Gamma measurement based on CMOS sensor and ARM microcontroller

Gamma measurement based on CMOS sensor and ARM microcontroller 1 first-author 1 1 corresp Corresponding author. Email: machunwang@126.com Corresponding author. Email: machunwang@126.com machunwang@126.com 2 College of Physics and Material Sciences, Henan Normal University, Xinxiang 453007 China College of Physics and Material Sciences, Henan Normal University, Xinxiang 453007 China College of Electronic and Electrical Engineering, Henan Normal University, Xinxiang 453007 China College of Electronic and Electrical Engineering, Henan Normal University, Xinxiang 453007 China A setup based on CMOS sensor and ARM microcontroller is designed to measure the γ-rays. STM32F103 is used as the main platform to control real-time on-line analysis of the image collected by the OV7670 CAMERACHIP TM , and send the image into the LCD at the desired frame rate. Accuracy of sampling interval can be regulated and controlled. The frame number of image is adjusted by STM32F103 to achieve better monitoring. Two methods are adopted to analyze the image data from the CMOS sensor. Although the CMOS sensor is of low efficiency for γ-ray detection, the results show that it is able to discriminate the 60 Co γ-rays. CMOS image sensor γ ray detection ARM microcontroller Image value processing 1 Introduction 1 As γ-rays penetrate the PN junction of a complementary metal-oxide-semiconductor (CMOS), electrons are produced [ 1 1 , 2 2 ] . This makes a CMOS sensor serve as a γ-ray detector. With a simple camera in a cell phone, or video camera, experiments were carried out to study the possibility of using a common CMOS to measure the dose rate of γ-ray sources [ 3 3 - 5 5 3-5 ] . And it is interesting that a smartphone camera can be used to detect γ-rays [ 5 5 , 6 6 ] , making a smartphone an easy and portable γ-detector. Also, because of their large numbers, smartphones are considered as the ground detector array for ultra-high energy cosmic rays, despite their small size and low detection efficiency [ 6 6 ] . A smartphone application with a simple and fast algorithm designed by Wei et al . is developed to extract radiation events from video stream captured by the built-in camera. It can be performed in real time without requiring covering the camera lens [ 7 7 ] . Wang et al . also focused on image analysis via the neural networks, without modify data processing of the cameras [ 3 3 , 4 4 ] . On recording γ-rays, the frame number of image is usually fixed. However, in many cases, a change in the frame number makes a better measurement of radiation. In this paper, OV7670 CAMERACHIP TM is used as the CMOS image sensor, which provides full-frame, sub-sampled or windowed 8-bit images in a wide range of formats, and is controlled through the Serial Camera Control Bus (SCCB) interface. The parameter settings, such as the frame number per second, image size, formatting and output data transfer, are realized by the writing program with the ARM microcontroller, for which the design functions are conducted to deal with the image data from the OV7670 CAMERACHIP TM firsthand. The image is shown on an LCD display for the online monitor. The gray value method proposed by Wang et al . is used to treat the γ-ray dose [ 3 3 ] . 2 Experimental setup 1 2.1. Hardware circuit 2 The detection system is shown schematically in Fig. 1 Fig. 1 . The OV7670 CAMERACHIP TM (OmniVision Technologies) is used as image sensor, with an image sensor array of 656×488 pixels, of which 640×480 pixels are active (307200 pixels). Its functional device is composed of the analog signal processor, A/D converters, digital signal processor, image scalar, timing generator and digital video port. The STM32F103 is used as main control ship. Its main function service consists of the analog-to-digital converter (ADC), digital-to-analog converter (DAC), general-purpose timers (TIM), secure digital input/output interface (SDIO), debug support and universal synchronous asynchronous receiver transmitter (USART). The STM32F103 receives the data from the OV7670 CAMERACHIP TM and analyzes them by designing self-defined functions. The image data are sent to the LCD display instantly. During the measurement, an aluminum foil blocks visible lights to the CAMERACHIP TM , to make it in dark. The aluminum foil also prevents α particles and electrons emitted by the radioactive source [ 3 3 ] . The CAMERACHIP TM can conveniently be controlled by the STM32f103 to set suitable storage frame rate. 2.2. Control of storage frame rate 2 Each byte of data storage has 8 bits ( Fig. 2 Fig. 2 ). The output signal of two bytes is transferred as RGB565, hence two bytes for a pixel. The first five bits of the high byte are denoted by R, the remaining three bits of the high byte and the first three bits of the low byte are denoted by G, and the remaining five bits of the low byte are denoted by B [ 8 8 ] . The image data output of OV7670 are controlled by the pixel clock (PCLK), vertical (VSYNC) and horizontal synchronization signals (HREF/HSYNC). The output sequence is shown in Fig. 2 Fig. 2 . Each PCLK sends a byte datum when the HREF is at the high level ( Fig. 2 Fig. 2 ). The frame can be adjusted by PCLK, VSYNC and HREF/HSYNC. In this work, the frequency of PCLK and HREF are used to adjust the frame rate of the output data. 2.3. Data analysis methods 2 The STM32F103 provides the driving signal and controls the parameters for the CMOS sensor, compiles the program to find the congruent relationship between the pixel (or the cumulative pixel) and the cumulative γ-ray dose, realized by the designed functions, and sends the image data to the display screen. There are two methods to analyze the image data from the CMOS sensor. Method 1 is to search the maximum pixel and its coordinates in the images, and to establish the correlation between the maximum pixel and the γ-ray dose. An ARM microcontroller controls the specific approach by changing the frequency of PCLK in OV7670 of the CMOS sensor, to set the frame rate (four or five frames per second), and to compare the pixel color value for the maximum color value and its coordinates. Then the correlation between the γ-ray dose and the maximum value is constructed. The maximum color value and its coordinates are displayed below the image. Method 2 is to acquire the correlation between the γ-ray dose and the accumulated pixel value, based on gray value of the figure. The gray value denotes brightness of an image, or degree of color shades [ 3 3 ] . The gray value method has been adopted in various image-processing applications for satellite images, aerial photographs, geophysical observations, etc . [ 9 9 ] If the color values of a certain pixel in an original image are ( Red , Green , Blue ), the gray value is calculated by G =(30 Red +59 Green +11 Blue )/100 [ 10 10 ] This analysis is controlled by the ARM microcontroller, with the PCLK frequency being four or five frames per second. The gray value of pixel is converted from color value. There are two maximum gray values, one with radiation and the other without radiation. A function for the two maximum gray values is calculated to obtain the threshold. The pixel of gray value being larger than or equal to the threshold is accumulated to acquire the correlation between the γ-ray dose and the gray value in radiation source of different doses. On the basis of the correlation, the dose of unknown γ-rays can be determined with the accumulated gray value obtained from the detector. 3 Results and Discussion 1 The 152 Eu (895.4 kBq calibrated on 1-Jan-14) and 60 Co (37.63 kBq calibrated on 1-Jan-14), both from Shanghai Institute of Applied Physics, Chinese Academy of Sciences, are adopted to test the system. While 60 Co emits 1.17 and 1.33 MeV γ-rays [ 11 11 ] , 152 Eu, as an important radionuclide for energy and efficiency calibration of γ spectrometers, has a complex decay scheme emitting over 140 γ-rays ranging from 122 keV to 1408 keV [ 12 12 ] . The original image obtained from the 60 Co source is shown in Fig.3(a) Fig.3(a) , in which the bright spots and black background can be clearly distinguished. The insert in the top-right corner is magnified bright spots. The background is due to the electronic noise or other interference noise. The pixel value of background will be too large if the bright spots are not obviously shown for low energy γ-rays. It is necessary to filter the noise in this case. An intervenient threshold value is to find between gray values of the background and a bright spot by statistical analysis. In order to get clearer spot of radiation, pixels of gray value being less than the threshold are filtered out. The image of 60 Co without background is shown in Fig. 3(b) Fig. 3(b) . The image data of 152 Eu are shown in Figs.3(c) and 3(d) Figs.3(c) and 3(d) . In Fig. 4 Fig. 4 , the images with and without background are compared in three-dimensional diagrams. One γ photon usually hits some adjacent pixels at the instant of imaging. Crosstalks always happen between the adjacent pixels in the CMOS sensor [ 13 13 ] . By filtering out the background, Method 2 is adopted to analyze image data. For an image of the 60 Co source, pixels in the dimensions of 3×3, 5×5 and 7×7 around the center of the maximum gray are summed. The histograms of color value and the gray value summation of the three windows are shown in Fig. 5 Fig. 5 . The counts of the max gray value and the summed gray value distribute a few peaks, being obvious in Fig.5(d) Fig.5(d) . In theory, the distribution of peaks should correspond to 1.17 and 1.33 MeV, but due to poor efficiency of the CMOS sensor, the γ-ray counts are not enough to discriminate the two γ-rays. However, in the summation for the 7×7 pixels, the different peaks appear, indicating that the CMOS sensor can be used to detect 60 Co γ-rays. 4 Conclusions 1 CMOS sensor and ARM microcontroller are used to detect γ-rays from 60 Co or 152 Eu source. STM32F103 is adopted as the main control platform to achieve real-time on-line analysis of the radiation image collected by OV7670 CAMERACHIP TM . The data are sent to the LCD at desired frame rate. 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(Science Press), Shanghai Institute of Applied Physics, the Chinese Academy of Sciences, ChineseNuclear Society and Springer Nature Singapore Pte Ltd. 2017 This is a post-peer-review, pre-copyedit version of an article published in Nuclear Science and Techniques. The final authenticated version is available online at: http://dx.doi.org/10.1007/s41365-017-0276-x http://dx.doi.org/10.1007/s41365-017-0276-x . 2017 Nuclear Science and Techniques 09 28