- Email: [email protected]
On the feasibility to apply a neural network processor for analyzing a gas response of a multisensor microarray
Sensors and Actuators A 190 (2013) 61–65
Contents lists available at SciVerse ScienceDirect
Sensors and Actuators A: Physical journal homepage: www.elsevier.com/locate/sna
On the feasibility to apply a neural network processor for analyzing a gas response of a multisensor microarray A.A. Maschenko a , V. Yu. Musatov a , A.S. Varezhnikov a , I. Kiselev b , M. Sommer b , V.V. Sysoev a,∗ a b
Gagarin State Technical University, Polytechnicheskaya 77, Saratov 410054, Russia Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
a r t i c l e
i n f o
Article history: Received 8 June 2012 Received in revised form 22 September 2012 Accepted 11 November 2012 Available online xxx Keywords: Electronic nose Neural network processor Gas sensor Pattern recognition Multisensor microarray
a b s t r a c t We describe an effort to implement a hardware neural network processor to carry out pattern recognition of signals generated by a multisensor microarray of electronic-nose type. The multisensor microarray is designed on a SnO2 thin film segmented by co-planar electrodes according to KAMINA (KArlsruhe Micro NAse) electronic-nose architecture. The response of this microarray to reducing gases mixed with synthetic air is processed by principal component analysis technique realized in conventional personal computer and hardware neural microprocessor NeuroMatrix NM6403. It is shown that the neural network processor is able to perform successfully the gas-recognition algorithms at a real time scale. The results open a way to fully mimicking a biology-inspired approach to analyze gas mixtures by hybrid chips consisting of a sensor array and a processing hardware based on neural networks. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Humans percept smell information coming from the surrounding world with the help of neural mechanisms which analyze multi-dimensional images generated by olfaction receptors [1]. This biology model inspired [2] the development of an artificial olfaction device which is frequently called in literature as electronic nose [3]. This instrument includes (i) the array of gas sensors, which generate signals in the presence of target gas or gas mixture, and (ii) pattern recognition technique(s), which sort out and discriminate the multi-dimensional sensor signals into classes corresponding to the test gases. The processing and recognition algorithms are based on extracting and analyzing statistical features as well as on employing artificial neural networks (ANNs) (for recent reviews, see [4,5]). The latter techniques directly follow the human approach, do not require a priori knowledge about statistics underlying the sensor array signals and could be considered as the most natural way to design the electronic nose fully mimicking the biological counterpart. Artificial neural networks (ANNs) implement algorithms that attempt to achieve a neurological related performance, such as learning from experience, making generalizations from similar situations and judging states where poor results were achieved in the past. There are many different kinds of ANN. Some of the most
∗ Corresponding author. Tel.: +7 8452 998649; fax: +7 8452 998604. E-mail address: [email protected] (V.V. Sysoev). 0924-4247/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.sna.2012.11.016
popular ones include multilayer perceptron (MLP), which is generally trained with the back-propagation error algorithm, learning vector quantization, radial basis function (RBF), Hopfield and Kohonen, to name a few [6]. Some ANNs are classified as feed-forward ones while others are recurrent, i.e. implementing feedback, in dependence on how data are processed along the network. Another way to classify ANNs is by their learning method (or training), as some ANN employ supervised training, while others are referred to as unsupervised or self-organizing [7]. In most cases, the ANNs are emulated by software developed for conventional personal computers (for recent examples, see [8–11] and references therein). But such implementations are often insufficient to meet the real-time requirements of many industries. Therefore, hardware platforms are recently developed to perform basic operations with ANN algorithms which benefit of parallel processing and are capable of increasing the speed compared to conventional digital processors [12]. Here, we consider using a hardware ANN processor, “dual-core” NeuroMatrix® NM6403 [13], for a gas recognition task based on signals generated by a singlechip multisensor array of KAMINA type [14]. 2. Experimental 2.1. The multisensor microarray and gas response measurements The multisensor microarray employed in this study has been fabricated according to earlier reported protocols [14]. In brief, the SnO2 :Pt film was deposited by r.f. magnetron sputtering on the
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A.A. Maschenko et al. / Sensors and Actuators A 190 (2013) 61–65
Fig. 1. Scheme of the experimental setup: a) KAMINA unit, fan and sensor chip in front, electronics behind; b) PC with in-built NM6403 processor; c) source of gases (liquids).
front side of Si:SiO2 substrate, over of approx. 4 × 8 mm2 square, followed by the sputtering of Pt co-planar electrode strips, 1 m (thickness), 100 m (width), 70 m (between-space), according to the KAMINA electronic nose architecture. In order to control the operating temperature, the chip front side is equipped with two Pt thermoresistors at the edges while the chip rear side carries four meander heaters. The electrode architecture allows one segmenting the metal oxide thin film so that to have the sensor array at the chip consisting of up to 38 chemiresistors (or sensor segments). Following the fabrication, the chip has been wired into PGA120 holder and housed with the KAMINA unit for measurements. In order to differentiate the sensing properties of the SnO2 :Pt film segments, the substrate has been inhomogeneously heated up to maintain a temperature gradient of approx. 7 ◦ C/mm over the chip substrate or the operating temperature difference of ca. 290–360 ◦ C (see, for details, ref. [15]). The sensor segment resistance between each pair of electrodes has been read out with 1 Hz frequency per a whole microarray in the range of 0.1–100 MOhm. The sensor signals are initially processed by the KAMINA unit electronics to be transferred through RS232 interface to PC for storage and visualization. The same interface is employed to manage the operating parameters of KAMINA unit and multisensor chip. The gas response measurements were performed in PCcontrolled experimental setup (Fig. 1). The sample gases were mixtures of pure air with solvent vapors and a mixture of pure air with a lemon smell. The whole setup emulated the conditions of real application of the multisensor chip [16]. Time of exposure to the test gas mixtures was ca. 10 min to ensure the resistance to be stabilized at a certain value. Following the exposure to gas the chips were flushed by synthetic air for 15 min. For gas-recognition purposes, the transient values of resistances received under the change of atmosphere were removed from the consideration and only the stationary resistance values were employed. To minimize the effect of possible long-term drift of resistance and the dependence on the gas concentration, all the signals were normalized prior feeding to the ANN as Ri → ri =
Ri , Rmed
(1)
where Ri is the sensor resistance of i sensor segment; Rmed is the resistance value normalized by the median resistance value over the whole microarray.
2.2. The NeuroMatrix neural network microprocessor The NeuroMatrix NM6403 neural processor is well documented in literature (see, for instance [13]). In brief, this processor has been designed for 32-bit and 64-bit data processing. It comprises original RISC core, vector coprocessor (VCP) operated with 40 MHz clock rate and some peripheral units. There are two identical programmable interfaces to work with different types of external memory, and two communication ports hardware compatible with the TMS320C4x ports, to be able to build multiprocessor systems. RISC core has 8 address registers, and 8 general purpose registers. This core prepares data to feed the vector coprocessor under operations of reading/recording into 64-bit words. Also, this core allows one to tune the vector coprocessor and to choose its operation regimes. The vector coprocessor treats, in general, the integer data which are packed into = {D1 ...D }, where k is the number of ele64-bit words–vectors D k ments. Each word-vector could contain data having various digit capacities. The processor architecture allows one carrying out a hardware support of vector-matrix or matrix-matrix multiplication, calculating on-chip saturation functions and vector composition. These features make the processor to be a rather universal tool to simulate ANNs. 2.3. The ANN algorithm In order to classify the multisensor microarray vector signals into groups corresponding to the test gas mixtures with the help of NM6403 neural network microprocessor, we have realized an algorithm based on correlation ANN or called as Hebb’s ones with self-organization [17]. During the training stage these ANNs find significant correlation dependences among signals via weights value adaptation. At a grade level these ANN find considerable dependence of correlation between signals by adaptation of values of scales. In our case we have targeted the ANN to decompose data into major components according to Principal Component Analysis (PCA) method [11]. This method performs the following linear transformation as y → Wr
(2)
A.A. Maschenko et al. / Sensors and Actuators A 190 (2013) 61–65
The matrix-vector device of NM6403 processor [13] possesses an ability to make the operation of the weighted summation, which is characteristic for matrix calculations, over the data with changeable word length. Because the neural processor operates with only integers it imposes a restriction applied to the word length of variables that directly affects their range. This feature does not allow one to realize a training algorithm of ANN with it. The ANN training is carried out by a host-program while the program of NM6403 processor carries out network simulation. Host-program has been realized in PC by means Borland C ++ Builder 6©. It possesses the convenient graphic interface and carries out the following functions: 1) loading the executed program for NM6403 processor into the PC - emulator; 2) loading data files from the KAMINA unit; 3) training the ANN and transferring its parameters to NM6403 processor; 4) exchanging data between PC and NM6403 processor; 5) displaying the received results, both in text and graphics. The ANN activation function has been chosen to be a linear one according to Hebb’s algorithm. The training has been carried out under 15 epoch with learning rate ŋ equal to 1·10−4 . The training sampling consisted of 239 signals received in each gas atmosphere. The array of input data for the ANN testing has been composed of 49 signals received in each gas. The efficiency of the gas recognition was estimated as a percent ratio of correctly determined signals to all test signals under processing.
Fig. 2. The ANN employed in the study to realize the PCA algorithm.
which transfers stationary stochastic N signals given as the vector r ∈ RN into the vector y ∈ RK via matrix W ∈ RK×N under condition of K < N where K are principal components calculated as uncorrelated orthogonal eigenvectors of the covariance matrix. The magnitude of a single eigenvector or percentage of “information” is expressed by the own eigenvalue which gives a measure of the variance related to this principal component. Thus, the output dimensionality of reduced scale keeps the major information about the natural clustering of the data points. The first (largest) principal component defines the direction which shows a maximum of data variation. The last (smallest) principal component defines the direction which shows a minimum of data variation. In our case, the transformation is performed by one-layer linear ANN employing the Hebb’s algorithm. The number of neurons composing the ANN corresponds to the number of principal components (Fig. 2). The ANN generates principal components according to the Senger’s rule [11]: yi =
N−1
3. Results and Discussion We have found that the distribution of sensor segment resistances over the multisensor array is varied under exposure to various gases (Fig. 3a). This difference is well visualized with PCA algorithm. In case of the studied data samplings the four employed PCA components cover a dispersion of 90.31%. Figure 3b shows the plot of 1st and 2nd PCA components as a result of gas recognition of test gas mixtures carried out with the help of the neural processor. As one can see, the sensor data related to different gases are grouped by PCA algorithm separately from each other to allow one to recognize the gases. The neuron weights are recorded into the corresponding file. Because the neural processor operates only with integer data, all the real weights and entry signals have been converted to this data format. This transformation is carried out as rinteger = T · rreal ,
wij · rj
(3)
j=0
where rj , yi , wij are inputs, outputs and weights, respectively. We have used 4 principal components which were found enough to carry out the pattern recognition from preliminary studies. The weights are specified as
wij (k + 1) = wij (k) + yi (k) rj (k) −
i
whj (k)yh (k)
63
(4)
h=1
where i is the number of neuron (in our case, i = 1.4); j the number of weights (in our case, j = 1,2,.N), wij the jth weight related to ith neuron; yi is the ith output neuron; N the number of sensor segments in the microarray; ŋ is a learning rate. The considered linear ANN of correlation type self-organization has been realized in NM6403 neural processor. This ANN possesses the following features: 1) two neuron layers - entry layer of N (N = 30–38) neurons equal to number of employed sensor segments and output layer of 4 neurons equal to the number of employed principal components; 2) entry and target values of neurons are given as 32–digit integers; 3) weight factors of neurons are given as 32–digit integers packed into 64–digit words.
(5)
where T is the transformation coefficient defined to be 1, 10, and 1000. The reduction of data from the real type to the integer one brings an error to be ı=
|(rreal − rinteger )| rreal
,
(6)
where rreal are calculated weight values; rinteger are weight values converted to the integer type. It was found out that using the factor T equal to 1000 is enough to have the error less than 0.3% (Fig. 4). The processor productivity primarily depends on clock rate, average quantity of steps needed for the instruction and quantities of performed instructions. To estimate the processor performance the quantity of instructions which the processor needs for calculating a neural network might be considered as a major parameter. The calculation of this parameter in case of neural processor is carried out with an initial code at assembler programming language. In case of the personal computer, this code has been primarily given in C programming language with further decoding into assembler. That means the calculation of quantity of instructions carried out in the personal computer is received also in a code of assembler programming language.
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Fig. 3. The results of gas identification using the vector signals of multisensor microarray: a) the change of exemplary sensor segment resistance under air and air/lemon aroma exposure; b) the PCA scores plot of the test gases (air, air/acetone, air/lemon) in the PC1-PC2 plane. The ellipses mark points related to one class for eye guiding.
Fig. 4. Percentage error, ı, of gas recognition with neural processor when K= 1000.
The Figure 5a depicts the dependence of time to perform the necessary calculations on a number of neurons composing the ANN. At the same time, the Figure 5b shows the estimation of number of carried out instructions in case of the neural processor and PC equipped with processor operated even at higher clock rate (Intel Pentium II, 266 MHz).
These data show that the realization of neural network algorithms in a neural processor requires much less time and less quantity of instructions in the programs when compared to performance of conventional PC. This advantage is greatly enhanced with increase of number of employed neurons. This is because the calculations in a neural processor are realized over vectors and matrixes
Fig. 5. The estimation of NM6403 neural processor (40 MHz) performance and PC (Intel Pentium II, 266 MHz) under different number of neurons in the ANN: a) the time to perform the necessary calculations; b) the number of instructions carried out.
A.A. Maschenko et al. / Sensors and Actuators A 190 (2013) 61–65
rather than over separate numbers as in the case of conventional PC. 4. Conclusions The possibility to apply the NM6403 neural processor for processing multisensor array vector signals has been considered with a purpose to identify a kind of gas mixture. It is shown that one of the major restrictions for such application is that the neural processor operates only with integer variables that require a transformation of neural network scales. The results of data processing with the neural network, received with the signals of the multisensor array of KAMINA type in response to gas mixtures under real conditions, show a good productivity of recognition. The performance of neural network method applied to gas recognition realized with the neural processor and a conventional personal computer has been compared. The features of neural processor architecture allow one to reach the higher speed of performance of recognition algorithm at rather low clock rate of the processor. Besides, specialized algorithms of recognition with reference to neural processor have more simple structure, than ones employed in conventional common processors used in PCs. These advantages may allow one to employ the neural processor as a processing unit when designing a mobile version of electronic nose prototypes. References [1] G.M. Shepherd, Smell images and the flavour system in the human brain, Nature 444 (2006) 316–321. [2] K. Persaud, G. Dodd, Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose, Nature 299 (1982) 352–354. [3] T.C. Pearce, S.S. Schiffman, H.T. Nagle, J.W. Gardner (Eds.), Handbook of machine olfaction: electronic nose technology, Wiley-VCH, Weinheim, 2003. [4] S.M. Scott, D. James, Z. Ali, Data analysis for electronic nose systems, Mikrochimica Acta 156 (2007) 183–207. [5] M.R.G. Meireles, P.E.M. Almeida, M.G. Simoes, A comprehensive review for industrial applicability of artificial neural networks, IEEE Transactions on Industrial Electronics 50 (2003) 585–601. [6] E. Lewis, C. Sheridan, M. O’Farrel, D. King, C. Flanagan, W.B. Lyons, C. Fitzpatrick, Principal component analysis and artificial neural network based approach to analyzing optical fibre sensors signals, Sensors and Actuators A: Physical 136 (2007) 28–38. [7] C.M. Bishop, Neural networks for pattern recognition, Oxford University Press, Oxford, 1995. [8] H.M. Zhang, M.X. Chang, J. Wang, S. Ye, Evaluation of peach quality indices using an electronic nose by MLR, QPST and BP network, Sensors and Actuators B: Chemical 134 (2008) 332–338. [9] H. Zhang, M.O. Balaban, J.C. Principe, Improving pattern recognition of electronic nose data with time-delay neural networks, Sensors and Actuators B: Chemical 96 (2003) 385–389. [10] D. Luo, H.G. Hosseini, J.R. Stewart, Application of ANN with extracted parameters from an electronic nose in cigarette brand identification, Sensors and Actuators B: Chemical 99 (2004) 253–257. [11] S. Osowski, K. Brudzewski, T. Markiewicz, J. Ulaczyk, Neural methods of calibration of sensors for gas measurements and aroma identification system, Journal of Sensory Studies 23 (2008) 533–557. [12] C.S. Lindsey, Th Lindblad, Survey of neural network hardware, Proceedings of SPIE 2492 (1995) 1194–1205.
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[13] P.A. Chevtchenko, D.V. Fomine, V.M. Tchernikov, P.E. Vixne, Using of microprocessor NM6403 for neural net emulation, Proceedings of SPIE 3728 (1999) 242–252. [14] J. Goschnick, An electronic nose for intelligent consumer products based on a gas analytical gradient microarray, Microelectronic Engineering 57-58 (2001) 693–704. [15] V.V. Sysoev, I. Kiselev, M. Frietsch, J. Goschnick, The temperature gradient effect on gas discrimination power of metal-oxide thin-film sensor microarray, Sensors 4 (2004) 37–46. [16] V. Yu Musatov, V.V. Sysoev, M. Sommer, I. Kiselev, Assessment of meat freshness with metal oxide sensor microarray Electronic Nose: a practical approach, Sensors and Actuators B: Chemical 144 (2010) 99–103. [17] T. Khanna, Foundations of neural networks, Addison-Wesley Publ, USA, 1990.
Biographies Artem A. Maschenko received his Master of Science degree in Systems of artificial intelligence from Gagarin State University (Russia) in 2008. Currently he is a PhD student at the same university. His scientific interests are pattern recognition algorithms, hardware and multisensor arrays. Vyacheslav Yu. Musatov is Docent in Artificial Intelligence Department in Gagarin State Technical University (GTU, Russia). He graduated from GTU in 1994 with Diploma of engineer for electrical and mechanical systems. In 2000 he was awarded with degree of Candidate in Engineering Sciences (Ph.D.). He worked few times as a visiting researcher in Karlsruhe Institute of Technology (Germany). His scientific interests are computer modeling of technical systems and data mining with artificial intelligence methods. Alexey S. Varezhnikov received his engineer degree in Systems of artificial intelligence from Gagarin State University (Russia) in 2008. Currently he is a PhD student at the same university. His scientific interests are multisensor arrays and pattern recognition algorithms. Ilia Kiselev received his diploma degree in physics from Moscow State University (Moscow, Russia) in 1978 with the thesis on carrier diffusion properties of semiconductor diodes and his PhD degree in physics and mathematics from Kyrgyz Science Academy (Bishkek, Kyrgyzstan) in 1992 with thesis on turbulent flows of plasma. In 2000 he joined gas sensor research group at Institute for Microstructure Techniques of Karlsruhe Institute of Technology (Germany) as a researcher. His main scientific interests include study of processes in the metal oxide structures and mathematical modeling. Martin Sommer received his diploma degree in physics from the Westfälische Wilhelmsuniversität Münster in 1992 with a thesis about the skipping motion of protons shot onto crystals under grazing angle of incidence. At the Forschungszentrum Karlsruhe (FZK, Germany), he elaborated his PhD thesis about the reduction of the matrix effect in mass spectrometry using the energy spectra of sputtered particles in 1996. Since 2008 he is the head of a group in the Institute for Microstructure Techniques of Karlsruhe Institute of Technology. His main scientific interest concerns fabrication and study of controllable nano-surfaces and functional nano-structures about their ability for sensor-actuator devices. Victor V. Sysoev received his Engineer-physicist degree in microelectronics and semiconductor devices from Saratov State University (SSU, Russia) in 1994, Candidate of Phys. & Math. Sc. degree (PhD) in physics of semiconductors and dielectrics from SSU in 1999, and Dr. of Sc. in Engineering from Gagarin State University in 2009. He worked many times as a visiting researcher in National Microelectronics Research Center (Cork, Ireland), Karlsruhe Institute of Technology (Karlsruhe, Germany) and Southern Illinois University (Carbondale, USA). Currently he holds a Professor position in Physics Department of Saratov State Technical University (Russia). He is author or coauthor of 2 monographs, 1 book chapter, 27 journal papers, and more than 60 conference presentations. His scientific interests are chemical sensors and multisensor arrays. He has been awarded with grants and scholarships from DAAD (Germany), INTAS (EU), Fulbright (USA) and Russian Federal Ministry for Higher Education.
Contents lists available at SciVerse ScienceDirect
Sensors and Actuators A: Physical journal homepage: www.elsevier.com/locate/sna
On the feasibility to apply a neural network processor for analyzing a gas response of a multisensor microarray A.A. Maschenko a , V. Yu. Musatov a , A.S. Varezhnikov a , I. Kiselev b , M. Sommer b , V.V. Sysoev a,∗ a b
Gagarin State Technical University, Polytechnicheskaya 77, Saratov 410054, Russia Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
a r t i c l e
i n f o
Article history: Received 8 June 2012 Received in revised form 22 September 2012 Accepted 11 November 2012 Available online xxx Keywords: Electronic nose Neural network processor Gas sensor Pattern recognition Multisensor microarray
a b s t r a c t We describe an effort to implement a hardware neural network processor to carry out pattern recognition of signals generated by a multisensor microarray of electronic-nose type. The multisensor microarray is designed on a SnO2 thin film segmented by co-planar electrodes according to KAMINA (KArlsruhe Micro NAse) electronic-nose architecture. The response of this microarray to reducing gases mixed with synthetic air is processed by principal component analysis technique realized in conventional personal computer and hardware neural microprocessor NeuroMatrix NM6403. It is shown that the neural network processor is able to perform successfully the gas-recognition algorithms at a real time scale. The results open a way to fully mimicking a biology-inspired approach to analyze gas mixtures by hybrid chips consisting of a sensor array and a processing hardware based on neural networks. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Humans percept smell information coming from the surrounding world with the help of neural mechanisms which analyze multi-dimensional images generated by olfaction receptors [1]. This biology model inspired [2] the development of an artificial olfaction device which is frequently called in literature as electronic nose [3]. This instrument includes (i) the array of gas sensors, which generate signals in the presence of target gas or gas mixture, and (ii) pattern recognition technique(s), which sort out and discriminate the multi-dimensional sensor signals into classes corresponding to the test gases. The processing and recognition algorithms are based on extracting and analyzing statistical features as well as on employing artificial neural networks (ANNs) (for recent reviews, see [4,5]). The latter techniques directly follow the human approach, do not require a priori knowledge about statistics underlying the sensor array signals and could be considered as the most natural way to design the electronic nose fully mimicking the biological counterpart. Artificial neural networks (ANNs) implement algorithms that attempt to achieve a neurological related performance, such as learning from experience, making generalizations from similar situations and judging states where poor results were achieved in the past. There are many different kinds of ANN. Some of the most
∗ Corresponding author. Tel.: +7 8452 998649; fax: +7 8452 998604. E-mail address: [email protected] (V.V. Sysoev). 0924-4247/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.sna.2012.11.016
popular ones include multilayer perceptron (MLP), which is generally trained with the back-propagation error algorithm, learning vector quantization, radial basis function (RBF), Hopfield and Kohonen, to name a few [6]. Some ANNs are classified as feed-forward ones while others are recurrent, i.e. implementing feedback, in dependence on how data are processed along the network. Another way to classify ANNs is by their learning method (or training), as some ANN employ supervised training, while others are referred to as unsupervised or self-organizing [7]. In most cases, the ANNs are emulated by software developed for conventional personal computers (for recent examples, see [8–11] and references therein). But such implementations are often insufficient to meet the real-time requirements of many industries. Therefore, hardware platforms are recently developed to perform basic operations with ANN algorithms which benefit of parallel processing and are capable of increasing the speed compared to conventional digital processors [12]. Here, we consider using a hardware ANN processor, “dual-core” NeuroMatrix® NM6403 [13], for a gas recognition task based on signals generated by a singlechip multisensor array of KAMINA type [14]. 2. Experimental 2.1. The multisensor microarray and gas response measurements The multisensor microarray employed in this study has been fabricated according to earlier reported protocols [14]. In brief, the SnO2 :Pt film was deposited by r.f. magnetron sputtering on the
62
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Fig. 1. Scheme of the experimental setup: a) KAMINA unit, fan and sensor chip in front, electronics behind; b) PC with in-built NM6403 processor; c) source of gases (liquids).
front side of Si:SiO2 substrate, over of approx. 4 × 8 mm2 square, followed by the sputtering of Pt co-planar electrode strips, 1 m (thickness), 100 m (width), 70 m (between-space), according to the KAMINA electronic nose architecture. In order to control the operating temperature, the chip front side is equipped with two Pt thermoresistors at the edges while the chip rear side carries four meander heaters. The electrode architecture allows one segmenting the metal oxide thin film so that to have the sensor array at the chip consisting of up to 38 chemiresistors (or sensor segments). Following the fabrication, the chip has been wired into PGA120 holder and housed with the KAMINA unit for measurements. In order to differentiate the sensing properties of the SnO2 :Pt film segments, the substrate has been inhomogeneously heated up to maintain a temperature gradient of approx. 7 ◦ C/mm over the chip substrate or the operating temperature difference of ca. 290–360 ◦ C (see, for details, ref. [15]). The sensor segment resistance between each pair of electrodes has been read out with 1 Hz frequency per a whole microarray in the range of 0.1–100 MOhm. The sensor signals are initially processed by the KAMINA unit electronics to be transferred through RS232 interface to PC for storage and visualization. The same interface is employed to manage the operating parameters of KAMINA unit and multisensor chip. The gas response measurements were performed in PCcontrolled experimental setup (Fig. 1). The sample gases were mixtures of pure air with solvent vapors and a mixture of pure air with a lemon smell. The whole setup emulated the conditions of real application of the multisensor chip [16]. Time of exposure to the test gas mixtures was ca. 10 min to ensure the resistance to be stabilized at a certain value. Following the exposure to gas the chips were flushed by synthetic air for 15 min. For gas-recognition purposes, the transient values of resistances received under the change of atmosphere were removed from the consideration and only the stationary resistance values were employed. To minimize the effect of possible long-term drift of resistance and the dependence on the gas concentration, all the signals were normalized prior feeding to the ANN as Ri → ri =
Ri , Rmed
(1)
where Ri is the sensor resistance of i sensor segment; Rmed is the resistance value normalized by the median resistance value over the whole microarray.
2.2. The NeuroMatrix neural network microprocessor The NeuroMatrix NM6403 neural processor is well documented in literature (see, for instance [13]). In brief, this processor has been designed for 32-bit and 64-bit data processing. It comprises original RISC core, vector coprocessor (VCP) operated with 40 MHz clock rate and some peripheral units. There are two identical programmable interfaces to work with different types of external memory, and two communication ports hardware compatible with the TMS320C4x ports, to be able to build multiprocessor systems. RISC core has 8 address registers, and 8 general purpose registers. This core prepares data to feed the vector coprocessor under operations of reading/recording into 64-bit words. Also, this core allows one to tune the vector coprocessor and to choose its operation regimes. The vector coprocessor treats, in general, the integer data which are packed into = {D1 ...D }, where k is the number of ele64-bit words–vectors D k ments. Each word-vector could contain data having various digit capacities. The processor architecture allows one carrying out a hardware support of vector-matrix or matrix-matrix multiplication, calculating on-chip saturation functions and vector composition. These features make the processor to be a rather universal tool to simulate ANNs. 2.3. The ANN algorithm In order to classify the multisensor microarray vector signals into groups corresponding to the test gas mixtures with the help of NM6403 neural network microprocessor, we have realized an algorithm based on correlation ANN or called as Hebb’s ones with self-organization [17]. During the training stage these ANNs find significant correlation dependences among signals via weights value adaptation. At a grade level these ANN find considerable dependence of correlation between signals by adaptation of values of scales. In our case we have targeted the ANN to decompose data into major components according to Principal Component Analysis (PCA) method [11]. This method performs the following linear transformation as y → Wr
(2)
A.A. Maschenko et al. / Sensors and Actuators A 190 (2013) 61–65
The matrix-vector device of NM6403 processor [13] possesses an ability to make the operation of the weighted summation, which is characteristic for matrix calculations, over the data with changeable word length. Because the neural processor operates with only integers it imposes a restriction applied to the word length of variables that directly affects their range. This feature does not allow one to realize a training algorithm of ANN with it. The ANN training is carried out by a host-program while the program of NM6403 processor carries out network simulation. Host-program has been realized in PC by means Borland C ++ Builder 6©. It possesses the convenient graphic interface and carries out the following functions: 1) loading the executed program for NM6403 processor into the PC - emulator; 2) loading data files from the KAMINA unit; 3) training the ANN and transferring its parameters to NM6403 processor; 4) exchanging data between PC and NM6403 processor; 5) displaying the received results, both in text and graphics. The ANN activation function has been chosen to be a linear one according to Hebb’s algorithm. The training has been carried out under 15 epoch with learning rate ŋ equal to 1·10−4 . The training sampling consisted of 239 signals received in each gas atmosphere. The array of input data for the ANN testing has been composed of 49 signals received in each gas. The efficiency of the gas recognition was estimated as a percent ratio of correctly determined signals to all test signals under processing.
Fig. 2. The ANN employed in the study to realize the PCA algorithm.
which transfers stationary stochastic N signals given as the vector r ∈ RN into the vector y ∈ RK via matrix W ∈ RK×N under condition of K < N where K are principal components calculated as uncorrelated orthogonal eigenvectors of the covariance matrix. The magnitude of a single eigenvector or percentage of “information” is expressed by the own eigenvalue which gives a measure of the variance related to this principal component. Thus, the output dimensionality of reduced scale keeps the major information about the natural clustering of the data points. The first (largest) principal component defines the direction which shows a maximum of data variation. The last (smallest) principal component defines the direction which shows a minimum of data variation. In our case, the transformation is performed by one-layer linear ANN employing the Hebb’s algorithm. The number of neurons composing the ANN corresponds to the number of principal components (Fig. 2). The ANN generates principal components according to the Senger’s rule [11]: yi =
N−1
3. Results and Discussion We have found that the distribution of sensor segment resistances over the multisensor array is varied under exposure to various gases (Fig. 3a). This difference is well visualized with PCA algorithm. In case of the studied data samplings the four employed PCA components cover a dispersion of 90.31%. Figure 3b shows the plot of 1st and 2nd PCA components as a result of gas recognition of test gas mixtures carried out with the help of the neural processor. As one can see, the sensor data related to different gases are grouped by PCA algorithm separately from each other to allow one to recognize the gases. The neuron weights are recorded into the corresponding file. Because the neural processor operates only with integer data, all the real weights and entry signals have been converted to this data format. This transformation is carried out as rinteger = T · rreal ,
wij · rj
(3)
j=0
where rj , yi , wij are inputs, outputs and weights, respectively. We have used 4 principal components which were found enough to carry out the pattern recognition from preliminary studies. The weights are specified as
wij (k + 1) = wij (k) + yi (k) rj (k) −
i
whj (k)yh (k)
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(4)
h=1
where i is the number of neuron (in our case, i = 1.4); j the number of weights (in our case, j = 1,2,.N), wij the jth weight related to ith neuron; yi is the ith output neuron; N the number of sensor segments in the microarray; ŋ is a learning rate. The considered linear ANN of correlation type self-organization has been realized in NM6403 neural processor. This ANN possesses the following features: 1) two neuron layers - entry layer of N (N = 30–38) neurons equal to number of employed sensor segments and output layer of 4 neurons equal to the number of employed principal components; 2) entry and target values of neurons are given as 32–digit integers; 3) weight factors of neurons are given as 32–digit integers packed into 64–digit words.
(5)
where T is the transformation coefficient defined to be 1, 10, and 1000. The reduction of data from the real type to the integer one brings an error to be ı=
|(rreal − rinteger )| rreal
,
(6)
where rreal are calculated weight values; rinteger are weight values converted to the integer type. It was found out that using the factor T equal to 1000 is enough to have the error less than 0.3% (Fig. 4). The processor productivity primarily depends on clock rate, average quantity of steps needed for the instruction and quantities of performed instructions. To estimate the processor performance the quantity of instructions which the processor needs for calculating a neural network might be considered as a major parameter. The calculation of this parameter in case of neural processor is carried out with an initial code at assembler programming language. In case of the personal computer, this code has been primarily given in C programming language with further decoding into assembler. That means the calculation of quantity of instructions carried out in the personal computer is received also in a code of assembler programming language.
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Fig. 3. The results of gas identification using the vector signals of multisensor microarray: a) the change of exemplary sensor segment resistance under air and air/lemon aroma exposure; b) the PCA scores plot of the test gases (air, air/acetone, air/lemon) in the PC1-PC2 plane. The ellipses mark points related to one class for eye guiding.
Fig. 4. Percentage error, ı, of gas recognition with neural processor when K= 1000.
The Figure 5a depicts the dependence of time to perform the necessary calculations on a number of neurons composing the ANN. At the same time, the Figure 5b shows the estimation of number of carried out instructions in case of the neural processor and PC equipped with processor operated even at higher clock rate (Intel Pentium II, 266 MHz).
These data show that the realization of neural network algorithms in a neural processor requires much less time and less quantity of instructions in the programs when compared to performance of conventional PC. This advantage is greatly enhanced with increase of number of employed neurons. This is because the calculations in a neural processor are realized over vectors and matrixes
Fig. 5. The estimation of NM6403 neural processor (40 MHz) performance and PC (Intel Pentium II, 266 MHz) under different number of neurons in the ANN: a) the time to perform the necessary calculations; b) the number of instructions carried out.
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rather than over separate numbers as in the case of conventional PC. 4. Conclusions The possibility to apply the NM6403 neural processor for processing multisensor array vector signals has been considered with a purpose to identify a kind of gas mixture. It is shown that one of the major restrictions for such application is that the neural processor operates only with integer variables that require a transformation of neural network scales. The results of data processing with the neural network, received with the signals of the multisensor array of KAMINA type in response to gas mixtures under real conditions, show a good productivity of recognition. The performance of neural network method applied to gas recognition realized with the neural processor and a conventional personal computer has been compared. The features of neural processor architecture allow one to reach the higher speed of performance of recognition algorithm at rather low clock rate of the processor. Besides, specialized algorithms of recognition with reference to neural processor have more simple structure, than ones employed in conventional common processors used in PCs. These advantages may allow one to employ the neural processor as a processing unit when designing a mobile version of electronic nose prototypes. References [1] G.M. Shepherd, Smell images and the flavour system in the human brain, Nature 444 (2006) 316–321. [2] K. Persaud, G. Dodd, Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose, Nature 299 (1982) 352–354. [3] T.C. Pearce, S.S. Schiffman, H.T. Nagle, J.W. Gardner (Eds.), Handbook of machine olfaction: electronic nose technology, Wiley-VCH, Weinheim, 2003. [4] S.M. Scott, D. James, Z. Ali, Data analysis for electronic nose systems, Mikrochimica Acta 156 (2007) 183–207. [5] M.R.G. Meireles, P.E.M. Almeida, M.G. Simoes, A comprehensive review for industrial applicability of artificial neural networks, IEEE Transactions on Industrial Electronics 50 (2003) 585–601. [6] E. Lewis, C. Sheridan, M. O’Farrel, D. King, C. Flanagan, W.B. Lyons, C. Fitzpatrick, Principal component analysis and artificial neural network based approach to analyzing optical fibre sensors signals, Sensors and Actuators A: Physical 136 (2007) 28–38. [7] C.M. Bishop, Neural networks for pattern recognition, Oxford University Press, Oxford, 1995. [8] H.M. Zhang, M.X. Chang, J. Wang, S. Ye, Evaluation of peach quality indices using an electronic nose by MLR, QPST and BP network, Sensors and Actuators B: Chemical 134 (2008) 332–338. [9] H. Zhang, M.O. Balaban, J.C. Principe, Improving pattern recognition of electronic nose data with time-delay neural networks, Sensors and Actuators B: Chemical 96 (2003) 385–389. [10] D. Luo, H.G. Hosseini, J.R. Stewart, Application of ANN with extracted parameters from an electronic nose in cigarette brand identification, Sensors and Actuators B: Chemical 99 (2004) 253–257. [11] S. Osowski, K. Brudzewski, T. Markiewicz, J. Ulaczyk, Neural methods of calibration of sensors for gas measurements and aroma identification system, Journal of Sensory Studies 23 (2008) 533–557. [12] C.S. Lindsey, Th Lindblad, Survey of neural network hardware, Proceedings of SPIE 2492 (1995) 1194–1205.
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[13] P.A. Chevtchenko, D.V. Fomine, V.M. Tchernikov, P.E. Vixne, Using of microprocessor NM6403 for neural net emulation, Proceedings of SPIE 3728 (1999) 242–252. [14] J. Goschnick, An electronic nose for intelligent consumer products based on a gas analytical gradient microarray, Microelectronic Engineering 57-58 (2001) 693–704. [15] V.V. Sysoev, I. Kiselev, M. Frietsch, J. Goschnick, The temperature gradient effect on gas discrimination power of metal-oxide thin-film sensor microarray, Sensors 4 (2004) 37–46. [16] V. Yu Musatov, V.V. Sysoev, M. Sommer, I. Kiselev, Assessment of meat freshness with metal oxide sensor microarray Electronic Nose: a practical approach, Sensors and Actuators B: Chemical 144 (2010) 99–103. [17] T. Khanna, Foundations of neural networks, Addison-Wesley Publ, USA, 1990.
Biographies Artem A. Maschenko received his Master of Science degree in Systems of artificial intelligence from Gagarin State University (Russia) in 2008. Currently he is a PhD student at the same university. His scientific interests are pattern recognition algorithms, hardware and multisensor arrays. Vyacheslav Yu. Musatov is Docent in Artificial Intelligence Department in Gagarin State Technical University (GTU, Russia). He graduated from GTU in 1994 with Diploma of engineer for electrical and mechanical systems. In 2000 he was awarded with degree of Candidate in Engineering Sciences (Ph.D.). He worked few times as a visiting researcher in Karlsruhe Institute of Technology (Germany). His scientific interests are computer modeling of technical systems and data mining with artificial intelligence methods. Alexey S. Varezhnikov received his engineer degree in Systems of artificial intelligence from Gagarin State University (Russia) in 2008. Currently he is a PhD student at the same university. His scientific interests are multisensor arrays and pattern recognition algorithms. Ilia Kiselev received his diploma degree in physics from Moscow State University (Moscow, Russia) in 1978 with the thesis on carrier diffusion properties of semiconductor diodes and his PhD degree in physics and mathematics from Kyrgyz Science Academy (Bishkek, Kyrgyzstan) in 1992 with thesis on turbulent flows of plasma. In 2000 he joined gas sensor research group at Institute for Microstructure Techniques of Karlsruhe Institute of Technology (Germany) as a researcher. His main scientific interests include study of processes in the metal oxide structures and mathematical modeling. Martin Sommer received his diploma degree in physics from the Westfälische Wilhelmsuniversität Münster in 1992 with a thesis about the skipping motion of protons shot onto crystals under grazing angle of incidence. At the Forschungszentrum Karlsruhe (FZK, Germany), he elaborated his PhD thesis about the reduction of the matrix effect in mass spectrometry using the energy spectra of sputtered particles in 1996. Since 2008 he is the head of a group in the Institute for Microstructure Techniques of Karlsruhe Institute of Technology. His main scientific interest concerns fabrication and study of controllable nano-surfaces and functional nano-structures about their ability for sensor-actuator devices. Victor V. Sysoev received his Engineer-physicist degree in microelectronics and semiconductor devices from Saratov State University (SSU, Russia) in 1994, Candidate of Phys. & Math. Sc. degree (PhD) in physics of semiconductors and dielectrics from SSU in 1999, and Dr. of Sc. in Engineering from Gagarin State University in 2009. He worked many times as a visiting researcher in National Microelectronics Research Center (Cork, Ireland), Karlsruhe Institute of Technology (Karlsruhe, Germany) and Southern Illinois University (Carbondale, USA). Currently he holds a Professor position in Physics Department of Saratov State Technical University (Russia). He is author or coauthor of 2 monographs, 1 book chapter, 27 journal papers, and more than 60 conference presentations. His scientific interests are chemical sensors and multisensor arrays. He has been awarded with grants and scholarships from DAAD (Germany), INTAS (EU), Fulbright (USA) and Russian Federal Ministry for Higher Education.