Marcin Bernas

Application of Electron Paramagnetic Resonance Spectroscopy to Comparative Examination of Different Groups of Free Radicals in Thermal Injuries Treated with Propolis and Silver Sulphadiazine

Different groups of free radicals expressed in burn wounds treated with propolis and silver sulphadiazine were examined. The thermal effect forms major types of free radicals in a wound because of the breaking of chemical bonds. Free radicals, located in the heated skin, were tested after 21 days of treating by these two substances. The aim of this work was to find the method for determination of types and concentrations of different groups of free radicals in wound after high temperature impact during burning. The effects of the therapy by propolis and silver sulphadiazine on free radicals were studied. Since the chemical methods of free radicals studies are destructive, the usefulness of the electron paramagnetic resonance spectroscopy was tested in this work. The electron paramagnetic resonance spectra measured with the microwave power of 2.2 mW were numerically fitted by theoretical curves of Gaussian and Lorentzian shapes. The experimental electron paramagnetic resonance spectra of tissue samples are best fitted by the sum of one Gauss and two Lorentz lines. An innovatory numerical procedure of spectroscopic skin analysis was presented. It is very useful in the alternative medicine studies.

Energy Aware Object Localization in Wireless Sensor Network Based on Wi-Fi Fingerprinting

The usage of GPS systems for indoor localization is limited, therefore multiple indirect localization techniques were proposed over the years. One of them is a localization method based on Wi-Fi (802.11) access point (AP) signal strength (RSSI) measurement. In this method, a RSSI map is constructed via Localization Fingerprinting (LF), which allows localizing object on the basis of a pattern similarity. The drawback of LF method is the need to create the RSSI map that is used as a training dataset. Therefore, in this study a Wireless Sensor Network (WSN) is used for this task. The introduced in this paper energy aware localization method allows to acquire the actual RSSI map or broadcast a localization signal, if there is not sufficient information to perform the localization by using nearby APs. To localize objects in a given cell, various classifiers were used and their localization accuracy was analyzed. Simulations were performed to compare the introduced solution with a state-of-the-art approach. The experimental results show that the proposed energy aware method extends the lifetime of WSN and improves the localization accuracy.

Microwave Saturation of Complex EPR Spectra and Free Radicals of Burnt Skin Treated with Apitherapeutic Agent

The effect of microwave power on the complex electron paramagnetic resonance spectra of the burn matrix after the therapy with propolis was examined. The spectra were measured with microwaves in the range of 2.2–79 mW. Three groups of free radicals were found in the damaged skin samples. Their spectral lines evolve differently with the microwave power. In order to detect these free radical groups, the lineshape of the spectra was numerically analysed. The spectra were a superposition of three component lines. The best fit was obtained for the deconvolution of the experimental spectra into one Gauss and two Lorentz lines. The microwave power changes also the lineshape of the spectra of thermally injured skin treated with the conventional agent—silver sulphadiazine. The spectral changes were different for propolis and for silver sulphadiazine. The number of individual groups of free radicals in the wound bed after implementation of these two substances is not equal. It may be explained by a higher activity of propolis than of silver sulphadiazine as therapeutic agents.

Period-aware local modelling and data selection for time series prediction

The introduced algorithm selects useful data for improved training of local models.A hybrid usefulness-related distance is proposed for training data selection.Data usefulness is evaluated by taking into account periodicity of time series.Autocorrelation function and Renyi entropy is used to reduce number of parameters.The proposed method offers lower prediction error than the state-of-the-art local and global models. The paper tackles with local models (LM) for periodical time series (TS) prediction. A novel prediction method is introduced, which achieves high prediction accuracy by extracting relevant data from historical TS for LMs training. According to the proposed method, the period of TS is determined by using autocorrelation function and moving average filter. A segment of relevant historical data is determined for each time step of the TS period. The data for LMs training are selected on the basis of the k-nearest neighbours approach with a new hybrid usefulness-related distance. The proposed definition of hybrid distance takes into account usefulness of data for making predictions at a given time step. During the training procedure, only the most informative lags are taken into account. The number of most informative lags is determined in accordance with the Kraskovs mutual information criteria. The proposed approach enables effective applications of various machine learning (ML) techniques for prediction making in expert and intelligent systems. Effectiveness of this approach was experimentally verified for three popular ML methods: neural network, support vector machine, and adaptive neuro-fuzzy inference system. The complexity of LMs was reduced by TS preprocessing and informative lags selection. Experiments on synthetic and real-world datasets, covering various application areas, confirm that the proposed period aware method can give better prediction accuracy than state-of-the-art global models and LMs. Moreover, the data selection reduces the size of training dataset. Hence, the LMs can be trained in a shorter time.

Fully connected neural networks ensemble with signal strength clustering for indoor localization in wireless sensor networks

The paper introduces a method which improves localization accuracy of the signal strength fingerprinting approach. According to the proposed method, entire localization area is divided into regions by clustering the fingerprint database. For each region a prototype of the received signal strength is determined and a dedicated artificial neural network (ANN) is trained by using only those fingerprints that belong to this region (cluster). Final estimation of the location is obtained by fusion of the coordinates delivered by selected ANNs. Sensor nodes have to store only the signal strength prototypes and synaptic weights of the ANNs in order to estimate their locations. This approach significantly reduces the amount of memory required to store a received signal strength map. Various ANN topologies were considered in this study. Improvement of the localization accuracy as well as speedup of learning process was achieved by employing fully connected neural networks. The proposed method was verified and compared against state-of-the-art localization approaches in real world indoor environment by using both stationary and mobile sensor nodes.

Fusion of Granular Computing and k–NN Classifiers for Medical Data Support System

The medical data and its classification should be particularly treated. The data can not be modified or altered, because this could lead to overestimation or false decisions. Some classifiers, using random factors, can generate false, higher overall accuracy of diagnosis. Medical support systems should be trustworthy and reliable even at the cost of system complexity. In this paper fusion of two classifiers has been proposed, where k–NN classifier and classifier based on a justified granulation paradigm were employed. Additionally, proposed solution allows to visualize obtained classification results. Accuracy of the proposed solution has been compared with various classifiers. All methods presented in this work were tested on real medical data coming from three medical datasets. Finally, some remarks for further research have been proposed.

Data Suppression Algorithms for Surveillance Applications of Wireless Sensor and Actor Networks

This paper introduces algorithms for surveillance applications of wireless sensor and actor networks (WSANs) that reduce communication cost by suppressing unnecessary data transfers. The objective of the considered WSAN system is to capture and eliminate distributed targets in the shortest possible time. Computational experiments were performed to evaluate effectiveness of the proposed algorithms. The experimental results show that a considerable reduction of the communication costs together with a performance improvement of the WSAN system can be obtained by using the communication algorithms that are based on spatiotemporal and decision aware suppression methods.

Edge Real-Time Medical Data Segmentation for IoT Devices with Computational and Memory Constrains.

The Internet of Things (IoT) becomes very important tool for data gathering and management in many environments. The majority of dedicated solutions register data only at time of events, while in case of medical data full records for long time periods are usually needed. The precision of acquired data and the amount of data sent by sensor-equipped IoT devices has vital impact on lifetime of these devices. In case of solutions, where multiple sensors are available for single device with limited computation power and memory, the complex compression or transformation methods cannot be applied - especially in case of nano device injected to a body. Thus this paper is focused on linear complexity segmentation algorithms that can be used by the resource-limited devices. The state-of-art data segmentation methods are analysed and adapted for simple IoT devices. Two segmentation algorithms are proposed and tested on a real-world dataset collected from a prototype of the IoT device.

Zone-Based VANET Transmission Model for Traffic Signal Control

The rising number of vehicles and slowly growing transport infrastructure results in congestion issue. Congestion becomes an important research topic for transportation and control sciences. The recent advances in vehicular ad-hoc networks (VANETs) allow the traffic control to be tackled as a real-time problem. Recent research works have proven that the VANET technology can improve the traffic control at the intersections by dynamically changing sequences of traffic signals. Transmission of all vehicle positions data in real-time to a traffic lights controller can generate a significant burden on the communication network, thus this paper is focused on the reduction of data transmitted to a control unit by vehicles. The time interval between data transfers from vehicles is defined by zones that are tuned for a given traffic control strategy using the proposed algorithm. The introduced zone-based approach reduces the number of transmitted messages, while maintaining the quality of traffic signal control. The results of experiments firmly show that the proposed method can be successfully used for various state-of-art traffic control algorithms.

Self-organizing Traffic Signal Control with Prioritization Strategy Aided by Vehicular Sensor Network

Preemption strategies are necessary for traffic signal control at intersections in a road network to ensure minimum delay of priority vehicles, such as ambulances or police cars. This paper introduces a decentralized algorithm, which extends the self-organizing signal control to provide preemption for the priority vehicles. The introduced algorithm enables effective utilisation of real-time data collected in vehicular sensor network (VSN). Results of simulation experiments show that the proposed approach ensures a quick passage of the priority vehicles and minimizes the negative effect of signal preemption on delays of non-priority vehicles. The new VSN-aided preemption strategy improves performance of the state-of-the-art methods that are based on road-side vehicle detectors and simple vehicle-to-infrastructure communication systems.