Miracle-dz/UniFAR-FTUs huggingface dataset:
Adding a Reproducible Airplane Model to the Austin RCS Benchmark Suite

A full-size airplane model (the EXPEDITE-RCS model) was developed as part of a benchmark suite for evaluating radar-cross-section (RCS) prediction methods. To generate accurate reference data for the benchmark problems formulated using the model, scale-model targets were additively manufactured, their material properties and RCS were measured, and the measurements were validated with a surface-integral-equation solver. To enable benchmarking of as many computational methods as possible, the following data are made available in a version-controlled online repository: (1) Exterior surface (outer mold line) of the CAD model in two standard file formats. (2) Triangular surface meshes. (3) Measured and predicted monostatic RCS data.
Current-Scaling Gain Compensation of Motor Drives Under Locked-Rotor Condition Considering Inequality of Phase Resistances

This article proposes a current-scaling gain-compen-sation algorithm for motor drive applications under the locked-rotor condition. In heavy industrial applications, such as elevators and cranes, initial commissioning tests are performed with the rotor locked. To compensate for errors in the current-scaling gain, the proposed method controls the current in the d–q axis using two-phase current-sensing methods in a stationary reference frame. The three-phase scaling gains are then estimated using the resulting d-axis current command and output voltage and are compensated for using these estimations. The proposed method delivers accurate compensations by considering the inequality between phase resistances. The effectiveness of the proposed algorithm is verified through simulations and experiments.
Glaucoma Detection: Joint Segmentation and Classification Framework via Deep Ensemble Network

Clinical research shows that glaucoma is primarily caused by pathological changes in the optic nerve structure, which may bring about irreversible damage of sight. In relevant literature research, the cup-to-disc ratio (CDR) is mainly used as an important indicator for glaucoma detection, which needs to segment optic disc (OD) and optic cup (OC) region clearly and accurately. However, due to the low contrast image of boundary, the segmentation of OD and OC is still a challenging problem. In this paper, a novel hybrid model based on the ensemble random-forest deep-neural-network (RF-DNN) is proposed for OD and OC segmentation, which can calculate more accurately for glaucoma detection. At the same time, the advantages of DNN and ensemble RF are combined to carry out the corresponding feature extraction and classification, which employs the winner-takes-all strategically to the segmentation and classification for glaucoma detection. Finally, the experiment result shows that the proposed method has reached the best evaluation level in terms of OD and OC segmentation results on ORIGA dataset and SCES dataset, which achieves highest diagnosis accuracy with AUC of 0.96 and 0.98 on ORIGA dataset and SCES dataset, respectively.
Phase-Controlled Pattern Diversity Antenna for 360°Continuous Beam Steering

In this paper, a novel phase-controlled pattern diversity Dielectric Resonator Antenna (DRA) is presented and studied for 360°continuous beam steering. The proposed DRA has two ports to excite diverse patterns. By changing the phase between diverse ports, unilateral radiation pattern can be steered in the horizontal plane. Compared to the conventional beam-switched antenna, the proposed methodology has continuous steering pattern instead of discrete patterns with limited number. The DRA operates at 2.95 GHz were simulated, showing characteristics of steering beam with stable gain (5dBi) and high efficiency (86.1%). The proposed DRA provide a low-cost beam steering solution to the indoor base station.
Design of a Low Input Voltage DC-DC Boost Converter with a Zero Current Detection Circuit for Energy Harvesting Applications

Energy harvesting circuits (EHV) are developed as an alternative solution to provide power to autonomous sensor nodes in wireless sensor networks (WSN) [1]. DC-DC boost converter raises a low input voltage harvested from an ambient energy source and provides a usable voltage for autonomous-powered wireless sensor. This converter uses a Zero Current Detection (ZCD) circuit. The converter sizing strategy was implemented using LTSPICE and Maple environment. The proposed system was simulated using 0. 18µm CMOS technology. Using a load of 10 KΩ, the voltage generated in the output is 1V from an input voltage of 0.3 V. The simulated conversion gain is 3.7and the output power is 164µW.
Hierarchical Interest-Driven Goal Babbling for Efficient Bootstrapping of Sensorimotor skills

We propose a novel hierarchical online learning scheme for fast and efficient bootstrapping of sensorimotor skills. Our scheme permits rapid data-driven robot model learning in a "learning while behaving" fashion. It is updated continuously to adapt to time-dependent changes and driven by an intrinsic motivation signal. It utilizes an online associative radial basis function network, which is the first associative dynamic network to be constructed from scratch with high stability. Moreover, we propose a parameter-sharing technique to increase efficiency, stabilize the online scheme, avoid exhaustive parameter tuning, and speed up the learning process. We apply our proposed algorithms on a 7-DoF physical robot manipulator and demonstrate their performance and efficiency.
Conformal Metasurface for Wideband RCS Reduction

A cylindrical conformal metasurface is introduced for wideband RCS reduction, utilizing the optimized multielement phase cancellation. The metasurface is divided into eight regions containing a total of 40 lattices that are printed on a cylindrically curved ground plane. For different regions of the curved surface, the incident angle and wave path difference caused by the bending of the conformal structure are considered. Hybrid array pattern synthesis and particle swarm optimization are used to control and optimize the interaction between multiple backscattered waves generated by the lattices, so as to realize wideband destructive interference. The designed metasurface can achieve a 10 dB reduction in backscattered RCS under normal incidence for HH polarization from 5 to 6.8 GHz and 7.3 to 10 GHz with the fractional bandwidths of 30.5% and 31.2%, respectively. The estimated and simulated results are in good agreement and prove that the proposed method can be used to solve the wideband RCS reduction of the conformal targets.
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy

Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions’ COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.
Modeling of Single-Pulse Operated Switched Reluctance Generator and Its Verification

This article proposes a mathematical model for a switched reluctance (SR) generator feeding a dc link through a power electronic converter. SR machines are operated in single-pulse mode at close to and above base speed. The SR generator is modeled as a current source, controllable with dc-link voltage <inline-formula><tex-math notation="LaTeX">$V_{\text{dc}}$</tex-math></inline-formula>, prime mover speed <inline-formula><tex-math notation="LaTeX">$\omega _m$</tex-math></inline-formula>, and switching angles (<inline-formula><tex-math notation="LaTeX">$\alpha _{\text{on}}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\alpha _{\text{off}}$</tex-math></inline-formula>) of the converter. This current source model is used to predict the dc-link dynamics for constant resistance, constant current, and constant power loads. This is further utilized to derive the voltage build-up condition for an SR generator. The generator model, dc-link dynamics and voltage build-up conditions are validated through simulations and experiments.
Mini-Batched Online Incremental Learning Through Supervisory Teleoperation with Kinesthetic Coupling*

We propose an online incremental learning approach through teleoperation which allows an operator to partially modify a learned model, whenever it is necessary, during task execution. Compared to conventional incremental learning approaches, the proposed approach is applicable for teleoperation-based teaching and it needs only partial demonstration without any need to obstruct the task execution. Dynamic authority distribution and kinesthetic coupling between the operator and the agent helps the operator to correctly perceive the exact instance where modification needs to be asserted in the agent’s behaviour online using partial trajectory. For this, we propose a variation of the Expectation-Maximization algorithm for updating original model through mini batches of the modified partial trajectory. The proposed approach reduces human workload and latency for a rhythmic peg-in-hole teleoperation task where online partial modification is required during the task operation.
Fake News Detection with Integration of Embedded Text Cues and Image Features

A novel approach using Convolution neural Network (CNN) and Long short-term memory (LSTM) has been proposed to find the reliability of the news. In this research, image visual feature with embedded text feature and headline texts have been considered to find the comprehensive results. First the semantic information from the images have been captured as text (news tag line) and this tag has been compared to the original headline text. Individually image and text both are insufficient to find the semantic knowledge of publish news. So, the cosine similarity index (CSI) has been used to predict the reliability of the news. The threshold of CSI has been constrained greater than 0.62 for the news real. A repository has been created named as” imaged fake news”. In this repository 1000 images have been considered with the headline texts, where 367 news were fake and 633 news were real. The accuracy of the proposed method is 91.07%. The result implies that the novel methodology is better than the state-of-the-art method.
Calibrating a Soft ERT-Based Tactile Sensor with a Multiphysics Model and Sim-to-real Transfer Learning

Tactile sensors based on electrical resistance tomography (ERT) have shown many advantages for implementing a soft and scalable whole-body robotic skin; however, calibration is challenging because pressure reconstruction is an ill-posed inverse problem. This paper introduces a method for calibrating soft ERT-based tactile sensors using sim-to-real transfer learning with a finite element multiphysics model. The model is composed of three simple models that together map contact pressure distributions to voltage measurements. We optimized the model parameters to reduce the gap between the simulation and reality. As a preliminary study, we discretized the sensing points into a 6 by 6 grid and synthesized single- and two-point contact datasets from the multiphysics model. We obtained another single-point dataset using the real sensor with the same contact location and force used in the simulation. Our new deep neural network architecture uses a de-noising network to capture the simulation-to-real gap and a reconstruction network to estimate contact force from voltage measurements. The proposed approach showed 82% hit rate for localization and 0.51 N of force estimation error performance in singlecontact tests and 78.5% hit rate for localization and 5.0 N of force estimation error in two-point contact tests. We believe this new calibration method has the possibility to improve the sensing performance of ERT-based tactile sensors.
Use of Multi-frequency Electrical Impedance Tomography as Tactile Sensor for Material Discrimination

Electrical Impedance Tomography (EIT) is an imaging technique used recently as a tactile sensor. The advantages are the absence of electrodes within the sensor area, low-cost design and material. Moreover, it also includes low electric consumption and it can be shaped freely. Although EIT reconstruction gives a low spatial resolution, the retrieved impedance provides other information. Usually, frequencies in EIT are chosen to match specific impedance material. This paper proposes a new approach to retrieve the contact type as a new modality for tactile sensors. We propose to use Multi-Frequency Analysis (MFA) to retrieve the contact type. Our device uses the frequency range from 1k to 200k Hz. The results show the ability to classify 4 different types of material using clustering algorithms from raw data. This method is also able to differentiate hands from different people. MFA shows the use of multi-frequencies allows discriminating material. Furthermore, within one type of material, sight differences are also detectable. This new approach could lead to a cheap multi-modal sensor and allows better robotics arms manipulation.
Real-Time Graph-Based SLAM with Occupancy Normal Distributions Transforms

Simultaneous Localization and Mapping (SLAM) is one of the basic problems in mobile robotics. While most approaches are based on occupancy grid maps, Normal Distributions Transforms (NDT) and mixtures like Occupancy Normal Distribution Transforms (ONDT) have been shown to represent sensor measurements more accurately. In this work, we slightly re-formulate the (O)NDT matching function such that it becomes a least squares problem that can be solved with various robust numerical and analytical non-linear optimizers. Further, we propose a novel global (O)NDT scan matcher for loop closure. In our evaluation, our NDT and ONDT methods are able to outperform the occupancy grid map based ones we adopted from Google's Cartographer implementation.
Stereo Visual Inertial Mapping Algorithm for Autonomous Mobile Robot

Simultaneous Localization and Mapping (SLAM) is a fundamental problem for autonomous mobile robots (AMRs). AMRs are widely used in automated warehousing, factory material transfer systems, flexible assembly systems, and other intelligent transportation sites. The visual Inertial Odometry (VIO) which consists of the camera and inertial-measurement-unit (IMU), is a popular approach to achieve accurate 6-DOF state estimation. However, such locally accurate VIO is prone to drift and cannot provide a global consistent map. The prerequisite for re-localizing the robot and ensuring precise autonomous navigation is an accurate and global consistent map of its environment. In this study, we propose a stereo visual-inertial mapping system. The front-end is a robust stereo VIO based on a tightly-coupled sliding window optimization. The core of the back-end is the global Bundle-Adjustment (BA) which is a nonlinear optimization, in which IMU is also added as a time-domain constraint. Meanwhile, stereo-camera-IMU extrinsic calibration is performed in BA to improve mapping accuracy. The selection principles of keyframes and map points are also designed according to the AMRs application characteristics. Further, the forward and backward Perspective-n-Point (PNP) method is also adopted to avoid the loop-detection mismatch. The performance of the system was validated and compared against other state-of-the-art algorithms. The findings revealed the effectiveness and robustness of this stereo visual-inertial mapping algorithm.
Robust Semiparametric Efficient Estimators in Complex Elliptically Symmetric Distributions

Covariance matrices play a major role in statistics, signal processing and machine learning applications. This paper focuses on the <italic>semiparametric</italic> covariance/scatter matrix estimation problem in elliptical distributions. The class of elliptical distributions can be seen as a semiparametric model where the finite-dimensional vector of interest is given by the location vector and by the (vectorized) covariance/scatter matrix, while the density generator represents an infinite-dimensional nuisance function. The main aim of this work is then to provide possible estimators of the finite-dimensional parameter vector able to reconcile the two dichotomic concepts of <italic>robustness</italic> and (semiparametric) <italic>efficiency</italic>. An <inline-formula><tex-math notation="LaTeX">$R$</tex-math></inline-formula>-estimator satisfying these requirements has been recently proposed by Hallin, Oja and Paindaveine for real-valued elliptical data by exploiting the Le Cam's theory of <italic>one-step efficient estimators</italic> and the <italic>rank-based statistics</italic>. In this paper, we firstly recall the building blocks underlying the derivation of such real-valued <inline-formula><tex-math notation="LaTeX">$R$</tex-math></inline-formula>-estimator, then its extension to complex-valued data is proposed. Moreover, through numerical simulations, its estimation performance and robustness to outliers are investigated in a finite-sample regime.
SW Runtime Estimation using Automata Theory and Deep Learning on HPC

People use high performance computers (HPCs) for computation-intensive tasks. Often these tasks require a lot of running time of their corresponding softwares. It is important to execute several tasks simultaneously for the system utilization while finishing all tasks within their desired deadlines. Therefore, it is important to know the runtime of each computation-intensive task without executing them in order to schedule the tasks on HPC and obtain better system performance. We propose a method for predicting runtime of MPI-based softwares on HPC using automata theory and deep learning. We first analyze a source code of an input program by representing the code as finite automata and measuring their state complexities. Next, we train the execution runtime of each module of our finite automata using deep neural network (DNN) together with its own state complexity. Then we combine all modules and make a single SW-runtime-prediction model. For experiment, we train the proposed model using OSU benchmark data, HPL and two in-house datasets, and present the usefulness of our model. We also demonstrate the adaptability of our model by updating the current model for new inputs using incremental DNN, which is an important feature for coping with new softwares or new systems.
Simple Expert System for Intelligent Control and HCI for a Wheelchair fitted with Ultrasonic Sensors

Using an expert system to make driving a powered-wheelchair less problematic is investigated. The system interprets sensor and joystick signals and then mixes them and improves that collaboration to control speed and direction. Ultrasonic sensors are used to identify hazardous circumstances and suggest a safer direction and speed. Results from drivers completing a series of timed routes are presented. Users completed tests using joysticks to control their chair with and then without a microcomputer and sensor system. A recent system is used to compare and contrast the results. This new system consistently performed quicker than the recent system. It also appears that the quantity of support provided by the sensors and microcomputer should be adjusted depending on situations and surroundings
Bitflow: An In Situ Stream Processing Framework

The timely processing of continuous data streams gains increasing importance in a variety of fields. Self-healing systems depend on efficient data analysis to detect problems and apply appropriate counter measures. This paper introduces Bitflow, a stream processing framework optimized for the data analysis tasks in self-healing IT systems. Numerous algorithmic contributions allow to mine monitoring data obtained from critical system components, in order to detect and classify anomalies, and to localize their root cause. These data analysis tasks are traditionally executed on big data processing platforms, which run on dedicated hosts and assume complete ownership over the occupied resources. Bitflow takes a different approach by analyzing the monitoring data directly at its source – i.e., in situ. We exploit the fact that IT systems are usually over-provisioned and a fraction of the computational resources can be allocated for self-healing functionality. Bitflow implements a dynamic modeling approach for dataflow graphs, which adapts to varying environments, such as changing data sources, or system components. Further, we describe Bitflow’s scheduling approach, which determines when it is beneficial to migrate a data analysis process to a remote host. Experimental data from practical data analysis tasks shows the applicability of our scheduling solution.
A Virtual Sensor Network for (WSNs): Architecture and Routing

Wireless Sensor Networks (WSNs) generate huge amounts of data and the number of physical sensor nodes used are increasing each day. However, to store and process the raw generated data, it is very crucial to use high-performance computing resources such as, distributed clusters, mainframe or Cloud Computing. In this context, a novel architecture based on WSN with cloud computing to support the big data processing and storing capabilities at different levels. The aim behind proposed architecture is to build a new virtual sensor Network (VSN) design based on Named Data Networking (NDN) paradigm response for routing sensed data between virtual sensors Network. The performance of the current architecture has been compared with different internal and external IP-based internet protocols. A primary evaluation of the proposed architecture tends to confirm its suitability to be adopted as a potential concurrent for WSN integration with the Cloud.