Browsing International Telemetering Conference Proceedings, Volume 55 (2019) by Authors
AN IMPROVED LOG-DOMAIN BELIEF PROPAGATION ALGORITHM OVER GRAPHS WITH SHORT CYCLESRaveendran, Nithin; Srinivasa, Shayan G.; Vasic, Bane; Univ Arizona, Dept Electrical and Computer Engineering; Indian Institute of Science, Dept Electronic Systems Engineering (International Foundation for Telemetering, 2019-10)We present a modified belief propagation (BP) algorithm for decoding low density parity check codes having graphs with short cycles. The modified algorithm in log domain is superior in terms of numerical stability, precision, computational complexity and ease of implementation when compared to the algorithm in the probability domain. Simulation results show improvement in decoding performance for the modified BP compared to the original algorithm. The modified approach is also generalized for graphs with isolated cycles of arbitrary length by considering the statistical dependency among messages passed in such cycles.
Neuro-OSVETA: A Robust Watermarking of 3D MeshesVasic, Bata; Raveendran, Nithin; Vasic, Bane; Univ Arizona, Dept Electrical and Computer Engineering; Univ Nis, Electronic Dept (International Foundation for Telemetering, 2019-10)Best and practical watermarking schemes for copyright protection of 3D meshes are required to be blind and robust to attacks and errors. In this paper, we present the latest developments in 3D blind watermarking with a special emphasis on our Ordered Statistics Vertex Extraction and Tracing Algorithm (OSVETA) algorithm and its improvements. OSVETA is based on a combination of quantization index modulation (QIM) and error correction coding using novel ways for judicial selection of mesh vertices which are stable under mesh simplification, and the technique we propose in this paper offers a systematic method for vertex selection based on neural networks replacing a heuristic approach in the OSVETA. The Neuro-OSVETA enables a more precise mesh geometry estimation and better curvature and topological feature estimation. These enhancements result in a more accurate identification of stable vertices resulting in significant reduction of deletion probability.