An Energy-Efficient IoT node for HMI applications based on an ultra-low power Multicore Processor

Developing wearable sensing technologies and unobtrusive devices is paving the way to the design of compelling applications for the next generation of systems for a smart IoT node for Human Machine Interaction (HMI). In this paper we present a smart sensor node for IoT and HMI based on a programmable Parallel Ultra-Low-Power (PULP) platform. We tested the system on a hand gesture recognition application, which is a preferred way of interaction in HMI design. A wearable armband with 8 EMG sensors is controlled by our IoT node, running a machine learning algorithm in real-time, recognizing up to 11 gestures with a power envelope of 11.84 mW. As a result, the proposed approach is capable to 35 hours of continuous operation and 1000 hours in standby. The resulting platform minimizes effectively the power required to run the software application and thus, it allows more power budget for high-quality AFE

Evaluating the features of Museum Websites (The Bologna Report)

MiLE (Milano – Lugano Evaluation Method) is an innovative method for evaluating the quality and usability of hypermedia applications. This paper focuses upon the specific “module” of MiLE concerning cultural heritage applications, synthesizing the results of research carried on by a group of seven museum experts of Bologna (Italy), with the joint coordination of IBC (Institute for the Cultural Heritage of the Emilia Romagna Region) and Politecnico di Milano. The “Bologna group” is composed of different professional figures working in the museum domain: museum curators of artistic, archaeological and historical heritage; museum communication experts; Web sites of cultural institutions’ communication experts. After illustrating the general features of MiLE and the specific features for Cultural Heritage, we will briefly show a few of the results which are to be published in the “Bologna Report”

Vega: A Ten-Core SoC for IoT Endnodes with DNN Acceleration and Cognitive Wake-Up from MRAM-Based State-Retentive Sleep Mode

The Internet-of-Things (IoT) requires endnodes with ultra-low-power always-on capability for a long battery lifetime, as well as high performance, energy efficiency, and extreme flexibility to deal with complex and fast-evolving near-sensor analytics algorithms (NSAAs). We present Vega, an IoT endnode system on chip (SoC) capable of scaling from a 1.7- μW fully retentive cognitive sleep mode up to 32.2-GOPS (at 49.4 mW) peak performance on NSAAs, including mobile deep neural network (DNN) inference, exploiting 1.6 MB of state-retentive SRAM, and 4 MB of non-volatile magnetoresistive random access memory (MRAM). To meet the performance and flexibility requirements of NSAAs, the SoC features ten RISC-V cores: one core for SoC and IO management and a nine-core cluster supporting multi-precision single instruction multiple data (SIMD) integer and floating-point (FP) computation. Vega achieves the state-of-the-art (SoA)-leading efficiency of 615 GOPS/W on 8-bit INT computation (boosted to 1.3 TOPS/W for 8-bit DNN inference with hardware acceleration). On FP computation, it achieves the SoA-leading efficiency of 79 and 129 GFLOPS/W on 32- and 16-bit FP, respectively. Two programmable machine learning (ML) accelerators boost energy efficiency in cognitive sleep and active states

Preferences across the Menstrual Cycle for Masculinity and Symmetry in Photographs of Male Faces and Bodies

Background: Previous studies have shown that women increase their preference for masculinity during the fertile phase of the menstrual cycle. Evidence for a similar preference shift for symmetry is equivocal. These studies have required participants to choose between subtle variations in computer-generated stimuli, and preferences for more natural stimuli have not been investigated. Methodology/Principal Findings: Our study employed photographs of individual males to investigate women’s preferences for face and body masculinity and symmetry across the menstrual cycle. We collected attractiveness ratings from 25 normally cycling women at high- and low-fertility days of the menstrual cycle. Attractiveness ratings made by these women were correlated with independent ratings of masculinity and symmetry provided by different sets of raters. We found no evidence for any cyclic shift in female preferences. Correlations between attractiveness and masculinity, and attractiveness and symmetry did not differ significantly between high- and low-fertility test sessions. Furthermore, there was no significant difference between high- and low-fertility ratings of attractiveness. Conclusions: These results suggest that a menstrual cycle shift in visual preferences for masculinity and symmetry may be too subtle to influence responses to real faces and bodies, and subsequent mate-choice decisions

Confirmation of ovulation from urinary progesterone analysis: assessment of two automated assay platforms

Urinary concentrations of the major progesterone (P4) metabolite pregnanediol-3-glucuronide (PDG) are used to confirm ovulation. We aimed to determine whether automated immunoassay of urinary P4 was as efficacious as PDG to confirm ovulation. Daily urine samples from 20 cycles in 14 healthy women in whom ovulation was dated by ultrasound, and serial weekly samples from 21 women in whom ovulation was unknown were analysed. Daily samples were assayed by two automated P4 immunoassays (Roche Cobas and Abbott Architect) and PDG ELISA. Serial samples were assayed for P4 by Architect and PDG by ELISA. In women with detailed monitoring of ovulation, median (95% CI) luteal phase increase was greatest for PDG, 427% (261–661), 278% (187–354) for P4 Architect and least for P4 Cobas, 146% (130–191), p 0.92). In serial samples classified as (an)ovulatory by PDG, P4 Architect gave ROC AUC 0.95 (95% CI 0.89 to 1.01), with sensitivity and specificity for confirmation of ovulation of 0.90 and 0.91 at a cutoff of 1.67 μmol/mol. Automated P4 may potentially be as efficacious as PDG ELISA but research from a range of clinical settings is required

3. Lo studio della pianura

Il capitolo si suddivide in 3 paragrafi: 3.1 - Il rilevamento geologico e pedologico (a cura di A. Amorosi e M. Guermandi); 3.2 - La stratigrafia del sottosuolo (a cura di A. Amorosi); 3.3 - La cartografia geologica e pedologica (a cura di A. Amorosi e M. Guermandi