3D Ray Tracing for device-independent fingerprint-based positioning in WLANs

We study the use of 3D Ray Tracing (RT) to construct radiomaps for WLAN Received Signal Strength (RSS) fingerprint-based positioning, in conjunction with calibration techniques to make the overall process device-independent. RSS data collection might be a tedious and time-consuming process and also the measured radiomap accuracy and applicability is subject to potential changes in the wireless environment. Therefore, RT becomes a more attractive and efficient way to generate radiomaps. Moreover, traditional fingerprint-based methods lead to radiomaps which are restricted to the device used to generate the radiomap and fail to provide acceptable performance when different devices are considered. We address both challenges by exploiting 3D RT-generated radiomaps and using linear data transformation to match the characteristics of various devices. We evaluate the efficiency of this approach in terms of the time spent to create the radiomap, the amount of data required to calibrate the radiomap for different devices and the positioning error which is compared against the case of using dedicated radiomaps collected with each device

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D

EFFECT OF TRH AND METOCLOPRAMIDE ON MATERNAL AND FETAL PROLACTIN SECRETION DURING LABOR

IN THE PRESENT WORK THE EFFECT OF TRH AND METOCLOPRAMIDE ON MATERNAL AND FETALPROLACTIN SECRETION WAS STUDIED IN 89 WOMEN WITH NORMAL SINGLETON TERM PREGNANCY. SIXTY TWO OF THEM WERE INJECTED INTRAVANESOULY (BOLUS) EITHER 200 ΜG TRH (25 WOMEN), 1000 ΜG TRH (16 WOMEN) OR SALINE SOLUTION (21 WOMEN) AT VARIOUS INTERVALS (90-110 MIN.) BEFORE VAGINAL DELIVERY. THE REMAINING 27 WOMEN WERE GIVEN EITHER AN INTRAVENOUS BOLUS OF 20 MG METOCLOPRAMIDE AT INTERVALS FROM 10 TO 90 MIN BEFORE VAGINAL DELIVERY (18 WOMEN), ORAL METOCLOPRAMIDE FOR THE LAST FOUR DAYS OF PREGNANCY UNTIL DELIVERY (4 WOMEN) OR PLACEBO ALSO FOR THE LASTFOUR DAYS OF PREGNANCY (5 WOMEN). (ABSTRACT TRUNCATED)ΣΤΗΝ ΠΑΡΟΥΣΑ ΕΡΓΑΣΙΑ ΜΕΛΕΤΗΘΗΚΕ Η ΕΠΙΔΡΑΣΗ ΤΟΥ TRH ΚΑΙ ΤΗΣ ΜΕΤΟΚΛΟΠΡΑΜΙΔΗΣ ΣΤΗΜΗΤΡΙΚΗ ΚΑΙ ΕΜΒΡΥΙΚΗ ΕΚΚΡΙΣΗ ΤΗΣ ΠΡΟΛΑΚΤΙΝΗΣ ΣΕ 89 ΦΥΣΙΟΛΟΓΙΚΕΣ ΕΓΚΥΕΣ ΓΥΝΑΙΚΕΣ ΠΟΥ ΕΙΧΑΝ ΗΛΙΚΙΑ ΚΥΗΣΕΩΣ ΑΠΟ 38-40 ΕΒΔΟΜΑΔΕΣ. ΣΤΙΣ 62 ΑΠ'ΑΥΤΕΣ ΧΟΡΗΓΗΘΗΚΕ ΚΑΤΑ ΤΗ ΔΙΑΡΚΕΙΑ ΤΟΥ ΤΟΚΕΤΟΥ ΕΦ'ΑΠΑΞ ΕΝΔΟΦΛΕΒΙΑ ΕΙΤΕ 200 ΜG TRH (25 ΓΥΝΑΙΚΕΣ), ΕΙΤΕ 1000 ΜG TRH (16 ΓΥΝΑΙΚΕΣ), ΕΙΤΕ ΦΥΣΙΟΛΟΓΙΚΟΣ ΟΡΟΣ (21 ΓΥΝΑΙΚΕΣ) ΣΕ ΧΡΟΝΙΚΑ ΔΙΑΣΤΗΜΑΤΑ ΑΠΟ 10 ΜΕΧΡΙ 110 MIN ΠΡΙΝ ΑΠΟ ΤΗ ΓΕΝΝΗΣΗ ΤΟΥ ΕΜΒΡΥΟΥ. ΣΤΙΣ ΥΠΟΛΟΙΠΕΣ 27 ΓΥΝΑΙΚΕΣ ΧΟΡΗΓΗΘΗΚΕ ΕΙΤΕ ΜΕΤΟΚΛΟΠΡΑΜΙΔΗ ΕΦ'ΑΠΑΞ ΕΝΔΟΦΛΕΒΙΑ ΣΕ ΔΟΣΗ 20 MG ΣΤΗ ΔΙΑΡΚΕΙΑ ΤΟΥ ΤΟΚΕΤΟΥ ΚΑΙ ΣΕ ΧΡΟΝΟΥΣ ΑΠΟ 10-90 MIN ΠΡΙΝ ΑΠΟ ΤΗ ΓΕΝΝΗΣΗ ΤΟΥ ΕΜΒΡΥΟΥ (18 ΓΥΝΑΙΚΕΣ) ΕΙΤΕ ΜΕΤΟΚΛΟΠΡΑΜΙΔΗ ΑΠΟ ΤΟ ΣΤΟΜΑ ΤΙΣ 4 ΤΕΛΕΥΤΑΙΕΣ ΗΜΕΡΕΣ ΤΗΣ ΚΥΗΣΕΩΣ (5 ΓΥΝΑΙΚΕΣ).(ΠΕΡΙΚΟΠΗ ΠΕΡΙΛΗΨΗΣ

3D Ray Tracing for device-independent fingerprint-based positioning in WLANs

We study the use of 3D Ray Tracing (RT) to construct radiomaps for WLAN Received Signal Strength (RSS) fingerprint-based positioning, in conjunction with calibration techniques to make the overall process device-independent. RSS data collection might be a tedious and time-consuming process and also the measured radiomap accuracy and applicability is subject to potential changes in the wireless environment. Therefore, RT becomes a more attractive and efficient way to generate radiomaps. Moreover, traditional fingerprint-based methods lead to radiomaps which are restricted to the device used to generate the radiomap and fail to provide acceptable performance when different devices are considered. We address both challenges by exploiting 3D RT-generated radiomaps and using linear data transformation to match the characteristics of various devices. We evaluate the efficiency of this approach in terms of the time spent to create the radiomap, the amount of data required to calibrate the radiomap for different devices and the positioning error which is compared against the case of using dedicated radiomaps collected with each device

Cross device fingerprint-based positioning using 3D Ray Tracing

This work proposes the use of 3D Ray Tracing (RT) to construct radiomaps for WLAN Received Signal Strength (RSS) fingerprint-based positioning, in conjunction with calibration techniques to tackle with the problem using different devices. In addition to the fact that RSS data collection might be a tedious and time-consuming process, the measured radiomap accuracy and applicability is subject to potential changes in the wireless environment. Therefore, RT becomes a very suitable and efficient solution to tackle this problem. Moreover, in traditional fingerprint-based methods, the underlying radiomap is restricted to the mobile device for which the radiomap has been created. To overcome this limitation, we propose the use of linear data transformation to match the characteristics of various devices. We address both challenges by using 3D RT-generated radiomaps and highlight the efficiency of this approach in terms of the time spent to create the radiomap, the amount of data required to calibrate the radiomap for different devices and the positioning error which is compared against the case of using dedicated radiomaps collected with each device