Implementing Homomorphic Encryption Based Secure Feedback Control for Physical Systems

This paper is about an encryption based approach to the secure implementation of feedback controllers for physical systems. Specifically, Paillier's homomorphic encryption is used to digitally implement a class of linear dynamic controllers, which includes the commonplace static gain and PID type feedback control laws as special cases. The developed implementation is amenable to Field Programmable Gate Array (FPGA) realization. Experimental results, including timing analysis and resource usage characteristics for different encryption key lengths, are presented for the realization of an inverted pendulum controller; as this is an unstable plant, the control is necessarily fast

Evaluating colour preference by using multidimensional approaches

Colour preference is a key factor in the design and evaluation of lighting systems, particularly with the emergence of multichannel LED systems which allow for greater control over the spectrum of light emitted and therefore the colour appearance of the illuminated objects. To more accurately and objectively measure colour preference, there has been a growing interest in the development of multidimensional evaluation algorithms that consider multiple dimensions of colour rendering, such as chroma and hue shift. The purpose of this study was to compare and evaluate the performance of different multidimensional evaluation algorithms for colour preference in lighting applications. Using computer-generated images of a coloured object displayed on a computer monitor under a fixed white point, we simulated the colour shifts of the object under different light sources and test subjects evaluated the results using a range of multidimensional methods. Our analysis revealed that there are significant differences in the performance of these algorithms, with some providing more accurate and reliable measures of colour preference than others. Considering all relevant criteria, genetic algorithms seem to provide the most promising approach, as they lead to a result quickly and reliably. These findings have important implications for the selection and use of multidimensional algorithms for evaluating colour preference in lighting, particularly in the context of multichannel LED systems, and can inform future research in this area

Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes

Current 3D segmentation methods heavily rely on large-scale point-cloud datasets, which are notoriously laborious to annotate. Few attempts have been made to circumvent the need for dense per-point annotations. In this work, we look at weakly-supervised 3D semantic instance segmentation. The key idea is to leverage 3D bounding box labels which are easier and faster to annotate. Indeed, we show that it is possible to train dense segmentation models using only bounding box labels. At the core of our method, \name{}, lies a deep model, inspired by classical Hough voting, that directly votes for bounding box parameters, and a clustering method specifically tailored to bounding box votes. This goes beyond commonly used center votes, which would not fully exploit the bounding box annotations. On ScanNet test, our weakly supervised model attains leading performance among other weakly supervised approaches (+18 mAP@50). Remarkably, it also achieves 97% of the mAP@50 score of current fully supervised models. To further illustrate the practicality of our work, we train Box2Mask on the recently released ARKitScenes dataset which is annotated with 3D bounding boxes only, and show, for the first time, compelling 3D instance segmentation masks.Comment: Project page: https://virtualhumans.mpi-inf.mpg.de/box2mask

Reality Check of Laboratory Service Effectiveness during Pandemic (H1N1) 2009, Victoria, Australia

TOC summary: The greatest challenges were insufficient staff and test reagents