A field-measurements-based LoRa network planning tool

Long range (LoRa) transmission technology enables energy-constrained devices such as the tiny sensor systems used in internet-of-things applications that are distributed over wide areas while still being able to establish appropriate connectivity. This has resulted in the development of an exponentially increasing number of different solutions and services based on LoRa, be they dedicated to the long-term monitoring of distributed plants and infrastructures or to human-centred applications such as safety-oriented sensor systems for use in the workplace. In dense LoRa networks, predicting the number of supported nodes in relation to their position and the propagation environment is essential for ensuring reliable and stable communication and minimising costs. In this paper, after comparing different path loss models based on a field measurement campaign for LoRa received signal strength indicator values within a university campus, two main modifications of the LoRa simulator tool were implemented. These were aimed at improving the accuracy of the prediction of the number of sustainable nodes in relation to the target data extraction rate set. The simulations based on field measurements demonstrated that through an improved path loss evaluation and the use of three gateways, the number of nodes could be increased theoretically from around 100 to around 6,000

04/07/2010 - Koresh Dance Company.pdf

Simultaneous and multipoint assessment of vibration velocities is an important issue for the development of advanced noncontact vibrometers. In this article a novel fiber optic vibrometer is presented. The architecture of the sensor is based on a simple optical layout and it is characterized by multiple fiber optic interferometric sensors which are operated in the homodyne mode. Optical configuration and operation of the single-point version of the sensor, as well as the two-points measurement version, are described and typical measured signals with the operating range are shown. The sensor can easily be configured in order to perform a higher number of point measurements. Some details regarding signal acquisition and processing are also given and the ways in which Doppler demodulation is performed are discussed. Finally tests with sinusoidal target excitation in the range 0–1.8 kHz have been conducted

The importance of physiological data variability in wearable devices for digital health applications

This paper aims at characterizing the variability of physiological data collected through a wearable device (Empatica E4), given that both intra- and inter-subject variability play a pivotal role in digital health applications, where Artificial Intelligence (AI) techniques have become popular. Inter-beat intervals (IBIs), ElectroDermal Activity (EDA) and Skin Temperature (SKT) signals have been considered and variability has been evaluated in terms of general statistics (mean and standard deviation) and coefficient of variation. Results show that both intra- and inter-subject variability values are significant, especially when considering those parameters describing how the signals vary over time. Moreover, EDA seems to be the signal characterized by the highest variability, followed by IBIs, contrary to SKT that results more stable. This variability could affect AI algorithms in classifying signals according to particular discriminants (e.g. emotions, daily activities, etc.), taking into account the dual role of variability: hindering a net distinction between classes, but also making algorithms more robust for deep learning purposes thanks to the consideration of a wide test population. Indeed, it is worthy to note that variability plays a fundamental role in the whole measurement chain, characterizing data reliability and impacting on the final results accuracy and consequently on decision-making processes

Heartbeat detection by laser doppler vibrometry and machine learning

Background: Heartbeat detection is a crucial step in several clinical fields. Laser Doppler Vibrometer (LDV) is a promising non-contact measurement for heartbeat detection. The aim of this work is to assess whether machine learning can be used for detecting heartbeat from the carotid LDV signal. Methods: The performances of Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN) were compared using the leave-one-subject-out cross-validation as the testing protocol in an LDV dataset collected from 28 subjects. The classification was conducted on LDV signal windows, which were labeled as beat, if containing a beat, or no-beat, otherwise. The labeling procedure was performed using electrocardiography as the gold standard. Results: For the beat class, the f1-score (f 1) values were 0.93, 0.93, 0.95, 0.96 for RF, DT, KNN and SVM, respectively. No statistical differences were found between the classifiers. When testing the SVM on the full-length (10 min long) LDV signals, to simulate a real-world application, we achieved a median macro-f 1 of 0.76. Conclusions: Using machine learning for heartbeat detection from carotid LDV signals showed encouraging results, representing a promising step in the field of contactless cardiovascular signal analysis

Assessment of Domestic Well-Being: From Perception to Measurement

Nowadays, there are plenty of sensing devices that enable the measurement of physiological, environmental, and behavioral parameters of people 24 hours a day, seven days a week and provide huge quantities of different data. Data and signals coming from sensing devices, installed in indoor or outdoor environments or often worn by the users, generate heterogeneous and complex structured datasets, most of the time not uniformly structured. The artificial intelligence (AI) algorithms applied to these sets of data have demonstrated capabilities to infer indices related to a subject's status and well-being [1]. Well-being is a key parameter in the World Health Organization (WHO) definition of health, considering its physical, mental, and social spheres. Quantitatively assessing a subject's well-being is of paramount importance if we want to assess the whole status of a person, which is particularly useful in the case of ageing people living alone. Assessment allows for continuous remote monitoring to improve people's quality of life (QoL) according to their perceptions, needs, and preferences. Technology undoubtedly plays a pivotal role in this regard, providing us new tools to support the objective evaluation of a subject's status, including her/his perception of the living environment. Its potential is huge, also in terms of support to the healthcare system and ageing people; however, there are several engineering challenges to consider, especially in terms of sensors integrability, connectivity, and metrological performance, in order to obtain reliable and accurate measurement systems

Metrological characterization of a therapeutic device for pressure wave therapy

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IR Kuiper Belt Constraints

We compute the temperature and IR signal of particles of radius $a$ and albedo $\alpha$ at heliocentric distance $R$, taking into account the emissivity effect, and give an interpolating formula for the result. We compare with analyses of COBE DIRBE data by others (including recent detection of the cosmic IR background) for various values of heliocentric distance, $R$, particle radius, $a$, and particle albedo, $\alpha$. We then apply these results to a recently-developed picture of the Kuiper belt as a two-sector disk with a nearby, low-density sector (40<R<50-90 AU) and a more distant sector with a higher density. We consider the case in which passage through a molecular cloud essentially cleans the Solar System of dust. We apply a simple model of dust production by comet collisions and removal by the Poynting-Robertson effect to find limits on total and dust masses in the near and far sectors as a function of time since such a passage. Finally we compare Kuiper belt IR spectra for various parameter values.Comment: 34 pages, LaTeX, uses aasms4.sty, 11 PostScript figures not embedded. A number of substantive comments by a particularly thoughtful referee have been addresse

Novel insights into the transport mechanism of the human amino acid transporter LAT1 (SLC7A5) : probing critical residues for substrate translocation

BACKGROUND: LAT1 (SLC7A5) is the transport competent unit of the heterodimer formed with the glycoprotein CD98 (SLC3A2). It catalyzes antiport of His and some neutral amino acids such as Ile, Leu, Val, Cys, Met, Gln and Phe thus being involved in amino acid metabolism. Interestingly, LAT1 is over-expressed in many human cancers that are characterized by increased demand of amino acids. Therefore LAT1 was recently acknowledged as a novel target for cancer therapy. However, knowledge on molecular mechanism of LAT1 transport is still scarce. METHODS: Combined approaches of bioinformatics, site-directed mutagenesis, chemical modification, and transport assay in proteoliposomes, have been adopted to unravel dark sides of human LAT1 structure/function relationships. RESULTS: It has been demonstrated that residues F252, S342, C335 are crucial for substrate recognition and C407 plays a minor role. C335 and C407 cannot be targeted by SH reagents. The transporter has a preferential dimeric structure and catalyzes an antiport reaction which follows a simultaneous random mechanism. CONCLUSIONS: Critical residues of the substrate binding site of LAT1 have been probed. This site is not freely accessible by molecules other than substrate. Similarly to LeuT, K+ has some regulatory properties on LAT1. GENERAL SIGNIFICANCE: The collected data represent a solid basis for deciphering molecular mechanism underlying LAT1 function