A methodological approach to fully automated highly accelerated life tests

Microsystem Technologies - Highly accelerated life test (HALT) is a test methodology to evaluate reliability of mechanical and electromechanical devices. HALT is often used on devices that must be...     

Design and test of a real-time shelf out-of-stock detector system

Microsystem Technologies - The shelf out-of-stock (SOOS) problem is relevant for retailers and producers since the absence of products on the shelf can lead to a significant reduction of...     

The amnion muscle combined graft (AMCG) conduits in nerves repair: an anatomical and experimental study on a rat model

The amnion muscle combined graft (AMCG) conduits showed good clinical results in peripheral nerves gap repair. It combines the human amniotic membrane with autologous skeletal muscle fibres. These results seem attributable to the biological characteristics of human amniotic membrane: Pluripotency, anti-inflammatory and low immunogenicity.We here evaluate the final outcome of nerve regeneration morphologically and functionally, across the AMCG compared to nerve autograft. Fourteen Wistar rats were divided into two groups: In Group A, including 6 rats, the left forelimb was treated performing a 1.5?cm length gap on median nerve that was then reconstructed with a reverse autograft. In Group B, including 8 rats, the gap was reconstructed with AMCG. Functional results were evaluated at 30, 60 and 90 days performing grasping tests. Morphological and stereological analyses were performed at T90 using high-resolution light microscopy and design-based stereology. The AMCG conduits revealed nerve fibres regeneration and functional recovery. Functional recovery was observed in both groups with AMCG conduits group showing lower values and a regeneration of median nerves with more myelinated fibres with the same axon size, but thinner myelin than the autograft group. Though the autograft remains the gold standard to restore wide nerve gaps, the AMCG conduit has proved to be effective in enabling nerve regeneration through a critical rat’s nerve gap of 15?mm. These findings empirically support the great clinical results obtained using AMCG conduit to restore traumatic nerve’s gap from 3 to 6?cm of mixed forearm nerves.

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Leptin and UCP1 genes are reciprocally regulated in brown adipose tissue

In a previous work we showed that only unilocular brown adipocytes express leptin. In order to investigate the relationship between leptin gene expression, brown adipocyte activity (UCP1) and morphology, we studied brown adipose tissues of mice (C57BL, female, 7 weeks old) acclimated at different temperatures (19 degrees C and 28 degrees C). Northern blot analysis revealed higher leptin and lower UCP1 mRNA levels in mice exposed to 28 degrees C than in the group acclimated at 19 degrees C. Also protein expression (immunohistochemistry) differed in the two groups: at 28 degrees C brown adipocytes were positive for leptin and only weakly positive for UCP1, while at 19 degrees C they were leptin-negative and UCP1-positive. In the former group the morphology was mainly unilocular. Our data suggest that in brown adipocytes of warm-acclimated mice leptin expression is closely related to their hypoactive functional stage, as evidenced by their low level of UCP1 synthesis and the morphological rearrangement of the lipid content (unilocularity).     

A Vision-Based Guidance System for UAV Navigation and Safe Landing using Natural Landmarks

In this paper a vision-based approach for guidance and safe landing of an Unmanned Aerial Vehicle (UAV) is proposed. The UAV is required to navigate from an initial to a final position in a partially known environment. The guidance system allows a remote user to define target areas from a high resolution aerial or satellite image to determine either the waypoints of the navigation trajectory or the landing area. A feature-based image-matching algorithm finds the natural landmarks and gives feedbacks to an onboard, hierarchical, behaviour-based control system for autonomous navigation and landing. Two algorithms for safe landing area detection are also proposed, based on a feature optical flow analysis. The main novelty is in the vision-based architecture, extensively tested on a helicopter, which, in particular, does not require any artificial landmark (e.g., helipad). Results show the appropriateness of the vision-based approach, which is robust to occlusions and light variations.     

A Visual Global Positioning System for Unmanned Aerial Vehicles Used in Photogrammetric Applications

The combination of photogrammetric aerial and terrestrial recording methods can provide new opportunities for photogrammetric applications. A UAV (Unmanned Aerial Vehicle), in our case a helicopter system, can cover both the aerial and quasi-terrestrial image acquisition methods. A UAV can be equipped with an on-board high resolution camera and a priori knowledge of the operating area where to perform photogrammetric tasks. In this general scenario our paper proposes vision-based techniques for localizing a UAV. Only natural landmarks provided by a feature tracking algorithm will be considered, without the help of visual beacons or landmarks with known positions. The novel idea is to perform global localization, position tracking and localization failure recovery (kidnapping) based only on visual matching between current view and available georeferenced satellite images. The matching is based on SIFT features and the system estimates the position of the UAV and its altitude on the base of the reference image. The vision system replaces the GPS signal combining position information from visual odometry and georeferenced imagery. Georeferenced satellite or aerial images must be available on-board beforehand or downloaded during the flight. The growing availability of high resolution satellite images (e.g., provided by Google Earth or other local information sources) makes this topic very interesting and timely. Experiments with both synthetic (i.e., taken from satellites or datasets and pre elaborated) and real world images have been performed to test the accuracy and the robustness of our method. Results show sufficient performance if compared with common GPS systems and give a good performance also in the altitude estimation, even if in this last case there are only preliminary results.     

Complete classification of raw LIDAR data and 3D reconstruction of buildings

LIDAR (LIght Detection And Ranging) data are a primary data source for digital terrain model (DTM) generation and 3D city models. This paper presents a three-stage framework for a robust automatic classification of raw LIDAR data as buildings, ground and vegetation, followed by a reconstruction of 3D models of the buildings. In the first stage the raw data are filtered and interpolated over a grid. In the second stage, first a double raw data segmentation is performed and then geometric and topological relationships among regions resulting from segmentation are computed and stored in a knowledge base. In the third stage, a rule-based scheme is applied for the classification of the regions. Finally, polyhedral building models are reconstructed by analysing the topology of building outlines, building roof slopes and eaves lines. Results obtained on data sets with different ground point density, gathered over the town of Pavia (Italy) with Toposys and Optech airborne laser scanning systems, are shown to illustrate the effectiveness of the proposed approach.     

A Framework for Simulation and Testing of UAVs in Cooperative Scenarios

Today, Unmanned Aerial Vehicles (UAVs) have deeply modified the concepts of surveillance, Search&Rescue, aerial photogrammetry, mapping, etc. The kinds of missions grow continuously; missions are in most cases performed by a fleet of cooperating autonomous and heterogeneous vehicles. These systems are really complex and it becomes fundamental to simulate any mission stage to exploit benefits of simulations like repeatability, modularity and low cost. In this paper a framework for simulation and testing of UAVs in cooperative scenarios is presented. The framework, based on modularity and stratification in different specialized layers, allows an easy switching from simulated to real environments, thus reducing testing and debugging times, especially in a training context. Results obtained using the proposed framework on some test cases are also reported.