E-Health & Innovation to Overcome Barriers in Neuromuscular Diseases. Report from the 3rd eNMD Congress: Pisa, Italy, 29-30 October 2021.

Neuromuscular diseases (NMDs), in their phenotypic heterogeneity, share quite invariably common issues that involve several clinical and socio-economical aspects, needing a deep critical analysis to develop better management strategies. From diagnosis to treatment and follow-up, the development of technological solutions can improve the detection of several critical aspects related to the diseases, addressing both the met and unmet needs of clinicians and patients. Among several aspects of the digital transformation of health and care, this congress expands what has been learned from previous congresses editions on applicability and usefulness of technological solutions in NMDs. In particular the focus on new solutions for remote monitoring provide valuable insights to increase disease-specific knowledge and trigger prompt decision-making. In doing that, several perspectives from different areas of expertise were shared and discussed, pointing out strengths and weaknesses on the current state of the art on topic, suggesting new research lines to advance technology in this specific clinical field.

ECG-Based Stress Detection and Productivity Factors Monitoring: The Real-Time Production Factory System.

Productivity and production quality have become primary goals for the success of companies in all industrial and manufacturing sectors. Performance in terms of productivity is influenced by several factors including machinery efficiency, work environment and safety conditions, production processes organization, and aspects related to workers' behavior (human factors). In particular, work-related stress is among the human factors that are most impactful and difficult to capture. Thus, optimizing productivity and quality in an effective way requires considering all these factors simultaneously. The proposed system aims to detect workers' stress and fatigue in real time using wearable sensors and machine learning techniques and also integrate all data regarding the monitoring of production processes and the work environment into a single platform. This allows comprehensive multidimensional data analysis and correlation research, enabling organizations to improve productivity through appropriate work environments and sustainable processes for workers. The on-field trial demonstrated the technical and operational feasibility of the system, its high degree of usability, and the ability to detect stress from ECG signals exploiting a 1D Convolutional Neural Network (accuracy 88.4%, F1-score 0.90).

SpaceART SpaceWire Sniffer for Link Monitoring: A Complete Communication Analysis in a Time-Constrained Test Scenario.

The on-board communication standard adopted in current generation space missions of the European Space Agency, and many other agencies as well, is SpaceWire (SpW). In a SpW network, data are exchanged as well-formed packets, whose structure offers low packet overhead and allows developers to tailor their implementation for SpW applications. The development of SpW-compliant devices requires a specific set of test instruments, namely Electrical Ground Support Equipment (EGSE), to verify the correct functionality of SpW units under test. An example of a SpW EGSE is the SpaceART EGSE Emulator, an EGSE for generation and processing of SpW packets for a time-constrained use-case scenario. This EGSE has been developed to address a mission-related SpW communication between a device and an Instrument Control Unit (ICU). It has allowed the generation and processing of specific SpW packets, which cannot be provided by the mostly used general-purpose SpW EGSEs. In this scope, the SpaceART SpW Sniffer, a SpW link analyser for unobtrusive monitoring of SpW link, has been employed to run a comprehensive set of tests and provide further information on the considered scenario. The SpaceART EGSE Emulator-ICU communication has been thoroughly tested through the SpaceART SpW Sniffer, unobtrusively analysing the exchanged data and allowing to assess the compliance with the defined time constraints from an external point of view. The use of the Sniffer has been crucial for testing the on-board communication, providing important support for the success of the mission employing the tested ICU.

Industrial functional safety assessment for WSN using QoS metrics.

Wireless Sensor Networks are increasingly getting deployed for the safety use cases in industrial applications. While several research papers discuss about the Quality & Reliability improvement techniques in WSN systems to achieve minimal delay, higher node life, optimal routing etc., very limited work is witnessed on assessment of safety integrity levels of WSN systems. In this paper we tried to bridge this gap by bringing out a QoS metric-based safety integrity assessment for the end-to-end industrial Wireless Sensor Network (WSN) system. To identify relevant QoS metrics for monitoring the safety integrity levels, we also bring out a 4-step mapping methodology to link the QoS metrics and communication defenses/safety mechanisms. This mapping approach is expected to serve the network safety design engineers. Finally, a simulation case example is discussed to illustrate safety integrity assessment and we conclude by bringing out future research opportunities to improve safety integrity levels of industrial WSN systems.

A Post-training Quantization Method for the Design of Fixed-Point-Based FPGA/ASIC Hardware Accelerators for LSTM/GRU Algorithms.

Recurrent Neural Networks (RNNs) have become important tools for tasks such as speech recognition, text generation, or natural language processing. However, their inference may involve up to billions of operations and their large number of parameters leads to large storage size and runtime memory usage. These reasons impede the adoption of these models in real-time, on-the-edge applications. Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs) have emerged as promising solutions for the hardware acceleration of these algorithms, thanks to their degree of customization of compute data paths and memory subsystems, which makes them take the maximum advantage from compression techniques for what concerns area, timing, and power consumption. In contrast to the extensive study in compression and quantization for plain feed forward neural networks in the literature, little attention has been paid to reducing the computational resource requirements of RNNs. This work proposes a new effective methodology for the post-training quantization of RNNs. In particular, we focus on the quantization of Long Short-Term Memory (LSTM) RNNs and Gated Recurrent Unit (GRU) RNNs. The proposed quantization strategy is meant to be a detailed guideline toward the design of custom hardware accelerators for LSTM/GRU-based algorithms to be implemented on FPGA or ASIC devices using fixed-point arithmetic only. We applied our methods to LSTM/GRU models pretrained on the IMDb sentiment classification dataset and Penn TreeBank language modelling dataset, thus comparing each quantized model to its floating-point counterpart. The results show the possibility to achieve up to 90% memory footprint reduction in both cases, obtaining less than 1% loss in accuracy and even a slight improvement in the Perplexity per word metric, respectively. The results are presented showing the various trade-offs between memory footprint reduction and accuracy changes, demonstrating the benefits of the proposed methodology even in comparison with other works from the literature.

Design and Test of an Integrated Random Number Generator with All-Digital Entropy Source.

In the cybersecurity field, the generation of random numbers is extremely important because they are employed in different applications such as the generation/derivation of cryptographic keys, nonces, and initialization vectors. The more unpredictable the random sequence, the higher its quality and the lower the probability of recovering the value of those random numbers for an adversary. Cryptographically Secure Pseudo-Random Number Generators (CSPRNGs) are random number generators (RNGs) with specific properties and whose output sequence has such a degree of randomness that it cannot be distinguished from an ideal random sequence. In this work, we designed an all-digital RNG, which includes a Deterministic Random Bit Generator (DRBG) that meets the security requirements for cryptographic applications as CSPRNG, plus an entropy source that showed high portability and a high level of entropy. The proposed design has been intensively tested against both NIST and BSI suites to assess its entropy and randomness, and it is ready to be integrated into the European Processor Initiative (EPI) chip.

Optimising Speaker-Dependent Feature Extraction Parameters to Improve Automatic Speech Recognition Performance for People with Dysarthria.

Within the field of Automatic Speech Recognition (ASR) systems, facing impaired speech is a big challenge because standard approaches are ineffective in the presence of dysarthria. The first aim of our work is to confirm the effectiveness of a new speech analysis technique for speakers with dysarthria. This new approach exploits the fine-tuning of the size and shift parameters of the spectral analysis window used to compute the initial short-time Fourier transform, to improve the performance of a speaker-dependent ASR system. The second aim is to define if there exists a correlation among the speaker's voice features and the optimal window and shift parameters that minimises the error of an ASR system, for that specific speaker. For our experiments, we used both impaired and unimpaired Italian speech. Specifically, we used 30 speakers with dysarthria from the IDEA database and 10 professional speakers from the CLIPS database. Both databases are freely available. The results confirm that, if a standard ASR system performs poorly with a speaker with dysarthria, it can be improved by using the new speech analysis. Otherwise, the new approach is ineffective in cases of unimpaired and low impaired speech. Furthermore, there exists a correlation between some speaker's voice features and their optimal parameters.

The Fight against COVID-19 on the Multi-Protease Front and Surroundings: Could an Early Therapeutic Approach with Repositioning Drugs Prevent the Disease Severity?

The interaction between the membrane spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the transmembrane angiotensin-converting enzyme 2 (ACE2) receptor of the human epithelial host cell is the first step of infection, which has a critical role for viral pathogenesis of the current coronavirus disease-2019 (COVID-19) pandemic. Following the binding between S1 subunit and ACE2 receptor, different serine proteases, including TMPRSS2 and furin, trigger and participate in the fusion of the viral envelope with the host cell membrane. On the basis of the high virulence and pathogenicity of SARS-CoV-2, other receptors have been found involved for viral binding and invasiveness of host cells. This review comprehensively discusses the mechanisms underlying the binding of SARS-CoV2 to ACE2 and putative alternative receptors, and the role of potential co-receptors and proteases in the early stages of SARS-CoV-2 infection. Given the short therapeutic time window within which to act to avoid the devastating evolution of the disease, we focused on potential therapeutic treatments-selected mainly among repurposing drugs-able to counteract the invasive front of proteases and mild inflammatory conditions, in order to prevent severe infection. Using existing approved drugs has the advantage of rapidly proceeding to clinical trials, low cost and, consequently, immediate and worldwide availability.

Telemonitoring in the Covid-19 Era: The Tuscany Region Experience.

Covid-19 has brought many difficulties in the management of infected and high-risk patients. Telemedicine platforms can really help in this situation, since they allow remotely monitoring Covid-19 patients, reducing the risk for the doctors, without decreasing the efficiency of the therapies and while alleviating patients' mental issues. In this paper, we present the entire architecture and the experience of using the Tel.Te.Covid19 telemedicine platform. Projected for the treatment of chronic diseases, it has been technologically updated for the management of Covid-19 patients with the support of a group of doctors in the territory when the pandemic arrived, introducing new sensors and functionalities (e.g., the familiar use and video calls). In Tuscany (Central Italy), during the first wave of outbreak, a model for enrolling patients was created and tested. Because of the positive results, the latter has been then adopted in the second current wave. The Tel.Te.Covid19 platform has been used by 40 among general practitioners and doctors of continuity care and about 180 symptomatic patients since March 2020. Both patients and doctors have good opinion of the platform, and no hospitalisations or deaths occurred for the monitored patients, reducing also the impact on the National Healthcare System.

On the Frequency Carrier Offset and Symbol Timing Estimation for CCSDS 131.2-B-1 High Data-Rate Telemetry Receivers.

In recent years there have been significant developments in satellite transmitter technology to follow the rapid innovation of sensors on-board new satellites. The CCSDS 131.2-B-1 standard for telemetry downlink, released in 2012, is part of the next generation of standards that aims to support the increased data-rate caused by these improvements in resolution. As a result of its relative novelty, this standard currently lacks in-depth analysis by researchers, but it is also strongly supported by the European Space Agency (ESA) for future missions. For these reasons, it seems important to evaluate how major receiver sub-components, such as timing recovery and carrier frequency correction, can be designed and implemented in new receivers that support this standard. The timing error detectors (TED) and frequency error detectors (FED) were therefore studied on the specific peculiarities of CCSDS 131.2-B-1 in its usual environment of Low Earth Orbit (LEO). Estimators have been evaluated highlighting performances, trade-offs and peculiarities of each one with respect to corresponding architectural choices. Finally, a receiver architecture derived from the paper considerations is proposed in the aim of supporting very different mission scenarios. Specifically, the realized architecture employs a parallel feedforward estimator for the timing recovery section and a novel multi-algorithm feedback frequency correction loop to efficiently cover both low symbol rates (5 Mbaud) and high data-rates (up to 500 Mbaud). This solution represents a good trade-off to support these scenarios in a very compact footprint by pushing the clock frequency to the FPGA limit. The FPGA resources occupation on a Zynq Ultrascale+ RFSoC XCZU28DR FPGA is 5202 LUT, 4851 FF, 5 BRAM, and 21 DSP for the timing recovery part, while the frequency recovery section occupies 1723 LUT, 1511 FF, 2.5 BRAM and 32 DSP.

An Easily Integrable Industrial System for Gamma Spectroscopic Analysis and Traceability of Stones and Building Materials.

In the building material and stones market, lots of restrictions are coming in different world zones. In Europe, a recent regulatory set up the maximum level of radiological emissions for materials intended for use in public and private building structures. For this reason, companies need to have a very efficient radiological measurements system in their production chain, in order to respect all the rules and to be competitive in the world market. This article describes CORSAIR, a Cloud-Oriented Measurement System for Radiological Investigation and Traceability of Stones. Our cyber-physical system consists of sensing nodes network connected to a data collection gateway through LoRaWAN protocol, and interfaces with a centralized cloud application. CORSAIR introduces a fast, repeatable, real-time and non-destructive method to measure radiological emissions and other parameters of each single building material item, uniquely identified by an applied RFID tag. The validity of this system is confirmed by in-situ measurement campaign compared with high-precision laboratory analysis. The results demonstrate the accuracy of the CORSAIR sensor and the possibility to easily integrate it in the company production chain without any change.

A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based Navigation.

Individual spacecraft manual navigation by human operators from ground station is expected to be an emerging problem as the number of spacecraft for space exploration increases. Hence, as an attempt to reduce the burden to control multiple spacecraft, future missions will employ smart spacecraft able to navigate and operate autonomously. Recently, image-based optical navigation systems have proved to be promising solutions for inexpensive autonomous navigation. In this paper, we propose a robust image processing pipeline for estimating the center and radius of planets and moons in an image taken by an on-board camera. Our custom image pre-processing pipeline is tailored for resource-constrained applications, as it features a computationally simple processing flow with a limited memory footprint. The core of the proposed pipeline is a best-fitting model based on the RANSAC algorithm that is able to handle images corrupted with Gaussian noise, image distortions, and frame drops. We report processing time, pixel-level error of estimated body center and radius and the effect of noise on estimated body parameters for a dataset of synthetic images.

Cryptographically Secure Pseudo-Random Number Generator IP-Core Based on SHA2 Algorithm.

In the context of growing the adoption of advanced sensors and systems for active vehicle safety and driver assistance, an increasingly important issue is the security of the information exchanged between the different sub-systems of the vehicle. Random number generation is crucial in modern encryption and security applications as it is a critical task from the point of view of the robustness of the security chain. Random numbers are in fact used to generate the encryption keys to be used for ciphers. Consequently, any weakness in the key generation process can potentially leak information that can be used to breach even the strongest cipher. This paper presents the architecture of a high performance Random Number Generator (RNG) IP-core, in particular a Cryptographically Secure Pseudo-Random Number Generator (CSPRNG) IP-core, a digital hardware accelerator for random numbers generation which can be employed for cryptographically secure applications. The specifications used to develop the proposed project were derived from dedicated literature and standards. Subsequently, specific architecture optimizations were studied to achieve better timing performance and very high throughput values. The IP-core has been validated thanks to the official NIST Statistical Test Suite, in order to evaluate the degree of randomness of the numbers generated in output. Finally the CSPRNG IP-core has been characterized on relevant Field Programmable Gate Array (FPGA) and ASIC standard-cell technologies.

A Complete EGSE Solution for the SpaceWire and SpaceFibre Protocol Based on the PXI Industry Standard.

This article presents a complete test equipment for the promising on-board serial high-speed SpaceFibre protocol, published by the European Committee for Space Standardization. SpaceFibre and SpaceWire are standard communication protocols for the latest technology sensor devices intended for on-board satellites and spacecrafts in general, especially for sensors based on image acquisition, such as scanning radiometers or star-tracking devices. The new design aims to provide the enabling tools to the scientific community and the space industry in order to promote the adoption of open standards in space on-board communications for current- and future-generation spacecraft missions. It is the first instrument expressly designed for LabVIEW users, and it offers tools and advanced features for the test and development of new SpaceFibre devices. In addition, it supports the previous SpaceWire standard and cross-communications. Thanks to novel cutting-edge design methods, the system complex architecture can be implemented on natively supported LabVIEW programmable devices. The presented system is highly customizable in terms of interface support and is provided with a companion LabVIEW application and LabVIEW Application Programming Interface (API) for user custom automated test-chains. It offers real-time capabilities and supports data rates up to 6.25 Gbps.The proposed solutions is then fairly compared with other currently available SpaceFibre test equipment. Its comprehensiveness and modularity make it suitable for either on-board device developments or spacecraft system integrations.

Embedded Systems and TensorFlow Frameworks as Assistive Technology Solutions.

In the field of deep learning, this paper presents the design of a wearable computer vision system for visually impaired users. The Assistive Technology solution exploits a powerful single board computer and smart glasses with a camera in order to allow its user to explore the objects within his surrounding environment, while it employs Google TensorFlow machine learning framework in order to real time classify the acquired stills. Therefore the proposed aid can increase the awareness of the explored environment and it interacts with its user by means of audio messages.

Bioimpedance based monitoring system for people with neurogenic dysfunction of the urinary bladder.

Patients with impaired bladder volume sensation have the necessity to monitor bladder level in order to avoid urinary tract infections and urinary reflux that can lead to renal failure. In this paper the the effectiveness of an embedded and wearable solution for bladder volume monitoring using the bioimpedance measurement is tested. Data are streamed real-time using Bluetooth wireless technology. The bioimpedance measurements on a healthy subject prove the effectiveness of the proposed solution. In the future the system will be evaluated in real world scenarios with patients affected by spinal paralysis and bladder neurogenic dysfunction.

Bridging the gap between high school and University studies for student with disability.

The choice of the university program represents an important and difficult step for a large part of high school students, especially for those who have to change city and lifestyle to follow their ambitions. In particular, for students with disabilities this choice is even more complicated due to their specific needs concerning both their educational and everyday life. In order to bridge the gap between high school and the University of Pisa, supporting the students during the selection of the program and their stay in Pisa, this paper presents a new model for matching the needs of the students and the existing services in Pisa, with particular attention to those with disabilities. It is based on questionnaires to assess the needs of the students and an accessible website to make available information about places and services offered in Pisa and its surrounding.

Real-time Pedestrian Crossing Recognition for Assistive Outdoor Navigation.

Navigation in urban environments can be difficult for people who are blind or visually impaired. In this project, we present a system and algorithms for recognizing pedestrian crossings in outdoor environments. Our goal is to provide navigation cues for crossing the street and reaching an island or sidewalk safely. Using a state-of-the-art Multisense S7S sensor, we collected 3D pointcloud data for real-time detection of pedestrian crossing and generation of directional guidance. We demonstrate improvements to a baseline, monocular-camera-based system by integrating 3D spatial prior information extracted from the pointcloud. Our system's parameters can be set to the actual dimensions of real-world settings, which enables robustness of occlusion and perspective transformation. The system works especially well in non-occlusion situations, and is reasonably accurate under different kind of conditions. As well, our large dataset of pedestrian crossings, organized by different types and situations of pedestrian crossings in order to reflect real-word environments, is publicly available in a commonly used format (ROS bagfiles) for further research.