Fragile Watermarking of 3D Models in Transformed Domain

This paper presents an algorithm aimed at the integrity protection of 3D models represented as a set of vertices and polygons. The proposed method defines a procedure to perform a fragile watermarking of the vertices’ data, namely 3D coordinates and polygons, introducing a very small error in the vertices’ coordinates. The watermark bit string is embedded into a secret vector space defined by the Karhunen–Loève transform derived from a key image. Experimental results show the good performance of the method and its security

6D object position estimation from 2D images: a literature review

The 6D pose estimation of an object from an image is a central problem in many domains of Computer Vision (CV) and researchers have struggled with this issue for several years. Traditional pose estimation methods (1) leveraged on geometrical approaches, exploiting manually annotated local features, or (2) relied on 2D object representations from different points of view and their comparisons with the original image. The two methods mentioned above are also known as Feature-based and Template-based, respectively. With the diffusion of Deep Learning (DL), new Learning-based strategies have been introduced to achieve the 6D pose estimation, improving traditional methods by involving Convolutional Neural Networks (CNN). This review analyzed techniques belonging to different research fields and classified them into three main categories: Template-based methods, Feature-based methods, and Learning-Based methods. In recent years, the research mainly focused on Learning-based methods, which allow the training of a neural network tailored for a specific task. For this reason, most of the analyzed methods belong to this category, and they have been in turn classified into three sub-categories: Bounding box prediction and Perspective-n-Point (PnP) algorithm-based methods, Classification-based methods, and Regression-based methods. This review aims to provide a general overview of the latest 6D pose recovery methods to underline the pros and cons and highlight the best-performing techniques for each group. The main goal is to supply the readers with helpful guidelines for the implementation of performing applications even under challenging circumstances such as auto-occlusions, symmetries, occlusions between multiple objects, and bad lighting conditions

Toward Supporting Maxillo-Facial Surgical Guides Positioning with Mixed Reality—A Preliminary Study

Following an oncological resection or trauma it may be necessary to reconstruct the normal anatomical and functional mandible structures to ensure the effective and complete social reintegration of patients. In most surgical procedures, reconstruction of the mandibular shape and its occlusal relationship is performed through the free fibula flap using a surgical guide which allows the surgeon to easily identify the location and orientation of the cutting plane. In the present work, we present a Mixed Reality (MR)-based solution to support professionals in surgical guide positioning. The proposed solution, through the use of a Head-Mounted Display (HMD) such as that of the HoloLens 2, visualizes a 3D virtual model of the surgical guide, positioned over the patient's real fibula in the correct position as identified by the medical team before the procedure. The professional wearing the HMD is then assisted in positioning the real guide over the virtual one by our solution, which is capable of tracking the real guide during the whole process and computing its distance from the final position. The assessment results highlight that Mixed Reality is an eligible technology to support surgeons, combining the usability of the device with an improvement of the accuracy in fibula flap removal surgery

Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance

The current study aimed to propose a Deep Learning (DL) and Augmented Reality (AR) based solution for a in-vivo robot-assisted radical prostatectomy (RARP), to improve the precision of a published work from our group. We implemented a two-steps automatic system to align a 3D virtual ad-hoc model of a patient's organ with its 2D endoscopic image, to assist surgeons during the procedure

A deep learning framework for real-time 3D model registration in robot-assisted laparoscopic surgery

Introduction The current study presents a deep learning framework to determine, in real-time, position and rotation of a target organ from an endoscopic video. These inferred data are used to overlay the 3D model of patient's organ over its real counterpart. The resulting augmented video flow is streamed back to the surgeon as a support during laparoscopic robot-assisted procedures. Methods This framework exploits semantic segmentation and, thereafter, two techniques, based on Convolutional Neural Networks and motion analysis, were used to infer the rotation. Results The segmentation shows optimal accuracies, with a mean IoU score greater than 80% in all tests. Different performance levels are obtained for rotation, depending on the surgical procedure. Discussion Even if the presented methodology has various degrees of precision depending on the testing scenario, this work sets the first step for the adoption of deep learning and augmented reality to generalise the automatic registration process

Multi-class queuing networks models for energy optimization

The increase of energy consumption and the related costs in large data centers has stimulated new researches on techniques to optimize the power consumption of the servers. In this paper we focus on systems that should process a peak workload consisting of different classes of applications. The objective is to implement a policy of load control which allows an efficient use of the power deployed to the resources. The proposed strategy controls the workload mix in order to achieve the maximum utilization of all the resources allocated. As a consequence, the power provision will be fully utilized and the throughput maximized. Thus, the costs to execute a given workload will be minimized, together with its energy consumption, since the required processing time is decreased

The detector control unit of the fine guidance sensor instrument on-board the ARIEL mission: design status

ARIEL is an ESA mission whose scientific goal is to investigate exoplanetary atmospheres. The payload is composed by two instruments: AIRS (ARIEL IR Spectrometer) and FGS (Fine Guidance System). The FGS detection chain is composed by two HgCdTe detectors and by the cold Front End Electronics (SIDECAR), kept at cryogenic temperatures, interfacing with the F-DCU (FGS Detector Control Unit) boards that we will describe thoroughly in this paper. The F-DCU are situated in the warm side of the payload in a box called FCU (FGS Control Unit) and contribute to the FGS VIS/NIR imaging and NIR spectroscopy. The F-DCU performs several tasks: drives the detectors, processes science data and housekeeping telemetries, manages the commands exchange between the FGS/DPU (Data Processing Unit) and the SIDECARs and provides high quality voltages to the detectors. This paper reports the F-DCU status, describing its architecture, the operation and the activities, past and future necessary for its development

Preliminary surface charging analysis of Ariel payload dielectrics in early transfer orbit and L2-relevant space environment

Ariel [1] is the M4 mission of the ESA’s Cosmic Vision Program 2015-2025, whose aim is to characterize by lowresolution transit spectroscopy the atmospheres of over one thousand warm and hot exoplanets orbiting nearby stars. The operational orbit of the spacecraft is baselined as a large amplitude halo orbit around the Sun-Earth L2 Lagrangian point, as it offers the possibility of long uninterrupted observations in a fairly stable radiative and thermo-mechanical environment. A direct escape injection with a single passage through the Earth radiation belts and no eclipses is foreseen. The space environment around Earth and L2 presents significant design challenges to all spacecraft, including the effects of interactions with Sun radiation and charged particles owning to the surrounding plasma environment, potentially leading to dielectrics charging and unwanted electrostatic discharge (ESD) phenomena endangering the Payload operations and its data integrity. Here, we present some preliminary simulations and analyses about the Ariel Payload dielectrics and semiconductors charging along the transfer orbit from launch to L2 include

Modelling replication in NoSQL datastores

11 full imprès, b/n. Els fulls d’aquesta sèrie corresponen a la divisió 12 x 8 de la malla de distribució del Mapa topográfico nacional de España 1:50 000.80 x 60 cm1:5 000254 pp