ENCODING PARAMETER ESTIMATION FOR RDTC OPTIMIZED COMPRESSION AND STREAMING OF IMAGE-BASED SCENE REPRESENTATIONS

ABSTRACT Remote navigation in image-based scene representations requires random access to parts of the compressed reference image data to compose virtual views. The degree of dependencies introduced during compression has an impact on the effort that is required to access reference image data and at the same time delimits the rate-distortion (RD) tradeoff that can be achieved. If a limited channel bitrate and computational power of client devices are taken into account, encoding can be performed in a RD optimal manner with respect to the expected maximum transmission data rate (T) and decoding complexity (C). In this work we present a practical framework for parameter estimation for RDTC optimal encoding of image-based scene representations

MCROOD: Multi-Class Radar Out-Of-Distribution Detection

Out-of-distribution (OOD) detection has recently received special attention due to its critical role in safely deploying modern deep learning (DL) architectures. This work proposes a reconstruction-based multi-class OOD detector that operates on radar range doppler images (RDIs). The detector aims to classify any moving object other than a person sitting, standing, or walking as OOD. We also provide a simple yet effective pre-processing technique to detect minor human body movements like breathing. The simple idea is called respiration detector (RESPD) and eases the OOD detection, especially for human sitting and standing classes. On our dataset collected by 60GHz short-range FMCW Radar, we achieve AUROCs of 97.45%, 92.13%, and 96.58% for sitting, standing, and walking classes, respectively. We perform extensive experiments and show that our method outperforms state-of-the-art (SOTA) OOD detection methods. Also, our pipeline performs 24 times faster than the second-best method and is very suitable for real-time processing.Comment: Accepted at ICASSP 202

A Distributed Public Key Infrastructure Based on Threshold Cryptography for the HiiMap Next Generation Internet Architecture

In this article, a security extension for the HiiMap Next Generation Internet Architecture is presented. We regard a public key infrastructure which is integrated into the mapping infrastructure of the locator/identifier-split addressing scheme. The security approach is based on Threshold Cryptography which enables a sharing of keys among the mapping servers. Hence, a more trustworthy and fair approach for a Next Generation Internet Architecture as compared to the state of the art approach is fostered. Additionally, we give an evaluation based on IETF AAA recommendations for security-related systems