Arterial mechanical motion estimation based on a semi-rigid body deformation approach

Arterial motion estimation in ultrasound (US) sequences is a hard task due to noise and discontinuities in the signal derived from US artifacts. Characterizing the mechanical properties of the artery is a promising novel imaging technique to diagnose various cardiovascular pathologies and a new way of obtaining relevant clinical information, such as determining the absence of dicrotic peak, estimating the Augmentation Index (AIx), the arterial pressure or the arterial stiffness. One of the advantages of using US imaging is the non-invasive nature of the technique unlike Intra Vascular Ultra Sound (IVUS) or angiography invasive techniques, plus the relative low cost of the US units. In this paper, we propose a semi rigid deformable method based on Soft Bodies dynamics realized by a hybrid motion approach based on cross-correlation and optical flow methods to quantify the elasticity of the artery. We evaluate and compare different techniques (for instance optical flow methods) on which our approach is based. The goal of this comparative study is to identify the best model to be used and the impact of the accuracy of these different stages in the proposed method. To this end, an exhaustive assessment has been conducted in order to decide which model is the most appropriate for registering the variation of the arterial diameter over time. Our experiments involved a total of 1620 evaluations within nine simulated sequences of 84 frames each and the estimation of four error metrics. We conclude that our proposed approach obtains approximately 2.5 times higher accuracy than conventional state-of-the-art techniques.The authors thank Ana Palomares for revising their English text. This work has been supported by the National Grant (AP2007-00275), the projects ARC-VISION (TEC2010-15396), ITREBA (TIC-5060), and the EU project TOMSY (FP7-270436)

Bidirectional recurrent learning of inverse dynamic models for robots with elastic joints: a real-time real-world implementation

Collaborative robots, or cobots, are designed to work alongside humans and to alleviate their physical burdens, such as lifting heavy objects or performing tedious tasks. Ensuring the safety of human–robot interaction (HRI) is paramount for effective collaboration. To achieve this, it is essential to have a reliable dynamic model of the cobot that enables the implementation of torque control strategies. These strategies aim to achieve accurate motion while minimizing the amount of torque exerted by the robot. However, modeling the complex non-linear dynamics of cobots with elastic actuators poses a challenge for traditional analytical modeling techniques. Instead, cobot dynamic modeling needs to be learned through data-driven approaches, rather than analytical equation-driven modeling. In this study, we propose and evaluate three machine learning (ML) approaches based on bidirectional recurrent neural networks (BRNNs) for learning the inverse dynamic model of a cobot equipped with elastic actuators. We also provide our ML approaches with a representative training dataset of the cobot's joint positions, velocities, and corresponding torque values. The first ML approach uses a non-parametric configuration, while the other two implement semi-parametric configurations. All three ML approaches outperform the rigid-bodied dynamic model provided by the cobot's manufacturer in terms of torque precision while maintaining their generalization capabilities and real-time operation due to the optimized sample dataset size and network dimensions. Despite the similarity in torque estimation of these three configurations, the non-parametric configuration was specifically designed for worst-case scenarios where the robot dynamics are completely unknown. Finally, we validate the applicability of our ML approaches by integrating the worst-case non-parametric configuration as a controller within a feedforward loop. We verify the accuracy of the learned inverse dynamic model by comparing it to the actual cobot performance. Our non-parametric architecture outperforms the robot's default factory position controller in terms of accuracy.IMOCOe4.0 [EU H2020RIA-101007311]Spanish national funding [PCI2021-121925INTSENSO [MICINN-FEDER-PID2019- 109991GB-I00]INTARE (TED2021-131466B-I00) projects funded by MCIN/AEI/10.13039/501100011033EU NextGenerationEU/PRTR to ERThe SPIKEAGE [MICINN629PID2020-113422GAI00]DLROB [TED2021 131294B-I00]Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRT

Precise Network Time Monitoring: Picosecond-level packet timestamping for Fintech networks

Network visibility and monitoring are critical in modern networks due to the increased density, additional complexity, higher bandwidth, and lower latency requirements. Precise packet timestamping and synchronization are essential to temporally correlate captured information in different datacenter locations. This is key for visibility, event ordering and latency measurements in segments as telecom, power grids and electronic trading in finance, where order execution and reduced latency are critical for successful business outcomes. This contribution presents Precise Network Time Monitoring (PNTM), a novel mechanism for asynchronous Ethernet packet timestamping which adapts a Digital Dual Mixer Time Difference (DDMTD) implemented in an FPGA. Picosecond-precision packet timestamping is outlined for 1 Gigabit Ethernet. Furthermore, this approach is combined with the White Rabbit (WR) synchronization protocol, used as reference for the IEEE 1588-2019 High Accuracy Profile to provide unprecedented packet capturing correlation accuracy in distributed network scenarios thanks to its sub-nanosecond time transfer. The paper presents different application examples, describes the method of implementation, integration of WR with PNTM and subsequently describes experiments to demonstrate that PNTM is a suitable picosecond-level distributed packet timestamping solutionNational project AMIGA7 RTI2018-096228-B-C32Andalusian project SINPA B-TIC-445-UGR1

Optimization of Efficient Neuron Models With Realistic Firing Dynamics. The Case of the Cerebellar Granule Cell

Biologically relevant large-scale computational models currently represent one of the main methods in neuroscience for studying information processing primitives of brain areas. However, biologically realistic neuron models tend to be computationally heavy and thus prevent these models from being part of brain-area models including thousands or even millions of neurons. The cerebellar input layer represents a canonical example of large scale networks. In particular, the cerebellar granule cells, the most numerous cells in the whole mammalian brain, have been proposed as playing a pivotal role in the creation of somato-sensorial information representations. Enhanced burst frequency (spiking resonance) in the granule cells has been proposed as facilitating the input signal transmission at the theta-frequency band (4–12 Hz), but the functional role of this cell feature in the operation of the granular layer remains largely unclear. This study aims to develop a methodological pipeline for creating neuron models that maintain biological realism and computational efficiency whilst capturing essential aspects of single-neuron processing. Therefore, we selected a light computational neuron model template (the adaptive-exponential integrate-and-fire model), whose parameters were progressively refined using an automatic parameter tuning with evolutionary algorithms (EAs). The resulting point-neuron models are suitable for reproducing the main firing properties of a realistic granule cell from electrophysiological measurements, including the spiking resonance at the theta-frequency band, repetitive firing according to a specified intensityfrequency (I-F) curve and delayed firing under current-pulse stimulation. Interestingly, the proposed model also reproduced some other emergent properties (namely, silent at rest, rheobase and negligible adaptation under depolarizing currents) even though these properties were not set in the EA as a target in the fitness function (FF), proving that these features are compatible even in computationally simple models. The proposed methodology represents a valuable tool for adjusting AdEx models according to a FF defined in the spiking regime and based on biological data. These models are appropriate for future research of the functional implication of bursting resonance at the theta band in large-scale granular layer network models.FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento under the EmbBrain project A-TIC-276-UGR18University of Granada under the Young Researchers FellowshipMinisterio de Economia y Competitividad (MINECO)-FEDER TIN2016-81041-REuropean Human Brain Project SGA2 ( H2020-RIA) 785907European Human Brain Project SGA3 (European Commission) ( H2020-RIA) 945539CEREBIO P18-FR-237

Ethernet-based timing system for accelerator facilities: The IFMIF-DONES case

This article presents the design of a timing system for accelerator facilities, which relies on a general networking approach based on standard Ethernet protocols that keeps all the devices synchronized to a common time reference. The case of the IFMIF-DONES infrastructure is studied in detail, providing a framework for the implementation of the timing system. The network time protocol (NTP) with software timestamping and the precision time protocol (PTP) with hardware timestamping are used to synchronize devices with sub-millisecond and sub-microsecond accuracy requirements, respectively. The design also considers the utilization of IEEE 1588 high accuracy default PTP profile (PTP-HA) to provide sub-nanosecond accuracy for the most demanding components. Three different solutions for the design of the timing system are discussed in detail. The first solution considers the deployment of one time-dedicated network for each synchronization protocol, while the second one proposes the integration of the synchronization data of NTP and PTP into the networks of the facility. The third solution relies on the single distribution of PTP-HA to all the systems. The final design aims to be fully based on standard technologies and to be cost-efficient, seeking for interoperability and scalability, and minimizing the impact on other systems in the facility. An experimental setup has been implemented to evaluate and discuss the suitability of the solutions for the timing system by studying the synchronization accuracy obtained with NTP, PTP and PTP-HA under different network conditions. It includes a timing evaluation platform that tries to resemble the network architecture foreseen in the facility. The measured results revealed that PTP is the most limiting protocol for the second solution. Using the default PTP configuration, it tolerates less than 20% of maximum bandwidth utilization for symmetric bidirectional flows, and around 30% in the case of unidirectional flows (server to client or client to server), with the current setup and using switches without enabled timing support. This case study provides a better understanding of the trade-off between bandwidth utilization, synchronization accuracy and cost in these kinds of facilities

Time-sensitive networking for interlock propagation in the IFMIF-DONES facility

In this study, we have proposed the use of time-sensitive networking (TSN) technologies for the distribution of the interlock signals of the machine protection system of the future IFMIF-DONES particle accelerator, required for implementing the protection mechanisms of the different systems in the facility. Such facilities usually rely on different fieldbus technologies or direct wiring for their transmission, typically leading to complex network infrastructures and interoperability problems. We provide insights of how TSN could simplify the deployment of the interlock network by aggregating all the traffic under the same network infrastructure, whilst guaranteeing the latency and timing constraints. Since TSN is built on top of Ethernet technology, it also benefits from other network services and all its related developments, including redundancy and bandwidth improvements. The main challenge to address is the transmission of the interlock signals with very low latency between devices located in different points of the facility. We have characterized our initial TSN architecture prototype, evaluated the latency and bandwidth obtained with this solution, identified applications to effectively shape the attainable determinism, and found shortcomings and areas of future improvements.Amiga-7 Grant RTI2018-096228-B-C32Programa Operativo FEDER/Junta de Andalucia SINPA Grant SINPA B-TIC-445-UGR18EU DAIS Project 101007273-2Spanish Government FPU20/01857, FPU20/05842Misiones CDTI 2021 framework (DONES-EVO) MIG-20211006European Union via the Euratom Research and Training Programme 10105220

Real-time architecture for robust motion estimation under varying illumination conditions

Motion estimation from image sequences is a complex problem which requires high computing resources and is highly affected by changes in the illumination conditions in most of the existing approaches. In this contribution we present a high performance system that deals with this limitation. Robustness to varying illumination conditions is achieved by a novel technique that combines a gradient-based optical flow method with a non-parametric image transformation based on the Rank transform. The paper describes this method and quantitatively evaluates its robustness to different illumination changing patterns. This technique has been successfully implemented in a real-time system using reconfigurable hardware. Our contribution presents the computing architecture, including the resources consumption and the obtained performance. The final system is a real-time device capable to computing motion sequences in real-time even in conditions with significant illumination changes. The robustness of the proposed system facilitates its use in multiple potential application fields.This work has been supported by the grants DEPROVI (DPI2004-07032), DRIVSCO (IST-016276-2) and TIC2007:”Plataforma Sw-Hw para sistemas de visión 3D en tiempo real”

An integrative methodology based on protein-protein interaction networks for identification and functional annotation of disease-relevant genes applied to channelopathies

Biologically data-driven networks have become powerful analytical tools that handle massive, heterogeneous datasets generated from biomedical fields. Protein-protein interaction networks can identify the most relevant structures directly tied to biological functions. Functional enrichments can then be performed based on these structural aspects of gene relationships for the study of channelopathies. Channelopathies refer to a complex group of disorders resulting from dysfunctional ion channels with distinct polygenic manifestations. This study presents a semi-automatic workflow using protein-protein interaction networks that can identify the most relevant genes and their biological processes and pathways in channelopathies to better understand their etiopathogenesis. In addition, the clinical manifestations that are strongly associated with these genes are also identified as the most characteristic in this complex group of diseases. This research provides a systems biology approach to extract information from interaction networks of gene expression. We show how large-scale computational integration of heterogeneous datasets, PPI network analyses, functional databases and published literature may support the detection and assessment of possible potential therapeutic targets in the disease. Applying our workflow makes it feasible to spot the most relevant genes and unknown relationships in channelopathies and shows its potential as a first-step approach to identify both genes and functional interactions in clinical-knowledge scenarios of target diseases.This work was supported by funds from MINECO-FEDER (TIN2016–81041-R to E.R.), European Human Brain Project SGA2 (H2020-RIA 785907 to M.J.S.), Junta de Andalucía (BIO-302 to F.J.E.) and MEIC (Systems Medicine Excellence Network, SAF2015–70270-REDT to F.J.E.)

Event-driven simulation scheme for spiking neural networks using lookup tables to characterize neuronal dynamics

Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.This work has been supported by the EU projects SpikeFORCE (IST-2001-35271), SENSOPAC (IST-028056) and the Spanish National Grant (DPI-2004-07032

Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation

Cerebellar Purkinje cells mediate accurate eye movement coordination. However, it remains unclear how oculomotor adaptation depends on the interplay between the characteristic Purkinje cell response patterns, namely tonic, bursting, and spike pauses. Here, a spiking cerebellar model assesses the role of Purkinje cell firing patterns in vestibular ocular reflex (VOR) adaptation. The model captures the cerebellar microcircuit properties and it incorporates spike-based synaptic plasticity at multiple cerebellar sites. A detailed Purkinje cell model reproduces the three spike-firing patterns that are shown to regulate the cerebellar output. Our results suggest that pauses following Purkinje complex spikes (bursts) encode transient disinhibition of target medial vestibular nuclei, critically gating the vestibular signals conveyed by mossy fibres. This gating mechanism accounts for early and coarse VOR acquisition, prior to the late reflex consolidation. In addition, properly timed and sized Purkinje cell bursts allow the ratio between long-term depression and potentiation (LTD/LTP) to be finely shaped at mossy fibre-medial vestibular nuclei synapses, which optimises VOR consolidation. Tonic Purkinje cell firing maintains the consolidated VOR through time. Importantly, pauses are crucial to facilitate VOR phase-reversal learning, by reshaping previously learnt synaptic weight distributions. Altogether, these results predict that Purkinje spike burst-pause dynamics are instrumental to VOR learning and reversal adaptation.This work was supported by the European Union (www.europa.eu), Project SpikeControl 658479 (recipient NL), the Spanish Agencia Estatal de Investigacio´n and European Regional Development Fund (www.ciencia.gob.es/ portal/site/MICINN/aei), Project CEREBROT TIN2016-81041-R (recipient ER), and the French National Research Agency (www.agence-nationalerecherche. fr) – Essilor International (www.essilor. com), Chair SilverSight ANR-14-CHIN-0001 (recipient AA)