Panasonic (Germany)

High-quality aspherical and freeform surfaces are in high demand, and the high-accuracy form measurement of such surfaces is a challenging task. To explore the current status of form measurement systems for complex surfaces such as aspheres and freeforms, interlaboratory comparison measurements are performed. This study presents the pseudonymized results obtained using three different surfaces (metal asphere, glass asphere, toroidal surface) in a total of six different round robins. These results were taken from a total of 13 different measurement instruments based on 9 different measurement principles and operated at 12 different laboratories. They were analyzed using a sophisticated procedure that was first developed in 2018 and then refined and tested on simulated data in 2022 to address the challenges of such a comparison at this level of accuracy. In the current study, we applied these refined methods to data acquired from tactile and optical point measurements as well as from optical areal measurements. As there are no absolutely measured and very well characterized reference standard aspherical and freeform surfaces available at the accuracy level of a few tens of nanometers root-mean-square, the approximated true forms of the surfaces were derived from the measurements and indicate the manufacturing accuracy of the surface forms. Then, the measurement’s differences to the approximated true forms were analyzed, which directly indicate the systematic measurement errors of the instruments. By also comparing the approximated true forms from the two different round robins for each surface, additional insights into the reliability and stability of these so-called virtual reference topographies were gained.

Due to the increasing use of nonlinear loads in modern power systems, harmonic currents have become a more prominent problem for power quality. Typically, harmonic currents are compensated by using shunt active power filters. Recently, a novel constrained linear state signal shaping model predictive controller has been proposed for shunt active power filter control. However, due to the high computational requirements of online quadratic programming solvers, the real-time implementation of this solution is quite challenging. Therefore, the present work proposes the use of a linear state signal shaping explicit model predictive control formulation, such that the optimizations are done offline. However, the generated offline data introduces a large memory footprint, hindering real-time implementation. To break the curse of dimensionality, a tensor representation is proposed, which can be efficiently compressed via tensor decomposition methods. The proposed approach was tested in simulation and was able to provide good results. Due to the use of efficient tensor decomposition methods, a considerable reduction of the memory requirement could be achieved. Harmonic compensation, explicit model predictive control, active power filter, tensor decomposition.

Despite the great scientific effort to capture adequately the complex environments in which autonomous vehicles (AVs) operate there are still use-cases that even SoA methods fail to handle. Specifically in odometry problems, on the one hand, geometric solutions operate with certain assumptions that are often breached in AVs, and on the other hand, deep learning methods do not achieve high accuracy. To contribute to that we present CarlaScenes, a large-scale simulation dataset captured using the CARLA simulator. The dataset is oriented to address the challenging odometry scenarios that cause the current state of art odometers to deviate from their normal operations. Based on a case study of failures presented in experiments we distinguished 7 different sequences of data. CarlaScenes besides providing consistent reference poses, includes data with semantic annotation at the instance level for both image and lidar. The full dataset is available at https://github.com/CarlaScenes/CarlaSence.git.

The evolution of automotive technology will eventually permit the automated driving system on the vehicle to handle all circumstances. Human occupants will be just passengers. This poses security issues that need to be addressed. This paper has two aims. The first one investigates strategies for robustifying scene analysis of adversarial road scenes. A taxonomy of the defense mechanisms for countering adversarial perturbations is initially presented, classifying those mechanisms in three major categories: those that modify the data, those that propose adding extra models, and those that focus on modifying the models deployed for scene analysis. Motivated by the limited number of surveys in the first category, we further analyze the approaches that utilize input transformation operations as countermeasures, further classifying them in supervised and unsupervised methods and highlighting both their strengths and weaknesses. The second aim of this paper is to publish CarlaScenes dataset produced using the CARLA simulator. An extensive evaluation study, on CarlaScenes, is performed testing the supervised deep learning approaches that have been either proposed for image restoration or adversarial noise removal. The study presents insights on the robustness of the aforementioned approaches in mitigating adversarial attacks in scene analysis operations.

In this paper we study packet scheduling algorithms which exploit multi-radio transmission diversity in a multi-radio access network. We view the packet scheduling process as a combination of user ...

This paper describes the in-loop deblocking filter used in the upcoming High Efficiency Video Coding (HEVC) standard to reduce visible artifacts at block boundaries. The deblocking filter performs detection of the artifacts at the coded block boundaries and attenuates them by applying a selected filter. Compared to the H.264/AVC deblocking filter, the HEVC deblocking filter has lower computational complexity and better parallel processing capabilities while still achieving significant reduction of the visual artifacts.

In the current video coding standard H.264/AVC, eight directional intra prediction modes are defined to predict edges in a block by using the already coded surrounding reference pixels. The H.264/AVC intra prediction scheme offers a limited set of prediction directions. Therefore, edges of certain directions cannot be accurately predicted. In the case of sharp edges, the inaccurate prediction leads to high frequency coefficients in the prediction error, that are expensive to code. To improve the prediction of edges, a new edge prediction method is introduced. This method detects the presence of an edge in the surrounding blocks and uses an extension of it along its exact direction for the prediction. Since this proposed method allows prediction in any direction without any additional overhead, bitrate reductions of 8% for sequences containing a lot of edges are achieved.

-A novel half-wavelength resonator with small size and high Q is proposed for the first time in the world. Two folded quarter-wavelength resonators are vertically stacked owing to the multilayer nature of LTCC (low temperature co-fired ceramic). Each electrode part is connected with via holes. It is named as VSRR (Vertical Split Ring Resonator). The super miniature size with high Q is obtained by this structure. The resonance frequency is easily calculated by circuit model and 3D EM simulation as well. The performance of experimental resonators shows good agreement with theory.