Optimized Biosignals Processing Algorithms for New Designs of Human Machine Interfaces on Parallel Ultra-Low Power Architectures

The aim of this dissertation is to explore Human Machine Interfaces (HMIs) in a variety of biomedical scenarios. The research addresses typical challenges in wearable and implantable devices for diagnostic, monitoring, and prosthetic purposes, suggesting a methodology for tailoring such applications to cutting edge embedded architectures. The main challenge is the enhancement of high-level applications, also introducing Machine Learning (ML) algorithms, using parallel programming and specialized hardware to improve the performance. The majority of these algorithms are computationally intensive, posing significant challenges for the deployment on embedded devices, which have several limitations in term of memory size, maximum operative frequency, and battery duration. The proposed solutions take advantage of a Parallel Ultra-Low Power (PULP) architecture, enhancing the elaboration on specific target architectures, heavily optimizing the execution, exploiting software and hardware resources. The thesis starts by describing a methodology that can be considered a guideline to efficiently implement algorithms on embedded architectures. This is followed by several case studies in the biomedical field, starting with the analysis of a Hand Gesture Recognition, based on the Hyperdimensional Computing algorithm, which allows performing a fast on-chip re-training, and a comparison with the state-of-the-art Support Vector Machine (SVM); then a Brain Machine Interface (BCI) to detect the respond of the brain to a visual stimulus follows in the manuscript. Furthermore, a seizure detection application is also presented, exploring different solutions for the dimensionality reduction of the input signals. The last part is dedicated to an exploration of typical modules for the development of optimized ECG-based applications

PULP-HD: Accelerating Brain-Inspired High-Dimensional Computing on a Parallel Ultra-Low Power Platform

Computing with high-dimensional (HD) vectors, also referred to as $\textit{hypervectors}$, is a brain-inspired alternative to computing with scalars. Key properties of HD computing include a well-defined set of arithmetic operations on hypervectors, generality, scalability, robustness, fast learning, and ubiquitous parallel operations. HD computing is about manipulating and comparing large patterns-binary hypervectors with 10,000 dimensions-making its efficient realization on minimalistic ultra-low-power platforms challenging. This paper describes HD computing's acceleration and its optimization of memory accesses and operations on a silicon prototype of the PULPv3 4-core platform (1.5mm$^2$, 2mW), surpassing the state-of-the-art classification accuracy (on average 92.4%) with simultaneous 3.7$\times$ end-to-end speed-up and 2$\times$ energy saving compared to its single-core execution. We further explore the scalability of our accelerator by increasing the number of inputs and classification window on a new generation of the PULP architecture featuring bit-manipulation instruction extensions and larger number of 8 cores. These together enable a near ideal speed-up of 18.4$\times$ compared to the single-core PULPv3

Compressed sensing based seizure detection for an ultra low power multi-core architecture

Extracting information from brain signals in advanced Brain Machine Interfaces (BMI) often requires computationally demanding processing. The complexity of the algorithms traditionally employed to process multi-channel neural data, such as Principal Component Analysis (PCA), dramatically increases while scaling-up the number of channels and requires more power-hungry computational platforms. This could hinder the development of low-cost and low-power interfaces which can be used in wearable or implantable real-Time systems. This work proposes a new algorithm for the detection of epileptic seizure based on compressively sensed EEG information, and its optimization on a low-power multi-core SoC for near-sensor data analytics: Mr. Wolf. With respect to traditional algorithms based on PCA, the proposed approach reduces the computational complexity by 4.4x in ARM Cortex M4-based MCU. Implementing this algorithm on Mr.Wolf platform allows to detect a seizure with 1 ms of latency after acquiring the EEG data for 1 s, within an energy budget of 18.4 μJ. A comparison with the same algorithm on a commercial MCU shows an improvement of 6.9x in performance and up to 18.4x in terms of energy efficiency

Coupling radio propagation and weather forecast models to maximize Ka-band channel transmission rate for interplanetary missions

Deep space (DS) missions for interplanetary explorations are aimed at acquiring information about the solar system and its composition. To achieve this result a radio link is established between the space satellite and receiving stations on the Earth. Significant channel capacity must be guaranteed to such spacecraft-to-Earth link considering their large separation distance as well. Terrestrial atmospheric impairments on the space-to-Earth propagating signals are the major responsible for the signal degradation thus reducing the link’s channel temporal availability. Considering the saturation and the limited bandwidth of the conventional systems used working at X-band (around 8.4 GHz), frequencies above Ku-band (12-18 GHz) are being used and currently explored for next future DS missions. For example, the ESA mission EUCLID, planned to be launched in 2020 to reach Sun-Earth Lagrange point L2, will use the K-band (at 25.5-27 GHz). The BepiColombo (BC) ESA mission to Mercury, planned to be launched in 2016, will use Ka-band (at 32-34 GHz) with some modules operating at X-band too. The W-band is also being investigated for space communications (Lucente et al., IEEE Systems J., 2008) as well as near-infrared band for DS links (Luini at al., 3rd IWOW, 2014; Cesarone et al., ICSOS, 2011). If compared with X-band channels, K-band and Ka-band can provide an appealing data rate and signal-to-noise ratio in free space due to the squared-frequency law increase of antenna directivity within the downlink budget (for the same physical antenna size). However, atmospheric path attenuation can be significant for higher frequencies since the major source of transmission outage is not only caused by convective rainfall, as it happens for lower frequencies too, but even non-precipitating clouds and moderate precipitation produced by stratiform rain events are detrimental. This means that accurate channel models are necessary for DS mission data link design at K and Ka band. A physical approach can offer advanced radiopropagation models to take into account the effects due to atmospheric gases, clouds and precipitation. The objective of this work is to couple a weather forecast numerical model with a microphysically- oriented radiopropagation model, providing a description of the atmospheric state and of its effects on a DS downlink. This work is developed in the framework of the RadioMeteorological Operations Planner (RMOP) program, aimed at performing a feasibility study for the BC mission (Biscarini et al., EuCAP 2014). The RMOP chain for the link budget computation is composed by three modules: weather forecast (WFM), radio propagation (RPM) and downlink budget (DBM). WFM is aimed at providing an atmospheric state vector. Among the available weather forecast models, for RMOP purposes we have used the Mesoscale Model 5. The output of the WFM is the input of the RPM for the computation of the atmospheric attenuation and sky-noise temperature at the receiving ground station antenna. RPM makes use of radiative transfer solver based on the Eddington approximations well as accurate scattering models. Time series of attenuation and sky-noise temperature coming from the RPM are converted into probability density functions and then ingested by the DBM to compute the received data volume (DV). Using the BC mission as a reference test case for the Ka-band ground station at Cebreros (Spain), this work will show the advantages of using a coupled WFM-RPM approach with respect to climatological statistics in a link budget optimization procedure. The signal degradation due to atmospheric effects in DS links in terms of received DV will be also investigated not only at Ka band, but also at X, K and W for intercomparison. The quality of the DS downlink will be given in terms of received DV and the results at different frequencies compared showing the respective advantages and drawbacks

Phytosociology and taxonomic notes on some endemic-rich associations of the Naples Gulf

The Gulf of Naples is an important centre of endemism, well known from the floristic point of view, but much less from the phytosociological one. In this paper we investigated the non-forest vegetation focusing on communities rich in endemics. We described two communities as new: Eryngio amethystini-Santolinetum neapolitanae for the garrigues on limestone, Globulario neapolitanae-Loniceretum stabianae for the vegetation on dolomitic rocks, both from the Lattari mountains, and we extend the area of Crithmo maritimi-Limonietum cumani for the vegetation on volcanic rocks and rarely on limestones along the coast, which was known for a few localities. The syntaxonomical position and the phytogeographical context of these communities are discussed. A few taxonomic notes are added on rare or interesting species retrieved in the course of the enquiry

A transprecision floating-point cluster for efficient near-sensor data analytics

Recent applications in the domain of near-sensor computing require the adoption of floating-point arithmetic to reconcile high precision results with a wide dynamic range. In this paper, we propose a multi-core computing cluster that leverages the fined-grained tunable principles of transprecision computing to provide support to near-sensor applications at a minimum power budget. Our design - based on the open-source RISC-V architecture - combines parallelization and sub-word vectorization with near-threshold operation, leading to a highly scalable and versatile system. We perform an exhaustive exploration of the design space of the transprecision cluster on a cycle-accurate FPGA emulator, with the aim to identify the most efficient configurations in terms of performance, energy efficiency, and area efficiency. We also provide a full-fledged software stack support, including a parallel runtime and a compilation toolchain, to enable the development of end-to-end applications. We perform an experimental assessment of our design on a set of benchmarks representative of the near-sensor processing domain, complementing the timing results with a post place-&-route analysis of the power consumption. Finally, a comparison with the state-of-the-art shows that our solution outperforms the competitors in energy efficiency, reaching a peak of 97 Gflop/s/W on single-precision scalars and 162 Gflop/s/W on half-precision vectors

Effect of Graphite Nanosheets on Properties of Poly(3-hydroxybutyrate-co-3-hydroxyvalerate)

The influence of different contents, 0.25, 0.50, and 1.00 wt%, of graphite nanosheets (GNS) on the properties of poly(3-hydroxybutyrate- co-3-hydroxyvalerate) (PHBV) nanocomposites obtained by solution casting method has been studied. GNS were prepared by three steps: intercalation (chemical exfoliation), expansion (thermal treatment), and the GNS obtainment (physical treatment by ultrasonic exfoliation). X-ray diffraction (XRD), Raman spectroscopy, and field emission gun-scanning electron microscopy (FE-SEM) showed that the physical, chemical, and thermal treatments preserved the graphite sheets structure. XRD and Raman results also showed that GNS were dispersed in the PHBV matrix. The degree of crystallinity (Xc) of the nanocomposites did not change when the graphite nanosheets were added. However, the GNS acted as nucleation agent for crystallizationthat is, in the second heating the samples containing GNS showed two melting peaks. The addition the GNS did not change the thermal stability of the PHBV.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Fed Univ Sao Paulo UNIFESP, Grp Polymers & Macromol, Inst Sci & Technol, Sao Jose Dos Campos, SP, BrazilNatl Inst Space Res INPE, Associated Lab Sensors & Mat LAS, Sao Jose Dos Campos, SP, BrazilInstitute of Science and Technology, Universidade Federal de São Paulo (UNIFESP), Group of Polymers and Macromolecules, São José dos Campos, SP, BrazilCNPq: 303287/2013-6CNPq: 158961/2014-5Web of Scienc

Mesoscale high-resolution meteorological and radiative transfer models for satellite downlink budget design at millimeter-wave frequencies

Deep space (DS) missions for interplanetary explorations are aimed at acquiring information about the solar system and its composition. To achieve this result a radio link is established between the space satellite and receiving stations on the Earth. Significant channel capacity must be guaranteed to such spacecraft-to-Earth link considering their large separation distance as well. Terrestrial atmospheric impairments on the space-to-Earth propagating signals are the major responsible for the signal degradation thus reducing the link’s channel temporal availability. Considering the saturation and the limited bandwidth of the conventional systems used working at X-band (around 8.4 GHz), frequencies above Ku-band (12-18 GHz) are being used and currently explored for next future DS missions. For example, the ESA mission EUCLID, planned to be launched in 2020 to reach Sun-Earth Lagrange point L2, will use the K-band (at 25.5-27 GHz). The BepiColombo (BC) ESA mission to Mercury, planned to be launched in 2016, will use Ka-band (at 32-34 GHz) with some modules operating at X-band too. The W-band is also being investigated for space communications (Lucente et al., IEEE Systems J., 2008) as well as near-infrared band for DS links (Luini at al., 3rd IWOW, 2014; Cesarone et al., ICSOS, 2011). If compared with X-band channels, higher frequency bands can provide an appealing data rate and signal-to-noise ratio in free space due to the squared-frequency law increase of antenna directivity within the downlink budget (for the same physical antenna size). In particular, W-band (75–110 GHz) can be one valid alternative to K- and Ka-bands; theoretically, W-band should provide high channel capacities due to the large bandwidth availability and a more robust immunity to signal interference. However, atmospheric path attenuation can be significant for higher frequencies since the major source of transmission outage is not only caused by convective rainfall, as it happens for lower frequencies too, but even non-precipitating clouds and moderate precipitation produced by stratiform rain events are detrimental. This means that accurate channel models are necessary for DS mission data link design. A physical approach can offer advanced radiopropagation models to take into account the effects due to atmospheric gases, clouds and precipitation. The objective of this work is to couple a weather forecast numerical model with a microphysically-oriented radiopropagation model, providing a description of the atmospheric state and of its effects on a DS downlink. This work is the continuation of a study developed in the framework of the RadioMeteorological Operations Planner (RMOP) program, aimed at performing a feasibility study for the BC mission (Biscarini et al., EuCAP 2014). The RMOP chain for the link budget computation is composed by three modules: weather forecast (WFM), radio propagation (RPM) and downlink budget (DBM). WFM is aimed at providing an atmospheric state vector. Among the available weather forecast models, for RMOP purposes we have used the Mesoscale Model 5. The output of the WFM is the input of the RPM for the computation of the atmospheric attenuation and sky-noise temperature at the receiving ground station antenna. RPM makes use of radiative transfer solver, based on the Eddington approximations well as accurate scattering models. Time series of attenuation and sky-noise temperature coming from the RPM are converted into probability density functions and then ingested by the DBM to compute the received data volume (DV). RMOP project was originally aimed at investigating the Ka-band for DS mission focusing the attention on the advantages of using a coupled WFM- RPM approach with respect to climatological statistics in a link budget optimization procedure. In this work we extended the study to the W- and K- band. The signal degradation, due to atmospheric effects in DS links in terms of received DV, is investigated and a comparison among K-, Ka-, W- and the more commonly used X-band is carried out. The quality of the DS downlink will be given in terms of received DV and the results at different frequencies compared showing the respective advantages and drawbacks

Analysis of the evolution of COVID-19 disease understanding through temporal knowledge graphs

The COVID-19 pandemic highlighted two critical barriers hindering rapid response to novel pathogens. These include inefficient use of existing biological knowledge about treatments, compounds, gene interactions, proteins, etc. to fight new diseases, and the lack of assimilation and analysis of the fast-growing knowledge about new diseases to quickly develop new treatments, vaccines, and compounds. Overcoming these critical challenges has the potential to revolutionize global preparedness for future pandemics. Accordingly, this article introduces a novel knowledge graph application that functions as both a repository of life science knowledge and an analytics platform capable of extracting time-sensitive insights to uncover evolving disease dynamics and, importantly, researchers' evolving understanding. Specifically, we demonstrate how to extract time-bounded key concepts, also leveraging existing ontologies, from evolving scholarly articles to create a single temporal connected source of truth specifically related to COVID-19. By doing so, current knowledge can be promptly accessed by both humans and machines, from which further understanding of disease outbreaks can be derived. We present key findings from the temporal analysis, applied to a subset of the resulting knowledge graph known as the temporal keywords knowledge graph, and delve into the detailed capabilities provided by this innovative approach