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

A virtual sensor for electric vehicles’ state of charge estimation

The estimation of the state of charge is a critical function in the operation of electric vehicles. The battery management system must provide accurate information about the battery state, even in the presence of failures in the vehicle sensors. This article presents a new methodology for the state of charge estimation (SOC) in electric vehicles without the use of a battery current sensor, relying on a virtual sensor, based on other available vehicle measurements, such as speed, battery voltage and acceleration pedal position. The estimator was derived from experimental data, employing support vector regression (SVR), principal component analysis (PCA) and a dual polarization (DP) battery model (BM). It is shown that the obtained model is able to predict the state of charge of the battery with acceptable precision in the case of a failure of the current sensor

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

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

Wave energy farm design in real wave climates: the Italian offshore

publisher: Elsevier articletitle: Wave energy farm design in real wave climates: the Italian offshore journaltitle: Energy articlelink: http://dx.doi.org/10.1016/j.energy.2017.01.094 content_type: article copyright: © 2017 Elsevier Ltd. All rights reserved

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

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting