Intelligent testing for Arduino UNO based on thermal ...

Computers and Electrical Engineering 58 (2017) 88–100

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Intelligent testing for Arduino UNO based on thermal imageR F. Al-Obaidy, F. Yazdani, F.A. Mohammadi∗ Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, M5B 2K3, Toronto, Ontario, Canada

a r t i c l e

i n f o

Article history: Received 28 July 2016 Revised 16 January 2017 Accepted 17 January 2017

Keywords: Thermal image Integrated circuits (ICs) Testing, Soft computing Arduino UNO Histogram Printed circuit boards (PCBs) Classification

a b s t r a c t The goal of this paper is to develop a tool that will aid manufacturers of Printed Circuit Boards (PCBs) in testing their production lines with an acceptable rate of fault detection and minimize the test time. The PCB unit, namely the Arduino UNO board, is used to generate the thermal profile for the unit under test. This work is based on using a classification approach that classifies the PCB defects into the Integrated Circuit (IC) level. In the proposed technique, histogram features are extracted from the ICs hotspots which are used as inputs into a classifier model. The number of effective features are minimized by the principal component analysis. The image classification and detection are performed based on three soft computing techniques; multilayer perceptron, support vector machine, and adaptive neuron-fuzzy inference system. The effectiveness of the models is evaluated by comparing their performance and accuracy of classification. © 2017 Published by Elsevier Ltd.

1. Introduction As technology has scaled down, Very Large Scale Integrated (VLSI) chip density has grown, exponentially. Consequently, mass VLSI production requires short cycle times and automated processes. This has led to a host of reliability challenges such as manufacturing defects of chips. The application of thermal testing as a non-destructive testing tool has become widespread in recent years because infrared (IR) testing is currently a very feasible tool for non-destructive VLSI testing. Moreover, infrared testing is an efficient, clean and safe technology that is used in such applications [1,2]. Some approaches have involved work on the thermal testing of Printed Circuit Boards (PCBs) with different techniques of thermal imaging. Thermal testing of PCBs is carried out in [3]; the proposed method is used to classify the Integrated Circuits (ICs) into the three main categories (e.g. functional fault free, non-functional, faulty, and less reliable). The classification system uses an artificial neural network to classify the samples extracted from the IR image. The major drawback of this approach is the time and cost of solution achievement to get the thermal signature for every test vector applied to the primary inputs of the PCB. In [4], a method for thermal analysis of the PCB was proposed using MATLAB. Depending on the loading condition of the PCB, two parameters are used for analysis of the thermal image, thermal image analysis, which are the highest temperature and maximum area of the highest temperature. But, this study is not recommended for the complex PCBs because as the choice of selecting a region of interest should be improved by clustering based on segmentation methods. In [5], a qualitative based measurement of thermal anomalies has done by detecting the feature point and region of interest with using the stable region algorithm and matching with Euclidian distance. Detection of the faulty region(s) on PCB by thermal image processing is presented in [6]. In this later work, the samples for thermal images of fault and fault free PCBs R ∗

Reviews processed and approved for publication by Editor-in-Chief Corresponding author. E-mail addresses: [email protected] (F. Al-Obaidy), [email protected] (F. Yazdani), [email protected] (F.A. Mohammadi).

http://dx.doi.org/10.1016/j.compeleceng.2017.01.014 0045-7906/© 2017 Published by Elsevier Ltd.

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Fig. 1. Radiation received by the infrared camera.

are experimentally obtained. The statistical analysis tool such as a Principal Component Thermography (PCT) technique is used to process the IR image sequences and increase the contrast of the processed data. Here, to reduce the amount of computations, Singular Value Decomposition (SVD) technique based on the PCT is used. The proposed method is conditional due to the excessive work of associate calculations. In [7], the analysis of PCBs for fault diagnosis by vector quantization is presented. The feeding data fed to Hopfield neural network and used based on the code word which is generated from the IR image. The main drawback of this study is that the mean values of the code words are nonlinearly distributed through the all codebooks, especially, for small mean values. Some other approaches have done on the thermal analysis of PCBs. Thermal analysis of PCBs has been done in [8]; in this paper, Finite -Element Model (FEM) via Galerkin approach applies to analyze the temperature behavior of PCBs for different width of copper and different amount of current. In addition, [9], describes another approach for analysis of thermal reliability of components on the PCBs, presented by ANSYS software to improve the reliability of the system. In this work, the thermal image is captured and enhanced through techniques such as filtering and Region of Interest (ROI) segmentation. Then, the statistical features including the mean, standard deviation, skewness, kurtosis, energy, contrast, homogeneity, correlation, and entropy are calculated by using the probability distribution of the intensity levels in the histogram bins of all IR images. To minimize the features extractions into linearly uncorrelated variables, Principal Component Analysis (PCA) is used. In order to classify the thermal image into two classes as normal and fault conditions, Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), and Adaptive Neuron-Fuzzy Inference System (ANFIS) techniques are used. We adapt the capabilities of MATLAB to achieve the above steps. The major contributions of this work is that it proposes a set of test techniques to reduce the test application time with different considerations. Also, this work has classified the defects of PCBs into IC level groups. Since a PCB pattern is produced in different processes, classification of defects can help in determining the sources which create errors and reduce production cost in the long run. Moreover, we arrange median with BM3D filters in series to increase the quality of IR image. This advanced filter is useful to eliminate the different thermal noises. The remainder of the paper is organized as follows: the proposed system set-up is introduced in Section 1. Section 2 presents the background of IR thermography technology and the fundamental of the IR temperature measurements. Section 3 describes the system set-up. Section 4 describes the IR image capturing. Image enhancement is presented in Section 5. Features extraction and minimization by PCA are elaborated in Section 6. Section 7 illustrates the classification process. Section 8 presents the experimental results, and Section 9 concludes the study. 2. Principles of infrared thermography Infrared thermography is defined as an equipment which detects Infrared radiation emitted from an object, converts it to temperature, and displays the image of the temperature distribution. This equipment comprises the IR camera and the thermography processing unit. Otherwise, Infrared radiation is a part of the electro-magnetic spectrum, and it is radiated from the surface of the target object. The total power of infrared radiation (Itot ) consists of three main parts: the emission from the main object (Iobj ), the emission from the surroundings (Erefl ) and the emission from the atmosphere (Iatm ). It can be expressed as the Eq. (1). In addition, the process is shown in Fig. 1.

Itot = Iob j + Ire f l + Iatm

(1)

The Eq. (1) can be rewritten at terms of three collected radiation power terms depending on the Stefan–Boltzmann law for a gray-body radiator as follows:

90 •

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The emission of the target object (Iobj ): It can be expressed as an Eq. (2). Where (ε obj ) is the emittance of the object, (τ atm ) is the transmittance of the atmosphere, (α ) is the Boltzmann constant, and (Tobj ) is the temperature of a gray body.

Iob j = εob j · τatm .α · (Tob j )4 •

(2)

The reflection emission from surrounding (Irefl ): It can be written by Eq. (3). Where the term (1- ε obj ) is the reflection of the object and (Trefl ) is the ambient temperature.





Ire f l = 1 − εob j · τatm .α · (Tre f l )4 •

(3)

The emission of the atmosphere (Iatm ): This term can be calculated using Eq. (4). Where (1 − τ atm ) is the emittance of the atmosphere, and (Tatm ) is the temperature of the atmosphere.

Iatm = (1 − τatm ) · α · (Tatm )4 •

(4)

Substituting Eqs. (2)–(4) in the main equation (2.1) to get the Eq. (5). The temperature variations of the target can be evaluated from the Eq. (6) [24,25].





Itot = εob j · τatm · ∝ ·(Tob j )4 + 1 − εob j · τatm · ∝ ·(Tre f l )4 + (1 − τatm )· ∝

 Tob j =

4





Itot − 1 − εob j · τatm · α · (Tre f l )

4

+ (1 − τatm ) · α · (Tatm )

εob j · τatm .α

(5)

4

(6)

However, to calculate the correct temperature of the observed target from the IR radiation received by the camera, the properties of the target surface, the temperature of the surrounding objects, camera to the object distance, temperature and the humidity of air must be set as input data to the camera software [10]. 3. Proposed system set-up The main imaging equipment used to capture IR images is listed in the next subsections. The units of the proposed experimental set-up include a project hardware board, FLIR infrared camera system, digital thermometer and data acquisition unit. 3.1. Real PCB under test Arduino UNO with a microcontroller board based on the ATmega328 is selected. According to the thermal profile of Arduino UNO board, the hot spots are expected to appear on the two main ICs on the board which are the ATmega328P and ATmega8U2, respectively. These ICs are considered as the Unit Under Test (UUT). 3.1.1. ATmega328P IC Plastic Dual Inline Package (PDIP) type. This chip is a rectangular-shaped package with two parallel rows of electrical connecting pins coming out of the two sides of the package, and typical dimensions are 34.798 × 7.493 × 4.5724 mm3 . More specifications of the IC are given in [11]. 3.1.2. ATmega8U2 IC Quad Flat No lead (QFN) with size 7 × 7 × 1.2 mm3 , lead pitch 0.8 mm. This package is a surface-mounted unit and should be stacked directly on the surface of the PCB. The technical description of the ATmega8U2 IC can be reviewed in [11]. 3.2. FLIR infrared camera unit The infrared camera used in the evaluation set-up is a FLIR SC40 0 0 camera system. This is high-speed, high-resolution, and high sensitivity science grade camera. Using the PC-link (Gigabit Ethernet), the camera system captures and transfers 125fps with 320 × 256 pixel imagery. As a result, the IR camera system can measure the temperature "through" the unit being tested [12,13]. 3.3. Data acquisition system and auxiliary unit The IR images are captured and analyzed using an integrated data acquisition unit of the optical system, supported by PC-based FLIR Researcher IR max software (version 4.20.2.74), for data acquisition, analysis, and image capturing. The acquired input data are recorded in real time for subsequent analysis using this software package. The other auxiliary unit is a digital thermometer which is used with a thermocouple probe to measure the atmospheric temperature of the surrounding area. The digital thermometer used in the present work is HH-23A from Omega Industries [13,14].

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Table 1 IR camera parameters set-up. Parameters

Value

Image format Distance Emissivity Lens focal length Resolution Atmospheric temperature

. png 0.65 m 0.94 25 mm 320 (H) × 256 (V) 23.0 ᵒC

Fig. 2. Real PCB with IR capturing system.

3.4. Experimental set-up In this step, the atmospheric temperature, emissivity value and distance of measurement are to be set before the image capturing [15]. Table 1 lists the main setting parameter for the IR cameras. In order to thermally excite the ICs in the PCB, the circuit board is programmed to exceed the total micro-controller current for a period of time by running at least 10 I/O pins (e.g. lighting 10 LED’s) on high mode and draw 20 mA from each one. According to Arduino UNO specifications, the total current sourced from all I/O pins must not exceed 200 mA. The experimental IR capturing equipment is set as shown in Fig. 2 4. IR image capture After ICs are excited; the thermal image sequence of the PCB is captured using the IR camera. These samples are captured at different thermal loading conditions with temperatures in the range of (34–42) °C. The temperatures measured for IC1 (ATmega328P) and IC2 (ATmega8U2) are (34.08–42.40) °C, and (34.76–40.919) °C, respectively. Fig. 3 illustrates the samples of the captured images for the temperature distribution on the PCB’s surface with the geometry. 5. IR image enhancement 5.1. Image de-noising The noise sources include the infrared camera system, electronic sensor, and turbulence in the environments, etc. [16]. In this work, in order to eliminate the confusing noise, many algorithms have suggested based on linear and nonlinear

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Fig. 3. Samples of captured IR image for real PCB.

filters for the de-noising process and each algorithm has its advantages and limitations [17]. We used two types of filter: a median filter and block matching and 3D filtering (BM3D). The experimental results led us to conclude that when we use the median and BM3D filters in series we obtain good results, as shown in Fig. 4. 5.2. Image segmentation for ROI Segmentation has two goals, first is to decompose an image into regions for further analysis and the second is to perform a change of the representation of an image for faster analysis. A thermal image of PCB is converted into a grayscale image, which carries the intensity information of the image. In the grayscale image, the faulty region shows more brightness than the normally repeated region in the PCB. The threshold method is useful and a very common method for detecting defects in thermal images. To describe it mathematically, if the original image is m (x, y), the threshold image w (a, b) is defined in Eq. (7), where (TTH ) is the threshold temperature value which is determined manually.



w(a, b) =

1, 0,

m(x, y ) > TT H m(x, y ) < TT H

(7)

Otsu’s segmentation method is used since it provides us with some of the most efficient results for image thresholding. Otsu’s algorithm can obtain sufficient segmentation results when it is applied to noisy images because it is simple in calculation and effective [18]. We use cropping tools to trim and efficiently remove unwanted portions of the image. The area is cropped by using the rectangle shape to enclose the segmented pattern in a tight window, and convert the selected region into another image. The width of ROI is adjusted manually such that all the thermal effect on the chip is preserved. Fig. 5 shows example of this process.

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Fig. 4. Filtering image result.

6. Features extraction and minimization The process of converting the input data into the set of features is called features extraction. The main goal of feature extraction is to extract the most relevant information from the original data (i.e. image) and represent that information in a lower dimensional space. In our work, nine histogram features are extracted by converting an image to gray color space and applying the histogram statistical analysis method. Mathematically, the histogram is the distribution of the probability function P(i) of the intensity levels (i) in the histogram bins, and defines using the following Eq. (8).

P (i ) =

No.o f pixels with gray level (i ) T otal number o f pixels in ROI

(8)

The extracted features are divided into two main groups. The first group is extracted from first-order histogram statistical analysis (mean value, standard deviation, skewness, and kurtosis). The second group is extracted from the Grey-Level Co-occurrence Matrix (GLCM) (energy, contrast, homogeneity, correlation, and Entropy). The basic difference between the two groups is that first order statistics estimate only the properties of individual pixel values while second order statistics estimate spatial relationships between pixel gray levels of the image happening positions relative to each other [19,20]. In order to increase the correlation of the extracted features, an additional process is conducted using the PCA approach. In PCA, the first step is to collect the data set to be analyzed. Mean value of the data set is calculated. The calculated mean

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Fig. 5. Image segmentation and cropping results.

value is subtracted from the data set to normalize the data. Then covariance matrix is calculated for the normalized data matrix. From the covariance matrix, we calculate the eigenvalues and the corresponding eigenvectors. The eigenvectors are arranged in the descending order of size in the eigenvector matrix. Then the eigenvector with the highest eigenvalue is considered as the principal component of the data set [21]. In our study, the added action is conducted using the PCA approach due to the number of features for each IR image sample is nine features. So that, by applied PCA analysis, minimizing these features into just three uncorrelated variables is achieved. 7. Classification process After processing the image, the next step is intelligent fault diagnosis. Three intelligent methods, MLP [22], SVM [23] and ANFIS [24,25], are used to develop the fault diagnosis algorithm. The dataset is divided randomly into two different sets: a training data set and testing data set. Of 274 samples, 192 (70% of the data set) were selected as a training data set and the remaining 82 (30% of the data set) are selected as testing data. In this classification, the value of the threshold temperature (TTH ) is selected as the valley point between the two categories: •

•

Fault-free condition: when the maximum temperature of the IC’s surface is lower than the threshold value (i.e. TTH < 40 °C). Fault condition: when the maximum temperature of the IC’s surface is equal to or higher than the threshold value (i.e. TTH ≥ 40 °C).

On the other hand, the accuracy percentage of classification is measured as the percentage of total samples classified correctly with respect to the total number of samples in the data set, as in the following equation.

Accuracy of Classification =

T otal No. o f sampl es cl assi f ied correctl y × 100 T otal No.o f samples in the data set

(9)

8. Results and discussion After capturing and processing the image, the final step is the intelligent fault diagnosis and making decisions. An example of the thermal data set and the output classification is shown in Table 2. The input to the classifier model consists of four variables, three extracted from PCA and the fourth being the IC index. The output from the classifier model represents the ICs condition; if all the ICs under test are working in a healthy mode the outputs will give an equivalent value of pass PCB. Otherwise, where there is any fault related to the functioning of the ICs operation, the corresponding output will classify the fault into one of two main classes: fault IC1 or fault IC2. From the experimental results, the optimal MLP architecture is shown in Fig. 6. The configuration that produces the minimum performance factor MLP consists of one hidden layer with 12 neurons. The input layer contains 4 neurons that take the value of the input data, while the output layer has 4 neurons corresponding to the UUT condition. The output layer transfer function is selected to be a Soft max transfer function which is suitable for the binary output. The convergence of the Levenberg–Marquardt BP training algorithm is computed based on the Mean Square Error (MSE) factor as in Fig. 7, where the performance factor (which is the mean square errors) is reduced to 1.187e-7 after 1634 epochs.

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Table 2 Samples for output results of experiment.

1 … 10 … 50 … 274

Histogram features (before PCA minimization)

Contrast

…

Entropy

0.659 … 0.873 … 0.567 … 0.566

… … … … … … …

0.089 … 0.332 … 0.248 … 0.140

Input to classifier model

Output results

PCA correlated features

IC index

Fea.1

Fea.2

Fea.3

Fea.4

Fault status

3.5099 … −3.713 … −1.160 … 1.540

2.617 … 0.248 … −1.00 … 0.161

−0.63 … 0.861 … 0.164 … −0.69

1 … 1 … 2 … 2

Pass PCB … Fault IC1 … Pass PCB … Fault IC2

Table 3 ANFIS parameters setting. Parameters

Description

Number of inputs Number of outputs Number of input membership functions Number of output membership functions Number of rules Output membership function type Fuzzy model type

4 1 24 6 6 Linear Sugeno

Fig. 6. Proposed neural network architecture layout.

Best Training Performance is 1.187e-07 at epoch 1634

0

10

Train Best

Mean Squared Error (mse)

Sample number

-2

10

-4

10

-6

10

0

200

400

600

800

1000

1634 Epochs Fig. 7. MSE behavior of MLP.

1200

1400

1600

96

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Fig. 8. ANFIS structure.

Performance evaluation

0.3

RMSE value

0.25

0.2

0.15

0.1

0.05

0

10

20

30

40

50 Epochs

60

Fig. 9. RMSE behavior of ANFIS.

70

80

90

100

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Training Phase 3.5 3

Output

2.5 2 1.5 1 Target 0.5 Output 0 0

50

100

150

200

250

Data set Index a: ANFIS training output.

Testing Phase

3.5

Output 3

Target

Output

2.5 2 1.5 1 0.5 0

0

20

40

60

80

100

Data Set index b: ANFIS testing output. Fig. 10. Graphical classification output based on ANFIS.

On the other hand, the model of the ANFIS classification topology is shown in Fig. 8, with the parameters setting as given in Table 3. According to the feature reduction results, the features were first selected by the PCA and then fed to the ANFIS classifier. The ANFIS system contains 6 rules, 4 inputs, and one single output. After taking 30 epochs, the Root Mean Square Error (RMSE) is 0.0300841. The decrement in the error while training the ANFIS is shown in Fig. 9. The graphical view of the ANFIS output is given in Fig. 10(a, b), where yellow squares represent the target of ANFIS while red circles represent the output values. Where the square and circle overlap each other, this means that the ANFIS target matches the output value, while a separate square and circle represent the difference in ANFIS output and the actual value for both phases: training and testing. Consequently, the SVM classifier model is implemented according to the parameters set in Table 4, and in this classifier, a kernel function called a quadratic function is used to the same effect as the hidden layers. The SVM method achieved an average accuracy of 88.54% and 88.90% in training and testing, respectively. Therefore, this method can correctly classify 170 samples out of 192 and 73 samples out of 82 in the training and testing phases, respectively. Finally, Table 5 shows that the MLP network presented training and testing with an average accuracy of 96.32% and 94.18%, respectively. Therefore, it correctly detected 186 samples out of 192, and 77 samples out of 82 with an epoch equal to 1643 in the training and testing process, respectively. The ANFIS produced the training and testing process with an aver-

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F. Al-Obaidy et al. / Computers and Electrical Engineering 58 (2017) 88–100 Table 4 SVM parameters setting. Parameters

Description

Number of inputs Number of outputs Kernel function Method for separating Tolerance value No. of classes

4 1 Quadratic Sequential minimal optimization 1e-3 4

Table 5 Comparative training and testing results for the proposed methods. Parameters

Total no. of samples No. of training samples Network structure Epochs No. of unclassified samples Average accuracy of classification

Training Process

Testing Process

MLP

ANFIS

SVM

SVM

MLP

ANFIS

274 192 4–12–4 1643 6 96.32%

274 192 4–16–1 30 4 97.44%

274 192 4–0–1 10,0 0 0 22 88.54%

274 82 4–0–1 10,0 0 0 9 88.90%

274 82 4–12–4 1643 5 94.18%

274 82 4–16–1 30 2 97.43%

age accuracy of 97.44% and 97.43%, respectively, with the epoch equal to 30 only, and from the 82 testing samples, only two unclassified samples were obtained with ANFIS, while there were five unclassified samples with MLP, and SVM, respectively. 9. Conclusion In this work, we present the technology of infrared thermal image processing and intelligent fault diagnosis. Classification models based on the three major techniques: MLP, SVM and ANFIS are proposed for ICs fault diagnosis. The proposed approach is carried out in four stages: first, IR image enhancement stage deals with noise removal by median and BM3D filters. Second stage, Otsu’s segmentation method is used for ROI thresholding. Third stage consists of features extraction, nine histogram features are calculated from the ROI. Finally, the extracted features are minimized by PCA approach and then fed as input to classifier model for classifying the UUT into normal or abnormal (i.e. faulty IC). The comparative test performance analysis concluded that the ANFIS model achieved less number of epochs and highest classification accuracy. References [1] Philemon Daniel P. Software-based self-test techniques for online test and diagnosis of embedded controllers and memories. Hamirpur, India: Dept. Electro. Comm. Eng. Nat. Inst. of Tech.; 2014. [2] Yabin Z, Bagnoli PE. A modeling methodology for thermal analysis of the PCB structure. Microelectr J. 2014;45(8):1033–52. [3] Moldovan H, Marco M, Vladutiu M. PCB testing using infrared thermal signature. In: Proc. IEEE int. conf. instrum. meas., vol. 3; 2005. p. 1970–4. [4] Varghese k J, Singh T, Mohan S. PCB thermal image analysis using MATLAB. Int J Adv Eng Tech 2014;2(3):46–52. [5] Taib S, Jadin M, Kabir S. Thermal imaging for qualitative-based measurements of thermal anomalies in electrical components. In: Proc. IEEE int. conf. electron. commun; 2011. p. 1–6. [6] Wagh CR, Baru VB. Detection of faulty region on printed circuit board with IR thermography. Int J Scientific Eng Res 2013;4(11):1–4. [7] Huang S, Mao C, Cheng K. A VQ-based approach to thermal image analysis for printed circuit boards diagnosis. IEEE Trans Instrum Meas 2006;54(Dec(6)):2381–8. [8] Norhisham M, Bugis I, Wani Jamaludin I, Ranom R. Thermal analysis on PCB using GALERKIN approach. In: IEEE 4th Int. Conf. Modeling, Simulation, and Appl. Optimization (ICMSAO); 2011. p. 1–6. [9] Xu S, Li X. Analysis on thermal reliability of key electronic components on PCB board. In: IEEE conf. quality, reliability, risk, maintenance, and safety engineering (ICQR2MSE); 2011. p. 52–4. [10] Thermo Vision SDK. User’s manual (no. T559014), program version. 26 SP2 ed. USA: FLIR Systems, Inc.; 2013. [11] ATmega48A/PA/88A/PA/168A/PA/328/P Complete, Atmel Corporation, Inc., http://www.atmel.com/devices/atmega328.aspx, 2013, (accessed 02 April 2016). [12] . FLIR Systems. Inc.; 2006 http://www.flir.com/assets/f606a0fb4c7d41869053907a8900a6ef.pdf accessed 02 April 2016). [13] Fakhry T. Optimization of a compact thermal model for a Ball Grid Array (BGA) package using experimental data M.Sc. thesis. Toronto, Canada: Dept. Elect. Eng., Ryerson Univ.; 2011. [14] Ailani S. Development of infrared thermal mapping technique for electronic devices M.Sc. thesis. Toronto, Canada: Dept. Elect. Eng., Ryerson Univ.; 2008. [15] Farrokhi F, Mohammadi FA. Temperature and power measurement of modern dual core processor by infrared thermography. In: Proc. IEEE int. conf. circuits syst. (ISCAS2010), Paris, May 30- June 02; 2010. p. 1603–6. [16] Mohammadi FA, Farrokhi F, Hossain S. A new approach for electro thermal analysis of electronic circuits. In: Proc. IEEE int. conf. circuits syst. (ISCAS2011), Rio de Janeiro, May 15- May 18; 2011. p. 1844–7. [17] Vijayalakshmi A, Titus C, Beaulah L. Image Denoising for different noise models by various filters: a brief survey. Int J Emerge Tech Comput Sci 2014;3(6):42–5. [18] Bansal S, Maini R. A comparative analysis of iterative and otsu’s thresholding techniques. Int J Comput Appl 2013;66(12):45–7. [19] Nummer MA. A DFT technique for testing high-speed circuits with arbitrarily slow testers M.Sc. thesis. Waterloo, Canada: Dept. Elect. Eng., Waterloo Univ.; 2001.

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Fural AL-Obaidy, received his M.Sc. Degree in Computer and Electrical Engineering from Ryerson University, Toronto, Canada in 2016. He also received M.Sc. Degree in Control and Computer Engineering from University of technology, Baghdad, Iraq in1999. He is currently pursuing the Ph.D. in Computer and Electrical Engineering from Ryerson University, Toronto, Canada. His area of research interests includes Microelectronics and VLSI testing. Farhang Yazdani is the President and CEO of BroadPak Corporation. He serves on various technical committees and is a frequent reviewer for IEEE Journal of Advanced Packaging. He received his degrees in Chemical Engineering and Mechanical Engineering from the University of Washington, Seattle. He is a member of AICHE, ASME, IEEE, IMAPS, SPE and the Society of Rheology. Farah Mohammadi is a professor at Ryerson University, Toronto, Canada. She is a senior member of the IEEE and several organizations active in the field of electronic thermal management and advance modeling of VLSI systems. She published more than 60 scientific papers.