Position-sensorless control of permanent-magnet-assisted synchronous reluctance motor

The sensorless control of permanent-magnet-assisted synchronous reluctance (PMASR) motors is investigated, in order to conjugate the advantages of the sensorless control with full exploitation of the allowed operating area, for a given inverter. An additional pulsating flux is injected in the d-axis direction at low and zero speed, while it is dropped out, at large speed, to save voltage and additional loss. A flux-observer-based control scheme is used, which includes an accurate knowledge of the motor magnetic behavior. This leads, in general, to good robustness against load variations, by counteracting the magnetic cross saturation effect. Moreover, it allows an easy and effective correspondence between the wanted torque and flux and the set values of the chosen control variables, that is d-axis flux and q-axis current. Experimental verification of the proposed method is given, both steady-state and dynamic performance are outlined. A prototype PMASR motor will be used to this aim, as part of a purposely assembled prototype drive, for light traction application (electric scooter

Human Arm Motion Tracking by Kinect Sensor Using Kalman Filter for Collaborative Robotics

The rising interest in collaborative robotics leads to research solutions in order to increase robot interaction with the environment. The development of methods that permit robots to recognize and track human motion is relevant for safety and collaboration matters. A large quantity of data can be measured in real time by Microsoft Kinect®, a well-known low-cost depth sensor, able to recognize human presence and to provide postural information by extrapolating a skeleton. However, the Kinect sensor tracks motion with relatively low accuracy and jerky behavior. For this reason, the effective use in industrial applications in which the measurement of arm velocity is required can be unsuitable. The present work proposes a filtering method that allows the measurement of more accurate velocity values of human arm, based on row data provided by the Kinect sensor. The estimation of arm motion is achieved by a Kalman filter based on a kinematic model and by the imposition of fixed lengths for the skeleton links detected by the sensor. The development of the method is supported by experimental tests. The achieved results suggest the practical applicability of the developed algorithms

A new method to identify subclasses among AGB stars using Gaia and 2MASS photometry

Aims: We explore the wealth of high quality photometric data provided by data release 2 of the Gaia mission for long period variables (LPVs) in the Large Magellanic Cloud. Our goal is to identify stars of various types and masses along the Asymptotic Giant Branch. Methods: For this endeavour, we developed a new multi-band approach combining Wesenheit functions W_{RP,BP-RP} and W_{K_s,J-K_s} in the Gaia BP, RP and 2MASS J, K_s spectral ranges, respectively, and use a new diagram (W_{RP,BP-RP}-W_{K_s,J-K_s}) versus K_s to distinguish between different kinds of stars in our sample of LPVs. We used stellar population synthesis models to validate our approach. Results:We demonstrate the ability of the new diagram to discriminate between O-rich and C-rich objects, and to identify low-mass, intermediate-mass and massive O-rich red giants, as well as extreme C-rich stars. Stellar evolution and population synthesis models guide the interpretation of the results, highlighting the diagnostic power of the new tool to discriminate between stellar initial masses, chemical properties and evolutionary stages.Comment: accepted for publication in A&A Letters; 7 figures, 2 appendice

GPU-based Real-time Triggering in the NA62 Experiment

Over the last few years the GPGPU (General-Purpose computing on Graphics Processing Units) paradigm represented a remarkable development in the world of computing. Computing for High-Energy Physics is no exception: several works have demonstrated the effectiveness of the integration of GPU-based systems in high level trigger of different experiments. On the other hand the use of GPUs in the low level trigger systems, characterized by stringent real-time constraints, such as tight time budget and high throughput, poses several challenges. In this paper we focus on the low level trigger in the CERN NA62 experiment, investigating the use of real-time computing on GPUs in this synchronous system. Our approach aimed at harvesting the GPU computing power to build in real-time refined physics-related trigger primitives for the RICH detector, as the the knowledge of Cerenkov rings parameters allows to build stringent conditions for data selection at trigger level. Latencies of all components of the trigger chain have been analyzed, pointing out that networking is the most critical one. To keep the latency of data transfer task under control, we devised NaNet, an FPGA-based PCIe Network Interface Card (NIC) with GPUDirect capabilities. For the processing task, we developed specific multiple ring trigger algorithms to leverage the parallel architecture of GPUs and increase the processing throughput to keep up with the high event rate. Results obtained during the first months of 2016 NA62 run are presented and discussed

Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUs

Over the past decade there has been a growing interest in the development of parallel hardware systems for simulating large-scale networks of spiking neurons. Compared to other highly-parallel systems, GPU-accelerated solutions have the advantage of a relatively low cost and a great versatility, thanks also to the possibility of using the CUDA-C/C++ programming languages. NeuronGPU is a GPU library for large-scale simulations of spiking neural network models, written in the C++ and CUDA-C++ programming languages, based on a novel spike-delivery algorithm. This library includes simple LIF (leaky-integrate-and-fire) neuron models as well as several multisynapse AdEx (adaptive-exponential-integrate-and-fire) neuron models with current or conductance based synapses, different types of spike generators, tools for recording spikes, state variables and parameters, and it supports user-definable models. The numerical solution of the differential equations of the dynamics of the AdEx models is performed through a parallel implementation, written in CUDA-C++, of the fifth-order Runge-Kutta method with adaptive step-size control. In this work we evaluate the performance of this library on the simulation of a cortical microcircuit model, based on LIF neurons and current-based synapses, and on balanced networks of excitatory and inhibitory neurons, using AdEx or Izhikevich neuron models and conductance-based or current-based synapses. On these models, we will show that the proposed library achieves state-of-the-art performance in terms of simulation time per second of biological activity. In particular, using a single NVIDIA GeForce RTX 2080 Ti GPU board, the full-scale cortical-microcircuit model, which includes about 77,000 neurons and 3 · 108 connections, can be simulated at a speed very close to real time, while the simulation time of a balanced network of 1,000,000 AdEx neurons with 1,000 connections per neuron was about 70 s per second of biological activity

NaNet: a Low-Latency, Real-Time, Multi-Standard Network Interface Card with GPUDirect Features

While the GPGPU paradigm is widely recognized as an effective approach to high performance computing, its adoption in low-latency, real-time systems is still in its early stages. Although GPUs typically show deterministic behaviour in terms of latency in executing computational kernels as soon as data is available in their internal memories, assessment of real-time features of a standard GPGPU system needs careful characterization of all subsystems along data stream path. The networking subsystem results in being the most critical one in terms of absolute value and fluctuations of its response latency. Our envisioned solution to this issue is NaNet, a FPGA-based PCIe Network Interface Card (NIC) design featuring a configurable and extensible set of network channels with direct access through GPUDirect to NVIDIA Fermi/Kepler GPU memories. NaNet design currently supports both standard - GbE (1000BASE-T) and 10GbE (10Base-R) - and custom - 34~Gbps APElink and 2.5~Gbps deterministic latency KM3link - channels, but its modularity allows for a straightforward inclusion of other link technologies. To avoid host OS intervention on data stream and remove a possible source of jitter, the design includes a network/transport layer offload module with cycle-accurate, upper-bound latency, supporting UDP, KM3link Time Division Multiplexing and APElink protocols. After NaNet architecture description and its latency/bandwidth characterization for all supported links, two real world use cases will be presented: the GPU-based low level trigger for the RICH detector in the NA62 experiment at CERN and the on-/off-shore data link for KM3 underwater neutrino telescope

Ex vivo experimental study on the Thulium laser system : new horizons for interventional endoscopy (with videos)

BACKGROUND AND STUDY AIMS: The Thulium laser system (TLS) is an emerging interventional tool adopted in many surgical specialties. Its 2.0-\u3bcm wavelength allows precise coagulation (0.2\u200a-\u200a0.4\u200amm in depth) and cutting, limiting the possibilities of collateral injuries. We tested the impact of the TLS for gastric endoscopic submucosal dissection (ESD) and per oral endoscopic myotomy (POEM) ex vivo in pigs. MATERIALS AND METHODS: Ex vivo porcine stomach and esophagus models underwent 2 POEMs, and 3 ESDs (mean diameter 3.5\u200acm) with TLS using a 272-\ub5m and a 365-\ub5m thick optical fibers. Both continuous and pulsed laser emission were evaluated. Subsequent histopathological analysis was performed by an expert GI pathologist on the whole porcine models. RESULTS: Complete POEMs and gastric ESDs were successfully performed in all cases in 30 to 70 and 15 to 20 minutes. Both optical fibers were equally effective and precise. The best power output for mucosal incision was 25 to 30\u200aW during ESD and 25\u200aW for POEM using continuous laser emission. During submucosal dissection and tunneling the favorite power output was 20\u200aW and 15 to 20\u200aW, respectively, operating in continuous mode. No transmural perforation occurred throughout the operations and histopathology confirmed the absence of accidental muscular layer damage. CONCLUSIONS: The TLS stands out as a precise and manageable instrument in ex vivo models. This technique appears to be a promising tool for advanced interventional endoscopy