Rotational Spectrum of the Formyl Cation, HCO+, to 1.2 THz

A variety of high-quality spectroscopic studies have contributed to knowledge of the formyl cation, HCO+, and its rare isotopologues, but technical limitations have previously limited precise determinations of the far-infrared, or terahertz spectrum. This study extends the microwave, millimeter, and submillimeter spectroscopy of HCO+ into the terahertz range. The resulting measurements and predictions are of sufficient coverage to adequately address astrophysical questions about this species using the Herschel Space Observatory or the Atacama Large Millimeter Array

Submillimeter Spectrum of Formic Acid

We have measured new submillimeter-wave data around 600 GHz and around 1.1 THz for the 13C isotopologue of formic acid and for the two deuterium isotopomers; in each case for both the trans and cis rotamer. For cis-DCOOH and cis-HCOOD in particular only data up to 50 GHz was previously available. For all species the quality and quantity of molecular parameters has been increased providing new measured frequencies and more precise and reliable frequencies in the range of existing and near-future submillimeter and far-infrared astronomical spectroscopy instruments such as Herschel, SOFIA and ALMA

Laboratory measurements and astronomical search of the hso radical

The sulphur chemistry in space is still quite puzzling although several S-bearing species have been detected in the interstellar medium (ISM) in our local system and outside our galaxy. In particular, we observe very large quantities of sulphur harbouring molecules, especially in high-mass star forming regions, that are in perfect accordance with its solar abundance, while in the cold, dense ISM a much lower abundance is observed compared to its solar one. To have a better understanding of the sulphur chemistry in space, it is crucial to derive the broadest picture of the chemical network involving the formation of sulphur species. In this work we report high-resolution spectra of a simple triatomic S-bearing molecule, the HSO radical, with experiments well into the THz region. Thanks to the spectroscopic results of this work, which provide accurate frequency predictions up to the THz, we have also performed a rigorous search for HSO in space. The main outcomes of our work will be briefly presented, showing in particular the synergy between the laboratory and the observations

The center for astrochemical studies at the max planck institute for extraterrestrial physics.

The Center for Astrochemical Studies (CAS), at the Max Planck Institute for Extraterrestrial Physics (MPE) in Garching, has been founded to incorporate scientists with different background to elucidate the physical-chemical processes that lead to the formation of stars and planets. The CAS group includes experts in observations (including millimetre and sub-millimetre interferometry, radio and infrared telescopes), theory (physical processes and dynamics, gas-grain chemical processes and dust evolution, molecular astrophysics and collisional/rate coefficients), and laboratory. The latter is mainly focused on spectroscopic characterisation of molecular species relevant in space, including ions, radicals and astronomically complex organic molecules. In this talk the laboratory group of the CAS will be briefly presented, including current projects and planned experiments

Exploring Artificial Neural Networks Efficiency in Tiny Wearable Devices for Human Activity Recognition

The increasing diffusion of tiny wearable devices and, at the same time, the advent of machine learning techniques that can perform sophisticated inference, represent a valuable opportunity for the development of pervasive computing applications. Moreover, pushing inference on edge devices can in principle improve application responsiveness, reduce energy consumption and mitigate privacy and security issues. However, devices with small size and low-power consumption and factor form, like those dedicated to wearable platforms, pose strict computational, memory, and energy requirements which result in challenging issues to be addressed by designers. The main purpose of this study is to empirically explore this trade-off through the characterization of memory usage, energy consumption, and execution time needed by different types of neural networks (namely multilayer and convolutional neural networks) trained for human activity recognition on board of a typical low-power wearable device. Through extensive experimental results, obtained on a public human activity recognition dataset, we derive Pareto curves that demonstrate the possibility of achieving a 4× reduction in memory usage and a 36× reduction in energy consumption, at fixed accuracy levels, for a multilayer Perceptron network with respect to more sophisticated convolution network model

A Review on Blockchain for the Internet of Medical Things: Definitions, Challenges, Applications, and Vision

none3noNowadays, there are a lot of new mobile devices that have the potential to assist healthcare professionals when working and help to increase the well-being of the people. These devices comprise the Internet of Medical Things, but it is generally difficult for healthcare institutions to meet compliance of their systems with new medical solutions efficiently. A technology that promises the sharing of data in a trust-less scenario is the Distributed Ledger Technology through its properties of decentralization, immutability, and transparency. The Blockchain and the Internet of Medical Things can be considered as at an early stage, and the implementations successfully applying the technology are not so many. Some aspects covered by these implementations are data sharing, interoperability of systems, security of devices, the opportunity of data monetization and data ownership that will be the focus of this review.openGioele Bigini;Valerio Freschi;Emanuele LattanziBigini, Gioele; Freschi, Valerio; Lattanzi, Emanuel

Supporting Preemptive Multitasking in Wireless Sensor Networks

Supporting the concurrent execution of multiple tasks on lightweight sensor nodes could enable the deployment of independent applications on a shared wireless sensor network, thus saving cost and time by exploiting infrastructures which are typically underutilized if dedicated to a single task. Existing approaches to wireless sensor network programming provide limited support to concurrency at the cost of reducing the generality and the expressiveness of the language adopted. This paper presents a java-compatible platform for wireless sensor networks which provides a thorough support to preemptive multitasking while allowing the programmers to write their applications in java. The proposed approach has been implemented and tested on top of VirtualSense, an ultra-low-power wireless sensor mote providing a java-compatible runtime environment. Performance and scalability of the solution are discussed in light of extensive experiments performed on representative benchmarks

A Statistical Geometry Approach to Distance Estimation in Wireless Sensor Networks

none3Algorithmic approaches to the estimation of pairwise distances between the nodes of a wireless sensor network are highly attractive to provide information for routing and localization without requiring specific hardware to be added to cost/resource-constrained nodes. This paper exploits statistical geometry to derive robust estimators of the pairwise Euclidean distances from topological information typically available in any network. Extensive Monte Carlo experiments conducted on synthetic benchmarks demonstrate the improved quality of the proposed estimators with respect to the state of the art.openV. Freschi; E. Lattanzi; A. BoglioloFreschi, Valerio; Lattanzi, Emanuele; Bogliolo, Alessandr

Unstructured Handwashing Recognition using Smartwatch to Reduce Contact Transmission of Pathogens

Current guidelines from the World Health Organization indicate that the SARS-CoV-2 coronavirus, which results in the novel coronavirus disease (COVID-19), is transmitted through respiratory droplets or by contact. Contact transmission occurs when contaminated hands touch the mucous membrane of the mouth, nose, or eyes so hands hygiene is extremely important to prevent the spread of the SARSCoV-2 as well as of other pathogens. The vast proliferation of wearable devices, such as smartwatches, containing acceleration, rotation, magnetic field sensors, etc., together with the modern technologies of artificial intelligence, such as machine learning and more recently deep-learning, allow the development of accurate applications for recognition and classification of human activities such as: walking, climbing stairs, running, clapping, sitting, sleeping, etc. In this work, we evaluate the feasibility of a machine learning based system which, starting from inertial signals collected from wearable devices such as current smartwatches, recognizes when a subject is washing or rubbing its hands. Preliminary results, obtained over two different datasets, show a classification accuracy of about 95% and of about 94% for respectively deep and standard learning techniques