to the development strategy. Then each main stage was broken into sub -steps according to the plan and study. Every sub -measure was planned as a distinct level, which could be filled out and tested separately, and then incorporated into the master task. Stage 1: Circuit Designing & PCB layout: In this stage, basic circuit has been designed by using simulation tools to check the overall working of the circuit to be used in the development of CBCS system. Several parts were gathered using the simulator tool and hence the working of circuit was tested on the simulator. Granting to the circuit designed, PCB Layout was made such that the hardware components can be set up accordingly. Stage 2: Assembling: At this stage of system evolution, all the elements are gathered together according to the circuit designed and PCB layout as presented in the old phases. After assembling of all the components, testing was performed to 952
check the working of every component with each other. Hence the hardware testing was done in parallel. Stage 3: Software: After detail study of hardware components and their assembling, as described in previous stages, next is to travel towards the software part of the system. Software part includes two sub-steps as stated below: a) Programming of a microcontroller: At initial stage of microcontroller programming, firstly an .asm file of the assembly code was coded for the working and performance of controller. Then microcontroller is burnt with the accumulated files of this codification. Secondly, C program is designed to do interaction with .asm file code. Functions of .asm file called in a C program and the interaction of the whole operation is being performed. b) Using image processing techniques using MATLAB: For the detection of value of coin, picture processing techniques have been used. These techniques include masking, feature extraction, Hough Transformation, pattern matching and hence on. Granting to the initial setup of hardware, here by a threshold range matching technique is being utilized for the detection of value of coins. Stage 4: Calculating the value of coins: After the development of above mention codes, one must integrate them with the hardware setup as assembled on stage 3. Microcontrollers code is debugged and complied and then the compiled files are transferred to the controller for its functioning. It lets the coin - holder to move in-front of the camera on detecting something inside it. And then the camera will send the real time captured image to the MATLAB for calculating its value and hence sends command to the controller to switch-on the power for a limited period. These are the four main stages covered for the designing of CBCS. An additional algorithm/technique can be applied to improve the working of the system. IV.
ALGORITHMS
The following algorithm (set parame set and confirm the threshold param rupee and two rupee coin. Algorithm: 1) 2)
Set the lower and upper limit for im rupee coin and two rupee coin. Capture image from an infinite inpu the camera and for five continues imag the following:
2015 1st International Conference on Next Generation Computing Technologies (NGCT-2015) Dehradun, India, 4-5 September 2015
Fig. 2.
Fig. 4.
Overall System Setup
Violet LED indicating Output unication can be set n
metal object. The primary goal of this project was satisfactorily accomplished. Nevertheless, the system works fine for the testing state becaus e working conditions were already being set for both the system and input. VI.
Fig. 3.
3)
Working Stage of System
a. Convert the RGB image to graysca separate the background from the reg (ROI). b. Check between which limits the av all five images lies between- the upper limit of either one rupee coin or two ru c. Based on which ever threshold interv value lies display the value of that coin At the end stop the video input. The following algorithm (run coin used to identify the coin in the image provide appropriate information microcontroller.
Algorithm: 1) 2) 3)
4)
Start a serial communication computing device and the microcontrol The port used for this commu by the user and is constantly open Capture image from an infinite the camera a. For five continues the following: a. Convert the RGB image to gr separate the background from th (ROI). b. Check between which limits t all five images lies between- the u limit of either one rupee coin or t c. Based on which ever threshold value lies send the appropriate the open serial communication p value of the coin recognized. At the end stop the video an serial COM port. V.
After understanding the related articles, literatures and analysis of a few similar projects, the current design strategy was selected. Various development stages were planned and finally the whole system was implemented. In entirety, the developed system is able to attain the primary objectives. Numerous subgoals is achieved like, value detection of the coin, controlling the communication between various components and mainly image processing. VII.
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FUTURE SCOPE
As discussed in the previous sections, there is a need of various enhancements that leads to the future scope of the proposed system. Firstly, image processing embedded systems techniques may improve the throughput of present system. Secondly, paper currency identification and recognition techniques can also be applied for the betterment of this project. With these enhancements one can commercialize it for the public usage. R EFERENCES [1]
Shatrughan Modi and Dr. Seema Bawa. Automated Coin Recognition System using ANN, India, International Journal of Computer Applications (0975-8887) Vol. 26-No.4, July 2011.
[2]
R. Bremananth, B. Balaji, M. Sankari and A. Chitra, A new approach to coin recognition using neural pattern analysis IEEE Indicon 2005 Conference, Chennai, India, 11-13 Dec. 2005.
[3]
Lu Zhang et al. Development of Counting Coins Program in Computer Vision a Approach using MATLAB, for the submission of dissertation, University of Bath, in 2005-06.
[4]
Khashman A., Sekeroglu B. And Dimililer K., Intelligent Coin Identification System, Proceedings of the IEEE International Symposium on Intelligent Control (ISIC06), Munich, Germany, 4-6 October 2006.
[5]
C.M.Velu and P.Vivekanandan et al. Indian Coin Recognition System of Image Segmentation by Heuristic Approach and Hough Transform (HT), Int. J. Open Problems Compt. Math., Vol. 2, No. 2, June 2009.
[6]
Al-Zoubi H.R., Efficient coin 00 a statistical approach, 2010 IEEE International Conference on Electro/Information Technology (EIT), 2010.
[7]
Velu C M, P.Vivekanadan, Kashwan K R. Indian Coin Recognition and Sum Counting System of Image Data Mining Using Artificial Neural Networks, International Journal of Advanced Science and Technology Vol. 31, June, 2011.
TESTING
The testing executed that the system has the ability to recognize the value of the coin inserted and it allow power supply for a limited period of time, based upon the value of the coin. Hence, it enables the user to charge their gadget in case of emergency. Also, the system is able to detect whether a coin is inserted or not hence, it cant be fooled by any other
CONCLUSION
2015 1st International Conference on Next Generation Computing Technologies (NGCT-2015) Dehradun, India, 4-5 September 2015
[8]
Gupta, V., Puri, R., Verma, M., Prompt Indian Coin Recognition with Rotation Invariance using Image Subtraction Technique, International Conference on Devices and Communications (ICDeCom), 2011.
[9] Shatrughan Modi, Dr. Seema Bawa, Automated Coin Recognition System using ANN International Journal of Computer Applications (0975-8887) Volume 26-No.4, July 2011,pp. 13-18. [10] Saranya das. Y. M, R. Pugazhenthi, HarrisHessian Algorithm for Coin
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Apprehension, International Journal of Advanced Research in Computer Engineering Technology (IJARCET) Volume 2, No 5, May 2013. [11] Deepika Mehta, Anil Sagar, An Efficient Way to Detect and Recognize the Overlapped Coins using Otsus Algorithm based on Ho ugh Transform technique, International Journal of Computer Applications (0975-8887) Volume 73- No.9, July 2013.
