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The technology of Unmanned Aerial Vehicles (UAV) has been the subject of pioneering research in recent years. Also, the subject has been used in various situations for both outdoor and indoor. In this paper, a cellular communication-based autonomous UAV Navigation is proposed that enables UAVs to maneuver independently in previously unknown and GPS-denied indoor environments with LiDAR sensors. The main idea of the proposed scheme is to implement LTE connection to UAV Navigation and compared it to a Wi-Fi connection. We have proposed that autonomous UAV navigation system for unknown environment like indoor. This system relies on the combination of ROS-based Hector SLAM systems, 2D-LiDAR sensor and LTE connection. The performance of proposed scheme is evaluated as the error distance and exploration time for unknown area like indoor environment. The mapping efficiency of LTE connection is 57.5% greater than the Wi-Fi connection. Computation time for flight is approximately 200 seconds.

This paper proposes novel compressed sensing (CS) of colored iris images using three RGB iterations of basis pursuit (BP) with sparsity averaging (SA), called RGB-BPSA. In RGB-BPSA, a sparsity basis is performed using the average of multiple coherent dictionaries to improve the BP reconstruction. In the experiment, first, the decomposition level of wavelet is studied to analyze the best reconstruction result. Second, the effect of compression rate (CR) is considered. Third, the effect of resolution is investigated. Last, the sparse basis of SA is compared to existing basis, i.e., curvelet, Daubechies-1 or haar, and Daubechies-8. The superior RGB-BPSA over existing CS is shown by better visual quality with higher signal to noise ratio (SNR) and structural similarity (SSIM) index in the same CR. In addition, reconstruction time also investigated where RGB-BPSA outperforms curvelet and two times longer than haar and Daubechies-8. INDEX TERMSCompressed sampling, basis pursuit (BP), sparsity averaging, iris images.

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