automatic gadget charger using coin detection

7/25/2019 Automatic Gadget Charger Using Coin Detection

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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] CoES, UPES, Dehradun, India, [email protected]

AbstractIn this communication era, mobile -telephone in-dustry has grown profoundly. The urban population uses thelatest mobile-phone technology while the rural population buyssecond hand ones, mostly with degraded battery, that requirefrequent charging. This battery-problem becomes a giant whenuser doesnt have a standard charger or an electricity connection.In this paper, researchers intent is to propose a public coinbased mobile battery charging system. By using image processingtechniques, value of the coin has been detected for a limited time,it will charge the device accordingly. A suitable microcontrolleris programmed for all the controlling applications. The sourcefor charging is obtained either from a direct power grid or bysolar energy.

Keywords- Image processing; Charging system; coin detec-tion; Circuit designing.

I. INTRODUCTION

Mobile phones, being an essential technological advance-ment, have enabled every individual to keep in contact withany niche of the globe. They have become a major source ofcommunication, be it with regards to the business aspect orpersonal communication.

These days mobile phones have also become a portablecomputing device that can serve multiple purposes. This in-

creases the usage of mobile telephone sets and its demandin the community. An increase in the usage of mobile phonefor various purposes consumes an equal measure of energy ineffect requiring frequent charging. As every application thatworks on mobiles require battery power, this results in fasterdischarge of the battery power.

In our regular lives (where our routines are fixed), themobile phone can be charged in such a manner that the batterypower is always available. But, in cases where the routinechanges due to any event or incident, there may be varioussituations where the battery power level becomes too low oris completely discharged. As a consequence, even emergencyphone calls are not possible. Many critics have argued that apublic mobile phone charging service would not be a lucrative

business because most of the users can charge their phonesat home, in their offices or in their vehicles. Life is neverpredictable, so in such unpredictability a public system wouldbe very useful.

Students, tourists and people utilizing public transportationwould become the prospective customers for a public mobilephone charger service. Coin Based Charging System (CBCS)brings a very wonderful solution for commuters and travelerswho need to charge their mobile phones and gadgets imme-diately. Coin operated mobile phone charging system couldbecome a new business milestone. The coin-based mobile

battery charging system is designed to resolve the problemof low battery. This could also be useful in the event ofunpredictable grid power and availability of abundant solarpower. This proposal for a coin based universal mobile batterycharger is presented in this paper.

The user has to plug the mobile phone into one of theadapters and insert a coin; the phone will then be given amicro-pulse for charging. It does not bring a mobile fromdead to fully charged state. The charging capacity of themobile is designed with the help of predefined values. It is, ofcourse, possible to continue charging the mobile by inserting

more coins.

The solar power application to battery charging has beenstudied in the past. Solar chargers convert light energy intodirect current for a range of voltage that can be used forcharging the battery. In this paper, the design and developmentof a coin based universal mobile battery charger based on themain power and solar power has been discussed. This is ofprime importance in rural areas where the mobiles are basicneeds for communication and the main power is not availableall the time.

II. LITERATURE SURVEY AND RELATED WORK

A. Theoretical BackgroundEspecially in India, one cannot imagine his/her life with-

out coins. A person uses coins in daily life, be it banks,supermarkets or grocery stores, etc. They have become anintegral part of transactions in our day to day life. Andthen there is a basic demand of highly exact and efficientautomatic coin recognition system. Alternative to daily uses,the coin recognition systems can also be used for the researchpurpose by the institutes or organizations that deal with theancient coins. There are three cases of coin recognition systemsavailable in the market based on different methods:

1) Mechanical method based systems2) Electromagnetic method based systems

3) Image processing based systems

The mechanical method based systems use parameters likediameter or radius, thickness, weight and magnetism of thecoin to differentiate between the coins. But these parameterscannot be used to differentiate between the different materialsof the coins. It implies, if we provide two coins, one originaland other fake having same diameter, thickness, weight andmagnetism, but made up of different materials to the mechan-ical method based coin recognition system then it will treatboth the coins as authentic so these systems can be fooledeasily.

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The electromagnetic method based systems can differenti-ate between different materials because, in these systems, thecoins pass through an oscillating coil at a certain frequencyand different materials bring different changes in the amplitudeand direction of frequency. Thus these changes and the otherparameters like diameter, thickness, weight and magnetism canbe utilized to distinguish between coins. The electromagneticmethod 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 thatfocuses only on the numerals rather than the use of otherimages presented on the front and rear side of the coin. Forexperiment they used 1-rupee, 2-rupee, and 5-rupee Indiancoin. Extract numeral image from the given coin image andthis image is used for character recognition process. Thissuggested approach can easily be carried out in whateverreal time business transactions. The system resulting fromthis 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 disser-tation work, for counting coins in computer vision approachusing MATLAB. The main purpose of this project is to applycomputer vision techniques to develop a program which shouldrecognize coins in an image, and enumerate their value. Thatis to have a computer, read the image and calculated the totalvalue of the coins which are on the image. In that respect arevarious techniques involved, such as image color segmentation,image edge detection, noise filtering, and Hough transforma-tion and hence along. The key to accomplish this project isthe color segmentation of coins and edge enhancement, whichseparates coins with their color difference and provides the

efficiency. After the computer programs are established, anexperiment which applies the programs with UK coins showsthat it works well and the error depends on the qualities of thecoins images. A database containing a large number of imagesis required for the use of this method. [3]

Adnan Khashman et al. in 2006 proposed an intelligentcoin identification system (ICIS) that uses coin patterns foridentification helps preventing confusion between differentcoins of similar physical dimensions. For recognition of rotatedcoins of various degrees, ICIS used pattern averaging and neu-ral network. In pre-processing phase ICIS apply thresholding,cropping, compressing, trimming, pattern averaging on images.And then neural network is trained using these images. ICISused 1 TL and 2 EURO coins in recognition. ICIS used a 3layer

back propagation neural network with 400 input neurons, 25hidden neurons and 2- output neuron. The neural network istrained using 20 images out of available 120 coin images. TheAccuracy rate achieved was 96.3%. [4]

C. M. Velu and P. Vivekanandan et al. in 2009 developeda system for the Indian coin recognition system of imagesegmentation by heuristic approach and Hough transformationmethod. This system is developed mainly to classify the coinsoffered in the Hundi by the devotees of Tirumala TirupatiDevasthanam (TTD), Tirupati, India. The objective is to countmoney by recognizing the coins and count the total sum based

on its value. The system is proposed to design coin recognitionby applying heuristic approach, based on the coin table. Thistable stores parameters of each coin. This paper concentrateson affine transformations such as scaling, shearing and soon. This method returns 97of result in recognizing the coinicon. [5] Hussein R. Al-Zoubi et al. in 2010 suggested acoin recognition method using a statistical approach to classifyJordanian coins. There are seven different coins used in Jordan:

500fils, 250fils, 100fils, 50fils, 25fils, 10fils, and 5fils. Colorand area of a coin was the central feature for sorting. Firstconvert the colored image into grey level and then apply thethreshold value to convert it into black and white image. Thenthe binary image is cleaned by opening and closing througherosion and dilation, after that calculate the value of each RGBcolor. Then on the basis of these value decisions is made thatto which category the coin belongs. Total 1050 experiments,150 for each coin were carried out to examine the proposedsystem. The Accuracy rate achieved was 9

C. M. Velu and P. Vivekanandan et al. in 2011 presented amethodology for Indian Coin Recognition and Sum CountingSystem of Image Data Mining Using Artificial Neural Net-works. The objective of this paper is to classify, recognize

and count the total value of newly released Indian coin s ofdifferent denomination, in terms of Indian National Rupees.The characteristics of old coins and new coins of differentdesignations are considered for classification. In this paper, itis proposed to introduce ML-CPNN approach. This approachis then compared with other approaches. The Roberts edgedetection method gives 93% of accuracy and Laplacian ofGaussian method 95% of the result, the Canny edge detectionmethod yields 97.25% result and the ML-CPNN approachyields 99.47% of recognition rate. [7]

Vaibhav Gupta et al. in 2011 presented a method based onimage subtraction for recognition of Indian coins of differentdenomination. The Process performs 3 checks (radius, coarse

and fine) on the input image. Instantly compute the radius ofthe input image and then based on the radius a test image isselected from the database. Then subtraction between the inputimage and database image is done. By plotting the resultantvalues we obtain a lower limit value which if less than astandard threshold provides the identification of the coin. [8]

Shatrughan Modi et al. in 2011 presented an ArtificialNeural Network based Automated Coin Recognition Systemfor Indian coins. They used Indian coins of denominations1, 2, 5, and 10. This system takes images of coins fromboth sides. First of all apply pre-processing for images likecropping, trimming, pattern averaging, etc. and then passedthe input data set to Neural Network for training. 4536 imagesare used for training and 252 images are used for validationand testing each. It eased back propagation neural networkwith 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 toclassify Indian coins discharged recently. This system is basedon Advanced Harris -Hessian Algorithm, used the parameterssuch as size, weight, surface, etc. of coins and also usedthe concept of rotation invariance. The primary goals of thisproject are: Recognize the coins, count the coins and then getthe total value. First, we apply preprocessing of the image andthen pre-processed images are passed to the Harris -Hessian

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detector, it detects interest points. Now these features are fedto the Hough Transform, it detects circles and calculates theradius of coins . It is a low cost system having recognitionrate close to 100%. [10]

Deepika Mehta et al. in 2013 presented a system to detectand recognize the Overlapped coins using Otsus Algorithmbased on the Hough Transform technique. This project includesthree step detection, extraction and recognition. For segmenta-

tion uses an Otsus algorithm, for detecting overlapping uses theHough transform and for recognition uses radius thresholding.The Detection rate of overlapped coins is 91% and recognitionrate is 40% to 50%. [11]

III. PLAN AND DESIGN

Before implementation of overall setup, the working condi-tions should be set up to improve the efficiency and effective-ness of the program. Because there are hundreds of conditionsabout how the coins would be displayed on an image. Forexample, the size and shape of the same coin could also changeaccording 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 bean ellipse. And likewise, if the camera placed near the coins,the size of the coin on the image captured by the camera willbe relatively larger as compared to the size of coins capturedby the camera which placed far from the coins.

A. Potential schemes for finding coins

The appropriate computation strategy used in the projectand described in this paper is a threshold range matching(upper and lower limit for coins), because of usage of thelow resolution camera. According to this strategy, when theimage is captured in real time, the range is being matchedby 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 lighteningconditions 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 mainstages according to the development strategy. Then each mainstage was broken into sub -steps according to the plan andstudy. Every sub -measure was planned as a distinct level,which could be filled out and tested separately, and thenincorporated into the master task.

Stage 1: Circuit Designing & PCB layout: In this stage,basic circuit has been designed by using simulation tools tocheck the overall working of the circuit to be used in thedevelopment of CBCS system. Several parts were gatheredusing the simulator tool and hence the working of circuit wastested on the simulator. Granting to the circuit designed, PCBLayout was made such that the hardware components can beset up accordingly.

Stage 2: Assembling: At this stage of system evolution,all the elements are gathered together according to the circuitdesigned and PCB layout as presented in the old phases. Afterassembling of all the components, testing was performed to

Fig. 1. Block diagram of overall system

check the working of every component with each other. Hencethe hardware testing was done in parallel.

Stage 3: Software:After detail study of hardware compo-nents 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 micro-controller programming, firstly an .asm file of the assemblycode was coded for the working and performance of controller.Then microcontroller is burnt with the accumulated files of thiscodification. Secondly, C program is designed to do interactionwith .asm file code. Functions of .asm file called in a Cprogram and the interaction of the whole operation is beingperformed. b) Using image processing techniques using MAT-LAB: For the detection of value of coin, picture processingtechniques have been used. These techniques include masking,feature extraction, Hough Transformation, pattern matchingand hence on. Granting to the initial setup of hardware, hereby a threshold range matching technique is being utilized forthe detection of value of coins.

Stage 4: Calculating the value of coins: After the devel-opment of above mention codes, one must integrate them withthe hardware setup as assembled on stage 3. Microcontrollerscode is debugged and complied and then the compiled files aretransferred to the controller for its functioning. It lets the coin- holder to move in-front of the camera on detecting somethinginside it. And then the camera will send the real time capturedimage to the MATLAB for calculating its value and hencesends command to the controller to switch-on the power for alimited period.

These are the four main stages covered for the designingof CBCS. An additional algorithm/technique can be applied toimprove the working of the system.

IV. ALGORITHMS

The following algorithm (set parame set and confirm thethreshold param rupee and two rupee coin.

Algorithm:

1) Set the lower and upper limit for im rupee coin andtwo rupee coin.

2) Capture image from an infinite inpu the camera andfor five continues imag the following:

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Fig. 2. Overall System Setup

Fig. 3. Working Stage of System

a. Convert the RGB image to graysca separate thebackground from the reg (ROI).b. Check between which limits the av all five imageslies between- the upper limit of either one rupee coinor two ruc. Based on which ever threshold interv value liesdisplay the value of that coin

3) At the end stop the video input. The followingalgorithm (run coin used to identify the coin inthe image provide appropriate information microcon-

troller.Algorithm:

1) Start a serial communication computing device andthe microcontrol

2) The port used for this commu by the user and isconstantly open

3) Capture image from an infinite the camera a. For fivecontinues the following:a. Convert the RGB image to gr separate the back-ground from th (ROI).b. Check between which limits t all five images liesbetween- the u limit of either one rupee coin or tc. Based on which ever threshold value lies send the

appropriate the open serial communication p value ofthe coin recognized.

4) At the end stop the video an serial COM port.

V. TESTING

The testing executed that the system has the ability torecognize the value of the coin inserted and it allow powersupply for a limited period of time, based upon the value ofthe coin. Hence, it enables the user to charge their gadget incase of emergency. Also, the system is able to detect whethera coin is inserted or not hence, it cant be fooled by any other

Fig. 4. Violet LED indicating Output unication can be set n

metal object. The primary goal of this project was satisfactorilyaccomplished. Nevertheless, the system works fine for thetesting state becaus e working conditions were already beingset for both the system and input.

VI. CONCLUSION

After understanding the related articles, literatures andanalysis of a few similar projects, the current design strategy

was selected. Various development stages were planned andfinally the whole system was implemented. In entirety, thedeveloped system is able to attain the primary objectives. Nu-merous subgoals is achieved like, value detection of the coin,controlling the communication between various componentsand mainly image processing.

VII. FUTURE SCOPE

As discussed in the previous sections, there is a needof various enhancements that leads to the future scope ofthe proposed system. Firstly, image processing embeddedsystems techniques may improve the throughput of presentsystem. Secondly, paper currency identification and recognition

techniques can also be applied for the betterment of thisproject. With these enhancements one can commercialize itfor the public usage.

REFERENCES

[1] Shatrughan Modi and Dr. Seema Bawa. Automated Coin RecognitionSystem using ANN, India, International Journal of Computer Applica-tions (0975-8887) Vol. 26-No.4, July 2011.

[2] R. Bremananth, B. Balaji, M. Sankari and A. Chitra, A new approachto coin recognition using neural pattern analysis IEEE Indicon 2005Conference, Chennai, India, 11-13 Dec. 2005.

[3] Lu Zhang et al. Development of Counting Coins Program in ComputerVision 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 Identi-fication System, Proceedings of the IEEE International Symposium onIntelligent Control (ISIC06), Munich, Germany, 4-6 October 2006.

[5] C.M.Velu and P.Vivekanandan et al. Indian Coin Recognition Systemof 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 IEEEInternational Conference on Electro/Information Technology (EIT),2010.

[7] Velu C M, P.Vivekanadan, Kashwan K R. Indian Coin Recognition andSum Counting System of Image Data Mining Using Artificial NeuralNetworks, International Journal of Advanced Science and TechnologyVol. 31, June, 2011.

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[8] Gupta, V., Puri, R., Verma, M., Prompt Indian Coin Recognition withRotation Invariance using Image Subtraction Technique, InternationalConference on Devices and Communications (ICDeCom), 2011.

[9] Shatrughan Modi, Dr. Seema Bawa, Automated Coin RecognitionSystem 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

Apprehension, International Journal of Advanced Research in ComputerEngineering Technology (IJARCET) Volume 2, No 5, May 2013.

[11] Deepika Mehta, Anil Sagar, An Efficient Way to Detect and Recog-nize the Overlapped Coins using Otsus Algorithm based on Ho ughTransform technique, International Journal of Computer Applications(0975-8887) Volume 73- No.9, July 2013.

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automatic gadget charger using coin detection

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