Wyniki wyszukiwania - Biblioteka Nauki

1

History aware Device-free Passive (DFP) Localisation

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Deak G. , Curran K. , Condell J.

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tom Vol. 16, no 3-4

21-30

EN

A novel Device-free Passive (DfP) Localisation technique that monitors wireless communications and location dependent signal characteristics is presented in this paper. The human body contains more than 70% water which is causing variances in the Received Signal Strength Indicator (RSSI) measurements. DfP is a technique to detect a person without the need for any physical devices i.e. tags or sensors. This paper focuses on communication protocols such as Radiogram Protocol, Transmission Control Protocol (TCP), and User Datagram Protocol (UDP), outlining the possibility of using these protocols in Wireless Sensor Networks (WSNs). Histograms and historical data are new concepts in a DfP scenario which can improve the accuracy of location estimation in DfP Localisation.

2

Filters for RSSI-based measurements in a device-free passive localisation scenario

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Deak G. , Curran K. , Condell J.

Image Processing & Communications

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2010

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tom Vol. 15, no 1

23-34

EN

There are a number of techniques used in modern Location aware systems such as Received Signal Strength Indicator (RSSI), Time of Arrival (TOA), Time Difference of Arrival (TDOA) and Angle of Arrival (AOA). However the benefit of RSSI-based location positioning technologies, is the possibility to develop location estimation systems without the need for specialised hardware. The human body contains more than 70% water which is causing changes in the RSSI measurements. It is known that the resonance frequency of the water is 2.4 GHz. Thus a human presence in an indoor environment attenuates the wireless signal. Device-free Passive (DfP) localisation is a technique to detect a person without the need for any physical devices i.e. tags or sensors. A DfP Localisation system uses the Received Signal Strength Indicator (RSSI) for monitoring and tracking changes in a Wireless Network infrastructure. The changes in the signal along with prior fingerprinting of a physical location allow identification of a person's location. This research is focused on implementing DfP Localisation built using a Wireless Sensor Network (WSN). The aim of this paper is the evaluation of various smoothing algorithms for the RSSI recorded in a Device-free Passive (DfP) Localisation scenario in order to find an algorithm that generates the best output. The best output is referred to here as results that can help us decide if a person entered the monitored environment. The DfP scenario considered in this paper is based on monitoring the changes in the wireless communications due to the presence of a human body in the environment. Thus to have a clear image of the changes caused by human presence indoors, the wireless recordings need to be smoothed. The following algorithms are demonstrated with results: five-point Triangular Smoothing Algorithm, Moving Average filter, Lowess filter, Loess filter, Rlowess filter, Rloess filter, 1-D median filter, Savittzky-Golay filter, and Kalman filter.

3

Support Vector Machine and Probability Neural Networks in a Device-Free Passive Localization (DFPL) Scenario

88%

Deak G. , Curran K. , Condell J. , Deak D. , Kiedrowski P.

Image Processing & Communications

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2012

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tom Vol. 17, no. 4

9--16

EN

The holy grail of tracking people indoors is being able to locate them when they are not carrying any wireless tracking devices. The aim is to be able to track people just through their physical body interfering with a standard wireless network that would be in most peoples home. The human body contains about 70% water which attenuates the wireless signal reacting as an absorber. The changes in the signal along with prior fingerprinting of a physical location allow identification of a person’s location. This paper is focused on taking the principle of Device-free Passive Localisation (DfPL) and applying it to be able to actually distinguish if there is more than one person in the environment. In order to solve this problem, we tested a Support Vector Machine (SVM) classifier with kernel functions such as Linear, Quadratic, Polynomial, Gaussian Radial Basis Function (RBF) and Multilayer Perceptron (MLP), and a Probabilistic Neural Network (PNN) in order to detect movement based on changes in the wireless signal strength.

4

The expression of erythropoietin and its regulation during the postnatal development of the rat retina

75%

Szabo A. , Vegvari D. , Magyar A. , Deak G. , Lukats A. , Berta A. , Szel A.

Acta Neurobiologiae Experimentalis

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2009

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tom 69

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nr 3

EN

In the present study, we examined developing postnatal and adult rat retinas by immunohistochemistry and western blot in order to identify the temporal and spatial onset of the expression of EPO and its presumptive regulating factors (HIF1-α and HIF2-α). In the adult the EPO was expressed in the photoreceptor inner segments, in the inner nuclear, ganglion (GCL), and both plexiform layers. At birth, we found strong immunoreactivities in the GCL and in the inner part of the neuroblast layer and weak staining in the outer part of the neuroblast layer. During the following days the immunoreactivity of the ganglion cells and presumptive amacrine cells became more prominent. From the middle of the fi rst week strongly stained horizontal cells were observed at the level of the separating OPL. The WB analysis resulted high protein levels in the developing retina samples and low protein amounts in the adult. Both hypoxia-induced factors showed similar expression pattern, being expressed in the inner retinal layers. At birth, only the HIF1-α was detectable. The HIF2-α was not detectable until the second half of the second postnatal week and its expression remained weak. The early appearance and distribution of the erythropoietin correlates in time with the main apoptotic processes of the postanatal rat retina. Since the EPO exerts neuroprotective properties in neural tissue, we hypothesize that it acts as a regulating factor during the retinal development.