Low-effort place recognition with WiFi fingerprints using deep learning

Using WiFi signals for indoor localization is the main localization modality of the existing personal indoor localization systems operating on mobile devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals are usually available indoors and can provide rough initial position estimate or can be used together with other positioning systems. Currently, the best solutions rely on filtering, manual data analysis, and time-consuming parameter tuning to achieve reliable and accurate localization. In this work, we propose to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results. Assuming the state-of-the-art hierarchical approach, we employ the DNN system for building/floor classification. We show that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification. The proposed architecture is verified on the publicly available UJIIndoorLoc dataset and the results are compared with other solutions

Role of anti-monopoly institution for development of railway market

Reformy kolei, jakie miały miejsce przez ostatnie dekady w Europie, miały za zadanie stworzenie warunków dla zaistnienia konkurencji na torach. Jednak między stworzeniem możliwości prawnej a rzeczywistym zaistnieniem na rynku leży tak wiele przeszkód, iż często wymaga to ogromnych interwencji ze strony instytucji powołanych do zwalczania monopoli

Railways in Latvia

Łotwa ma 2,4 mln mieszkańców, gęstość zaludnienia (36,5 mieszk./km2) jest 3,5-krotnie mniejsza niż w Polsce, długość linii wynosi 2331 km, z czego zelektryfikowanych jest 258 km. Koleje na Łotwie w 2001 r. przewiozły 20,1 mln pasażerów oraz 37,9 mln t, czyli piątą część tego co PKP. Oprócz kolei państwowych LDZ funkcjonują na Łotwie także prywatni operatorzy

On augmenting the visual slam with direct orientation measurement using the 5-point algorithm

This paper presents the attempt to merge two paradigms of the visual robot navigation: Visual Simultaneous Localization and Mapping (VSLAM) and Visual Odometry (VO). The VSLAM was augmented with the direct, visual measurement of the robot orientation change using the 5-point algorithm. The extended movement model of the robot was proposed and additional measurements were introduced to the SLAM system. The efficiency of the 5-point and 8-point algorithms was compared. The augmented system was compared with the state of the art VSLAM solution and the proposed modification allowed to reduce the tracking error by over 30%

Comparative assessment of point feature detectors in the context of robot navigation

This paper presents evaluation of various contemporary interest point detector and descriptor pairs in the context of robot navigation. The robustness of the detectors and descriptors is assessed using publicly available datasets: the first gathered from the camera mounted on the industrial robot [17] and the second gathered from the mobile robot [20]. The most efficient detectors and descriptors for the visual robot navigation are selected

A hardware system for muscle force and tiredness estimation from electromyo-graphic signal

W pracy przedstawiono implementację układu służącego do estymacji siły oraz zmęczenia mięśni na podstawie sygnału elektromiograficznego (EMG), rejestrowanego za pomocą dwukanałowego wzmacniacza, oraz położenia stawu mierzonego za pomocą enkodera kwadraturowego. W matrycy FPGA zaimplementowano struktury obliczające aktualną wartość średniej częstotliwości (MNF) oraz wartości średniokwadratowej (RMS) sygnału i kąta, co umożliwia estymację aktualnej siły oraz zmęczenia. Opracowane rozwiązanie jest skalowalne i umożliwia równoległą obsługę liczby kanałów ograniczonej wyłącznie zasobami matrycy FPGA.This paper presents an FPGA implementation of the muscle force and fatigue estimation unit based on the analysis of an electromyography (EMG) signal measured with a two-channel amplifier and the joint position measured with a quadratic encoder. The contemporary systems use the root mean square (RMS) of the EMG signal and muscle length to estimate the contraction force and decrease in the median frequency of the EMG signal to detect the muscle fatigue [2]. The proposed system consists of (Fig. 1): an infinite impulse response (IIR) high-pass filter with the cut-off frequency of 10 Hz, a dedicated RMS calculation block for the 512 samples window (Fig. 2.), the Fast Fourier Transform (FFT) block and a MicroBlaze processor. The muscle length is estimated using measurements from the encoder placed on the joint. The mean value of the EMG signal frequencies is used as the approximation of the median-frequency. The system was tested using the Xilinx SP605 evaluation kit and the obtained results were verified. The resources usage is presented in Table 1. Due to the FPGA inherent ability to parallelize computation, additional measurement channels can be easily added without increase in the processing time. The presented system is portable and can be used as a part of any mobile solution requiring feedback from the muscles-state (e.g. exoskeleton). Due to its scalability, it can be easily extended into a larger muscle-analysis system. Moreover, it can be modified to facilitate analysis of other biomedical signals

The registration system for the evaluation of indoor visual slam and odometry algorithms

This paper presents the new benchmark data registra- tion system aimed at facilitating the development and evaluation of the visual odometry and SLAM algorithms. The WiFiBOT LAB V3 wheeled robot equipped with three cameras, XSENS MTi atitude and heading reference system (AHRS) and Hall encoders can be used to gather data in indoor exploration scenarios. The ground truth trajectory of the robot is obtained using the visual motion tracking system. Additional static cameras simulating the surveillance network, as well as artificial markers augmen ting the navigation are incorporated in the system. The datasets registered with the presented system will be freely available for research purposes