Longitude and Latitude Based Received Signal Strength Difference Localization Models

In order to use the latitude and longitude coordinates for received signal strength difference (RSSD) localization, the errors of several spherical distance calculation methods and the error of arc length relative to string length were compared. The distance-calculation RSSD localization equations were established, including spherical accurate calculation RSSD, spherical approximate calculation RSSD, and normal cylindrical projection RSSD. And then, the optimization RSSD localization models based on geodetic coordinates and corresponding to the above equations were established, and the models were verified using the point by point search method with good convergence. The numerical results show there are a lot of weak localization areas for the RSSD localization networks lack of central stations with 4,5,6 stations. Among networks with central stations, there are only a small number of weak-localization areas for the concave 4 stations network, while there are no weak-localization areas for the networks composed of more stations. When the measurement errors and the additional losses of radio wave propagation are not considered, the localization errors of the spherical accurate model, the spherical approximate model and the equianglular projection model are very small, among which the second model has the shortest localization time. The localization errors of equidistance projection model and equal-area projection model are large, neither of which is suitable for the middle latitude and high latitude areas.

Kinovea tracking guide and distance calculation R script v1

This protocol can be used to determine the travelled distances of larvae on a black-stained agar plate. For this purpose, videos of the moving animals are recorded, the movement is then tracked using the Kinovea program and the resulting files are evaluated using an R script.

The Many Faces of Distance – A Typology of Distance in Management

Purpose: Based on the various streams of distance literature in management, a framework is provided to make discourses on distance clear. Design / Method / Approach: After a review of the most relevant distance concepts, a typology with five categories is introduced, which are then applied to the most relevant distance concepts in management. Findings: A clear typology helps to clearly define and differentiate the various streams of distance literature. Theoretical Implications: With the conceptual categories ‘topic’, ‘dimensionality’, and ‘level of analysis’, and the empirical categories ‘variables’ and ‘distance calculation’, distance concepts become more consistent. Practical Implications: With a terminology for the different aspects of distance, practitioners can focus more clearly on specific remedies to bridge distance. Originality / Value: The paper offers a new typology of different elements of distance. Research Limitations / Future Research: The introduced typology will aid in the discussion of distance in management. Paper type: Conceptual

Radio Transmitter Identification Under Small Sample Conditions

Radio transmitter identification is an emerging technology that uses RFfingerprints to identify different radiation sources, and can find important applications in the field of wireless security.The choice of classifier is one of the key factors affecting recognition performance. Aiming at the shortcomings of a single classifier model, the author proposes a radio transmitter identification method based on improved SVM-KNN. First, the square integral bispectrum(SIB) algorithm is used to extract the signal fingerprint features from the steady-state signal of the mobile phone. Then, instead of the traditional Euclidean distance calculation method, the manifold geodesic distance calculation method is used to calculate the distance between the test sample and each support vector.Experimental studies using the same model of mobile phone show that the improved SVM-KNN algorithm is less affected by the choice of kernel function parameters, and has higher recognition accuracy without increasing the computational complexity. We also proved the robustness of our method in a wide range of signal-to-noise ratio.