_"J #:~~ ~? •• 3 (.~J lkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA '" X1JlENFIR lXENAN INFCRMATICNlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA THE EVENT CONTACT In the beginning DETAILS Promoter ABEN - Brazilian Nuclear Energy Association alternative to guarantee the planet, bringing a sustainable together future for safe energy supply Besides being an energy souree that helps to reduce - Brazil greenhouse benefit Contads gas emissions, Edson Kuramoto - Chair - ELETRONUCLEAR ekura@eletronuclear.gov.br Parejo Calvo - Chair nuclear areas like medicine, agriculture. Wilson Aparecido nuclear presents itself as an efficient with respect for the environment. Rua Mena Barreto, 161 - Botafogo Rio de Janeiro/RJ - CEP 22271-100 of the 21 st eentury, technology Nuclear applications industry and energy will have an important role to play in the world's expansion and teehnologieal energy innovations in the next deeades. IPEN-CNEN/SP - wapcalvo@ipen.br Web: www.inac2009.com.br In this context, the Brazilian Phone: + 5521 3797-1751 Fax: + 55213797-1751 Association Nuclear Atlantie Conferenee will be held at the Windsor Information Full Time Assessoria & Eventos - organization generallogistics Executive of ExpolNAC and 2009. secretariat Full Time Assessoria & Eventos Rua Alberto Veiga, 69 - Marape Santos/SP - CEP 11070-030 - Brazil Web: www.inac2009.com.br Eliete Fraga KEY for a Sustainable pretends eontemporary Brazilian 2 nd , in Nuclear Future", the to diseuss the eontribution in the nuclear sedor can give to society in order for it to develop a sustainable manner. and foreign in To achieve this goal, specialists will be invited to involving on Nuclear perspectives, the environment Meeting REMEMBER alongside the XVI Reactor Physics and Thermal (Enfir) and the IX Meeting Applications 55 13 3252-1290 TO Inae 2009 will be promoted Hydraulies 55 13 3877-5027 DATES "Innovations conference Meeting eliete@fulltime-eventos.com.br + 2Jfh to Odober Technology that innovations (Inae 2009), whieh Barra Hotel, in Rio de and energy security. lombardi@fulltime-eventos.com.br + With the theme as well as questions Contacts Fax: 2009. from September Energy the International debate and analyze teehnologieal Vanessa Lombardi Phone: Janeiro, Nuclear (Aben) is promoting (Enan). lhe on Nuclear on Nuclear event will also host the I Industry. Also a part of the event's official program Expolnae, an exhibition public and private eompanies, in which nuclear is and non:nucl.ear, . will • take part, offering produds and servrces IA Set-up dates wvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA various areas of applieation. Expolnae 2009 will • 26 'h Seplember, 2009 from 08:00 em 1018:00 pm present tendencies, alternatives, studies, pr?ieds and accomplishments Exhibition opening hours • 27 'h Seplember 2009 from 15:00 pm 10 23:00 pm • 28 'h Seplember 2009 from 08:00 em 10 19:00 pm • 29 'h Seplember 2009 from 08:00 em 1019:00 pm • 30 'h Seplember 2009 from 08:00 em 1019:00 pm • 01 " October 2009 from 08:00 em 10 19:00 pm nd 02 October 2009 from 08:00 em 10 19:00 pm • or indirectly theme. laking engineering, innovation • / Move-out 03,d Oclober, 2009 from 08:00 em 10 18:00 pm IA part in the exhibition consulting companies areas directly central will be and technological and service providers. Equipment and machine cornponiés that work with management manufadurers research and development, transmission Dismantling developed linked to theconference's lubricants, efficiency institutions and technical participate fuels and sanitation will also be present. and technological sof!ware, energy generatlon, and distribution, infrastrudure, and and energy Educational and scientific, associations business will likewise in Inac 2009. Bring your eompany to this eventl 2 2 0 0 9 1 n te r n a lio n a l N u c le a r A tla n tic C o n J e r e n c e - I N A C R io d e J a n e ir o .R l, B r a z il, S e p le m b e r 2 7 10 O c to b e r 2009 wvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA 2. 2 0 0 9 A S S O C IA Ç Ã O B R A S IL E IR A D E E N E R G IA N U C L E A R - A B E N IS B N : 9 7 8 -8 5 -9 9 1 4 1 -0 3 -8 PATTERN RECOGNITION OF NUCLEAR TRACES PRODUCED ALPHA PARTICLES IN SOLID STATE DETECTORS Ananda C. Trindade 1 ,2,\ BY Paulo E. Cruvíneli, Nivaldo C. da Silva 3 and José H. Salto'lkjihgfedcbaZY de Computação I Departamento Universidade Federal de São Carlos - UFSCar Rodovia Washington Luis, km 235 13565-905 - São Carlos, SP anandat@gmail.com, saito@dc.ufscar.br 2 3 Laboratório Embrapa Instrumentação Agropecuária Rua XV de Novembro, 1452 13560-970 São Carlos, SP cruvinel@cnpdia.embrapa.br de Poços de Caldas - Comissão Nacional de energia Nuclear, Rodovia Poços de Caldas - Andradas, km 13 37701-970 Poços de Caldas, MO ncsi Iva@cnen.gov.br LAPOC/CNEN ABSTRACT A great concem about the spread of radon gas in human living environments requires an improvement of the detection techniques used nowadays and the development of new technologies that provides greater reliability, accuracy and agility in the process of analyzing the results generated by emission of radon. Aiming to increase the analytical capacity of Radon Laboratory, unit of Brazilian Commission ofNuclear Energy, located in Poços de Caldas, the automation ofthe XY table ofthe optical microscope was proposed and implemented and also a new technique of nuclear traces analysis generated by alpha particles from radon using digital imaging processing and artificial neural networks as pattem classifiers. 1. INTRODUCTION Radon and its decay products contribute to the majority of ionizing radiation received by worldwide population. Studies carried out a long of the years confirmed the association between exposure to radon and lung cancer. Radon when inhaled is eliminated almost immediately, however, its decay products are deposited in the lungs which emits alpha particles that interact with the lung tissue and may also reach the bloodstream, affecting other organs like the bone marrow. According to UNSCEAR (United Nations Scientific Committee on the Effects of Atomic Radiation), radon contributes approximately 50% of the natural radiation dose received by the worldwide population annually. Outdoors concentrations ofradon are very low, however, high concentrations are observed in underground mines, particularly in uranium mines. Considerable concentrations of radon are also found in homes depending on the characteristics ofthe building materiais, underground and ventilation. Thus, this work presents an automated technique for obtaining images produced by alpha particles in solid-state detectors through a digital optical microscope coupled with a digital camera CCD and motors capable of moving it along the X and Y axes, which guarantees a complete scan of the detector, increasing agility in the process of capturing images and also the development of an algorithm to analyze those images, based on techniques of digital image processing using Hough transform to identify traces and Artificial Neural Networks as pattems classifiers and indicators that qualify the alpha particle in terms of its angle of incidence and energy. 2. NUCLEAR TRACES PRODUCED BY NUCLEAR PARTICLES 2.1. Radon Radon was discovered by Owens and Emest Rutherford in 1899 and it is the product of alpha decay of 226Ra, result the alpha decay of radioactive series of 238U. It is a colorless, odorless, tasteless and has half \ife of 3.823 days and for being a noble gas is easily disseminated to human living environments through building materiais, soil and water. 2.2. Radon detection techniques The most widely used techniques for radon detection can be divided into two groups: active detection and passive detection, where each of these techniques can be used to detect 222Rn and its decay products [1]. Active techniques detection consists of a few liters of air samples which are placed in a radiation detection system. The main techniques used are the Lucas cells and ionizations chambers. In the passive techniques detections so\id state nuclear track detectors (SSNTD) are exposed to the environment for a certain period of time, with a diffusion chamber, which are permeable only for 222Rn [1]. 2.3. Tracks formation and etching process Tracks formation occurs when a heavy particle (alpha particles, protons, etc.) incises on a SSNTD causing damage in the material. This damage is named latent track, Figure 1, and each one has different characteristics according to their energy and angle of incidence baZYXWVUTSRQPON ! L _.~ - . - r - - - - - - - - - , lkjihgfedcbaZYXWVUT / / / __ ~..__ ..._~ LATENT TRACK SSNTD ...._ ._ Figura 1. Latent track on a SSNTD. To the latent tack be observed, the SSNTD must go through a process of etching, which consists of a chemical attack that etches its surface, increasing the diameter of the latent track. This method is called EPC (Chemical Pre Etching). There is also another process of etching called ECE (Electro Chemical Etching). The difference between then is that the ECE during the chemical process of etching applies a voltage in SSNTD [2]. The most used chemical reagents are potassium hydroxide (KOH) and sodium hydroxide (NaOH). The appearance of an alpha particle incident on a SSNTD after chemical etching process can be seen in Figure 2, which iIIustrates the surface of CR-39 detector exposed to an environment with high radon concentrations. lNAC 2009, Rio de Janeiro, RJ, Brazil. Figura 2. Surface of an SSNTD exposed to an environment with a high radon concentration after chemical etching process in NaOH solution. 3. DIGITAL IMAGES PROCESSING AND ARTIFICIAL NEURAL NETWORKS In the last 30 years, considerable advances have been developed in pattems recognition, and image processing with applications to vision systems. These advances have led to a great need to develop methods, software and hardware. From the initial concepts of signal processing and systems theory, image processing depends mainly on linear filters and convolution masks. Recently, image processing has been developed mainly in areas of frequency analysis, nonlinear analysis, space-variant filtering and analysis based on methods that provide better results than the traditional techniques. Digital image processing can be used in several applications in various levels of knowledge, as geoprocessing, medicine, X-ray images or MRI, astronomy, recognition of celestial bodies or applications for facial and retina recognition and also as a form of classification and pattems recognition. 3.1 Hough transform Duda and Hart [3] suggested the use of the Hough Transform for straight adapted to circles. Using the circle equation given by Equation 4, where a and b are its center coordinates and c its radius, it is possible to use the Hough Transform. The implementation of the Hough Transform consists to change the image from the Cartesian plane in a parameter space, i.e. each pixel on the Cartesian plane is converted into a circle in the parameter space. The intersection of circles in the parameter space will define the center coordinates of a circle and the total radius ofthe circle [4]. (4) Figure 6 (a) illustrates a circle with five pixels and radius equal to baZYXWVUTSRQPONMLKJIHGFEDCBA 1 /.fi in the Cartesian plane (x, y). Figure 6 (b) illustrates image mapping from the Cartesian plane to a parameter space. INAC 2009, Rio de Janeiro, RJ, Brazil. 4,0 4, 3,5 3,5 3, 3,0 3,0 3,5 4,0 5,0 3,03,5 4,0 (a) Figura 6. (a) CircIe in a Cartesian 5,0 wvutsrqponmlkjihgfedcbaZYXWVUTSRQ (b) b. plane x, y; (b) parameter space a and baZYXWVUTSRQPONMLKJIHGFE Each pixel, Figure 6 (a) creates a circle in the parameter space, Figure 6 (b). Thus, points a and b generated in the parameter space are stored in an accumulator array. The intersection of circles in parameter space indicates the circle center coordinates and its sum indicates the pixels belonging to the circle. The circle equation is given by Equation 5. (5) For this approach, where the radius is known, the size of the accumulator the dimensions shown in Equation 6. A C C [X max + 2 .r a io J [ Y max + 2 .r a io ] array should have (6) However, when you want to detect a circle with radius value is unknown is necessary to use a three dimensional accumulator array with the dimensions shown in equation 7. A C C (X max + L r a io ).(Y max + L r a io ).(rm ax - rm in + 1) (7) 3.2 Artificial neural networks An artificial neural network is a mathematical model or computational model that tries to simulate the structure and functional aspects of biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. Neuron has a structure represented by Figure 7. Neurons receives input signals, xl, x2 ,..., xm and are equipped with synapses, which modulate the received signal, represented by the synaptic weight wj, INAC 2009. Rio de Janeiro. RJ. Brazil. Yj lkjihgfedcbaZYXWVUTSRQPONMLKJIH Output Activation Function Xm ---f Synaptic Weights Input Bias Figura 7. Artificial neuron or artificial neuron network. The activation function process the stimuli weighted sum by the synaptic weights and is defined by Equation 8, where Yj is the output generated by each neuron in the layer above. (8) With the multilayer perceptron networks was possible to solve non-linear problems, ending with the Iimitations of single-Iayer perceptron networks. The supervised leaming process consists to adjust the weights of the network so that they achieve a configuration that is capable of mapping entries in the desired outputs. Backpropagation is the most popular algorithm to make these weight adjustments and to minimize errors. Backpropagation uses a sequence of two steps, where the first consists in the pattems presents to the input layer and that cover the entire network until a response is obtained by the output layer. ln the second step, the output obtained is compared with the desired response and its error is calculated and then propagated from output layer to input layer and the weights are adjusted and modified [6]. 4. lMAGE ACQUISITION To image acquisition is used an automatic capture system of images which consists of a digital optical microscope, Figure 8 (a), which has motors capable of moving it along the X and Y axes, Figure 8 (b). The motors are controlled by a programmable microcontroller PIC type 16F877A, Figure 9. INAC 2009, Rio de Janeiro, RJ, Brazil. (a) (b) Figura 8. (a) Digital optical microscope used to automatic capture images. (b) Digital optical microscope and its motors capable of moving it along X and Y axes. Figura 9. Programmable microcontroller PIe type 16F877A. The algorithm developed for the management of the system is able to send instructions to the microcontrolIer device in the hardware and perform reading of the current situation. This algorithm is also responsible for the process of capturing images from the microscope to be analyzed later. For perfect control of the X and Y axes, were developed two algorithms, which the first one is installed on a compute r and the other one recorded in a microcontroller. The software installed on a computer is responsible for positioning the X and Y axes in an initial position and adjust alI the settings necessary to start the process of image acquisition. After the initial setup, the system begins to acquire images. When the first image is acquired, the software in the microcontrolIer sends a control sign indicating a new position for the X and Y axes, and acquired a new image. This process is repeated until ali images are captured. The software INAC 2009, Rio de Janeiro, RJ, Brazil. recorded in the microcontroller receives from serial port commands from computer and send signals that wil\ put the motors in motion. 5. PATTERNS To create a database, which consists of images and the bank's standards, were carried out a series of experimental studies to determinate the behavior of the alpha partic\es incident on CR-39 due to its energy and angle of incidence. The objective of these studies was to obtain traces of alpha partic\es of known ener~y and incident angles. The experiments were performed using a radioactive source of 24 Am, which emits alpha particles with energy of 5.486 Me V, restricting the angle of incidence at 30 0, 45 0, 60 0, 70 lkjihgfedcbaZYXWVUTSRQPONMLKJIHGFED ° and 90 ° with respect to the normal surface. To restrict the angles were used collimators made of acrylic and with central holes of 1 mm with pre-determined angles. Figure 10 shows collimators with incidence angles of90 0, 75 0, 60 0, 45 ° and 30 0. Figura 10. Collimators with incidence angles of 90°,75°,60°,45° e 30°. To avoid the energy loss of alpha particles emitted by radioactive sources, a vacuum camera was used, Figure 11 Therefore, we can ensure that partic\e's energy wont decrease until reaches detector surface. For each experiment performed in the vacuum camera, a similar was performed in the air using the same radioactive source and collimator. Figura 11. Vacuum camera. INAC 2009, Rio de Janeiro, RJ, Brazil. 6. ACQUIRE PROCESSING AND IMAGE ANAL YSIS The project under development is based on three steps consisting digitization), processing and analysis (pattern c1assification). of: acquisition (image The step of image acquisition involves: microscopy, XY table, control signals and acquiring images. This stage of processing consists on image acquisition from a plastic detecto r using the automatic control of the XY table and the camera attached to the microscope. The next step is image processing which includes noise correction acquired images of the microscope and preparing it to analysis step. and focus from the Image analysis, consists to identify patterns in image using the Hough transform, which is able to identify circular and elliptical patterns, providing the largest and smallest diameters of each event. Pattern recognition uses an artificial neural network that receives as parameters data obtained from patterns identification and c1assifies them according to their energy and angle of incidence. A bank of images was designed with the purpose of storing images at ali stages of processing, i.e., the original images, pre-processed images and images with patterns identified. 7. CONCLUSIONS The automation process based on capturing and analysis of traces in images produced by alpha particles in solid-state detectors can increase the analytical capacity of the Radon Laboratory, CNEN unit, installed in Poços de Caldas. Therefore, based on this presented work it will be possible improve the efficiency to determinate the radon concentration in indoor environments, reducing errors and risk of false positives comparing with the manual method used routinely. REFERENCES 1. PAULO, S. R. de,baZYXWVUTSRQPONMLKJIHGFEDCBA " D o s im e tr ia A m b ie n ta ! d e R n - 2 2 2 e filh o s : a b s o lu ta do C R -3 9 le v a n d o em c o n ta o s e fe ito s d o p la te - o u t m e d id a e fa to r e s de e fic iê n c ia a m b ie n ta is " , Editora da Unicamp, Campinas, Brazil, 1991. Tese de Doutorado, Unicamp. 2. TOMMASINO, L., "Electrochemical Etching Process for the Detection of Neutrons and & R a d ia tio n P r o te c tio n , v. 19, n. 1, pp. 12Radon-decay Products", N u c le a r T e c h n o lo g y lkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA 19,2004. 3. DUDA, R. O.; HART, P. E.; STORK, D. G. - P a tte r n C la s s ific a tio n , John Wiley and Sons, 2001. 4. MARTINEZ, A. C. "Um Novo Método para Medidas de Gotas de Chuva com Técnicas do Processamento Digital de Imagens", Universidade de São Paulo - Escola de Engenharia de São Carlos, 2002. 5. HAYKIN, S. " N e u r a l N e tw o r k s : A C o m p r e h e n s iv e F o u n d a tio n " , Prentice Hall, 1999. 6. SUZUMURA, Y. F. "Método para Avaliação da Eficiência de Pulverização Agrícola Baseado em Processamento de Imagens e Rede Neural, UNITAU - Universidade de Taubaté,2005. INAC 2009, Rio de Janeiro, RJ, Brazil.