ROBI’: A prototype mobile manipulator for agricultural applications

The design of ROBI', a prototype mobile manipulator for agricultural applications devised following low-cost, low-weight, simplicity, flexibility and modularity requirements, is presented in this work. The mechanical design and the selection of the main components of the motion control system, including sensors and in-wheel motors, is described. The kinematic and dynamic models of the robot are also derived, with the aim to support the design of a trajectory tracking system and to make a preliminary assessment of the design choices, as well. Finally, two simulations, one~specifically related to a realistic trajectory in an agricultural field, show the validity of these choices

Human segmentation in surveillance video with deep learning

Advanced intelligent surveillance systems are able to automatically analyze video of surveillance data without human intervention. These systems allow high accuracy of human activity recognition and then a high-level activity evaluation. To provide such features, an intelligent surveillance system requires a background subtraction scheme for human segmentation that captures a sequence of images containing moving humans from the reference background image. This paper proposes an alternative approach for human segmentation in videos through the use of a deep convolutional neural network. Two specific datasets were created to train our network, using the shapes of 35 different moving actors arranged on background images related to the area where the camera is located, allowing the network to take advantage of the entire site chosen for video surveillance. To assess the proposed approach, we compare our results with an Adobe Photoshop tool called Select Subject, the conditional generative adversarial network Pix2Pix, and the fully-convolutional model for real-time instance segmentation Yolact. The results show that the main benefit of our method is the possibility to automatically recognize and segment people in videos without constraints on camera and people movements in the scene (Video, code and datasets are available at http://graphics.unibas.it/www/HumanSegmentation/index.md.html)

Solid and Effective Upper Limb Segmentation in Egocentric Vision

Upper limb segmentation in egocentric vision is a challenging and nearly unexplored task that extends the well-known hand localization problem and can be crucial for a realistic representation of users' limbs in immersive and interactive environments, such as VR/MR applications designed for web browsers that are a general-purpose solution suitable for any device. Existing hand and arm segmentation approaches require a large amount of well-annotated data. Then different annotation techniques were designed, and several datasets were created. Such datasets are often limited to synthetic and semi-synthetic data that do not include the whole limb and differ significantly from real data, leading to poor performance in many realistic cases. To overcome the limitations of previous methods and the challenges inherent in both egocentric vision and segmentation, we trained several segmentation networks based on the state-of-the-art DeepLabv3+ model, collecting a large-scale comprehensive dataset. It consists of 46 thousand real-life and well-labeled RGB images with a great variety of skin colors, clothes, occlusions, and lighting conditions. In particular, we carefully selected the best data from existing datasets and added our EgoCam dataset, which includes new images with accurate labels. Finally, we extensively evaluated the trained networks in unconstrained real-world environments to find the best model configuration for this task, achieving promising and remarkable results in diverse scenarios. The code, the collected egocentric upper limb segmentation dataset, and a video demo of our work will be available on the project page1

A Preliminary Investigation into a Deep Learning Implementation for Hand Tracking on Mobile Devices

Hand tracking is an essential component of computer graphics and human-computer interaction applications. The use of RGB camera without specific hardware and sensors (e.g., depth cameras) allows developing solutions for a plethora of devices and platforms. Although various methods were proposed, hand tracking from a single RGB camera is still a challenging research area due to occlusions, complex backgrounds, and various hand poses and gestures. We present a mobile application for 2D hand tracking from RGB images captured by the smartphone camera. The images are processed by a deep neural network, modified specifically to tackle this task and run on mobile devices, looking for a compromise between performance and computational time. Network output is used to show a 2D skeleton on the user's hand. We tested our system on several scenarios, showing an interactive hand tracking level and achieving promising results in the case of variable brightness and backgrounds and small occlusions

Electromagnetic shielding properties of LPBF produced Fe2.9wt.%Si alloy

Ferromagnetic materials are used in various applications such as rotating electrical machines, wind turbines, electromagnetic shielding, transformers, and electromagnets. Compared to hard magnetic materials, their hysteresis cycles are featured by low values of coercive magnetic field and high permeability. The application of additive manufacturing to ferromagnetic materials is gaining more and more attraction. Indeed, thanks to a wider geometrical freedom, new topological optimized shapes for stator/rotor shapes can be addressed to enhance electric machines performances. However, the properties of the laser powder bed fusion (LPBF) processed alloy compared to conventionally produced counterpart must be still addressed. Accordingly, this paper presents for the first time the use of the LPBF for the manufacturing of Fe2.9wt.%Si electromagnetic shields. The process parameter selection material microstructure and the magnetic shielding factor are characterized

Adaptive manifold-mapping using multiquadric interpolation applied to linear actuator design

In this work a multilevel optimization strategy based on manifold-mapping combined with multiquadric interpolation for the coarse model construction is presented. In the proposed approach the coarse model is obtained by interpolating the fine model using multiquadrics in a small number of points. As the algorithms iterates, the response surface model is improved by enriching the set of interpolation points. This approach allows to accurately solve the TEAM Workshop Problem 25 using as little as 33 finite element simulations. Furthermore is allows a robust sizing optimization of a cylindrical voice-coil actuator with seven design variables. Further analysis is required to gain a better understand of the role that the initial coarse model accuracy plays the convergence of the algorithm. The proposed allows to carry out such analysis by varying the number of points included in the initial response surface model. The effect of the trust-region stabilization in the presence of manifolds of equivalent solutions is also a topic of further investigations

Characterization of LPBF Produced Fe2.9wt.%Si for Electromagnetic Actuator

This study aims to produce Fe2.9wt.%Si ferromagnetic material via laser powder bed fusion (L-PBF) for the realization of electromagnetic actuators (EMA). This study is necessary as there are no documents in scientific literature regarding the manufacturing of Iron-Silicon plungers using the L-PBF additive manufacturing (AM) technique. The microstructure, and magnetic properties were characterized using various techniques. The results indicate that the samples produced via L-PBF process exhibit good magnetic properties (μ = 748, H C= 87.7 [A/m] ) especially after annealing treatment at 1200° C for 1h (μ = 3224, H C= 69.1 [A/m]), making it a promising material for use in electromagnetic actuators

Oxidative stress biomarkers in Fabry disease: is there a room for them?

Background: Fabry disease (FD) is an X-linked lysosomal storage disorder, caused by deficient activity of the alpha-galactosidase A enzyme leading to progressive and multisystemic accumulation of globotriaosylceramide. Recent data point toward oxidative stress signalling which could play an important role in both pathophysiology and disease progression. Methods: We have examined oxidative stress biomarkers [Advanced Oxidation Protein Products (AOPP), Ferric Reducing Antioxidant Power (FRAP), thiolic groups] in blood samples from 60 patients and 77 healthy controls. Results: AOPP levels were higher in patients than in controls (p < 0.00001) and patients presented decreased levels of antioxidant defences (FRAP and thiols) with respect to controls (p < 0.00001). In a small group of eight treatment-naïve subjects with FD-related mutations, we found altered levels of oxidative stress parameters and incipient signs of organ damage despite normal lyso-Gb3 levels. Conclusions: Oxidative stress occurs in FD in both treated and naïve patients, highlighting the need of further research in oxidative stress-targeted therapies. Furthermore, we found that oxidative stress biomarkers may represent early markers of disease in treatment-naïve patients with a potential role in helping interpretation of FD-related mutations and time to treatment decision