No abstract

Path loss prediction is essential for network planning in any wireless communication system. For cellular networks, it is usually achieved through extensive received signal power measurements in the target area. When the 3D model of an area is available, ray tracing simulations can be utilized; however, an important drawback of such an approach is the high computational complexity of the simulations. In this paper, we present a fundamentally different approach for path loss distribution prediction directly from 2D satellite images based on deep convolutional neural networks. While training process is time consuming and completed offline, inference can be done in real time. Another advantage of the proposed approach is that 3D model of the area is not needed during inference since the network simply uses an image captured by an aerial vehicle or satellite as its input. Simulation results show that the path loss distribution can be accurately predicted for different communication frequencies and transmitter heights. INDEX TERMS Path loss, deep learning, convolutional neural networks.

H.264 video coding standard supports several interprediction coding modes that use macroblock partitions with variable block sizes. Rate-distortion optimal selection of both the motion vectors and the coding mode of each macroblock is essential for an H.264 encoder to achieve superior coding efficiency. Unfortunately, searching for optimal motion vectors of each possible subblock incurs a heavy computational cost. In this paper, in order to reduce the computational burden of integer-pel motion estimation without sacrificing from the coding performance, we propose a rate-distortion and complexity joint optimization framework. Within this framework, we develop a simple method that determines for each macroblock which partitions are likely to be optimal. Motion vector search is carried out for only the selected partitions, thus reducing the complexity of the motion estimation step. The mode selection criteria is based on a measure of spatio-temporal activity within the macroblock. The procedure minimizes the coding loss at a given level of computational complexity either for the full video sequence or for each single frame. For the latter case, the algorithm provides a tight upper bound on the worst case complexity/execution time of the motion estimation module. Simulation results show that the algorithm speeds up integer-pel motion estimation by a factor of up to 40 with less than 0.2 dB loss in coding efficiency.

Anatomic variability and anastomosis of the angular artery of the facial artery with the other arteries are important for both anatomists and surgeons. In particular, the angular artery is a significant landmark in dacryocystorhinostomy. Because of variations on anatomy of the angular artery, there are limited numbers of anatomic studies on the flaps of facial region. Hence, the aim of the cadaveric study was to evaluate the anatomic features of the angular artery in detail to help surgical procedures.The artery was represented under ×4 loop magnification in 32 sides of 16 formalin-fixed adult cadavers. The angular artery's position, diameter, and branch patterns relevant to the nose arterial supply were evaluated. The facial artery ended symmetrically in 10 (62.5%) of the cadavers. The facial artery was terminated as angular artery in all of the cases. The types of the angular artery were as follows: classical angular type in 8 cases (25.0%), nasal type in 15 cases (46.9%), alar type in 4 cases (12.5%), and labial type in 5 cases (15.6%) on the facial halves. We studied the topographic anatomic features of the angular artery for increasing reliability of the flaps on the region. The angular arterial anatomic details are critical and essential for surgical cosmetic and functional results.

In recent literature, there exist many high-performance wavelet coders that use different spatially adaptive coding techniques in order to exploit the spatial energy compaction property of the wavelet transform. Two crucial issues in adaptive methods are the level of flexibility and the coding efficiency achieved while modeling different image regions and allocating bitrate within the wavelet subbands. In this paper, we introduce the "spherical coder," which provides a new adaptive framework for handling these issues in a simple and effective manner. The coder uses local energy as a direct measure to differentiate between parts of the wavelet subband and to decide how to allocate the available bitrate. As local energy becomes available at finer resolutions, i.e., in smaller size windows, the coder automatically updates its decisions about how to spend the bitrate. We use a hierarchical set of variables to specify and code the local energy up to the highest resolution, i.e., the energy of individual wavelet coefficients. The overall scheme is nonredundant, meaning that the subband information is conveyed using this equivalent set of variables without the need for any side parameters. Despite its simplicity, the algorithm produces PSNR results that are competitive with the state-of-art coders in literature.