Miracle-dz/UniFAR-FTUs huggingface dataset:
Interband Retrieval and Classification Using the Multilabeled Sentinel-2 BigEarthNet Archive

Conventional remote sensing data analysistechniques have a significant bottleneck of operating on a selectively chosen small-scale dataset. Availability of an enormous volume of data demands handling large-scale, diverse data, which have been made possible with neural network-based architectures. This article exploits the contextual information capturing ability of deep neural networks, particularly investigating multispectral band properties from Sentinel-2 image patches. Besides, an increase in the spatial resolution often leads to nonlinear mixing of land-cover types within a target resolution cell. We recognize this fact and group the bands according to their spatial resolutions, and propose a classification and retrieval framework. We design a representation learning framework for classifying the multispectral data by first utilizing all the bands and then using the grouped bands according to their spatial resolutions. We also propose a novel triplet-loss function for multilabeled images and use it to design an interband group retrieval framework. We demonstrate its effectiveness over the conventional triplet-loss function. Finally, we present a comprehensive discussion of the obtained results. We thoroughly analyze the performance of the band groups on various land-cover and land-use areas from agro-forestry regions, water bodies, and human-made structures. Experimental results for the classification and retrieval framework on the benchmarked BigEarthNet dataset exhibit marked improvements over existing studies.
Point Cloud Segmentation via Edge-fused Local Graph Learning

Traditional convolution for capturing local structures and relationships remains a key technical limit in 3D semantic segmentation, which neglects the certain influence of the adjacent points on the central point in the disordered local point clouds. In this paper, we propose a novel joint-edge graph convolution neural network (JEGCN), which can extract the dynamic features of each local area and transfer the edge information between the vertex pairs to the adjacent vertices. In the proposed graph convolution module, the adjacent vertices are selected with high classification confidence which can guide the central vertex, and then reweight these vertices. Considering the lack of texture features in 3D point clouds, we incorporate 2D image features to adjacent feature propagation to effectively extract the local and global features of point clouds. The experimental results based on ScanNet and S3DIS datasets demonstrate the effectiveness of the proposed method.
A Probabilistic Estimation of PV Capacity in Distribution Networks from Aggregated Net-load Data

Globally, solar photovoltaic (PV) installations on distribution LV feeders have increased significantly. The increased penetration leads to several technical problems on existing networks and impacts utilities’ business models as energy sales drop. For proactive management of these challenges, utilities need to continually monitor the capacity of installed PV. To this end, some utilities typically require PV installations to be registered and sometimes use GIS mapping to approximate the installed PV capacity. However, these GIS PV capacity estimations methods are unreliable. Therefore, to obtain reliable PV capacity estimates at a distribution level, comprehensive modeling is required to accurately represent the generation output and the distribution load. This paper proposes a novel probabilistic PV estimation method that uses time-series historical sets of load and irradiance data to estimate the embedded PV capacity while considering the uncertainty in solar irradiance and the measured net load. Uncertainty characterization is implemented using empirical probability density functions, and simulation is performed stochastically using Monte-Carlo methods. A novel quantile analysis approach is developed and used in the computation of the final PV estimates. The proposed methodology is tested using measured load data from Ausgrid customers, Australia, and achieves reasonable accuracy (between 86% and 90% for the tested cases) and a 10% mean absolute percentage error. This approach is robust to the effects of input uncertainty and can be used by distribution utilities to estimate and monitor PV capacity installed on the distribution networks without incurring extra advanced metering investment costs.
A Flexible Magnetic Field Mapping Model For Calibration of Magnetic Manipulation System

Magnetic manipulation provides a versatile, remote, noninvasive, and cost-effective strategy in a variety of applications. Till now, many different configurations of magnetic manipulation systems have been developed to address different needs on force, torque, accuracy, and accessibilities. Magnetic field mapping can help to explore the exact map of the magnetic field in the working space and guarantee the homogeneity of the magnetic field. In this paper, a flexible mapping method is employed to solve the scalar potential of the magnetic source by using the separation of variables in Cartesian coordinates. Levenberg-Marquardt Algorithm (LMA) and Whale Optimization Algorithm (WOA) are set to the solver of the model. The work is evaluated in the mapping of an eight-pole magnetic manipulation system. The result of numerical simulation shows that the coefficient of determination R2 of the model reaches99.81%, and the actual system mapping obtains R2 value of 99.57%. This technique can directly be used to calculate the magnetic flux density and gradient field in a short period (≈1ms). Finally, the manipulation of a permanent magnet under the control magnetic field mapping and PID controller demonstrates the effectiveness of the proposed method.
Predictive Theory of Covid-19 Infections at European Countries Through Bessel Functions: Past and Present

The data of infections by Covid-19 is modeled through the integer-order Bessel functions that have been parametrized in according to the morphology of data. In particular, the modeling is focused on official data belonging to UK, Germany, Italy and Netherlands. The free parameters of model have been coherently linked to data. Interestingly, it was seen that a "silent period" with the lowest cases of infections play a relevant role for new pandemics as well as the apparition of new strains, such as the most recent "delta-variant".
Explainable anomaly detection for Hot-rolling industrial process*

Anomaly detection is emerging trend in manufacturing processes and may be considered as part of the Industry 4.0 revolution. It can serve both as diagnostic tool in predictive maintenance task, as well as trace back mechanism for assessing quality of production or services. In this paper we describe and approach for explainable anomaly detection in industrial data which contains sequential and static features. We based our solution on modified autoencoder architecture with Long Short-Term Memory layers. To address a problem of explinability in deep learning and find origin of the anomalies we have engaged the SHAP method, which gives both local and global explanations of the model. Analysis of SHAP explanations allowed us to determine the source of majority of anomalies detected by deep learning model. We demonstrated the feasibility of our approach on synthetic, reproducible dataset and on real-life data gathered from hot rolling industrial process.
Optimal scaling of dynamic safety zones for collaborative robotics

We propose a safety control approach based on the online optimal scaling of the size of bounding volumes used as dynamic safety zones for collaborative robotics. Intersection tests between bounding volumes surrounding robot and human allow the safety controller to identify possible collisions. Our proposed approach optimizes online smooth stop trajectories, to be engaged if a potential collision is detected. Unlike other approaches, the robot dynamics and its torque constraints are here considered to plan optimal trajectories that minimize the size of safety zones surrounding the robot. Simulation results on a validated model of a robot with seven degrees-of-freedom verify the feasibility of the proposed approach.
How to Earn Money with an EMI Problem: Static Energy Meters Running Backwards

The increased use of non-linear appliances in households has resulted in several conducted electromagnetic interference issues, such as misreadings of static energy meters used for billing purposes of the households’ energy consumption. In this paper a case is presented where a static energy meter indicates a power generation, while power is actually being consumed. A perceived power generation of more than 430 W is measured by a static energy meter installed in a household when a television with a commercial off the shelf remote controlled switch with dimming functionalities consumed 21 W. The same situation is reproduced in a controlled lab environment, to eliminate possible influences of other appliances in the grid, which confirmed the on-site results. The current waveforms causing this supposed generation of power are investigated and it is observed that the phase firing angle of the current pulse drawn by the load in combination with the commercial off the shelf remote controlled switch affects the metering errors and determines whether the errors indicate a false generation, a too high consumption of power, or no error at all. A combination of the household equipment and a basic unloaded switched mode power supply in conjunction with two remote controlled switches resulted in a perceived power generation of more than 600 W. Having these loads connected for the entire day would counteract the total consumption of an average household and could even “generate” energy, and thus generate money for the consumer.
Learning Seed Placements and Automation Policies for Polyculture Farming with Companion Plants

Polyculture farming is a sustainable farming technique based on synergistic interactions between differing plant types that make them more resistant to diseases and pests and better able to retain water. Reduced uniformity can reduce use of pesticides, fertilizer, and water, but is more labor intensive and more challenging to automate. We describe a scaled physical testbed (1.5m×3.0m) that uses a high resolution camera and soil sensors to monitor polyculture plants to facilitate tuning of plant growth, companion effects, and irrigation parameters for a first-order garden simulator. We use this simulator to develop a novel seed placement algorithm that increases coverage and diversity, and a learned pruning policy. In simulation experiments, the seed placement algorithm yields 60% more coverage and 10% more diversity than random seed placement and the learned pruning policy runs 1000X faster than a procedural lookahead policy to achieve high leaf coverage and plant diversity on adversarial gardens that include plant species with diverse growth rates. These models and policies provide the groundwork for a fully-automated system under development. Code, datasets and supplementary material can be found at https://github.com/BerkeleyAutomation/AlphaGarden/.
A Low-Complexity Shifting-Based Conflict-Free Memory-Addressing Architecture for Higher-Radix FFT

Conflict-free addressing (CFA) techniques are necessary for Fast Fourier Transform (FFT) hardware. For low radices, well-proven XOR-based addressing architectures are available in the literature. Applications such as wireless communication use higher FFT radices, more processing elements and a larger number of memory sets for a continuous flow of data. In the existing CFA techniques, the complexity increases with increasing radices or memory sets. In the proposed technique, the higher number of memory sets and radix are leveraged to an advantage. A novel scheme is suggested to reduce the complexity using a progressive shifting technique. The mathematical basis of the scheme is derived here and illustrated with an example of 512-point radix-16 FFT. The proposed and existing CFA architectures are designed using Verilog and implemented in the Semiconductor Laboratory, Chandigarh, 180 nm SCL library. The synthesis results show that the proposed scheme achieves a 33% area reduction compared to the best existing schemes. The area reduction factor further improves with a higher number of FFT points.
Improving Portuguese Semantic Role Labeling with Transformers and Transfer Learning

The Natural Language Processing task of determining “Who did what to whom” is called Semantic Role Labeling. For English, recent methods based on Transformer models have allowed for major improvements in this task over the previous state of the art. However, for low resource languages, like Portuguese, currently available semantic role labeling models are hindered by scarce training data. In this paper, we explore a model architecture with only a pre-trained Transformer-based model, a linear layer, softmax and Viterbi decoding. We substantially improve the state-of-the-art performance in Portuguese by over 15 F1. Additionally, we improve semantic role labeling results in Portuguese corpora by exploiting cross-lingual transfer learning using multilingual pre-trained models, and transfer learning from dependency parsing in Portuguese, evaluating the various proposed approaches empirically.
Referring Image Segmentation via Language-Driven Attention

This paper aims to tackle the problem of referring image segmentation, which is targeted at reasoning the region of interest referred by a query natural language sentence. One key issue to address the referring image segmentation is how to establish the cross-modal representation for encoding the two modalities, namely, the query sentence and the input image. Most existing methods are designed to concatenate the features from each modality or to gradually encode the cross-modal representation concerning each word’s effect. In contrast, our approach leverages the correlation between the two modalities for constructing the cross-modal representation. To make the resulting cross-modal representation more discriminative for the segmentation task, we propose a novel mechanism of language-driven attention to encode the cross-modal representation for reflecting the attention between every single visual element and the entire query sentence. The proposed mechanism, named as Language-Driven Attention (LDA), first decouples the cross-modal correlation to channel-attention and spatial-attention and then integrates the two attentions for obtaining the cross-modal representation. The channel attention and the spatial attention respectively reveal how sensitive each channel or each pixel of a particular feature map is with respect to the query sentence. With a proper fusion of the two kinds of feature attention, the proposed LDA model can effectively guide the generation of the final cross-modal representation. The resulting representation is further strengthened for capturing the multi-receptive-field and multi-level-semantic for the intended segmentation. We assess our referring image segmentation model on four public benchmark datasets, and the experimental results show that our model achieves state-of-the-art performance
Semantic Feature Mining for 3D Object Classification and Segmentation

Deep learning on 3D point clouds has drawn much attention, due to its large variety of applications in intelligent perception for automated and robotic systems. Unlike structured 2D images, it is challenging to extract features and implement convolutional networks over these unordered points. Although a number of previous works achieved high accuracies for point cloud recognition, they tend to process local point information in such a way that semantic information is not fully encoded. In this paper, we propose a deep neural network for 3D point cloud processing that utilizes effective feature aggregation methods emphasizing both generalizability and relevance. In particular, our method uses fixed-radius grouping for pooling layers and spherical kernel convolution for semantics mining. To address the issue of gradient degradation and memory consumption of a deep network, a parallel feature feed-forward mechanism and bottleneck layers are implemented to reduce the number of parameters. Experiments show that our algorithm achieves state-of-the-art results and competitive accuracy in both classification and part segmentation while maintaining an efficient architecture.
Learning from Demonstration for Real-Time User Goal Prediction and Shared Assistive Control

In shared autonomy, the user input is blended with the assistive motion to accomplish a task where the user goal is typically unknown to the robot. Transparency between the human and robot is essential for effective collaboration. Prior works have provided methods for the robot to infer the user goal; however, they are usually dependent on the distance between the robot and object, which may not be directly associated with the real-time user control intention and thus cause low control feelings. Here, we propose a real-time goal prediction method driven by assistive motion generated by learning from demonstration (LfD) allowing more reactive assistive behaviors. This LfD-generated assistive motion is blended with the user input based on goal predictions to achieve targeted tasks. The LfD policy was learned offline and used with different users. To evaluate our proposed method, we compared it with a state-of-the-art Partially Observable Markov Decision Process (POMDP) based method using a distance cost, and a direct control method (i.e., joystick). A pilot study (N = 6) was conducted to control a 6-DoF Kinova Mico robotic arm to carry out three tasks: (1) reaching-and-grasping, (2) pouring, and (3) object-returning with the three control methods. We used both objective and subjective measures in the comparative study. Results show that our method has the shortest task completion time, the lowest amount of joystick control inputs among all three control methods, as well as a significantly lower angular difference between the user input and assistive motion compared to the POMDP-based method. Besides, it obtains the highest subjective score in the user preference and perceived speed ratings, and the second-highest in the control feeling and the robot did what I wanted ratings.
Distributed Rendezvous Control of Networked Uncertain Robotic Systems with Bearing Measurements

In this paper, the distributed rendezvous control problem of networked uncertain robotic systems with bearing measurements is investigated. The network topology of the multi-robot systems is described by an undirected graph. The dynamics of robots is modeled by Euler-Lagrange equation with unknown inertial parameters, which is more general than simple kinematics considered in existing works on rendezvous problem of multi-robot systems. To achieve rendezvous, a distributed adaptive force/torque control law is developed for each robot, which uses bearings with respect to its neighbors instead of relative displacements or distances. It is shown that the resulting closed-loop multi-robot systems are globally asymptotically stable. Then, the rendezvous control problem of multiple wheeled mobile robots is further solved by the proposed approach. Finally, on-site experiment on networked TurtleBot3 Burger mobile robots is conducted and the results demonstrate effectiveness of the proposed approach.
Thermo- and Magnetoresistive Properties of Multicomponent Film Materials Based on Magnetic and Non-magnetic Metals

In the phenomenological approximation, the relations for the differential temperatures coefficient of resistance $\left(\beta_{T}=\frac{\partial\ln\rho}{\partial T}\right)$ and magnetic coefficient of resistance $\left(\beta_{B}=\frac{\partial\ln\rho}{\partial B}\right)$, where $\rho=\sum_{(i)}c_{i}\rho_{i}$, for multicomponent high-entropy film alloys (HEA) based on magnetic and non-magnetic metals are obtained. Experimental values $\beta_{T},\beta_{B}^{MR}$ and $\beta_{B}^{GMR}\quad$ agree well with the calculations, which allows for the prognosis of the value $\beta_{T}$ for normal $\beta_{B}^{M/R}$ and for giant $\beta_{B}^{GMR}$ magnetoresistance based on experimental data for temperature and magnetic coefficients of individual components.
A Smart Parametric 3D Printing Hand Assistive Device with A Practical Physically Intervention Feedback Based on The Embedded IMU Sensor

Assistive devices are one of the crucial medical instruments for rehabilitation. Customized designs are required to match the geometry and dimensions of the affected sites of human bodies. The existing practice of assistive device development remains mostly a manual process, in which an experienced physiotherapist employs simple two-dimensional measurements and visual estimation to handcraft each device. The resulting devices not only vary substantially in fit to wearers, but also are not compatible to modern developmental technologies such as three-dimensional (3D) printing. This study incorporated substantial quantities of anthropometric data to construct a parametric 3D hand model. This study employed a customized assistive device - splint for carpal tunnel syndrome as a target object for testing the feasibility of the proposed method and verified the practicability through 3D printing implementation. Moreover, a wearable device embedded with inertial measurement unit sensor and vibration module will be integrated into the customized assistive device to offer a smart way for rehabilitation. With the help of this developed wearable device, continuously tracking user’s activity wirelessly and provide the real-time physically intervention feedback of vibration module based on back-end monitoring system platform. This new concept by integrating customized design and the IMU sensor and vibration module open a new window for the field of customized assistive device and improve its practical values.
VINSEval: Evaluation Framework for Unified Testing of Consistency and Robustness of Visual-Inertial Navigation System Algorithms

The research community presented significant advances in many different Visual-Inertial Navigation System (VINS) algorithms to localize mobile robots or hand-held devices in a 3D environment. While authors of the algorithms of-ten do compare to, at that time, existing competing approaches, their comparison methods, rigor, depth, and repeatability at later points in time have a large spread. Further, with existing simulators and photo-realistic frameworks, the user is not able to easily test the sensitivity of the algorithm under examination with respect to specific environmental conditions and sensor specifications. Rather, tests often include unwillingly many polluting effects falsifying the analysis and interpretations. In addition, edge cases and corresponding failure modes often remain undiscovered due to the limited breadth of the test sequences. Our unified evaluation framework allows, in a fully automated fashion, a reproducible analysis of different VINS methods with respect to specific environmental and sensor parameters. The analyses per parameter are done over a multitude of test sets to obtain both statistically valid results and an average over other, potentially polluting effects with respect to the one parameter under test to mitigate biased interpretations. The automated performance results per method over all tested parameters are then summarized in unified radar charts for a fair comparison across authors and institutions.