Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of PMU Based Distributed Wide Area Monitoring in Smart Grid

Determination of accurate operating states of the power system is one of the most challenging tasks due to integration of large number of solar PVs into the power system. Since these sources of energy are inertialess generations, hence may cause instability issues if highly penetrated. Hence, precise monitoring and control of such systems with higher PV penetration is a critical issue to address both in terms of the total number of PV sources in the system and the percentage of penetration. Phasor Measurement Units (PMUs), with their time synchronizing abilities, have made this task a bit easier. However, traditional centralized data handling architecture at control centers are becoming redundant due to various limitations such as data handling, computational constraints etc. To overcome this, a distributed PMU-PDC architecture approach is utilized in this paper. Since local PDC (Phasor Data Concentrator) in an n-area power system can run separate optimization algorithms, by combining the results of these optimization algorithms of n-area PDCs, we can get a much more accurate global consensus about the current operating state of the system. The presented work is divided into two parts. First, the analysis of power system stability is performed in terms of the total number of Photovoltaic Source (PV) in the system and the percentage of PV penetration. Then, the accuracy of the method is tested both in terms of the number of PMUs in each area along with their channel capacities. Low-frequency oscillations have been simulated on IEEE-68 standard bus system using MATLAB, and the modes of oscillation are estimated using the Alternating Direction Method of Multipliers (ADMM) algorithm.
A Locally-Adaptive, Parallel-Jaw Gripper with Clamping and Rolling Capable, Soft Fingertips for Fine Manipulation of Flexible Flat Cables

Flexible flat cables (FFC) are very popular for connecting different components in modern electronics (e.g., mobile phones, laptops, tablets, etc.). The manipulation of FFCs typically relies on highly trained workers that spend hours performing the same repetitive processes, or on autonomous robotic systems that are equipped with simple clamping mechanisms or pneumatically driven suction cups. Such robotic systems are difficult to program and reprogram and often rely on sophisticated sensing elements and complicated control laws. Moreover, the performance and robustness of such systems is far from sufficient, hindering their mass adoption. The manipulation of FFCs is also quite challenging. A good gripper should be able to pinch the cable steadily and execute insertion tasks of the cable connector with ease. The suction cup based solution is a good approach for holding the cable, but it makes the cable connector insertion very challenging as it can only apply limited shear forces. In this paper, we propose a locally-adaptive, pneumatic, parallel-jaw robot gripper equipped with fingertips that are able to both pinch the cable with a soft clamping mechanism and roll the cable surface on the soft fingertip structure until it reaches the desired connector. The gripper base accommodates a camera that allows for the recognition and pose estimation of the flat, flexible cables and other electronic components. The gripper is of low-cost and low-complexity and it can facilitate the efficient and robust execution of FFC grasping and assembly tasks.
Modelling and optimisation of a mechanism-based metamaterial for a wrist flexion-extension assistive device

In this paper we present a methodology for optimising the design of a metamaterial structure with one degree of freedom that is able to simultaneously bend and stretch. The structure is intended for assisting flexion-extension of the wrist joint. The metamaterial is comprised of serially connected, individually designed cells. The design parameters can be chosen to optimally fit a desired planar curve such as the curvature of the skin on a plane normal to the flexion/extension axis of the wrist joint. A tool for the optimised design is described and the experimental validation of the output is conducted, to show the ability of the mechanism to conform to a 2D curve, also exhibiting a change in length, which is desirable for reducing sliding and shear on the skin. The design tool allows the generation of metamaterials optimised for a multitude of other applications where actuated mechanisms must be worn by a user for rehabilitation or assistance purposes.
Fruit Quality Analysis using modern Computer Vision Methodologies

The field of agriculture is one of the most profitable fields for a country. The produce of this industry i.e., fruits and vegetables, is tremendous and thus the quality insurance of these products are of utmost importance. Evaluation of fruits can be done manually but due to inconsistent results and huge time consumption, it is necessary to have the automated systems to perform the quality tests. In this study, computer vision has been used to build an architecture that is competent to detect whether the fruit is rotten or fresh. VGG16 CNN (Convolutional Neural Network) model is employed to extract the features from the images of Apples, Bananas, Guava and Oranges. With the help of extracted features, the classification is performed through Decision Tree, Support Vector Machines (SVM), and Logistic regression models. Support Vector Machine performed the best classification with an accuracy of 99%
Plant Disease Detection in Imbalanced Datasets using Efficient Convolutional Neural Networks with Stepwise Transfer Learning

Convolutional neural networks have demonstrated state-of-the-art performance in image classification and various other computer vision tasks. Plant disease detection is an important area of deep learning which has been addressed by many recent methods. However, there is a dire need to optimize these solutions for resource-constrained portable devices such as smartphones. This is a challenging problem because deep learning models are resource extensive in nature. This paper proposes an efficient method to systematically classify plant disease symptoms using convolutional neural networks. These networks are memory efficient and when coupled with the proposed training configuration it enables rapid development of industrial applications by reducing the training times. Another critical problem arises with the improper distribution of samples among classes known as the class imbalance problem, which is addressed by employing a simple statistical methodology. Transfer learning is a known technique for training small datasets which transfers pre-trained weights learned on a large dataset. However, during transfer learning, negative transfer learning is a common problem. Therefore, a stepwise transfer learning approach is proposed which can help in fast convergence, while reducing overfitting and preventing negative transfer learning during knowledge transfer across domains. The system is trained and evaluated on two plant disease datasets i.e., PlantVillage (a publicly available dataset) and pepper disease dataset provided by the National Institute of Horticultural and Herbal Science, Republic of Korea. The pepper dataset is particularly challenging as it contains images from different parts of the plant such as the leaf, pulp, and stem. The proposed system has dominated the previous works on the PlantVillage dataset and achieved 99% and 99.69% accuracy on the Pepper dataset and PlantVillage datasets, respectively.
Design and Modeling of a Biomimetic Gastropod-like Soft Robot with Wet Adhesive Locomotion

Crawling through various terrains has been a long research interest. In recent years, quite a number of soft crawling robots have been developed. However, locomoting in an elastic, humid, and slippery environment remains a challenge. In nature, gastropods, such as snails, live in humid environment and could crawl through all kinds of surface conditions by using wet adhesion. In the wet adhesive locomotion, the mucus is crucial in adhering the gastropod while allowing forward motion. Previously, we presented one snail-like soft robot that mimics the gastropods. In this work, we propose a second version and present a theoretical model of the mucus simulant. In addition, the dynamic model of the soft robot’s wet adhesive locomotion is developed for the first time. Results show that the speed of the current version is 5 times than that of the previous one through the optimization of design. Also shown by the results that the mucus helps to speed up the robot by at least 2.7 times.
Model-based Design and Digital Implementation to Improve Control of the da Vinci Research Kit Telerobotic Surgical System

The da Vinci Research Kit (dVRK) was introduced in 2012 to provide an affordable, open-source platform for research in robotic minimally-invasive surgery. It provides access to all levels of control but, until now, has relied on an analog controller for the motor current, which cannot easily be customized to improve performance. This paper aims to implement the low-level control digitally and to improve the overall control performance. To enable model-based controller design, the system is first identified using measurements provided by the encoders and internal electronics. The digital current controller is then implemented on the existing field programmable gate array (FPGA). Experiments demonstrate that the new digital current controller yields superior performance compared to the original analog design. In addition, the identified system model is used to design an improved position controller that is also implemented on the FPGA and provides better trajectory tracking than the position controller currently implemented on the control PC. The comparison between simulation and measurement, for both the current and position control, verifies the validity of the model based on the system identification, enabling utilization for future adaptations. The improved low-level control enlarges the possibilities for more accurate operation and the achieved digital implementation enables researchers worldwide to easily adapt the low-level control in future versions of the dVRK.
Foot Control of a Surgical Laparoscopic Gripper via 5DoF Haptic Robotic Platform: Design, Dynamics and Haptic Shared Control

Foot devices have been ubiquitously used in surgery to control surgical equipment. Most common applications are foot switches for electro-surgery, endoscope positioning and tele-robotic consoles. Switches fall short of providing continuous control as required for precise use of instruments. We developed a haptic foot interface to provide continuous assistance in surgical procedures. This paper concerns the foot control of simultaneous five degrees of freedom (DoF) of a surgical laparoscopic gripper. We assess systematically precision at controlling position and orientation at the target and closing of the forceps. Our controller provides position:position mapping between the foot and the robotic tool, as well as haptic feedback, compensating for gravity of the lower limb of the operator so as to alleviate fatigue. A dynamic model compensation and closed loop force feedback is used to achieve high transparency and backdrivability. The assistance is based on a novel type of haptic fixtures combining spring-damper with selective dynamic compensation in the direction aligned with the task of grasping, so as to simplify control of certain poses, made difficult due to the coupling between human lower limbs’ DoF’s. We experimentally evaluated the control strategy with six users on a position control surgical task in simulation. Results show the proposed assistance greatly eases the foot grasping task leading to higher completeness, efficiency, and lower mental and physical load.
Rate Mode Bilateral Teleoperation Based on Passivity Tanks and Variable Admittance Control

Bilateral teleoperation under rate mode is known to be a difficult problem in terms of stability, especially when the slave manipulator interacts with a time-varying environment. This paper presents an energy based variable admittance control approach, whose principle combines the monitoring and the regulation of the energy exchanges with a passivity tank. It allows stable interactions with force feedback for any desired inertia, damping and stiffness parameters. Experiments are conducted to assess the efficiency of the proposed approach using an experimental setup with a variable stiffness environment. The obtained results illustrate the ability of the proposed strategy to stabilize a system otherwise unstable, with little effect on the transparency of the teleoperation system.
A Study on Staircase Artifacts in Total Variation Image Restoration

The total variation (TV) regularization is used in various image processing domains such as image super-resolution, reconstruction, compressed sensing, and restoration mainly due to its edge-preserving capabilities. However, the main problem when using the TV regularization is the staircase artifacts. For image restoration, the staircase artifacts manifest themselves by producing a smeared and blocky restored image, especially when the noise level is high. This problem has been a long-standing problem, and various improvements to TV regularization have been proposed. This paper studies the effects of the staircase artifacts produced by two different noises; Gaussian noise and salt-and-pepper noise. For this purpose, we compare three well-known algorithms, the alternating direction method of multipliers (ADMM), alternating minimization (AM), and accelerated AM, and observe the effects of staircase artifacts produced between the three algorithms. As a by-product, the accelerated AM tested for the salt-and-pepper noise can be seen as a new extension of the existing accelerated AM method. Results show that it is interesting to study further the effects of different types of noise and the algorithms to mitigate the staircase artifacts produced.
A data-set and a method for pointing direction estimation from depth images for human-robot interaction and VR applications

3D pointing devices are indispensable in virtual reality (hereafter VR) and human-robot interaction scenarios. Existing devices are cumbersome or non-immersive or have a limited volume of operation. Hand gesture-based interfaces do not suffer from these problems and can be used for 3D pointing purposes. However, there is a lack of robust, accurate hand gesture-based pointing techniques which can be attributed to the non-existence of large and accurate data-set for the same. To overcome this barrier, we propose a data-set consisting of depth images with a large number (107000) of samples collected from 11 subjects, with accurate ground-truth and adequate variation in the orientation and distance of the hand w.r. t. the camera. We propose a 3D convolutional neural network based technique that works on the proposed data-set and achieves an accuracy of 94.49% for an angle error threshold of 10 degrees. The proposed data-set may be used for developing more accurate, robust, less computationally expensive methods.
Constellation Design Method for Large Scale Satellite Optical Networks

With the integration progress of space- and ground-based Internet, the demand for large-scale satellite networks is increasing. However, the efforts on constellation design for large-scale satellite networks are rarely observed. Based on the single satellite coverage model and multiple coverage theory of circular orbit constellation, this paper proposes a large-scale satellite constellation design method to meet the requirements of multi-coverage. Finally, the influence of constellation parameters on coverage performance is simulated and analyzed, and the correctness of constellation parameters is verified by simulation.
Learning hierarchical relationships for object-goal navigation

Direct search for objects as part of navigation poses a challenge for small items. Utilizing context in the form of object-object relationships enable hierarchical search for targets efficiently. Most of the current approaches tend to directly incorporate sensory input into a reward-based learning approach, without learning about object relationships in the natural environment, and thus generalize poorly across domains. We present Memory-utilized Joint hierarchical Object Learning for Navigation in Indoor Rooms (MJOLNIR), a target-driven navigation algorithm, which considers the inherent relationship between target objects, and the more salient contextual objects occurring in its surrounding. Extensive experiments conducted across multiple environment settings show an $82.9\%$ and $93.5\%$ gain over existing state-of-the-art navigation methods in terms of the success rate (SR), and success weighted by path length (SPL), respectively. We also show that our model learns to converge much faster than other algorithms, without suffering from the well-known overfitting problem. Additional details regarding the supplementary material and code are available at https://sites.google. com/eng.ucsd. edu/mjolnir.
PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris

Current segmentation methods have limitations for multi-source heterogeneous iris segmentation since differences of acquisition devices and acquisition environment conditions lead to images of greatly varying quality from different iris datasets. Thus, different segmentation algorithms are generally applied to distinct datasets. Meanwhile, deep-learning-based iris segmentation models occupy more space and take a long time. Therefore, a lightweight, precise, and fast segmentation network model, PFSegIris, aimed at the multi-source heterogeneous iris is proposed by us. First, the iris feature extraction modules designed were used to fully extract heterogeneous iris feature information, reducing the number of parameters, computation, and the loss of information. Then, an efficient parallel attention mechanism was introduced only once between the encoder and the decoder to capture semantic information, suppress noise interference, and enhance the discriminability of iris region pixels. Finally, we added a skip connection from low-level features to catch more detailed information. Experiments on four near-infrared datasets and three visible datasets show that the segmentation precision is better than that of existing algorithms, and the number of parameters and storage space are only 1.86 M and 0.007 GB, respectively. The average prediction time is less than 0.10 s. The proposed algorithm can segment multi-source heterogeneous iris images more precisely and quicker than other algorithms.
Learning Hierarchical Task Networks with Preferences from Unannotated Demonstrations

: We address the problem of learning Hierarchical Task Networks (HTNs) from unannotated task demonstrations, while retaining action execution preferences present in the demonstration data. We show that the problem of learning a complex HTN structure can be made analogous to the problem of series/parallel reduction of resistor networks, a foundational concept in Electrical Engineering. Based on this ﬁnding, we present the CircuitHTN algorithm, which constructs an action graph representing the demonstrations, and then reduces the graph following rules for reducing combination electrical circuits. Evaluation on real-world household kitchen tasks shows that CircuitHTN outperforms prior work in task structure and preference learning, successfully handling large data sets and exhibiting similar action selection preferences as the demonstrations.
RDF Knowledge Graph Mining over Data Cubes

Existing eﬀorts in RDF knowledge graph (KG) mining solely addressed fact-level KGs. Many KGs, especially those published by government entities, however contain aggregated data modeled using data cubes. We applied a state-of-the-art KG mining tool on RDF datasets of two government institutions and on interlinked fact-level KGs (Wikidata and YAGO), in multiple settings. Some of the results are meaningful, the main challenges being the low level of support of individual rules and their diﬃcult readability.
VOWLMap: graph-based ontology alignment visualization and editing

Manual validation of automated ontology alignments remains essential to produce high quality ontology alignments. However, few alignment systems feature user interfaces for alignment visualization, validation and editing, and those that do, support a limited number of requirements. We developed VOWLMap — an extension for the web application, WebVOWL — for visualizing, editing, and validating ontology alignments. VOWLMap employs modularization techniques to facilitate the visualization of large alignments while maintaining the context of each mapping and supports direct interaction with and editing of graph representations.
Sparql2GraphServer: a Server-side Tool for Extracting Networks from Linked Data for Data Analysis

This paper presents a server-side tool for constructing graph representations from a Linked Data for network analysis. The main features for the tool are: 1) Support to read data from a SPARQL endpoint, 2) easy adaptation to virtually any dataset and data model, and 3) easy usage in web portals for data analysis. The tool is in use.
A DBpedia-based Benchmark for Ontology-mediated Query Answering

Ontology-mediated query answering (OMQA) is a framework for querying data with a background ontology. Detailed evaluation of OMQA systems remains a challenge due to limitations in existing benchmarks. In this paper, we propose a new benchmark for OMQA based on natural language questions over DBpedia. In particular, the data are sampled from DBpedia with adjustable volumes and can easily reach a scale that is diﬃcult for existing OMQA systems to handle. Logical rules are automatically extracted from DBpedia using a rule learner, and the queries come from real-life natural language questions over DB-pedia. We evaluated two state-of-the-art systems under various settings, to demonstrate the potential of our benchmark in benchmarking and analyzing the behavior of OMQA systems.
Autonomous Decentralized Shape-Based Navigation for Snake Robots in Dense Environments

In this work, we focus on the autonomous navigation of snake robots in densely-cluttered environments, where collisions between the robot and obstacles are frequent, which could happen often in disaster scenarios, underground caves, or grassland/forest environments. This work takes the view that obstacles are not to be avoided, but rather exploited to support and direct the motion of the snake robot. We build upon a decentralized state-of-the-art compliant controller for serpenoid locomotion, and develop a bi-stable dynamical system that relies on inertial feedback to continuously steer the robot toward a desired direction. We experimentally show that this controller allows the robot to autonomously navigate dense environments by consistently locomoting along a given, global direction of travel in the world, which could be selected by a human operator or a higher level planner. We further equip the robot with an onboard vision system, allowing the robot to autonomously select its own direction of travel, based on the obstacle distribution ahead of its position (i.e., enacting feedforward control). In those additional experiments on hardware, we show how such an exteroceptive sensor can allow the robot to steer before hitting obstacles and to preemptively avoid challenging regions where proprioception-only (i.e., torque and inertial) feedback control would not suffice.