Miracle-dz/UniFAR-FTUs huggingface dataset:
Air Pollution Uncertainty Modelling based on urban API: a case of Beijing, China

This paper models the uncertainty of Beijing API by introducing the econometric models widely used in financial econometrics in this field. In particular, this research focuses on three aspects: comparing the estimation and forecast performance of GARCH, GJR-GARCH, EGARCH and GARCH-M models; examining the seasonal dust effect of data, and detecting the existence of asymmetry in the data. With model diagnostic criteria, EGARCH outperforms other models, while out-of- data static forecasting performance does not.
Temperature Prediction in Microgrids Using LSTMs: A Case Study

This paper presents a case of study of land-level temperature prediction using LSTMs in the context of renewable energy generation in microgrids. We explore the temperature forecast through LSTMs trained with pre-processed datasets consisting of historic temperatures along with relevant historic features, such as humidity, dew point, pressure, and wind speed. The results shows that the LSTM models are able to fit our available data and generate reasonable future values of temperature according to the obtained loss values. Moreover, we explore the forecast of temperature considering its historic values in an unsupervised manner using rolling LSTMs. The findings suggest that the approach presented in this paper can be used as a foundation of a machine learning-based microgrid control.
Target Detection on Hyperspectral Images Using MCMC and VI Trained Bayesian Neural Networks

Neural networks (NN) have become almost ubiquitous with image classification, but in their standard form produce point estimates, with no measure of confidence. Bayesian neural networks (BNN) provide uncertainty quantification (UQ) for NN predictions and estimates through the posterior distribution. As NN are applied in more high-consequence applications, UQ is becoming a requirement. Automating systems can save time and money, but only if the operator can trust what the system outputs. BNN provide a solution to this problem by not only giving accurate predictions and estimates, but also an interval that includes reasonable values within a desired probability. Despite their positive attributes, BNN are notoriously difficult and time consuming to train. Traditional Bayesian methods use Markov Chain Monte Carlo (MCMC), but this is often brushed aside as being too slow. The most common method is variational inference (VI) due to its fast computation, but there are multiple concerns with its efficacy. MCMC is the gold standard and given enough time, will produce the correct result. VI, alternatively, is an approximation that converges asymptotically. Unfortunately (or fortunately), high consequence problems often do not live in the land of asymtopia so solutions like MCMC are preferable to approximations. We apply and compare MCMC- and VI-trained BNN in the context of target detection in hyperspectral imagery (HSI), where materials of interest can be identified by their unique spectral signature. This is a challenging field, due to the numerous permuting effects practical collection of HSI has on measured spectra. Both models are trained using out-of-the-box tools on a high fidelity HSI target detection scene. Both MCMC- and VI-trained BNN perform well overall at target detection on a simulated HSI scene. Splitting the test set predictions into two classes, high confidence and low confidence predictions, presents a path to automation. For the MCMC-trained BNN, the high confidence predictions have a 0.95 probability of detection with a false alarm rate of 0.05 when considering pixels with target abundance of 0.2. VI-trained BNN have a 0.25 probability of detection for the same, but its performance on high confidence sets matched MCMC for abundances >0.4. However, the VI-trained BNN on this scene required significant expert tuning to get these results while MCMC worked immediately. On neither scene was MCMC prohibitively time consuming, as is often assumed, but the networks we used were relatively small. This paper provides an example of how to utilize the benefits of UQ, but also to increase awareness that different training methods can give different results for the same model. If sufficient computational resources are available, the best approach rather than the fastest or most efficient should be used, especially for high consequence problems.
Assessing Mars Curiosity Rover Wheel Damage

An alarming rate of wheel skin cracks was first ob-served on the Mars Science Laboratory (MSL) Curiosity rover about 14 months after the start of its surface mission. Nine years later (as of August 2, 2021), Curiosity has four broken wheel grousers, three on the left middle wheel and one on the right middle wheel. There are a substantial number of wheel skin cracks on the left front, left middle, and right middle wheels such that the number of grousers on each that are considered at risk of breaking are 10, 7, and 11, respectively. Although the current level of wheel damage does not significantly limit Cu-riosity's mission objectives, a higher damage rate could impact surface operations, so the damage rate is closely monitored. On November 30, 2013, the MSL Surface Operations team began periodically acquiring images of Curiosity's wheels. In this paper, we describe the process the Mobility Operations team uses to assess wheel damage, the current state of Curiosity's wheels, and how the wheel damage assessments have influenced driving guidelines and wheel imaging frequency.
Exploring Optimal Deep Learning Models for Image-based Malware Variant Classification

Analyzing a large amount of malware is a major burden for security analysts. Since emerging malware is often a variant of existing malware, automatically classifying malware into known families greatly reduces a part of their burden. Image-based malware classification with deep learning is an attractive approach due to its simplicity, versatility, and affinity with the latest technologies. However, the impact of differences in deep learning models and the degree of transfer learning on the classification accuracy of malware variants has not been fully studied. In this paper, we conducted an exhaustive study of deep learning models using 24 ImageNet pre-trained models and five fine-tuning parameters, totaling 120 combinations, on two platforms. As a result, we found that the highest classification accuracy was obtained by fine-tuning one of the latest deep learning models with a relatively low degree of transfer learning, and we achieved the highest classification accuracy ever in cross-validation on the Malimg and Drebin datasets. We also confirmed that this trend is true for recent mal ware variants using the VirusTotal 2020 Windows and Android datasets.
Multi-Semantic Path Representation Learning for Travel Time Estimation

Travel time estimation of a given path is a crucial task of Intelligent Transportation Systems (ITS). Accurate travel time estimation can benefit multiple downstream applications such as route planning, real-time navigation, and urban construction. However, it is a challenging problem since the travel time is largely affected by multiple complicated factors including spatial factors, temporal factors and external factors, and obtaining informative representations of a given path is not trivial. Most previous works solved this problem in either Euclidean space or non-Euclidean space, which was unilateral to represent the actual traveling path and led to relatively poor performance. To address this, this paper proposes a multi-semantic path representation method to exploit information in Euclidean space and non-Euclidean space simultaneously. First, since the path is composed of several segments, we generate semantic representations of segments in non-Euclidean space by taking both the time information and the historical co-occurrence into consideration. Second, as the path could be equally represented as several travelled intersections, semantic representations of intersection sequences are also extracted to improve the capability of the method by considering information in Euclidean space. Meanwhile, semantic representations from properties, including the length and the type of segments, are also incorporated into the model. Finally, a sequence learning component is added on the top to aggregate the information along the entire path and provides the final estimation. Extensive experiments were conducted on two real-world taxi trajectories datasets, and the experimental results demonstrate the superiority of the proposed method.
Analysis of Multiple Positions for the Intrinsic and Extrinsic Calibration of a Multi-Beam LiDAR

The usage of light detection and ranging sensors (LiDARs) has grown rapidly in recent years. The ability to directly capture 3D point clouds is a big advantage compared to other visual systems like cameras. One disadvantage is that uncertainty information is difficult to obtain for these systems, although this information is crucial for the decisions based on the measurements. This becomes even more important, when LiDARs are used in conjunction with other sensors in multi-sensor systems (MSS). The sensor data fusion with different sensors requires an extrinsic calibration, which describes the transformation between the LiDAR frame and the body frame of the platform. This can be done utilizing object space information measured by the LiDAR, which is used to infer the origin of the sensor frame based on reference geometries. This process can be used to additionally determine intrinsic parameters of the sensor. Possible intrinsic parameters are corrections for the distance measurements or approximations for the uncertainty of the measurement elements. In this work, the determination of extrinsic and intrinsic parameters is combined for the first time with the approximation of a stochastic model for a multi-beam LiDAR. This is demonstrated on a real-data set of a Velodyne VLP-16, for which the transformation parameters between sensor frame and body frame are determined. Additionally, a distance offset is determined and the variance components are estimated to establish a better approximation for the stochastic model. The impact of the calibration field and choice of positions within this calibration field are shown and discussed. The results are evaluated in a separate experiment using a kinematic MSS.
Drone Ego-Noise Cancellation for Improved Speech Capture using Deep Convolutional Autoencoder Assisted Multistage Beamforming

We propose a multistage approach for enhancing speech captured by a drone-mounted microphone array. The key challenge is suppressing the drone ego-noise, which is the major source of interference in such captures. Since the location of the target is not known a priori, we first apply a UNet-based deep convolutional autoencoder (AE) individually to each microphone signal. The AE generates a time-frequency mask ∈ [0, 1] per signal, where high values correspond to time-frequency points with relatively good signal-to-noise ratios (SNRs). The masks are pooled across all microphones and the aggregated mask is used to steer an adaptive, frequency domain beamformer, yielding a signal with an improved SNR. This beamformer output, after being fed back to the AE, now yields an improved mask - which is used for re-focussing the beamformer. This combination of AE and beamformer, which can be applied to the signals in multiple ‘passes' is termed multistage beamforming. The approach is developed and evaluated on a self-collected database. For the AE - when used to steer a beamformer - a training target that preserves more speech at the cost of less noise suppression outperforms an aggressive training target that suppresses more noise at the cost of more speech distortion. This, in combination with max-pooling of the multi-channel mask - which also lets through more speech (and noise) compared with median pooling - performs best. The experiments further demonstrate that the multistage approach brings extra benefit to the speech quality and intelligibility when the input SNR is 2:-10 dB, and yields comprehensible outputs when the input has a SNR above -5 dB.
Solar PV based 51-level Hybrid Multilevel Inverter

Multilevel inverter (MLI) topologies have been well presented to produce a high-level of AC output voltage with reduced number of component count. However, without utilizing any AC filtering devices, the total harmonic distortion (THD) of the generated AC voltage at unity modulation is still unable to satisfy the IEEE-519 harmonic standard. The emergence of hybrid MLI has been developed to solve the aforementioned issue by using asymmetrical configurations (i.e., unequal DC-sources). But no research work has been conducted to integrate renewable energy conversion system with hybrid MLI. This paper extended the findings of 51-level MLI topology in hybrid configuration (hereafter known as hybrid MLI) by replacing each isolated asymmetrical DC source with photovoltaic (PV) based DC-DC buck converter. The DC-DC buck converter is controlled by proportional integral derivative (PID) controller while the hybrid MLI is driven using nearest level control (NLC) method to generate 51-level of AC output voltage waveform. To demonstrate the feasibility of the hybrid MLI, PV-battery and PV-non-battery based hybrid MLI are presented in this paper. The effectiveness and the theoretical analysis of the proposed systems for resistive-inductive RL loading condition are verified through simulation studies using MATLAB SIMULINK.
Mapping Earth's Dust-EMITting Regions from the ISS with the EMIT Imaging Spectrometer

The Earth Surface Mineral Dust Source Investigation (EMIT) is a Dyson-type imaging spectrometer which will operate from the EXPRESS Logistics Carrier (ELC) 1 platform of the International Space Station (ISS) starting in 2022 with the objective of mapping Earth's dust-EMITting land regions for the purpose of determining the composition of key constituent minerals and their impact on radiative forcing. Designed to operate for a minimum of one year, the EMIT instrument will collect over 50 Terabits of data representing billions of individual spectra for processing on the ground, significantly improving the state of knowledge about the composition and regional and global distribution of these key minerals. Acquiring, storing, processing and downlinking such large amounts of data requires a well-orchestrated planning effort in concert with an on-board high-performance computing embedded system. Adding to the challenge, and because the science requires a cloud-free measurement, significant effort has been expended in both attempting to predict the impact of clouds on the coverage as well as removing cloudy acquisitions on-board to reduce the pressure on the shared ISS downlink pipeline. In this paper we will explore the ways in which the EMIT mission design has been shaped by the performance requirements imposed by the science on data sufficiency and quality along with the constraints imposed by ISS operations and hardware limitations. Various sensitivity analyses are presented investigating the impact of ISS orbit altitude and seasonality on the coverage of the science target mask, along with data compression and throughput, cloudiness assumptions, and downlink data rate effects on on-board storage sizing and margins.
Radiation Test and in Orbit Performance of MpSoC AI Accelerator

Φ-Sat-1 is part of the European Space Agency initiative to promote the development of disruptive innovative technology and capabilities on-board EO missions. The Φ-Sat-l satellite represents the first-ever on-board Artificial Intelligence (AI) deep Convolutional Neural Network (CNN) inference on a dedicated chip attempting to exploit artificial Deep Neural Network (DNN) capability for Earth Observation. It utilises the Myriad Vision Processing Unit (VPU), a System On Chip (SOC) that has been designed ex novo for high-performance edge compute for vision applications. In order to support Myriad's deployment on Φ-Sat-l, the first mission using AI processing for operational purposes, and future applications in general, the SOC has undergone radiation characterisation via several test campaigns in European test facilities. The first AI application developed for in-flight inference was CloudScout, a segmentation neural network that was designed specifically for Φ-Sat-l in order to achieve high detail and good granularity in the classification result, and eventually discard on-board the cloudy images acquired by the hyperspectral sensor, thus greatly enhancing the data throughput capability of the mission. In addition to the CloudScout cloud detection AI SW results acquired during Φ-Sat-l's mission, in-flight performance data was also acquired for the hardware inference engine. Four separate VPU-based inference engine test phases were executed over 70 days during the mission. The in-flight diagnostics tests for the VPU inference engine indicate that the device performed as expected on-board Φ-Sat-l without experiencing any functional upsets, or any functional degradation effects due to radiation. All future installations of the Myriad VPU in space will be equipped with this Built-In Self Test (BIST) that will allow monitoring the performance of the inference engine hardware.
Classification of Sugarcane Leaf Disease using Deep Learning Algorithms

Early disease identification and detection have been an interest of experts to enhance productivity and performance in agriculture. This study aims to use deep learning algorithms to classify sugarcane diseases using leaf images. Deep learning algorithms are implemented to create models that can classify sugarcane diseases using 16,800 images of training data, 4,800 images for validation tasks, and 2400 images for testing. Results show that the InceptionV4 algorithm outperforms other models in classifying sugarcane leaf diseases at 99.61 accuracy. Different models such as VGG16, ResnetV2-152, and AlexNet achieve high accuracies of 98.88%, 99.23%, and 99.24%, respectively. Hence, this study provides evidence that deep learning models can perform better in classification problems. This study suggests some improvements to further its contribution.
Robust Vision-based Multi-spacecraft Guidance Navigation and Control using CNN-based Pose Estimation

In this paper, we present an end-to-end simulation framework for tracking an uncooperative Target spacecraft in Low Earth Orbit using a CubeSat-class Ego spacecraft outfitted with a camera. Currently, capturing high-fidelity realistic images in space for this scenario is difficult and exorbitantly expensive. Therefore, we developed a framework to simulate the spacecraft orbits in Basilisk software and generate high-fidelity realistic images of spacecraft in Unreal Engine, including the effects from Sun, Earth, Moon and stars. The Ego spacecraft uses cameras to capture images of the uncooperative Target and estimates its position and attitude using a CNN based 6DOF pose estimation pipeline, eliminating need for large SWAP-C(Size, Weight, Power and Cost) sensors like LI-DAR or reliance on inter-spacecraft communication, This CNN, which is motivated by ESA's Pose Estimation challenge of 2019, is trained using simulated data from our end-to-end simulation framework. We compare the performance of two distinct CNN-based algorithms for pose estimation along a nominal trajectory. In presence of non-Gaussian modeling uncertainties, the state-dependent estimation error is characterized with a quadratic upper-bound. The quadratically-bounded error can be used by a robust controller to maneuver Ego spacecraft to track the uncooperative Target.
Self-Alignment for Black-Box Domain Adaptation of Image Classification

Recently, black-box domain adaptation attracts a lot of attention. It is a new concept to realize domain adaptation of instance image classification with only a cloud API service, reflecting the focus on development of cloud services and concerns about data security. However, the existing black-box domain adaptation methods always only use high-confidence image samples which limits their classification performance. We propose a self-alignment approach to realize black-box domain adaptation. First, an initial model is constructed for target domain. Then, we put target data into source model API to obtain the pseudo-labels and divide the target data into high-confidence and low-confidence parts according to their pseudo-label confidences. By matching the data distributions between these two parts and self-supervised learning on high-confidence part, the classification performance of initial model on both parts samples can be boosted respectively. Information maximization is also applied to further improve classification performance. Experiment results confirm that our method achieves the state-of-the-art performance on image classification.
Co-simulated Digital Twin on the Network Edge: the case of platooning

This paper presents an approach to create high fidelity Digital-Twin models for distributed multi-agent cyber-physical systems based on the combination of simulating components, generated from different modeling languages, each tailored for the specific domain of the subsystem. The approach specifically addresses the wireless communication domain, exploiting a Python module as a simulating component to evaluate the impact of network delay among the distributed elements of the system under analysis. A case study with a platoon of four vehicles following a leading car, all modeled in Simulink, is used to show the applicability of the approach, allowing the comparison between a Vehicle-to-Vehicle communication against a centralized multi-access edge computing paradigm.
SC-NET: Spatial and Channel Attention Mechanism for Enhancement in Face Recognition

This paper proposes a spatial and channel attention mechanism module called SC-NET which is a lightweight yet effective method for deep convolutional neural networks. Recently, channel attention mechanism has been researched extensively and proved to be efficient in improvement of performance. However after carrying out rigorous empirical analysis, we find that channel attention and spatial channel attention improve the network's performance more efficiently. Therefore we incorporate both spatial information and cross-channel interaction in our SC-NET architecture. SC-NET is validated through extensive experiments on CASIA- WebFace and VGGFace2 datasets. By comparing our SC-NET with other methods, SC-NET has the best performance. Then when we apply our SC-NET to FaceNet(A Unified Embedding for Face Recognition and Clustering), FaceNet with SC-NET has achieved higher recognition accuracy than the original FaceNet and has reached state-of-the-art performance.