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]} †

† _{CIT CoES, UPES, Dehradun, India, [email protected]}_{CIT CoES, UPES, Dehradun, India, [email protected]} ‡

‡_{CIT CoES, UPES, Dehradun, India, [email protected]}_{CIT CoES, UPES, Dehradun, India, [email protected]}

Abstract

Abstract—In —In this communithis communicatiocation n era, mobile -telephonera, mobile -telephone e in- in-dustr

dustry y has has growgrown n profprofoundloundly. The y. The urban populatiurban population on uses theuses the lates

latest t mobimobile-ple-phone hone techntechnology ology whilwhile e the the rural populatirural population on buysbuys secon

second d hand ones, hand ones, mostlmostly y with degraded batterywith degraded battery, , that requirthat requiree frequent charging. This battery-problem becomes a giant when frequent charging. This battery-problem becomes a giant when user doesnt have a standard charger or an electricity connection. user doesnt have a standard charger or an electricity connection. In

In thithis s pappaperer, , rereseasearcrcherhers s intintent ent is is to to prpropoopose se a a pubpublilic c coicoinn based mobile battery charging system. By using image processing based mobile battery charging system. By using image processing techniques, value of the coin has been detected for a limited time, techniques, value of the coin has been detected for a limited time, it will charge the device accordingly. A suitable microcontroller it will charge the device accordingly. A suitable microcontroller is

is progprogrammrammed ed for all for all the the contrcontrollinolling g appliapplicatiocations. ns. The The soursourcece for charging is obtained either from a direct power grid or by for charging is obtained either from a direct power grid or by solar energy.

solar energy. Keyw

Keywordsords- - Image processImage processing; ing; CharCharging ging systesystem; m; coin coin detec detec--tion; Circuit designing.

tion; Circuit designing.

I.

I. INTINTRORODUCDUCTITIONON

Mobile phones, being an essential technological Mobile phones, being an essential technological advance-ment, have enabled every individual to keep in contact with ment, have enabled every individual to keep in contact with any niche of the globe. They have become a major source of any niche of the globe. They have become a major source of comm

communicunicatioation, n, be be it with it with regaregards to rds to the businethe business aspect orss aspect or personal communication.

personal communication.

These days mobile phones have also

These days mobile phones have also becombecome e a a portportableable computing device that can serve multiple purposes. This computing device that can serve multiple purposes. This in-cre

creaseases s the the usausage ge of of mobmobile ile teltelephephone one setsets s and and its its demdemandand in the community. An increase in the usage of mobile phone in the community. An increase in the usage of mobile phone for various purposes consumes an equal measure of energy in for various purposes consumes an equal measure of energy in eff

effect ect requrequiriniring g frequfrequent ent charchargingging. . As As evevery ery appliapplicatication on thatthat works on mobiles require battery power, this results in faster works on mobiles require battery power, this results in faster discharge of the battery power.

discharge of the battery power. In

In our our reregulgular ar liliveves s (wh(where ere our our rouroutintines es are are ﬁxﬁxed)ed), , thethe mobile phone can be charged in such a manner that the battery mobile phone can be charged in such a manner that the battery po

power wer is is alwalways ays avavailailablable. e. ButBut, , in in cascases es whewhere re the the rouroutintinee cha

changenges s due due to to anany y evevent or ent or incincideident, nt, thethere re may be may be vavarioriousus situations where the battery power level becomes too low or situations where the battery power level becomes too low or is completely discharged. As a consequence, even emergency is completely discharged. As a consequence, even emergency phone calls are not possible. Many critics have argued that a phone calls are not possible. Many critics have argued that a public mobile phone charging service would not be a lucrative public mobile phone charging service would not be a lucrative busi

business becausness because e most of most of the users can the users can charcharge their phonesge their phones at

at homhome, e, in in thetheir ir ofofﬁceﬁces s or or in in thetheir ir vevehichiclesles. . LifLife e is is neneveverr predictable, so in such unpredictability a public system would predictable, so in such unpredictability a public system would be very useful.

be very useful.

Students, tourists and people utilizing public transportation Students, tourists and people utilizing public transportation would become the prospective customers for a public mobile would become the prospective customers for a public mobile phone charger service. Coin Based Charging System (CBCS) phone charger service. Coin Based Charging System (CBCS) brings a very wonderful solution for commuters and travelers brings a very wonderful solution for commuters and travelers who need to charge their mobile phones and gadgets who need to charge their mobile phones and gadgets imme-diat

diatelyely. . Coin Coin operoperated mobile ated mobile phone chargiphone charging ng systesystem m coulcouldd bec

become ome a a nenew w bubusinsiness ess milmilestestoneone. . The The coicoin-bn-baseased d mobmobileile

batt

battery charginery charging g systsystem em is is desidesigned to gned to resolresolve the ve the problproblemem of

of lolow w babattttereryy. . ThThis is cocoululd d alalso so be be ususefeful ul in in ththe e evevenent t of of unpre

unpredictadictable ble grid grid powpower er and and avavailaailabilibility ty of of abuabundant solarndant solar power. This proposal for a coin based universal mobile battery power. This proposal for a coin based universal mobile battery charger is presented in this paper.

charger is presented in this paper. Th

The e ususer er hahas s to to plplug ug ththe e momobibile le phphonone e ininto to onone e of of ththee ada

adaptepters rs and inseand insert a rt a coicoin; n; the phonthe phone e wilwill l thethen n be be gigiveven n aa mic

micro-ro-pulpulse se for charfor charginging. g. It It doedoes s not bring not bring a a mobmobile fromile from ’’deadead’ d’ to to fulfully ly chachargrged ed stastate. te. The The chachargrging ing capcapaciacity ty of of thethe mobile is designed with the help of predeﬁned values. It is, of mobile is designed with the help of predeﬁned values. It is, of course, possible to continue charging the mobile by inserting course, possible to continue charging the mobile by inserting more coins.

more coins.

The solar power application to battery charging has been The solar power application to battery charging has been studi

studied ed in in the past. the past. SolaSolar r charchargers convgers convert light ert light enerenergy gy intointo di

direrect ct cucurrrrenent t fofor r a a rarangnge e of of vovoltltagage e ththat at cacan n be be usused ed foforr charging the battery. In this paper, the design and development charging the battery. In this paper, the design and development of a coin based universal mobile battery charger based on the of a coin based universal mobile battery charger based on the main power and solar power has been discussed. This is of main power and solar power has been discussed. This is of prime importance in rural areas where the mobiles are basic prime importance in rural areas where the mobiles are basic needs for communication and the main power is not available needs for communication and the main power is not available all the time.

all the time. II.

II. LITERALITERATURE TURE SURSURVEY VEY AND AND RELARELATED TED WORKWORK A.

A. TheoretTheoretical ical BackgrBackground ound

Especially in India, one cannot imagine his/her life Especially in India, one cannot imagine his/her life with-ou

out t cocoinins. s. A A pepersrson on ususes es cocoinins s in in dadailily y lilifefe, , be be it it babanknks,s, sup

supermermarkarkets ets or or grogrocercery y stostoresres, , etcetc. . TheThey y hahave ve becbecome ome anan in

intetegrgral al papart rt of of trtranansasactctioions ns in in ouour r daday y to to daday y lilifefe. . AnAndd the

then n thethere re is is a a basbasic ic demdemand and of of highighly exact and hly exact and efefﬁciﬁcientent automatic coin recognition system. Alternative to daily uses, automatic coin recognition system. Alternative to daily uses, the coin recognition systems can also be used for the research the coin recognition systems can also be used for the research purpose by the institutes or organizations that deal with the purpose by the institutes or organizations that deal with the ancient coins. There are three cases of coin recognition systems ancient coins. There are three cases of coin recognition systems available in the market based on different methods:

available in the market based on different methods: 1)

1) MeMechachanicnical al metmethod hod basbased ed syssystemtemss 2)

2) ElectrElectromaomagnegnetic tic metmethod hod basbased sed systystememss 3)

3) Image Image proprocescessinsing bg baseased sd systystememss

The mechanical method based systems use parameters like The mechanical method based systems use parameters like diam

diameter or eter or radiradius, us, thicthicknesskness, , weighweight t and magnetisand magnetism m of of thethe coin to differentiate between the coins. But these parameters coin to differentiate between the coins. But these parameters cannot be used to differentiate between the different materials cannot be used to differentiate between the different materials of the coins. It implies, if we provide two coins, one original of the coins. It implies, if we provide two coins, one original and

and other fake other fake havihaving ng same diametesame diameter, thicknesr, thickness, s, weighweight t andand magnetism, but made up of different materials to the magnetism, but made up of different materials to the mechan-ical method based coin recognition system then it will treat ical method based coin recognition system then it will treat bot

both h the coins as the coins as autauthenhentic so tic so thethese se syssystemtems s can be can be foofooledled easily.

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The electromagnetic method based systems can differenti-ate between different mdifferenti-aterials 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 disser-tation 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 transforma-tion 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 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. 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

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 Net-works. 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

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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 segmenta-tion 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]

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 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. 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

Fig. 1. Block diagram of overall system

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 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 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 MAT-LAB: 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 devel-opment 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) Set the lower and upper limit for im rupee coin and two rupee coin.

2) Capture image from an inﬁnite inpu the camera and for 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 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

3) At the end stop the video input. The following algorithm (run coin used to identify the coin in the image provide appropriate information microcon-troller.

Algorithm:

1) Start a serial communication computing device and the microcontrol

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

3) Capture image from an inﬁnite the camera a. For ﬁve continues 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 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.

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

Fig. 4. 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. CONCLUSION

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. Nu-merous subgoals is achieved like, value detection of the coin, controlling the communication between various components and mainly image processing.

VII. 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.

REFERENCES

[1] Shatrughan Modi and Dr. Seema Bawa. Automated Coin Recognition System 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 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 Identi-ﬁcation 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.

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[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

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

Recog-nize 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.