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Pedro Machado

Publications and source records attributed to Pedro Machado.

At least 37 records · Page 2Linked to original sources
arXiv2025

Does the Sun have a Dark Disk?

The Sun is not quite a perfect sphere, and its oblateness, thought to be induced through its rotation, has been measured using optical observations of its radius. Its gravitational quadrupole moment can then be deduced using solar models, or through helioseismology, and it can also be determined from measurements of its gravitational effects on Mercury's orbit. The various assessments do not appear to agree, with the most complete and precise orbital assessments being in slight excess of other determinations. This may speak to the existence of a non-luminous disk or ring, where we also note evidence for a circumsolar dust ring within Mercury's orbit from the Solar TErrestrial RElations Observatory (STEREO) mission. Historically, too, a protoplanetary disk may have been key to reconciling the Sun's metallicity with its neutrino yield. The distribution of the non-luminous mass within Mercury's orbit can modify the relative size of the optical and orbital quadrupole moments in different ways. We develop how we can use these findings to limit the mass of a dark disk, ring, or halo in the immediate vicinity of the Sun, and we note how future observational studies of the inner solar system can not only refine these constraints but also help to identify and to assess the mass of its dark-matter component.

hep-ph↗
arXiv2025

Clash of the Titans: ultra-high energy KM3NeT event versus IceCube data

KM3NeT has reported the detection of a remarkably high-energy through-going muon. Lighting up about a third of the detector, this muon likely originated from a neutrino exceeding 10 PeV in energy. The crucial question we need to answer is where this event comes from and what its source is. Intriguingly, IceCube has been operating with a much larger effective area for a considerably longer time, yet it has not reported neutrinos above 10~PeV. We quantify the tension between the KM3NeT event and the absence of similar high-energy events in IceCube. Through a detailed analysis, we determine the most likely neutrino energy to be in the range of 23 - 2400 PeV. We find a $3.5σ$ tension between the two experiments, assuming the neutrino is from the diffuse isotropic neutrino flux. Alternatively, assuming the event is of cosmogenic origin and considering three representative models, this tension still falls within 3.1 - 3.6$σ$. The least disfavored scenario is a steady or transient point source, though still leading to $2.9σ$ and $2.0σ$ tensions, respectively. The lack of observation of high-energy events in IceCube seriously challenges the explanation of this event coming from any known diffuse fluxes. Our results indicate the KM3NeT event is likely the first observation of a new astrophysical source.

astro-ph.HE↗
arXiv2024

Deep Reinforcement Learning for Energy-Efficient on the Heterogeneous Computing Architecture

The growing demand for optimal and low-power energy consumption paradigms for IOT devices has garnered significant attention due to their cost-effectiveness, simplicity, and intelligibility. In this article, an AI hardware energy-efficient framework to achieve optimal energy savings in heterogeneous computing through appropriate power consumption management is proposed. The deep reinforcement learning framework is employed, utilising the Actor-Critic architecture to provide a simple and precise method for power saving. The results of the study demonstrate the proposed approach's suitability for different hardware configurations, achieving notable energy consumption control while adhering to strict performance requirements. The evaluation of the proposed power-saving framework shows that it is more stable, and has achieved more than 34.6% efficiency improvement, outperforming other methods by more than 16%.

eess.SP↗
arXiv2024

Enhancing Pollinator Conservation towards Agriculture 4.0: Monitoring of Bees through Object Recognition

In an era of rapid climate change and its adverse effects on food production, technological intervention to monitor pollinator conservation is of paramount importance for environmental monitoring and conservation for global food security. The survival of the human species depends on the conservation of pollinators. This article explores the use of Computer Vision and Object Recognition to autonomously track and report bee behaviour from images. A novel dataset of 9664 images containing bees is extracted from video streams and annotated with bounding boxes. With training, validation and testing sets (6722, 1915, and 997 images, respectively), the results of the COCO-based YOLO model fine-tuning approaches show that YOLOv5m is the most effective approach in terms of recognition accuracy. However, YOLOv5s was shown to be the most optimal for real-time bee detection with an average processing and inference time of 5.1ms per video frame at the cost of slightly lower ability. The trained model is then packaged within an explainable AI interface, which converts detection events into timestamped reports and charts, with the aim of facilitating use by non-technical users such as expert stakeholders from the apiculture industry towards informing responsible consumption and production.

cs.CV↗
arXiv2024

SmartAntenna: Enhancing Wireless Range with Autonomous Orientation

The SmartAntenna proposes a novel approach to extend wireless communication, focusing on autonomous orientation to extend range and optimize performance. Through meticulous evaluation, various aspects of its functionality were assessed, revealing both strengths and areas for improvement. Notably, the antenna tracking mechanism exhibited remarkable efficacy. The SmartAntenna demonstrated robust functionality throughout extensive testing, underscoring its reliability even amidst complex operational scenarios. However, challenges emerged during target tracking, particularly evident in 360-degree sweeps, necessitating further refinement to enhance accuracy. Despite reliance on the HC-12 module, LoRa, performance limitations surfaced, prompting concerns regarding its suitability for production systems, especially within noisy frequency bands. Nevertheless, the SmartAntenna's adaptability across various wireless technologies holds promise, opening avenues for extended communication ranges and diverse applications. SmartAntenna research contributes valuable insights into optimizing wireless communication systems, paving the way for enhanced performance and expanded capabilities in diverse operational environments.

eess.SY↗
arXiv2024

WeedScout: Real-Time Autonomous blackgrass Classification and Mapping using dedicated hardware

Blackgrass (Alopecurus myosuroides) is a competitive weed that has wide-ranging impacts on food security by reducing crop yields and increasing cultivation costs. In addition to the financial burden on agriculture, the application of herbicides as a preventive to blackgrass can negatively affect access to clean water and sanitation. The WeedScout project introduces a Real-Rime Autonomous Black-Grass Classification and Mapping (RT-ABGCM), a cutting-edge solution tailored for real-time detection of blackgrass, for precision weed management practices. Leveraging Artificial Intelligence (AI) algorithms, the system processes live image feeds, infers blackgrass density, and covers two stages of maturation. The research investigates the deployment of You Only Look Once (YOLO) models, specifically the streamlined YOLOv8 and YOLO-NAS, accelerated at the edge with the NVIDIA Jetson Nano (NJN). By optimising inference speed and model performance, the project advances the integration of AI into agricultural practices, offering potential solutions to challenges such as herbicide resistance and environmental impact. Additionally, two datasets and model weights are made available to the research community, facilitating further advancements in weed detection and precision farming technologies.

cs.RO↗
arXiv2024

ACE Science Workshop Report

We summarize the Fermilab Accelerator Complex Evolution (ACE) Science Workshop, held on June 14-15, 2023. The workshop presented the strategy for the ACE program in two phases: ACE Main Injector Ramp and Target (MIRT) upgrade and ACE Booster Replacement (BR) upgrade. Four plenary sessions covered the primary experimental physics thrusts: Muon Collider, Neutrinos, Charged Lepton Flavor Violation, and Dark Sectors. Additional physics and technology ideas were presented from the community that could expand or augment the ACE science program. Given the physics framing, a parallel session at the workshop was dedicated to discussing priorities for accelerator R\&D. Finally, physics discussion sessions concluded the workshop where experts from the different experimental physics thrusts were brought together to begin understanding the synergies between the different physics drivers and technologies. In December of 2023, the P5 report was released setting the physics priorities for the field in the next decade and beyond, and identified ACE as an important component of the future US accelerator-based program. Given the presentations and discussions at the ACE Science Workshop and the findings of the P5 report, we lay out the topics for study to determine the physics priorities and design goals of the Fermilab ACE project in the near-term.

hep-ex↗
arXiv2023

Ground-breaking Exoplanet Science with the ANDES spectrograph at the ELT

In the past decade the study of exoplanet atmospheres at high-spectral resolution, via transmission/emission spectroscopy and cross-correlation techniques for atomic/molecular mapping, has become a powerful and consolidated methodology. The current limitation is the signal-to-noise ratio during a planetary transit. This limitation will be overcome by ANDES, an optical and near-infrared high-resolution spectrograph for the ELT. ANDES will be a powerful transformational instrument for exoplanet science. It will enable the study of giant planet atmospheres, allowing not only an exquisite determination of atmospheric composition, but also the study of isotopic compositions, dynamics and weather patterns, mapping the planetary atmospheres and probing atmospheric formation and evolution models. The unprecedented angular resolution of ANDES, will also allow us to explore the initial conditions in which planets form in proto-planetary disks. The main science case of ANDES, however, is the study of small, rocky exoplanet atmospheres, including the potential for biomarker detections, and the ability to reach this science case is driving its instrumental design. Here we discuss our simulations and the observing strategies to achieve this specific science goal. Since ANDES will be operational at the same time as NASA's JWST and ESA's ARIEL missions, it will provide enormous synergies in the characterization of planetary atmospheres at high and low spectral resolution. Moreover, ANDES will be able to probe for the first time the atmospheres of several giant and small planets in reflected light. In particular, we show how ANDES will be able to unlock the reflected light atmospheric signal of a golden sample of nearby non-transiting habitable zone earth-sized planets within a few tenths of nights, a scientific objective that no other currently approved astronomical facility will be able to reach.

astro-ph.IM↗
arXiv2023

Estimating the Power Consumption of Heterogeneous Devices when performing AI Inference

Modern-day life is driven by electronic devices connected to the internet. The emerging research field of the Internet-of-Things (IoT) has become popular, just as there has been a steady increase in the number of connected devices. Since many of these devices are utilised to perform CV tasks, it is essential to understand their power consumption against performance. We report the power consumption profile and analysis of the NVIDIA Jetson Nano board while performing object classification. The authors present an extensive analysis regarding power consumption per frame and the output in frames per second using YOLOv5 models. The results show that the YOLOv5n outperforms other YOLOV5 variants in terms of throughput (i.e. 12.34 fps) and low power consumption (i.e. 0.154 mWh/frame).

cs.AR↗
arXiv2023

Dynamic Training of Liquid State Machines

Spiking Neural Networks (SNNs) emerged as a promising solution in the field of Artificial Neural Networks (ANNs), attracting the attention of researchers due to their ability to mimic the human brain and process complex information with remarkable speed and accuracy. This research aimed to optimise the training process of Liquid State Machines (LSMs), a recurrent architecture of SNNs, by identifying the most effective weight range to be assigned in SNN to achieve the least difference between desired and actual output. The experimental results showed that by using spike metrics and a range of weights, the desired output and the actual output of spiking neurons could be effectively optimised, leading to improved performance of SNNs. The results were tested and confirmed using three different weight initialisation approaches, with the best results obtained using the Barabasi-Albert random graph method.

cs.NE↗
arXiv2023

Mitigating Adversarial Attacks in Deepfake Detection: An Exploration of Perturbation and AI Techniques

Deep learning constitutes a pivotal component within the realm of machine learning, offering remarkable capabilities in tasks ranging from image recognition to natural language processing. However, this very strength also renders deep learning models susceptible to adversarial examples, a phenomenon pervasive across a diverse array of applications. These adversarial examples are characterized by subtle perturbations artfully injected into clean images or videos, thereby causing deep learning algorithms to misclassify or produce erroneous outputs. This susceptibility extends beyond the confines of digital domains, as adversarial examples can also be strategically designed to target human cognition, leading to the creation of deceptive media, such as deepfakes. Deepfakes, in particular, have emerged as a potent tool to manipulate public opinion and tarnish the reputations of public figures, underscoring the urgent need to address the security and ethical implications associated with adversarial examples. This article delves into the multifaceted world of adversarial examples, elucidating the underlying principles behind their capacity to deceive deep learning algorithms. We explore the various manifestations of this phenomenon, from their insidious role in compromising model reliability to their impact in shaping the contemporary landscape of disinformation and misinformation. To illustrate progress in combating adversarial examples, we showcase the development of a tailored Convolutional Neural Network (CNN) designed explicitly to detect deepfakes, a pivotal step towards enhancing model robustness in the face of adversarial threats. Impressively, this custom CNN has achieved a precision rate of 76.2% on the DFDC dataset.

cs.LG↗
arXiv2023

Computational models of object motion detectors accelerated using FPGA technology

This PhD research introduces three key contributions in the domain of object motion detection: Multi-Hierarchical Spiking Neural Network (MHSNN): A specialized four-layer Spiking Neural Network (SNN) architecture inspired by vertebrate retinas. Trained on custom lab-generated images, it exhibited 6.75% detection error for horizontal and vertical movements. While non-scalable, MHSNN laid the foundation for further advancements. Hybrid Sensitive Motion Detector (HSMD): Enhancing Dynamic Background Subtraction (DBS) using a tailored three-layer SNN, stabilizing foreground data to enhance object motion detection. Evaluated on standard datasets, HSMD outperformed OpenCV-based methods, excelling in four categories across eight metrics. It maintained real-time processing (13.82-13.92 fps) on a high-performance computer but showed room for hardware optimisation. Neuromorphic Hybrid Sensitive Motion Detector (NeuroHSMD): Building upon HSMD, this adaptation implemented the SNN component on dedicated hardware (FPGA). OpenCL simplified FPGA design and enabled portability. NeuroHSMD demonstrated an 82% speedup over HSMD, achieving 28.06-28.71 fps on CDnet2012 and CDnet2014 datasets. These contributions collectively represent significant advancements in object motion detection, from a biologically inspired neural network design to an optimized hardware implementation that outperforms existing methods in accuracy and processing speed.

cs.CV↗
arXiv2023

Secure Video Streaming Using Dedicated Hardware

Purpose: The purpose of this article is to present a system that enhances the security, efficiency, and reconfigurability of an Internet-of-Things (IoT) system used for surveillance and monitoring. Methods: A Multi-Processor System-On-Chip (MPSoC) composed of Central Processor Unit (CPU) and Field-Programmable Gate Array (FPGA) is proposed for increasing the security and the frame rate of a smart IoT edge device. The private encryption key is safely embedded in the FPGA unit to avoid being exposed in the Random Access Memory (RAM). This allows the edge device to securely store and authenticate the key, protecting the data transmitted from the same Integrated Circuit (IC). Additionally, the edge device can simultaneously publish and route a camera stream using a lightweight communication protocol, achieving a frame rate of 14 frames per Second (fps). The performance of the MPSoC is compared to a NVIDIA Jetson Nano (NJN) and a Raspberry Pi 4 (RPI4) and it is found that the RPI4 is the most cost-effective solution but with lower frame rate, the NJN is the fastest because it can achieve higher frame-rate but it is not secure, and the MPSoC is the optimal solution because it offers a balanced frame rate and it is secure because it never exposes the secure key into the memory. Results: The proposed system successfully addresses the challenges of security, scalability, and efficiency in an IoT system used for surveillance and monitoring. The encryption key is securely stored and authenticated, and the edge device is able to simultaneously publish and route a camera stream feed high-definition images at 14 fps.

cs.CR↗
arXiv2023

NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector

Vertebrate retinas are highly-efficient in processing trivial visual tasks such as detecting moving objects, yet a complex challenges for modern computers. In vertebrates, the detection of object motion is performed by specialised retinal cells named Object Motion Sensitive Ganglion Cells (OMS-GC). OMS-GC process continuous visual signals and generate spike patterns that are post-processed by the Visual Cortex. Our previous Hybrid Sensitive Motion Detector (HSMD) algorithm was the first hybrid algorithm to enhance Background subtraction (BS) algorithms with a customised 3-layer Spiking Neural Network (SNN) that generates OMS-GC spiking-like responses. In this work, we present a Neuromorphic Hybrid Sensitive Motion Detector (NeuroHSMD) algorithm that accelerates our HSMD algorithm using Field-Programmable Gate Arrays (FPGAs). The NeuroHSMD was compared against the HSMD algorithm, using the same 2012 Change Detection (CDnet2012) and 2014 Change Detection (CDnet2014) benchmark datasets. When tested against the CDnet2012 and CDnet2014 datasets, NeuroHSMD performs object motion detection at 720x480 at 28.06 Frames Per Second (fps) and 720x480 at 28.71 fps, respectively, with no degradation of quality. Moreover, the NeuroHSMD proposed in this paper was completely implemented in Open Computer Language (OpenCL) and therefore is easily replicated in other devices such as Graphical Processing Units (GPUs) and clusters of Central Processing Units (CPUs).

cs.NE↗
arXiv2022

Benchmarking Edge Computing Devices for Grape Bunches and Trunks Detection using Accelerated Object Detection Single Shot MultiBox Deep Learning Models

Purpose: Visual perception enables robots to perceive the environment. Visual data is processed using computer vision algorithms that are usually time-expensive and require powerful devices to process the visual data in real-time, which is unfeasible for open-field robots with limited energy. This work benchmarks the performance of different heterogeneous platforms for object detection in real-time. This research benchmarks three architectures: embedded GPU -- Graphical Processing Units (such as NVIDIA Jetson Nano 2 GB and 4 GB, and NVIDIA Jetson TX2), TPU -- Tensor Processing Unit (such as Coral Dev Board TPU), and DPU -- Deep Learning Processor Unit (such as in AMD-Xilinx ZCU104 Development Board, and AMD-Xilinx Kria KV260 Starter Kit). Method: The authors used the RetinaNet ResNet-50 fine-tuned using the natural VineSet dataset. After the trained model was converted and compiled for target-specific hardware formats to improve the execution efficiency. Conclusions and Results: The platforms were assessed in terms of performance of the evaluation metrics and efficiency (time of inference). Graphical Processing Units (GPUs) were the slowest devices, running at 3 FPS to 5 FPS, and Field Programmable Gate Arrays (FPGAs) were the fastest devices, running at 14 FPS to 25 FPS. The efficiency of the Tensor Processing Unit (TPU) is irrelevant and similar to NVIDIA Jetson TX2. TPU and GPU are the most power-efficient, consuming about 5W. The performance differences, in the evaluation metrics, across devices are irrelevant and have an F1 of about 70 % and mean Average Precision (mAP) of about 60 %.

cs.CV↗
arXiv2022

Exploiting High Quality Tactile Sensors for Simplified Grasping

Robots are expected to grasp a wide range of objects varying in shape, weight or material type. Providing robots with tactile capabilities similar to humans is thus essential for applications involving human-to-robot or robot-to-robot interactions, particularly in those situations where a robot is expected to grasp and manipulate complex objects not previously encountered. A critical aspect for successful object grasp and manipulation is the use of high-quality fingertips equipped with multiple high-performance sensors, distributed appropriately across a specific contact surface. In this paper, we present a detailed analysis of the use of two different types of commercially available robotic fingertips (BioTac and WTS-FT), each of which is equipped with multiple sensors distributed across the fingertips' contact surface. We further demonstrate that, due to the high performance of the fingertips, a complex adaptive grasping algorithm is not required for grasping of everyday objects. We conclude that a simple algorithm based on a proportional controller will suffice for many grasping applications, provided the relevant fingertips exhibit high sensitivity. In a quantified assessment, we also demonstrate that, due in part to the sensor distribution, the BioTac-based fingertip performs better than the WTS-FT device, in enabling lifting of loads up to 850g, and that the simple proportional controller can adapt the grasp even when the object is exposed to significant external vibrational challenges.

cs.RO↗
arXiv2022

Deep Learning approach for Classifying Trusses and Runners of Strawberries

The use of artificial intelligence in the agricultural sector has been growing at a rapid rate to automate farming activities. Emergent farming technologies focus on mapping and classification of plants, fruits, diseases, and soil types. Although, assisted harvesting and pruning applications using deep learning algorithms are in the early development stages, there is a demand for solutions to automate such processes. This paper proposes the use of Deep Learning for the classification of trusses and runners of strawberry plants using semantic segmentation and dataset augmentation. The proposed approach is based on the use of noises (i.e. Gaussian, Speckle, Poisson and Salt-and-Pepper) to artificially augment the dataset and compensate the low number of data samples and increase the overall classification performance. The results are evaluated using mean average of precision, recall and F1 score. The proposed approach achieved 91%, 95% and 92% on precision, recall and F1 score, respectively, for truss detection using the ResNet101 with dataset augmentation utilising Salt-and-Pepper noise; and 83%, 53% and 65% on precision, recall and F1 score, respectively, for truss detection using the ResNet50 with dataset augmentation utilising Poisson noise.

cs.CV↗