13 Signal and image processing

Goals

These subtle defects, e.g. blow wrinkles and ruptures, are very harmful to paper maker causing breaks at winders, supercalanders or, in the worst case, in the printing house. The early identification of them decreases the down time of paper machines and prevents returns of paper shipments from printing houses.

Results

The test pilot system (Figure 1) was used to verify results with actual samples from paper mills.

(Figure is missing, sorry.)

Figure 1. Test pilot system with rotating cylinder, light source and signal processing unit.

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13.2 Dynamic load and weight measuring system

Goals

• To measure loads of moving objects even at speeds of up to 110 km/h (approx. 70 miles/h).
• To create, with the help of neural computing, a classifier that recognizes overpassing vehicles or items.

Results

• During this project we have developed software and solutions using neural computing in cooperation with Prof. Lampinen.
• Neural computing makes it possible to carry out a training event that helps you to find out, for example, the weight of a vehicle or material flow.
• Training software operates extremely well even at very high speeds.
• We have developed a completely new generation of measuring system.
• As an example, the WIM scale measures the weight and classifies the type of overpassing vehicle.
• Neural computing has given us a great technical advantage over our competitors, but commercially we still have some problems due to a lack of references.
• We have a lot of new cooperation and buying offers, but the latter still hesitate due the above reason.
• The project with the classifier has not yet finished.

Figure 1. Installing the WIM scale in Luleå, Sweden.

Methods

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Participants

• Oy Omni Weight Control Ltd
• Lappeenranta University of Technology
• Helsinki University of Technology
• University of Jyväskylä

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13.3 European licence plates and optical character recognition

Goals

Results and impacts

We also discovered the power of nature - rain, storms, sunlight, fog, mud, snow and dust. We knew that we would face these problems when operating under the clear sky at the Russian border. Using a slightly bit different technique these problem were resolved.

Now we have the keys to reliable operational system for customers all over the Europe.

Methods

This also means that we must have a lot of training material. We collected the material from across Europe, which was very expensive and demanded a lot of work. We also got test material at Vaalimaa (a splendid place to collect Russian plates). Because we had sufficient test material, we were able to repair the systematic bugs of the OCR engine.

The advantage of the Visy system is that we can add as many countries as needed very easily.

We also tested the use of different types of lights and cameras for achieving better results.

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Participants

All algorithms were developed and implemented by Visy Oy.

The advice of local authorities of the Finnish Customs was very useful when developing the features of the user interface and functions of the program.

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13.4 Intelligent alarm management for mobile telecommunication networks

Goals and results

In Sonera’s NeuroFMS project we focused on developing an intelligent fault management system to operate in a multivendor cellular network. The result of the project was an entirely new fault management environment. This environment includes an intelligent diagnosis part that utilises e.g. traffic measurement information and experiential knowledge.

Figure 1. NeuroFMS architecture.

Sonera is utilising Gensym's G2 Operations Expert (OpEx) software as the basis for the system. The NeuroFMS system includes four main functional parts (Figure 1): a diagnosis part (IDS), a performance measurement analyser (PMA), a Java based user interface application and an interface layer which adapts data for the system. The topology of a network has been modelled for the diagnosis system so that correlation and alarm filtering can be carried out based on the model. Analysis and diagnosis are implemented in IDS by graphical procedures that can be easily maintained and modified. The basic functions of IDS are described in Figure 2.

Figure 2. Some functions of IDS.

The diagnostic system receives alarms from the network elements as well as from the PMA module. The alarms from the PMA module can be correlated with alarms that are generated by network elements. NeuroFMS system can also actively carry out maintenance tasks on network elements, such as testing and resetting BTS units, or forward alarms automatically to trouble ticketing systems without manual intervention.

Figure 3. PMA system.

The PMA system carries out pre-processing of performance measurements collected from BSS. The PMA system looks for symptoms of decreased service grade and pinpoints problems for the operator. The use of fuzzy rules makes the system more adaptable.

The NeuroFMS system supports centralised as well as distributed approaches to organise network management processes. Flexibility of the system is achieved by means of Java and Corba interfaces. We have gathered into NeuroFMS system the most useful features which operators need in daily network supervising tasks.

Mobile networks are very dynamic, because plenty of new services and standards will be introduced with each passing year. Hence, we have had to take into account the adaptability of the NeuroFMS system, otherwise our application would be useless after a while. One interesting new application area could be monitoring the fulfilment of service level agreements between service providers and customers. At the moment, the NeuroFMS system needs some improvements to fulfil the requirements set for a SLA monitoring system.

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13.5 Knowledge-based alarm processing and fault diagnostics in GSM networks

Goals

The GSM mobile telephone network is a complex system that includes mobile telephone exchanges, base station controllers, a great number of base stations and transmission facilities connecting all of these. The operation and maintenance of the network is centralized in a network management system (NMS). The NMS collects fault information (alarms) from the network elements, for instance. Quite often a single fault in the network causes an alarm burst, and it is difficult to distinguish the actual fault from secondary alarms. For example, disturbances in a transmission line may cause repeated alarm bursts from transmission equipment and all base stations and base station controllers that are using the services of that transmission line.

Alarm processing was improved in the project by employing knowledge-based techniques. Improvements are designed for base stations and network management system.

The goals of the project were:

• to separate primary faults from secondary alarms, and to display only primary faults to the O&M personnel,
• to simplify the view of the network for the operator´s O&M personnel, e.g. concealing the differences in base station configurations that are not relevant to the fault situation.

Results and impacts

Methods

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Participants

• Nokia Telecommunications
• VTT Electronics

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13.6 Measuring and recognising colours under varying outdoor conditions by a trainable system based on a CCD colour video camera and PC

Goals

The system can be used for colour measurement and recognition in different applications. Installation is possible without tailoring the program code by simply adjusting parameters and teaching the application specific colours.

The pilot application was recognition of the colours of shipping and transportation containers.

The system can measure a restricted set of colours reliably under outdoor lighting conditions varying with season, weather and time of day.

Results and impacts

The system can be used for different applications and is now one part of Visy Oy machine vision “toolkit”. At the time of writing, the applicability of the colour measurement subsystem to a new application is being evaluated by Visy. If the system is found to be suitable, then an object oriented C++ implementation will be carried out.

Methods

The colour measurement is based on calibration of the colour information contained in digital images captured from a colour PAL composite-video signal. Calibration is performed by using a reference white. A physical white reference board is positioned so as to be visible in a constant location in the images, typically in one specific corner. The calibration algorithm produces data values for reference white from the image data for the area of the calibration board by image processing and analysis.

A robust method, not sensitive to noise, is used to extract colour features inside a specific region of interest. The extracted colour features are then transformed into calibrated features by using the data values of reference white.

The calibrated colour features form a colour sample. The features are fed into a multilayer perceptron (MLP) based neural network classifier which outputs the colour (class) in which the input colour sample belongs. The network also outputs a classification confidence value.

The implementation of the neural network comprises two main parts, the classifier network and a “teacher”. The number of layers in a network and the number of nodes in each layer can be set by parameters. The teacher creates a binary neural network configuration file describing the neural net. An application reads in the  neural network configuration file when initialising recognition functionality. The very same neural network implementation is also applied to optical character recognition (OCR) by Visy.

Project information

Participants

The algorithms were developed and implemented by Visy Oy and ATE Oy. ATE worked as a supplier for Visy.

ATE developed the colour calibration and measuring algorithms and created the preliminary C-program language implementations. ATE also tested the colour measuring system with a simple decision tree classifier capable of separating a restricted set of known container colours.

Visy enhanced the preliminary implementation of colour measuring system by ATE to conform to Visy software development conventions. Visy merged the colour measuring system software and an earlier artificial neural network software implemented by Visy to create a learning system for colour recognition. Adjustment of parameters and system testing was carried out by Visy.

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13.7 Navigating wheelchair

Goals

Methods

Results

Impacts

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Participants

• The Finnish Association for the Disabled
• VTT Automation

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More information

Pertti Peussa, Ari Virtanen, and Tiina Johansson, "Improving the Mobility of Severely Disabled". in Proc. The 2nd European Conference on Disability, Virtual Reality and Associated Technologies, Skövde, Sweden, Sep. 10-11, 1998, pp. 169-176.

Pertti Peussa and Ari Virtanen, "A General Purpose Obstacle Avoidance System". in Proc. 2nd Tampere International Conference on Machine Automation (ICMA '98), Tampere, Finland, Sep. 15-18, 1998, pp. 211-220.

Pertti Peussa and Ari Virtanen, "Make your wheelchair autonomous". Automation Technology Review 1998, VTT Automation, Helsinki, Dec. 1998, pp. 18-22.

(Figure is missing, sorry.)

Figure 1. The navigating wheelchair is being tested by the Finnish Association for the Disabled.

13.8 Neural algorithms for radio communication systems

Goals

Results and impacts

Studies on neural detection methods in difficult environments led to conference papers, one journal article and finally to one doctoral thesis. These have increased Nokia’s visibility in scientific forums and will indirectly pave way for Nokia products onto the markets.

Methods

The studies on detection in a hostile environment concentrated on using self-organizing networks (SOMs). The main finding was that neural methods may provide improved performance in detection problems where nonlinearities are involved. The performance comparisons were made using computer simulations.

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Publications

Kimmo Raivio, Ari Hämäläinen, Jukka Henriksson and Olli Simula: Performance of Two Neural Receiver Structures in the Presence of Co-Channel Interference. In Proceedings of the ICNN, volume 4, pp. 2080-2084, Houston, Texas, USA, June 9 – 12, 1997.

Kimmo Raivio, Jukka Henriksson and Olli Simula: Neural receiver structures based on Self-Organizing Maps in nonlinear multipath channels. In Proceedings of the IWANNT, pp. 241-247, Melbourne, Australia, June 9 – 11, 1997.

Kimmo Raivio, Jukka Henriksson and Olli Simula: Neural detection of QAM signal with strongly nonlinear receiver.  Neurocomputing (21) 1-3 (1998), pp. 159-171.

Ari Hämäläinen, Jukka Henriksson: A Recurrent Neural Decoder for Convolution Codes. To be published in Proceedings of  ICC'99, S 33.5. Vancouver, June 6-10, 1999.

A Hämäläinen and J Henriksson: Menetelmä ja järjestely konvoluutiokoodauksen toteuttamiseksi. Patenttihakemus FI-981168. 26.6.1998. (In Finnish.)

Kimmo Raivio: Receiver Structures Based on Self-Organizing Maps. Ph.D. thesis, Helsinki University of Technology, January 1999. Acta Polytechnica Scandinavica, Mathematics, Computing and Management in Engineering Series No. 96.

Figure 1. An example of a neural decoder for a convolutional code.

jukka.iivarinen@hut.fi
http://www.cis.hut.fi/neuronet/Tekes/13.shtml
Wednesday, 29-Nov-2000 10:39:42 EET