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1

Decentralized control of traffic signals with priority for ambulances

Lewandowski M., Płaczek B., Bernas M.

Journal of Medical Informatics & Technologies

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2017

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Vol. 26

9--17

EN

In this paper delays and average travel times of vehicles are analyzed for various decentralized traffic control algorithms that can provide priority for ambulances. Decentralized control strategy is scalable and can be used in road networks where traffic lights are controlled autonomously for multiple intersections of different types. The experiments were performed in a realistic simulation model of complex road network, which is typical for European cities. It was shown that utilization of detailed traffic data from vehicular sensor network significantly improves the performance of signal control algorithms. After proper selection of algorithm parameters, the decentralized control strategy not only provides a quick transition of ambulances, but also has minimal effect on the delay of non-priority vehicles. Research for mesh road network organization has been performed in previous work [16].

2

The visual detection and steering of medical autonomous vehicles

Bernas M.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

133--140

EN

The paper presents initial research on method, which improves precise indoor localization and steering of autonomous mobile devices that can be used for medical applications like: patient’s state monitoring, medicine distribution or environmental data collection before medical intervention (in case of biohazard or ﬁre). The localization of object is based on optical codes, which are modified to be easily identified from distance in low light. Multiple codes modification was tested to find optimal ones. The visual recognition system is using Hough transform and Canny edge detection to read values from code. The novelty of the proposed method is reading values directly from image, without scaling and rotation. Moreover, the steering algorithm for identified device is proposed. It takes distance and decision uncertainty under consideration. The proposed method was verified against state-of-the-art optical codes in real-world indoor environment. Finally, the further research directions are discussed.

3

ANN as justified granular computing mechanism for medical data classification

Bernas M.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

85--90

EN

The medical data and its classification have to be treated in particular way. The data should not be modified or altered, because this could lead to false decisions. Most state-of-the-art classifiers are using random factors to produce higher overall accuracy of diagnosis, however the stability of classification can vary significantly. Medical support systems should be trustworthy and reliable, therefore this paper proposes fusion of multiple classifiers based on artificial Neural Network (ANN). The structure selection of ANN is performed using granular paradigm, where granulation level is defined by ANN complexity. The classification results are merged using voting procedure. Accuracy of the proposed solution was compared with state-of-the-art classifiers using real medical data coming from two medical datasets. Finally, some remarks and further research directions have been discussed.

4

Separation of groups of free radicals from noised EPR spectrum using genetic algorithm and gradient method

Bernas M., Ramos P.

Journal of Medical Informatics & Technologies

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2013

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Vol. 22

117--123

EN

Different groups of free radicals exist in biological material like animal tissues or plants parts. The processes like heating or cooling creates additional types of free radicals groups in this organic matter, due to changes in chemical bonds. The paper proposes a method to determine types and concentrations of different groups of free radicals in the matter processed in various temperatures. The method extracts the spectrum of free radicals using electron paramagnetic resonance with the microwave power of 2.2 mW. Then an automatic method to find a best possible fit using limited number of theoretical mathematical functions is proposed. The match is found using spectrum filtration, and a genetic algorithm implementation supported by a Gradient Method. The obtained results were compared against the samples prepared by an expert. Finally, some remarks were given and new possibilities for future research were proposed.

5

Quantum road traffic model for ambulance travel time estimation

Bernas M., Wisniewska J.

Journal of Medical Informatics & Technologies

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2013

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Vol. 22

257--264

EN

Efficient management of ambulance utilisation is a vital issue for life saving. Knowledge of the amount of time needed for an ambulance to get to the hospital and when it will be available for a new task, can be estimated using modern Intelligent Transport Systems. Their main feature is an ability to simulate the state of traffic not only in long term, but also the real time events like accidents or high congestion, using microscopic models. The paper introduces usage of Quantum Computing paradigm to propose a quantum model of road traffic, which can track the state of traffic and estimate the travel time of vehicles. Model, if run on quantum computer can simulate the traffic in vast areas in real time. Proposed model was verified against the cellular automata model. Finally, application of quantum microscopic traffic models for ambulance vehicles was taken into consideration.