Virtual and rapid prototyping of an underactuated space end effector

A fast and reliable verification of an initial concept is an important need in the field of mechatronics. Usually, the steps for a successful design require multiple iterations involving a sequence of design phases-the initial one and several improvements-and the tests of the resulting prototypes, in a trial and error scheme. Now a day’s software and hardware tools allow for a faster approach, in which the iterations between design and prototyping are by far reduced, even to just one in favorable situation. This work presents the design, manufacturing and testing of a robotic end effector for space applications, realized through virtual prototyping, followed by rapid prototyping realization. The first process allows realizing a mathematical model of the robotic system that, once all the simulations confirm the effectiveness of the design, can be directly used for the rapid prototyping by means of 3D printing. The workflow and the results of the process are described in detail in this paper, showing the qualitative and quantitative evaluation of the performance of both the virtual end effector and the actual physical robotic hand

Intra-Domain Pathlet Routing

Internal routing inside an ISP network is the foundation for lots of services that generate revenue from the ISP's customers. A fine-grained control of paths taken by network traffic once it enters the ISP's network is therefore a crucial means to achieve a top-quality offer and, equally important, to enforce SLAs. Many widespread network technologies and approaches (most notably, MPLS) offer limited (e.g., with RSVP-TE), tricky (e.g., with OSPF metrics), or no control on internal routing paths. On the other hand, recent advances in the research community are a good starting point to address this shortcoming, but miss elements that would enable their applicability in an ISP's network. We extend pathlet routing by introducing a new control plane for internal routing that has the following qualities: it is designed to operate in the internal network of an ISP; it enables fine-grained management of network paths with suitable configuration primitives; it is scalable because routing changes are only propagated to the network portion that is affected by the changes; it supports independent configuration of specific network portions without the need to know the configuration of the whole network; it is robust thanks to the adoption of multipath routing; it supports the enforcement of QoS levels; it is independent of the specific data plane used in the ISP's network; it can be incrementally deployed and it can nicely coexist with other control planes. Besides formally introducing the algorithms and messages of our control plane, we propose an experimental validation in the simulation framework OMNeT++ that we use to assess the effectiveness and scalability of our approach.Comment: 13 figures, 1 tabl

Hyperuniformity in amorphous speckle patterns

Hyperuniform structures possess the ability to confine and drive light, although their fabrication is extremely challenging. Here we demonstrate that speckle patters obtained by a superposition of randomly arranged sources of Bessel beams can be used to generate hyperunifrom scalar fields. By exploiting laser light tailored with a spatial filter, we experimentally produce (without requiring any computational power) a speckle pattern possessing maxima at locations corresponding to a hyperuniform distribution. By properly filtering out intensity fluctuation from the same speckle pattern, it is possible to retrieve an intensity profile satisfying the hyperuniformity requirements. Our findings are supported by extensive numerical simulations.Comment: 13 pages, 7 figure

Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid

Deep neural networks have been widely adopted in recent years, exhibiting impressive performances in several application domains. It has however been shown that they can be fooled by adversarial examples, i.e., images altered by a barely-perceivable adversarial noise, carefully crafted to mislead classification. In this work, we aim to evaluate the extent to which robot-vision systems embodying deep-learning algorithms are vulnerable to adversarial examples, and propose a computationally efficient countermeasure to mitigate this threat, based on rejecting classification of anomalous inputs. We then provide a clearer understanding of the safety properties of deep networks through an intuitive empirical analysis, showing that the mapping learned by such networks essentially violates the smoothness assumption of learning algorithms. We finally discuss the main limitations of this work, including the creation of real-world adversarial examples, and sketch promising research directions.Comment: Accepted for publication at the ICCV 2017 Workshop on Vision in Practice on Autonomous Robots (ViPAR

Prevalence, Characteristics, Association Factors of and Management Strategies for Low Back Pain Among Italian Amateur Cyclists: an Observational Cross-Sectional Study

Background Low back pain (LBP) is a burdensome problem affecting amateur cyclists. This cross-sectional study analysed Italian amateur cycling cohort's demographic and sport-specific characteristics, the prevalence and characteristics of LBP among this population, its possible association factors, the management strategies adopted to deal with LBP and the sample's beliefs among possible LBP triggers. A web-based cross-sectional survey was created. The questionnaire included 56 questions divided into six sections, querying the sample's demographic, clinical, and cycling characteristics. Binomial logistic regression with a Wald backward method was performed to ascertain the effects of some covariates ("Sex", "Age", "Body Mass Index", "Sleep hours", "Work type", "Cycling year", "Number of training sessions per week", "Stretching sessions", "Being supervised by a coach or following a scheduled training", "Other sports practised regularly", "Number of cycling competitions per year", "Past biomechanic visits", "Specific pedal training", "LBP before cycling") on the likelihood of developing LBP in the last 12 months. Results A total of 1274 amateur cyclists answered the survey. The prevalence of LBP appeared to be 55.1%, 26.5% and 10.8% in life, in the last 12 months and the last 4 weeks, respectively. The final model of the logistic regression included the covariates "Sex", "Work type", "Cycling year", "Being supervised by a coach or following a scheduled training", "Other sports practised regularly", "Specific pedal training", "LBP before cycling", among which "Cycling year" (variable "Between 2 and 5 years" vs. "Less than 2 years", OR 0.48, 95% CI [0.26-0.89]), "Being supervised by a coach or following a scheduled training" (OR 0.53, 95% CI [0.37-0.74]), "Specific pedal training" (OR 0.69, 95% CI [0.51-0.94]), and "LBP before cycling" (OR 4.2, 95% CI [3.21-5.40]) were found to be significant. Conclusions The prevalence of LBP among Italian amateur cyclists seems to be less frequent compared to the general population. Moreover, undergoing previous specific pedal training and being supervised by a coach or following scheduled training drew a negative association with LBP development. This evidence highlights the importance of being overseen by specific sport figures that could offer a tailored evidence-based training to reach good physical level and to practise sports safely

Effects of Oil Warm up Acceleration on the Fuel Consumption of Reciprocating Internal Combustion Engines

Abstract The homologation cycle of vehicles for private passenger transportation or for light duty applications considers a cold start from ambient temperature. The most part of harmful substances (≈ 60-65%) are produced during the thermal engine stabilization which occurs in the very of the driving cycle. This strongly influences also engine efficiency, i.e. fuel consumption. The more recent commitments on CO2, therefore, reinforce the concept of reducing warm up time encountering it in the low carbon engine technologies. Due to this importance, engine thermal management has been the subject of a huge interest opening the way to new components, technologies and control strategies. This regards not only the coolant fluid, which undoubtedly influences engine warm up, but also the lubricant:an its heating acceleration produces much faster benefits.. The purpose of this paper is to assess the effect of a faster oil heating during the homologation cycle on the fuel consumption. An experimental campaign has been done on an 3L Iveco F1C engine mounted on a dynamometer test bench operated in order to reproduce the NEDC. The engine OEM has been characterized and the effect of the oil temperature has been studied according to: (a) an external heat source which brings the oil at its stabilized temperature value before engine start, (b) an internal heat source represented by the exhaust gases which almost immediately reach a temperature value able to heat-up the oil. The effects on CO2 emissions during the cycle have been evaluated. The benefits are noteworthy and justify some oil circuit modifications

Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks

Transferability captures the ability of an attack against a machine-learning model to be effective against a different, potentially unknown, model. Empirical evidence for transferability has been shown in previous work, but the underlying reasons why an attack transfers or not are not yet well understood. In this paper, we present a comprehensive analysis aimed to investigate the transferability of both test-time evasion and training-time poisoning attacks. We provide a unifying optimization framework for evasion and poisoning attacks, and a formal definition of transferability of such attacks. We highlight two main factors contributing to attack transferability: the intrinsic adversarial vulnerability of the target model, and the complexity of the surrogate model used to optimize the attack. Based on these insights, we define three metrics that impact an attack's transferability. Interestingly, our results derived from theoretical analysis hold for both evasion and poisoning attacks, and are confirmed experimentally using a wide range of linear and non-linear classifiers and datasets

GRB 110709B in the Induced Gravitational Collapse paradigm

Context: GRB110709B is the first source for which Swift BAT triggered twice, with a time separation of ~10 min. The first emission (Ep. 1) goes from 40s before the 1{\deg} trigger up to 60s after it. The second (Ep. 2) goes from 35s before the 2{\deg} trigger to 100s after it.[...] Within the Induced Gravitational Collapse (IGC) model, we assume the progenitor to be a close binary system composed of a core of an evolved star and a Neutron Star (NS). The evolved star explodes as a Supernova (SN) and ejects material that is partially accreted by the NS. We identify this process with Ep. 1. The accretion process brings the NS over its critical mass, thus gravitationally collapsing to a BH. This process leads to the GRB emission, Ep. 2.[...] Aims: We analyze the spectra and time variability of Ep. 1 and 2 and compute the relevant parameters of the binary progenitor[...] in the IGC paradigm. Methods: We perform a time-resolved spectral analysis of Ep. 1 with a blackbody (BB) plus a power-law (PL) spectral model. We analyze Ep. 2 within the Fireshell model, identifying the Proper-GRB (P-GRB) and simulating the light curve and spectrum. We establish the redshift to be z=0.75 [...]. Results: We find for Ep. 1 a BB temperature following a broken PL with time, with the PL slopes at early and late times \alpha=0 and \beta=-4+/-2, respectively, and a break at t=41.21s. The total energy of Ep. 1 and 2 are E_{iso}^1=1.42x10^{53}erg and E_{iso}^2=2.43x10^{52}erg, respectively. We find at transparency a Lorentz factor \Gamma~173, laboratory radius of 6.04x10^{13}cm, P-GRB observed temperature kT_{P-GRB}=12.36keV, baryon load B=0.0057 and P-GRB energy E_{P-GRB}=3.44x10^{50}erg. [...] Conclusions: We interpret GRB110709B as a member of the IGC sources, together with GRB970828, GRB090618 and GRB101023. The XRT data during Ep. 2 offers an unprecedented tool for improving the diagnostic of GRBs emission.Comment: 12 pages, 17 figures, to appear on A&