Least-squares multirate FIR filters

The authors propose a new least-squares design procedure for multirate FIR filters with any desired shape of the (band-limited) frequency response. The aliasing, inherent in such systems, is implicitly taken into account in the approximation criterion

An active feedback recovery technique from disruption events induced by m=2 n=1 tearing modes in ohmically heated tokamak plasmas

We present experimental results of magnetic feedback control on the m=2, n=1 tearing mode in RFX-mod operated as a circular ohmically heated tokamak. The feedback suppression of the non-resonant m=2, n=1 Resistive Wall Mode (RWM) in q(a)<2 plasmas is a well-established result of RFX-mod. The control of the tearing counterpart, which develops in q(a)>2 equilibrium, is instead a more difficult issue. In fact, the disruption induced by a growing amplitude m=2, n=1 tearing mode can be prevented by feedback only when the resonant surface q=2 is close to the plasma edge, namely 2<q(a)<2.5, and the electron density does not exceed approximately half of the Greenwald limit. A combined technique of tearing mode and q(a) control has been therefore developed to recover the discharge from the most critical conditions: the potentially disruptive tearing mode is converted into the relatively benign RWM by suddenly decreasing q(a) below 2. The experiments demonstrate the concept with 100% of successful cases. The q(a) control has been performed through the plasma current, given the capability of the toroidal loop-voltage power supply of RFX-mod. We also propose a path for controlling q(a) by acting on the plasma shape, which could be applied to medium size elongated tokamaks

From on-road to off : transfer learning within a deep convolutional neural network for segmentation and classification of off-road scenes.

Real-time road-scene understanding is a challenging computer vision task with recent advances in convolutional neural networks (CNN) achieving results that notably surpass prior traditional feature driven approaches. Here, we take an existing CNN architecture, pre-trained for urban road-scene understanding, and retrain it towards the task of classifying off-road scenes, assessing the network performance within the training cycle. Within the paradigm of transfer learning we analyse the effects on CNN classification, by training and assessing varying levels of prior training on varying sub-sets of our off-road training data. For each of these configurations, we evaluate the network at multiple points during its training cycle, allowing us to analyse in depth exactly how the training process is affected by these variations. Finally, we compare this CNN to a more traditional approach using a feature-driven Support Vector Machine (SVM) classifier and demonstrate state-of-the-art results in this particularly challenging problem of off-road scene understanding

Reactive direction control for a mobile robot: A locust-like control of escape direction emerges when a bilateral pair of model locust visual neurons are integrated

Locusts possess a bilateral pair of uniquely identifiable visual neurons that respond vigorously to the image of an approaching object. These neurons are called the lobula giant movement detectors (LGMDs). The locust LGMDs have been extensively studied and this has lead to the development of an LGMD model for use as an artificial collision detector in robotic applications. To date, robots have been equipped with only a single, central artificial LGMD sensor, and this triggers a non-directional stop or rotation when a potentially colliding object is detected. Clearly, for a robot to behave autonomously, it must react differently to stimuli approaching from different directions. In this study, we implement a bilateral pair of LGMD models in Khepera robots equipped with normal and panoramic cameras. We integrate the responses of these LGMD models using methodologies inspired by research on escape direction control in cockroaches. Using ‘randomised winner-take-all’ or ‘steering wheel’ algorithms for LGMD model integration, the khepera robots could escape an approaching threat in real time and with a similar distribution of escape directions as real locusts. We also found that by optimising these algorithms, we could use them to integrate the left and right DCMD responses of real jumping locusts offline and reproduce the actual escape directions that the locusts took in a particular trial. Our results significantly advance the development of an artificial collision detection and evasion system based on the locust LGMD by allowing it reactive control over robot behaviour. The success of this approach may also indicate some important areas to be pursued in future biological research

Start of SPIDER operation towards ITER neutral beams

Heating Neutral Beam (HNB) Injectors will constitute the main plasma heating and current drive tool both in ITER and JT60-SA, which are the next major experimental steps for demonstrating nuclear fusion as viable energy source. In ITER, in order to achieve the required thermonuclear fusion power gain Q=10 for short pulse operation and Q=5 for long pulse operation (up to 3600s), two HNB injectors will be needed [1], each delivering a total power of about 16.5 MW into the magnetically-confined plasma, by means of neutral hydrogen or deuterium particles having a specific energy of about 1 MeV. Since only negatively charged particles can be efficiently neutralized at such energy, the ITER HNB injectors [2] will be based on negative ions, generated by caesium-catalysed surface conversion of atoms in a radio-frequency driven plasma source. A negative deuterium ion current of more than 40 A will be extracted, accelerated and focused in a multi-aperture, multi-stage electrostatic accelerator, having 1280 apertures (~ 14 mm diam.) and 5 acceleration stages (~200 kV each) [3]. After passing through a narrow gas-cell neutralizer, the residual ions will be deflected and discarded, whereas the neutralized particles will continue their trajectory through a duct into the tokamak vessels to deliver the required heating power to the ITER plasma for a pulse duration of about 3600 s. Although the operating principles and the implementation of the most critical parts of the injector have been tested in different experiments, the ITER NBI requirements have never been simultaneously attained. In order to reduce the risks and to optimize the design and operating procedures of the HNB for ITER, a dedicated Neutral Beam Test Facility (NBTF) [4] has been promoted by the ITER Organization with the contribution of the European Union\u2019s Joint Undertaking for ITER and of the Italian Government, with the participation of the Japanese and Indian Domestic Agencies (JADA and INDA) and of several European laboratories, such as IPP-Garching, KIT-Karlsruhe, CCFE-Culham, CEA-Cadarache. The NBTF, nicknamed PRIMA, has been set up at Consorzio RFX in Padova, Italy [5]. The planned experiments will verify continuous HNB operation for one hour, under stringent requirements for beam divergence (< 7 mrad) and aiming (within 2 mrad). To study and optimise HNB performances, the NBTF includes two experiments: MITICA, full-scale NBI prototype with 1 MeV particle energy and SPIDER, with 100 keV particle energy and 40 A current, aiming at testing and optimizing the full-scale ion source. SPIDER will focus on source uniformity, negative ion current density and beam optics. In June 2018 the experimental operation of SPIDER has started