Rotation invariant texture descriptor plays an important role in texture-based object classification. However the classification accuracy may decrease due to the inconsistent performance of texture descriptor with respect to various rotated angles. In this paper we propose a consistent rotation invariant texture descriptor named Sorted Neighborhood Differences (SND). SND is derived from the integration of sorted neighborhood and binary patterns. Experimental results show that overall texture classification accuracy of SND with respect to different rotations using OUTEX TC 0010 texture database is 91.81% whereas those of LBPriu and LBP-HF are 86.42% and 88.28%, respectively. The texture and coin classification accuracies of SND are also consistent in various rotation angles and illumination levels.
Indoor positioning has become popular in this decade and is used to locate users or objects in indoor environments. This is because global positioning system (GPS) is not efficient for indoor use due to the multipath fading effect. This research is about development bluetooth low energy (BLE) indoor positioning system with the aid of long range (LoRa) network and guideline on selection of the BLE beacons. Next, positioning systems are developed consisting of BLE beacons, a transceiver of hybrid BLE-LoRa module, a LoRa receiver and Raspberry Pi as real-time monitoring. The received signal strength indicator (RSSI) and BLE Mac address from BLE beacons received via LoRa network are analyzed using the positioning algorithm designed in MATLAB. The positioning algorithm incorporates distance estimation, filter implementation and trilateration technique. The estimated location is analyzed with the root mean square error (RMSE) and cumulative distribution function (CDF). According to the results, implementing the filter reduces the positioning accuracy error, achieving 90% accuracy of positioning error less than 1.20 meters for the whole testbed. Finally, the algorithm is embedded into Raspberry Pi to view the location via desktop.
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