High-Speed Airborne Particle Monitoring 
Using Artificial Neural Networks 
Alistair Ferguson 
ERDC, Univ. of Hertfordshire 
A.Ferguson@herts.ac.uk 
Theo Sabisch 
Dept. Electrical and Electronic Eng. 
Univ. of Hertfordshire 
Paul Kaye 
ERDC, Univ. of Hertfordshire 
Laurence C. Dixon 
NOC, Univ. of Hertfordshire 
Hamid Bolouri 
ERDC, Univ. of Hertfordshire, Herts, ALl0 9AB, UK 
Abstract 
Current environmental monitoring systems assume particles to be 
spherical, and do not attempt to classify them. A laser-based sys- 
tem developed at the University of Hertfordshire aims at classify- 
ing airborne particles through the generation of two-dimensional 
scattering profiles. The performances of template matching, and 
two types of neural network (HyperNet and semi-linear units) are 
compared for image classification. The neural network approach is 
shown to be capable of comparable recognition performance, while 
offering a number of advantages over template matching. 
I Introduction 
Reliable identification of low concentrations of airborne particles requires high speed 
monitoring of large volumes of air, and incurs heavy computational overheads. An 
instrument to detect particle shape and size from spatial light scattering profiles has 
High-speed Airborne Particle Monitoring Using Artificial Neural Networks 981 
previously been described [6]. The system constrains individual particles to traverse 
a laser beam. Thus, spatial distributions of the light scattered by individual particles 
may be recorded as two dimensional grey-scale images. 
Due to their highly distributed nature, Artificial Neural Networks (ANNs) offer the 
possibility of high-speed non-linear pattern classification. Their use in particulate 
classification has already been investigated. The work by Kohlus [7] used contour 
data extracted from microscopic images of particles, and so was not real-time. While 
using laser scattering data to allow real-time analysis, Bevan [2] used only three 
photomultipliers, from which very little shape information can be collected. 
This paper demonstrates the plausibility of particle classification based on shape 
recognition using an ANN. While capable of similar recognition rates, the neural 
networks are shown to offer a number of advantages over template matching. 
2 The HyperNet Architecture 
HyperNet is the term used to denote the hardware model of a RAM-based sigma-pi 
neural architecture developed by Gurney [5]. The architecture is similar in nature 
to the pRAM of Gorse and Taylor (references in [4]). The amenability of these 
nodes to hardware realisation has been extensively investigated, leading to custom 
VLSI implementations of both nodes [3, 4]. Each HyperNet node is termed a multi- 
cube unit (MCU), and consists of a number of subunits, each with an arbitrary 
number of inputs. j references the nodes, with i - 1,..., I 1 indexing the subunits. 
/ denotes the site addresses, and is the set of bit strings/1,.. ,/, where n denotes 
the number of inputs to the subunit. zc refers to the c th real-valued input, with 
Zc E [0,1] and c -- (1 - Zc). For each of the 2" site store locations, two sets are 
defined: c E Mff0 if/c = 0; c  MJl if/c = 1. The access probability P(l ij) for 
location/ in subunit i of hidden layer node j is therefore 
P(lii)- H  H Zc (1) 
The activation (a i) is formed by accumulating the proportional site values (S,,j) 
from every subunit. The activation is then passed through a sigmoidal transfer 
function to yield the node output 
1 
YJ = tr(aJ) -- 1 + e "i/o (3) 
where p is a positive parameter determining the steepness of the sigmoidal curve. 
By combining equations (1) and (2), it becomes apparent that the node is a higher- 
order or sigma-pi node [9]. A wide variety of learning algorithms have been tailored 
for these nodes, notably reward-penalty and back-propagation [5]. 
982 A. FERGUSON, T. SABISCH, P. KAYE, L. C. DIXON, H. BOLOURI 
3 Description of the Particle Monitoring System 
The instrument draws air through the laser scattering chamber at approximately 
1.5 min -, and is constrained to a column of approximately 0.8mm diameter at the 
intersection with the laser beam. Light scattered into angles between 30  and 141  
to the beam direction is reflected through the optics and onto the photocathode of 
an intensified CCD (charge-coupled device), thus giving rise to the scattering profile. 
The imaging device used has a pixel resolution of 385 x 288, which is quantised into 
2562 8-bit pixels by the frame grabbing processor card of the host computer. 
Data was collected on eight particle types, namely: long and short caffeine fibres; 
3pm and 12pm micro-machined silicon dioxide fibres; copper flakes (2-5pm in length 
and 0.1pm thick); 3pm and 4.3pm polystyrene spheres; and salt crystals. An exem- 
plar profile for each class is given in figure 1. Almost all the image types are highly 
variable. In particular, the scattering profile obtained for a fibrous particle is af- 
fected by its orientation as it passes through the laser beam. The scattering profiles 
are intrinsically centred, with the scaling giving important information regarding 
the size of the particle. The experiments reported here use 100 example scattering 
profries for each of the eight particle classes. For each class, 50 randomly selected 
images were used to construct the templates or train the neural network (training 
set), and the remainder used to test the performance of the pattern classifiers. 
4 Experimental Results 
The performance of template matching is compared to both HyperNet and networks 
of semi-linear units. In all experiments, high-speed classification is emphasised by 
Figure 1: Exemplar Image Profile For Each Of The Eight Benchmark Classes 
High-speed Airborne Particle Monitoring Using Artificial Neural Networks 983 
avoiding image preprocessing operations such as transformation to the frequency 
domain, histogram equalisation, and other filtering operations. Furthermore, all 
experiments use the scatter profile image as input, and include no other information. 
The current monitoring system produces a 2562 8-bit pixel image. The sensitivity of 
the camera is such that a single pixel can represent the registration of a single photon 
of light. Two possible methods of reducing computation, implementable through 
the use of a cheaper, less sensitive camera were investigated. The first grouped 
neighbouring pixels to form a single average intensity value. The neighbourhood 
size was restricted to powers of two, producing images ranging in size from 2562 to 
42 pixels. The second banded grey levels into groups, again in powers of two. Each 
pixel could therefore range from eight bits down to one. 
4.1 Template Matching Results 
The construction of reference templates is crucial to successful classification. Two 
approaches to template construction were investigated 
(J) Single reference image for each class. Various techniques were applied rang- 
ing from individual images, to mode, median, and mean averaged templates. 
Mean averaged templates were found to lead to the highest classification 
rates. In this approach, each pixel location in the template takes on the 
averaged value of that location across the 50 training images. 
 Multiple templates per class. A K-means clustering algorithm [1] was used 
to identify clusters of highly correlated images within each class. The initial 
cluster centres were hand selected. The maximum number of clusters within 
each class was limited to six. For each cluster, the reference template was 
constructed using the mean averaging approach above. 
Tables I and 2 summarise the recognition rates achieved using single, and multiple 
mean averaged templates for each particle class. In both cases, the best average 
recognition rate using this approach was gained with 1282 3-bit pixel images. With 
a single template this lead to a recognition rate of 78.2%, increasing to 85.2% for 
multiple templates. However, the results for both 162 and 82 pixel images are 
reasonable approximations of the best performance, and represent an acceptable 
trade-off between computational cost and performance. With few exceptions, mul- 
tiple templates per class led to higher recognition rates than for the corresponding 
single template results. This is attributable to the variability of the particles within 
a class. As expected, the effect of grey level quantisation is inversely proportional 
to that of local averaging. 
In order to evaluate the efficiency of the template construction methods, every image 
in the training set was used as a reference template. 2562 8-bit, 1282 3-bit, and 642 
2-bit pixel images were used for these experiments. However, the recognition rate 
did not exceed 85%, demonstrating the success of the template generation schemes 
previously employed. 
984 A. FERGUSON, T. SABISCH, P. KAYE, L. C. DIXON, H. BOLOURI 
Table 1: Single Template Per Class % Recognition Rates 
grey levels image size 
2562 12821 642 322 162 I 82 42 
256 73.5 75.0 74.7 74.7 74.7 75.0 67.2 
128 73.5 75.0 74.7 74.7 74.5 75.0 68.5 
64 73.0 75.0 74.5 74.5 74.2 74.7 66.2 
32 73.0 74.7 75.2 75.5 74.7 74.2 66.5 
16 74.0 76.0 76.7 76.0 75.0 75.5 56.0 
8 7'5.5 7'8.2 7'7.5 7'7.5 7'6.0 73.7 38.7 
4 68.4 69.7 71.0 70.7 69.7 58.5 18.7 
2 69.7 68.7 65.5 66.2 46.2 23.0 16.6 
Table 2: Multiple Templates Per Class % Recognition Rates 
grey levels image size 
2562 112s 2 [ 642 322 162 ] 82 42 
256 78.0 80.0 80.2 80.5 79.0 76.7 7'0.2 
128 78.5 80.2 80.5 80.5 79.0 77.0 69.7 
64 78.7 80.2 80.2 80.5 79.2 76.0 69.2 
8 82.2 85.2 84.5 84.7 81.0 80.0 43.5 
4 72.7 74.5 72.2 72.2 69.5 61.2 39.2 
2 69.7 70.2 70.7 62.7 51.7 51.7 0.03 
4.2 Neural Network Results 
A fully connected three layer feed-forward network was used in all experiments. The 
number of hidden layer neurons was equal to the square root of the number of pixels. 
The target patterns were chosen to minimise the number of output layer nodes, while 
ensuring an equitable distribution of zeros and ones. Six output layer neurons were 
used to give a minimum Hamming distance of two between target patterns. The 
classification of a pattern was judged to be the particle class whose target pattern 
was closest (lowest difference error). The HyperNet architecture was trained using 
steepest descent, though the line search was hardware based and inexact. The semi- 
linear network was trained using a variety of back-propagation type algorithms, with 
the best results obtained reported. Both networks were randomly initialised. Due 
to the enormous training overhead, only 162 and 82 pixel images were tried. The 
recognition rates achieved are given in table 3. 
Both neural networks are significantly better than the single, and some of the mul- 
tiple template matching results. With optimisation of the network structures, it is 
likely that the ANNs could exceed the performance of multiple templates. 
High-speed Airborne Particle Monitoring Using Artificial Neural Networks 985 
Table 3: Neural Network % Recognition Rates 
Quantisation Levels 
Classifier 
1624 bit I 162 3-bit 182 4-bit 182 3obit 
HyperNet 83.8 82.3 83.0 76.8 
Semi-linear 84.5 86.3 77.8 76.0 
le+09 
 le+08 
1+07 
le+06 
le+05 . - ' ' Template matching 
- ' HyperNet --- 
Semi-linear network - - - 
le+04 I I I I 
82 162 322 642 1282 2562 
Number of pixels 
Figure 2: Hardware classification speeds for a single pattern against image size 
5 Speed Considerations 
Single processor, pipelined hardware implementations of the three classification 
techniques have been considered. A fast (45ns) multiply-accumulate chip (Logic 
Devices Ltd, LMA2010) was utilised for semi-linear units. Both template matching 
and HyperNet were implemented using the Logic Devices LGC381 ALU (26ns per 
accumulate). The cost of these devices is approximately the same (10-20). The 
HyperNet implementation uses a bit-stream approach to eliminate the probability 
multiplications [8], with a stream length of 256 bits. Figure 2 plots single pattern 
processing time for each classifier against image size. 
For small image resolutions, the semi-linear network offers the best performmace, be- 
ing almost three times faster than template matching. However, template matching 
and HyperNet yield faster performmace at higher image resolutions. At the op- 
timum (indicated by template matching results (4.1); 1282 pixels), HyperNet is 
almost seven times faster than the comparable implementation of semi-linear units. 
While the hardware performance of template matching is similar to HyperNet, it 
suffers from a number of disadvantages to which the neural approaches are immune 
Recognition rate is dependent on the choice of reference images. 
Multiple reference images must be used to achieve good recognition rates 
986 A. FERGUSON, T. SABISCH, P. KAYE, L. C. DIXON, H. BOLOURI 
which drastically increases the amount of computation required. 
New reference images must be found whenever a new class is introduced. 
Difficult to make behaviour adaptive, ie. respond to changing conditions. 
6 Conclusions 
The feasibility of constructing an airborne particle monitoring system capable of 
reliable particle identification at high speeds has been demonstrated. Template 
matching requires multiple reference images and is cumbersome to develop. The 
neural networks offer easier training procedures and equivalent recognition rates. In 
addition, HyperNet has the advantage of high speed operation at large image sizes. 
Acknowledgements 
The authors would like to thank Dr. Eric Dykes and Dr. Edwin Hirst at the Uni- 
versity of Hertfordshire, Dr. Kevin Gurney at Brunel University, and the EPSRC 
and the Royal Society for financial support. 
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[5] Kevin N Gurney. Learning in networks of structured hypercubes. PhD thesis, 
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[6] Paul H Kaye et al. Airborne particle shape and size classification from spatial 
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PART IX 
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