arXiv subjects

Pedro Machado

Publications and source records attributed to Pedro Machado.

53 records · Page 3Linked to original sources

arXiv2022

Towards Achieving Thermal Comfort through Physiologically Cloud based controlled HVAC System

Thermal comfort in shared spaces is essential to occupants well-being and necessary in the management of energy consumption. Existing thermal control systems for indoor shared spaces adjust temperature set points mechanically, making it difficult to intelligently achieve thermal comfort for all. Recent studies have shown that thermal comfort in a shared space is difficult to achieve due to individual preferences and the inability of occupants to reach a thermal compromise on temperature set points. This paper proposes a thermal comfort system to automatically adjust the temperature set-points in a shared space whilst recognising individual preferences. The control strategy of the proposed system is based on an algorithm to adjust the temperature set point of the shared space using the individual thermal preferences and predicted thermal comfort value of the occupants. The thermal preferences of the occupants are determined first and used as part of the occupants profile, which is mapped to thermal comfort values predicted from the occupants measured physiological data and environmental data. A consensus is reached by the algorithm to find the optimal temperature set-point, which takes into account individual thermal preferences and their physiological responses.

eess.SY↗

arXiv2022

Real-Time Gesture Recognition with Virtual Glove Markers

Due to the universal non-verbal natural communication approach that allows for effective communication between humans, gesture recognition technology has been steadily developing over the previous few decades. Many different strategies have been presented in research articles based on gesture recognition to try to create an effective system to send non-verbal natural communication information to computers, using both physical sensors and computer vision. Hyper accurate real-time systems, on the other hand, have only recently began to occupy the study field, with each adopting a range of methodologies due to past limits such as usability, cost, speed, and accuracy. A real-time computer vision-based human-computer interaction tool for gesture recognition applications that acts as a natural user interface is proposed. Virtual glove markers on users hands will be created and used as input to a deep learning model for the real-time recognition of gestures. The results obtained show that the proposed system would be effective in real-time applications including social interaction through telepresence and rehabilitation.

cs.CV↗

arXiv2022

Constraining Feeble Neutrino Interactions with Ultralight Dark Matter

If ultralight $(\ll$ eV), bosonic dark matter couples to right handed neutrinos, active neutrino masses and mixing angles depend on the ambient dark matter density. When the neutrino Majorana mass, induced by the dark matter background, is small compared to the Dirac mass, neutrinos are "pseudo-Dirac" fermions that undergo oscillations between nearly degenerate active and sterile states. We present a complete cosmological history for such a scenario and find severe limits from a variety of terrestrial and cosmological observables. For scalar masses in the "fuzzy" dark matter regime ($\sim 10^{-20}$ eV), these limits exclude couplings of order $10^{-30}$, corresponding to Yukawa interactions comparable to the gravitational force between neutrinos and surpassing equivalent limits on time variation in scalar-induced electron and proton couplings.

hep-ph↗

arXiv2022

Physics Opportunities for the Fermilab Booster Replacement

This white paper presents opportunities afforded by the Fermilab Booster Replacement and its various options. Its goal is to inform the design process of the Booster Replacement about the accelerator needs of the various options, allowing the design to be versatile and enable, or leave the door open to, as many options as possible. The physics themes covered by the paper include searches for dark sectors and new opportunities with muons.

hep-ph↗

arXiv2022

Asteroid astrometry by stellar occultations: Accuracy of the existing sample from orbital fitting

Context: Stellar occultations, greatly enhanced by the publication of the Gaia data releases, permit not only the determination of asteroid size and shape, but also the retrieval of additional, accurate astrometry, with a possible relevant impact on the study of dynamical properties. The use of Gaia as reference catalogue and the recent implementation of an improved error model for occultation astrometry offer the opportunity to test its global astrometric performance on the existing data set of observed events, dominated by minor planets belonging to the main belt. Aims: We aim to explore the performance on orbit accuracy brought by reducing occultations by stellar positions given in Gaia Data Release 2 (DR2) and Early Data Release 3 (EDR3), exploited jointly with the new occultation error model. Our goal is to verify that the quality of DR2 and EDR3 provides a logical progression in the exploitation of occultation astrometry with respect to previous catalogues. We also want to compare the post-fit residuals to the error model. Methods: We began with accurate orbit adjustment to occultation data, either alone or joined to the other available ground-based observations. We then analyzed the orbit accuracy and the post-fit residuals. Results: Gaia EDR3 and DR2 bring a noticeable improvement to the accuracy of occultation data, bringing an average reduction of their residuals upon fitting an orbit of about a factor of 5 when compared to other catalogues. This is particularly visible when occultations alone are used, resulting in very good orbits for a large fraction of objects. We demonstrate that occultation astrometry can reach the performance of Gaia on small asteroids. The joint use of archival data and occultations remains more challenging due to the higher uncertainties and systematic errors of other data, mainly obtained by traditional CCD imaging.

astro-ph.EP↗

arXiv2021

Object recognition for robotics from tactile time series data utilising different neural network architectures

Robots need to exploit high-quality information on grasped objects to interact with the physical environment. Haptic data can therefore be used for supplementing the visual modality. This paper investigates the use of Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) neural network architectures for object classification on Spatio-temporal tactile grasping data. Furthermore, we compared these methods using data from two different fingertip sensors (namely the BioTac SP and WTS-FT) in the same physical setup, allowing for a realistic comparison across methods and sensors for the same tactile object classification dataset. Additionally, we propose a way to create more training examples from the recorded data. The results show that the proposed method improves the maximum accuracy from 82.4% (BioTac SP fingertips) and 90.7% (WTS-FT fingertips) with complete time-series data to about 94% for both sensor types.

cs.RO↗

arXiv2021

HSMD: An object motion detection algorithm using a Hybrid Spiking Neural Network Architecture

The detection of moving objects is a trivial task performed by vertebrate retinas, yet a complex computer vision task. Object-motion-sensitive ganglion cells (OMS-GC) are specialised cells in the retina that sense moving objects. OMS-GC take as input continuous signals and produce spike patterns as output, that are transmitted to the Visual Cortex via the optic nerve. The Hybrid Sensitive Motion Detector (HSMD) algorithm proposed in this work enhances the GSOC dynamic background subtraction (DBS) algorithm with a customised 3-layer spiking neural network (SNN) that outputs spiking responses akin to the OMS-GC. The algorithm was compared against existing background subtraction (BS) approaches, available on the OpenCV library, specifically on the 2012 change detection (CDnet2012) and the 2014 change detection (CDnet2014) benchmark datasets. The results show that the HSMD was ranked overall first among the competing approaches and has performed better than all the other algorithms on four of the categories across all the eight test metrics. Furthermore, the HSMD proposed in this paper is the first to use an SNN to enhance an existing state of the art DBS (GSOC) algorithm and the results demonstrate that the SNN provides near real-time performance in realistic applications.

cs.CV↗

arXiv2021

Ariel: Enabling planetary science across light-years

Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey, was adopted as the fourth medium-class mission in ESA's Cosmic Vision programme to be launched in 2029. During its 4-year mission, Ariel will study what exoplanets are made of, how they formed and how they evolve, by surveying a diverse sample of about 1000 extrasolar planets, simultaneously in visible and infrared wavelengths. It is the first mission dedicated to measuring the chemical composition and thermal structures of hundreds of transiting exoplanets, enabling planetary science far beyond the boundaries of the Solar System. The payload consists of an off-axis Cassegrain telescope (primary mirror 1100 mm x 730 mm ellipse) and two separate instruments (FGS and AIRS) covering simultaneously 0.5-7.8 micron spectral range. The satellite is best placed into an L2 orbit to maximise the thermal stability and the field of regard. The payload module is passively cooled via a series of V-Groove radiators; the detectors for the AIRS are the only items that require active cooling via an active Ne JT cooler. The Ariel payload is developed by a consortium of more than 50 institutes from 16 ESA countries, which include the UK, France, Italy, Belgium, Poland, Spain, Austria, Denmark, Ireland, Portugal, Czech Republic, Hungary, the Netherlands, Sweden, Norway, Estonia, and a NASA contribution.

astro-ph.IM↗

arXiv2021

Flavor-dependent radiative corrections in coherent elastic neutrino-nucleus scattering

We calculate coherent elastic neutrino-nucleus scattering cross sections on spin-0 nuclei (e.g. $^{40}$Ar and $^{28}$Si) at energies below 100 MeV within the Standard Model and account for all effects of permille size. We provide a complete error budget including uncertainties at nuclear, nucleon, hadronic, and quark levels separately as well as perturbative error. Our calculation starts from the four-fermion effective field theory to explicitly separate heavy-particle mediated corrections (which are absorbed by Wilson coefficients) from light-particle contributions. Electrons and muons running in loops introduce a nontrivial dependence on the momentum transfer due to their relatively light masses. These same loops, and those mediated by tau leptons, break the flavor universality because of mass-dependent electromagnetic radiative corrections. Nuclear physics uncertainties significantly cancel in flavor asymmetries resulting in subpercent relative errors. We find that for low neutrino energies, the cross section can be predicted with a relative precision that is competitive with neutrino-electron scattering. We highlight potentially useful applications of such a precise cross section prediction ranging from precision tests of the Standard Model, to searches for new physics and to the monitoring of nuclear reactors.

hep-ph↗

arXiv2020

Strawberry Detection Using a Heterogeneous Multi-Processor Platform

Over the last few years, the number of precision farming projects has increased specifically in harvesting robots and many of which have made continued progress from identifying crops to grasping the desired fruit or vegetable. One of the most common issues found in precision farming projects is that successful application is heavily dependent not just on identifying the fruit but also on ensuring that localisation allows for accurate navigation. These issues become significant factors when the robot is not operating in a prearranged environment, or when vegetation becomes too thick, thus covering crop. Moreover, running a state-of-the-art deep learning algorithm on an embedded platform is also very challenging, resulting most of the times in low frame rates. This paper proposes using the You Only Look Once version 3 (YOLOv3) Convolutional Neural Network (CNN) in combination with utilising image processing techniques for the application of precision farming robots targeting strawberry detection, accelerated on a heterogeneous multiprocessor platform. The results show a performance acceleration by five times when implemented on a Field-Programmable Gate Array (FPGA) when compared with the same algorithm running on the processor side with an accuracy of 78.3\% over the test set comprised of 146 images.

cs.RO↗

arXiv2020

Tau neutrinos at DUNE: new strategies, new opportunities

We propose a novel analysis strategy, that leverages the unique capabilities of the DUNE experiment, to study tau neutrinos. We integrate collider physics ideas, such as jet clustering algorithms in combination with machine learning techniques, into neutrino measurements. Through the construction of a set of observables and kinematic cuts, we obtain a superior discrimination of the signal ($S$) over the background ($B$). In a single year, using the nominal neutrino beam mode, DUNE may achieve $S/\sqrt{B}$ of $3.3$ and $2.3$ for the hadronic and leptonic decay channels of the tau respectively. Operating in the tau-optimized beam mode would increase $S/\sqrt{B}$ to $8.8$ and $11$ for each of these channels. We premier the use of the analysis software Rivet, a tool ubiquitously used by the LHC experiments, in neutrino physics. For wider accessibility, we provide our analysis code.

hep-ph↗

arXiv2020

Summary of Workshop on Common Neutrino Event Generator Tools

A neutrino community workshop was held at Fermilab in Jan 2020, with the aim of developing an implementation plan for a set of common interfaces to Neutrino Event Generators. This white paper summarizes discussions at the workshop and the resulting plan.

hep-ex↗

arXiv2020

A survey for occultation astrometry of Main Belt: expected astrometric performances

Context: Occultations of stars by asteroids are an efficient method to study the properties of minor bodies, and can be exploited as tools to derive very precise asteroid astrometry relative to the target star. With the availability of stellar astrometry thanks to the ESA mission Gaia, the frequency of good predictions and the quality of the astrometry have been strongly enhanced. Aims: Our goal is to evaluate the astrometric performance of a systematic exploitation of stellar occultations, with a homogeneous data set and a given instrument setup. As a reference instrument, we adopt the example of a robotic 50 cm telescope, which is under construction at the Observatoire de la Côte d Azur. We focus in particular on single chord occultations. Methods: We created a data set of simulated light curves, that are modelled by a Bayesian approach. To build the final statistics, we considered a list of predicted events over a long time span, and stellar astrometry from Gaia data release 2. Results: We derive an acceptable range of observability of the events, with clear indications of the expected errors in terms of timing uncertainties. By converting the distribution of such errors to astrometric uncertainties, we show that the precision on a single chord can reach levels equivalent to the performance of Gaia (sub milli arcseconds). The errors on the asteroid position are dominated by the uncertainty on the position of the occultation chord with respect to the barycentre of the object. Conclusions: The limiting factor in the use of occultation astrometry is not the light curve uncertainty, but our knowledge of the shape and size of the asteroid. This conclusion is valid in a wide range of flux drops and magnitudes of the occulted star. The currently increasing knowledge of the shape, spin properties, and size, must be used to mitigate this source of error.

astro-ph.EP↗

arXiv2020

Decaying Sterile Neutrinos and the Short Baseline Oscillation Anomalies

The MiniBooNE experiment has observed a significant excess of electron neutrinos in a muon neutrino beam at source-detector distances too short to be compatible with standard neutrino oscillations. The most straightforward explanation for this signal in terms of oscillations between Standard Model neutrinos and a new, sterile, neutrino, is disfavored by null results from experiments looking for muon neutrino disappearance. Here, we discuss the possibility that MiniBooNE data are instead explained by a sterile neutrino that decays quickly back into active neutrinos plus a light boson. The flavor composition of the secondary neutrinos is determined by the sterile neutrino mixing angles, and we show that the data is best explained if the sterile neutrino mixes mostly with electron neutrinos. The preferred range for the mass of the sterile neutrino is between 100 eV and 1 keV. We argue that the model can easily satisfy cosmological constraints because it has the "secret interactions" mechanism built-in. Accommodating in addition to the MiniBooNE anomaly also the LSND, reactor, and gallium anomalies is possible, but in this case the model needs to be extended to avoid cosmological limits.

hep-ph↗

arXiv2019

NatCSNN: A Convolutional Spiking Neural Network for recognition of objects extracted from natural images

Biological image processing is performed by complex neural networks composed of thousands of neurons interconnected via thousands of synapses, some of which are excitatory and others inhibitory. Spiking neural models are distinguished from classical neurons by being biological plausible and exhibiting the same dynamics as those observed in biological neurons. This paper proposes a Natural Convolutional Neural Network (NatCSNN) which is a 3-layer bio-inspired Convolutional Spiking Neural Network (CSNN), for classifying objects extracted from natural images. A two-stage training algorithm is proposed using unsupervised Spike Timing Dependent Plasticity (STDP) learning (phase 1) and ReSuMe supervised learning (phase 2). The NatCSNN was trained and tested on the CIFAR-10 dataset and achieved an average testing accuracy of 84.7% which is an improvement over the 2-layer neural networks previously applied to this dataset.

cs.NE↗

arXiv2019

Constraining Sterile Neutrino Interpretations of the LSND and MiniBooNE Anomalies with Coherent Neutrino Scattering Experiments

Results from the LSND and MiniBooNE experiments have been interpreted as evidence for a sterile neutrino with a mass near the electronvolt scale. Here we propose to test such a scenario by measuring the coherent elastic scattering rate of neutrinos from a pulsed spallation source. Coherent scattering is universal across all active neutrino flavors, and thus can provide a measurement of the total Standard Model neutrino flux. By performing measurements over different baselines and making use of timing information, it is possible to significantly reduce the systematic uncertainties and to independently measure the fluxes of neutrinos that originate as $ν_μ$ or as either $ν_e$ or $\barν_μ$. We find that a 100 kg CsI detector would be sensitive to the large fraction of the sterile neutrino parameter space that could potentially account for the LSND and MiniBooNE anomalies.

hep-ph↗

arXiv2018

Nightside Winds at the Lower Clouds of Venus with Akatsuki/IR2: Longitudinal, local time and decadal variations from comparison with previous measurements

We present measurements of the wind speeds at the nightside lower clouds of Venus from observations by JAXA's mission Akatsuki during 2016, complemented with new wind measurements from ground-based observations acquired with TNG/NICS in 2012 and IRTF/SpeX in 2015 and 2017. Zonal and meridional components of the winds were measured from cloud tracking on a total of 466 Akatsuki images of Venus acquired by the camera IR2 using the 2.26-$\mathrm{μm}$ filter, with spatial resolutions ranging 10--80 km per pixel and covering from 2016 March 22 to October 31. More than 149,000 wind vectors were obtained with an automatic technique of template matching, and 2,947 wind vectors were inferred with the manual procedure. The meridional profiles for both components of the winds are found to be consistent with results from the Venus Express mission during 2006--2008, although stronger wind variability is found for the zonal component at equatorial latitudes where Akatsuki observations have better viewing geometry than Venus Express. The zonal winds at low latitudes also suggest a zonal variability that could be associated with solar tides or vertically propagating orographic waves. Finally, the combination of our wind measurements from TNG/NICS, IRTF/SpeX and Akatsuki images with previously published and based in data from 1978 to 2017 suggests variations of up to 30 m s$^{-1}$ in the winds at the lower clouds of the Venus nightside.

astro-ph.EP↗