The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power level measurement by using multiple anchors and multiple radio channels and, consequently, considers different approaches to aligning the actual measurements with the recorded values. The dataset is available online. This article focuses on the very popular radio technology Bluetooth Low Energy to explore the possible improvement of the system accuracy through different machine learning approaches. It shows how the accuracy–complexity trade-off influences the possible candidate algorithms on an example of three-channel Bluetooth received signal strength based fingerprinting in a one dimensional environment with four static anchors and in a two dimensional environment with the same set of anchors. We provide a literature survey to identify the machine learning algorithms applied in the literature to show that the studies available can not be compared directly. Then, we implement and analyze the performance of four most popular supervised learning techniques, namely k Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network. In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%.
This paper deals with the properties of a compression method based on the 3D DCT During the video compression process with 3D DCT the image data are transformed into frequency domain and the crosscorrelation between adjacent pixels and even between frames is being removed without any quality loss. By implementing the quantization and thresholding operations, the number of3D DCT coefficients is reducing. The situation is even worse while a group ofencodedframes contains several types ofvideo sequences, i.e. the cut is presents. In decompressed sequence the disturbing cross-fade effect is perceptible. In the next text a methodfor removing this effect based on the 3D DCT coder modifications is described
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