Miracle-dz/UniFAR-FTUs huggingface dataset:
Solar PV Array Integrated UPQC for Power Quality Improvement Based on Modified GI

In this paper, a modified generalized integrator (GI) control technique with a reduced number of integrators, is presented for the control of grid interfaced single stage solar PV array integrating with unified power quality conditioner (SPVA-UPQC) system. This controller has a reduced number of integrators to estimate the fundamental active components of the distorted signals enabling DC offset elimination. Further, these fundamental signals are used to generate the reference signals for shunt compensator. The SPVA-UPQC helps to mitigate both current and voltage related power quality (PQ) problems such as it eliminates harmonics, load unbalancing, reactive power compensation, voltage sag-swell elimination, as well as it provides clean energy generation, by interfacing solar PV array at the common DC link of UPQC. In order to evaluate the performance of this system with presence of nonlinear load, simulation results are presented in environment of MATLAB/Simulink. The experimental performance of the SPVA-UPQC system is validated through developed prototype and the corresponding results are presented herein for various conditions like grid voltage sag, grid voltage swell, load unbalancing, solar irradiation variation and harmonics. The total harmonic distortion (THD) of the grid voltages and currents are attained within the limits as per the IEEE-519 and IEEE-1159 standards.
Robust Navigation under Incomplete Localization Using Reinforcement Learning

This paper proposes a novel system for navigation under incomplete localization using reinforcement learning. Our system mainly consists of two modules: a Bayesian module and an action module. The Bayesian module is applied to update belief, a probabilistic distribution over all locations, through history information of actions and observations. The action module is responsible for decision-making based on the current belief, which is separated into two stages. In the first stage, we use Deep Q Network (DQN) to move the agent from an ordinary place to a transportation hub which is a place of relatively high localization certainty. In the second stage, actions are selected by weighting the shortest distance to destination with the current belief. Our system can successfully solve the large-scale navigation problem that general POMDP models cannot be figure out due to computational cost and difficulty in model construction. Different from traditional navigation methods which passively receive localization results, our method has the exploration capability by executing actions selected by DQN, which actively increases the localization accuracy. We perform extensive experiments and results show that our model is able to robustly navigate with a high success rate under incomplete localization. In the case of 30% localization uncertainty, the navigation success rate can surpass 80%.
Online Temporal Calibration Based on Modified Projection Model for Visual-Inertial Odometry

Due to the sensors’ latency, unsynchronized clocks, triggering, and data jams, there usually exists an inevitable time offset between image streams and inertial measurement unit (IMU) data sequences in visual-inertial systems. Such a time offset has a significant effect on the robustness and accuracy of the system. In this article, an online temporal calibration algorithm is proposed based on a modified projection model. In particular, we introduce the time offset as an estimated state variable in an extended Kalman filter (EKF)-based visual-inertial odometry (VIO). In addition, in order to align camera and IMU measurements in the time domain, the projection model is sophisticatedly given, and we propose a systematic method to obtain features’ virtual observations with respect to the time offset. Our approach is general and can be easily applied to different EKF-based VIO frameworks. Experimental results demonstrate that the time offset is calibrated quickly and accurately even compared with other existing offline tools, and the proposed online temporal calibration method significantly improves the performance of VIO.
A Novel High-Gain Sum and Difference Conical Beam-Scanning Reflector Antenna

A novel sum and difference (SD) conical beam-scanning reflector antenna is proposed in this paper. The reflector surface, illuminated by two omnidirectional horns, is a body of revolution obtained by revolving the generating parabola about a symmetry axis. The horns stacked along the symmetry axis of the reflector are fed by a nest coaxial waveguide. Teflon lens is used to improve the side lobe levels and reflection coefficients of the feed horns. By tilting the generating parabola and using the offset feeding method, two conical beams, with different beam pointing angles, were generated. A compact feed network was also designed to feed the two horns, which includes two rectangular TE10 mode-to-coaxial TEM mode transitions and a Magic-T to implement SD beams. The scanning performance of conical beams can be achieved by mechanically moving the feed antennas along the symmetry axis and taking full advantage of offset focus characteristic of the parabolic reflector. The error in angle measurement was also discussed. The SD conical beam-scanning antenna was formed by a parabolic dish with a radius of $18~\lambda $ operating at 24 GHz. The proposed antenna was designed, simulated, and fabricated. The measured results show that the difference beam null-depth is −25 dB and the sum beam gain is 15 dBi at 40°. The sum beam gain varies by less than 1.5 dB across the scan coverage from 30.8° to 45.2°. The measured results are found to be in good agreement with the simulated ones.
On the Detection of Persistent Attacks using Alert Graphs and Event Feature Embeddings

Intrusion Detection Systems (IDS) generate a high volume of alerts that security analysts do not have the resources to explore fully. Modelling attacks, especially the coordinated campaigns of Advanced Persistent Threats (APTs), in a visually-interpretable way is a useful approach for network security. Graph models combine multiple alerts and are well suited for visualization and interpretation, increasing security effectiveness. In this paper, we use feature embeddings, learned from network event logs, and community detection to construct and segment alert graphs of related alerts and networks hosts. We posit that such graphs can aid security analysts in investigating alerts and may capture multiple aspects of an APT attack. The eventual goal of this approach is to construct interpretable attack graphs and extract causality information to identify coordinated attacks.
Continuous Moving Object Clustering in Dynamic Road Network

Location based updation and clustering is important issue in dynamic road network. In dynamic road network moving object clustering is needed because huge number of moving objects are moving in the constrained road network. In this paper introduced new clustering algorithm for grouping similar objects in dynamic road network. The proposed clustering method is called density-based clustering. In this work the spatial and temporal objects are clustered separated and combined as per the requirements. The spatial distance between the moving objects are calculated with the help of Non-Ecludeian Distance. The Experiment is conducted using Geo-life GPS trajectory data and results are compared with K-mean moving object clustering (K-MCL) and moving object clustering methods (MC). The results are compared using accuracy, updation cost and speed of clustering parameters.
Adaptive Extended Kalman Filter approach for Sensorless Induction Motor Drive

This paper presents sensorless control of induction motor using Adaptive Extended Kalman Filter (AEKF). The performance of EKF depends on the value of system noise covariance matrix (Q) and measurement noise covariance matrix (R). Usually (Q, R) are considered as constants, they are determined by trial-and-error method. But in fact, they change with operating conditions of Induction Motor, for this reason we should updated (Q, R) according the operating conditions. The performances have been tested by simulation using Matlab /Simulink. The Simulation results show high performances of the proposed AEKF.
Acoustic Positioning and Navigation System for GNSS Denied/Challenged Environments

Current navigation sensors mostly rely on electromagnetic signals for getting the position, velocity, and time (PVT) information. But it can be observed that mammals like bats use acoustic waves, mostly ultrasound, for echolocation. Acoustic waves are also used by cetaceans like dolphins and sperm whales for echolocation. This paper investigates the performance of the acoustic positioning and navigation system (APNS). Acoustic sensors have relatively lower cost, size, weight, and power (C-SWAP) and are easy to deploy. Additionally, being based on acoustic signals, this technique is immune to signal-in-space electromagnetic interferences. The attenuation of sound in air is discussed along with potential ranging errors and signal delays. A multistatic arrangement of sensors is discussed in detail, with an optimized arrangement of transmitters in a given test geometry. The transmitters broadcast their respective signals following a Time Division Multiple Access (TDMA) scheme. The receiver position is calculated based on ranging measurements from a minimum of three transmitters. The range is calculated based on the Time of Arrival (TOA) of acoustic waves from the transmitter to the receiver. The transmitters are arranged optimally to minimize Position Dilution of Precision (PDOP) as well as maximizing sensor availability. The error in positioning due to platform dynamics is also discussed. This analysis lead to an optimized arrangement of transmitters, thus supporting subsequent experimental activities.
Digital Beamforming Based RFI Extraction for an On-Ground Correction of Synthetic Aperture Radar Data

Radar remote sensing instruments have played an increasingly important role in understanding Earth and its dynamic processes. Their operational frequencies are predetermined based on the physical properties that are to be retrieved. Simultaneously, the use of their allocated frequency bands in P- to C-band by other services has increased. A reliable mitigation of Radio Frequency Interference (RFI) is therefore critical for future Earth observation missions.New space-borne multi-channel Synthetic Aperture Radar (SAR) instruments are capable of Digital Beamforming (DBF). Each receive channel is recorded individually to form the digital antenna pattern after data acquisition. This opens up new opportunities for spatial RFI filtering. Unfortunately, this approach requires significant processing power on board the satellite as it is unfeasible to downlink the increased data volume. This paper presents a new method based on auxiliary beams that relaxes the requirement for the on-board processing power and only affects the downlinked data volume minimally. The information measured with the auxiliary beam can be used for an on-ground RFI correction.
A Study on Human Behavior based Color Psychology using K-means Clustering

Now-a-days the technology growth and the development of day to day life is varying and increasing vast in its features. There is no separate rising movement or any awareness or any other special learning logos for the connectivity between colors and human emotions that pave an emerging innovative study and get high step productive knowledge in it. The path from colors to emotional intelligence behavior not only shifts its actions but also gives a cohesive image determination on human civilization from human to gentleman. There also arises a technical aspect of sentimental analysis and opinion mining regarding human behavior. The purpose of this paper is to connect and produce a relation between color and human emotions that illustrate the whole meaning of what is life and to promote the originality of the behavior evolving in it. This brings innovative ideas to expose the ground- breaking technology in various fashions regardless to life matching emotions. Here gives a clear cut idea of what defines color technology into human emotion wheel in different moods of recognition.
A Novel Neural Network Method for Face Recognition With a Single Sample Per Person

Face Recognition (FR) problem is one of the significant fields in computer vision. FR is used to identify the faces that appear over distributed cameras over the network. The problem of face recognition can be divided into two categories, the first is recognition with more than one sample per person, which can be called traditional face recognition problem. The second is the recognition of faces using only a Single Sample Per Person (SSPP). The efficiency of face recognition systems decreases because of limited references especially (SSPP) and faces taken in the Operational Domain (OD) different from faces in the Enrollment Domain (ED) in illumination, pose, low-resolution, and blurriness. This paper proposed a method that deals with all problems related to face recognition with SSPP. 3D face reconstruction is used to increase the reference gallery set with different poses and generate a design domain dictionary to overcome the problem of limited reference. Besides, the design domain dictionary is used to feed different deep learning models. Face illumination transfer techniques are utilized to overcome the illumination problem. Labeled Faces in the Wild (LFW) dataset is used to train Super-Resolution Generative Adversarial Network (SRGAN) to overcome the low-resolution problem. Deblur Generative Adversarial Network (DeblurGAN) is trained on the LFW dataset to overcome the problem of blurriness. The proposed method is evaluated using the Chokepoint dataset and COX-S2V dataset. The final results confirm an overall enhancement in accuracy compared to techniques that use SSPP for face recognition (generic learning and face synthesizing approaches). Also, the proposed method outperforms of Traditional and Deep Learning (TDL) method accuracy, which uses SSPP for face recognition.
Sensitivity Analysis of Bridge Structure Systems’ Reliability

The sensors used in vehicle can be modelled as Bridge Structure Systems (BSS) which are System with Complex Interconnections (SwCI). Reliability of these systems cannot be simplified to single equivalent components or blocks by a combination of parallel and series reductions. Therefore Reliability Block Diagram (RBD) and Fault Tree Analysis (FTA) methods cannot be applied to determine reliability of the SwCI. The aims of this paper are to apply the True Table Method (TTM) and Linear Sensitivity Model of System Reliability (LSMoSR) to determine reliabilities of Bridge Structure System and to analyze their sensitivities in different cases.
Learning Acoustic Word Embeddings With Dynamic Time Warping Triplet Networks

In the last years, acoustic word embeddings (AWEs) have gained significant interest in the research community. It applies specifically to the application of acoustic embeddings in the Query-by-Example Spoken Term Detection (QbE-STD) search and related word discrimination tasks. It has been shown that AWEs learned for the word or phone classification in one or several languages can outperform approaches that use dynamic time warping (DTW). In this paper, a new method of learning AWEs in the DTW framework is proposed. It employs a multitask triplet neural network to generate the AWEs. The triplet network learns acoustic representations of words through a comparison of DTW distances. In addition, a multitask objective, including a conventional word classification component, and a triplet loss component is proposed. The triplet loss component applies the DTW distance for the word discrimination task. The multitask objective ensures that the embeddings can be used with DTW directly. Experimental validation shows that the proposed approach is well-suited, but not necessarily restricted to the QbE-STD search. A comparison with several baseline methods shows that the new method leads to a significant improvement of the results on the word discrimination task. An evaluation of the word clustering in the learned embedding space is presented.
Population abundance and biomass of large-bodied birds in Amazonian flooded and unflooded forests

Abstract Little is known about the contribution of different forest types to the beta-diversity, abundance and biomass of the avifauna in lowland Amazonia. This paper presents data on the large-bodied bird assemblages of adjacent upland (terra firme) and seasonally flooded (várzea and igapó) forests in the lower Rio Purús region of central-western Brazilian Amazonia. We focus our analysis on 23 large-bodied canopy and terrestrial bird species from 10 families, on the basis of 2,044 bird sightings obtained during line-transect censuses conducted over a two-year period. Large toucans comprised the most numerically abundant large birds in terra firme and igapó forests, whereas macaws were the most abundant in nutrient-rich várzea forests that were seasonally inundated by white-water. The aggregate population density of all bird species in terra firme forest was slightly higher than that in várzea forest. Igapó forest, which was seasonally inundated by black-water, sustained the lowest population densities. Terra firme and várzea forests differed considerably in species composition and abundance whereas igapó forest shared many species with both terra firme and várzea. Our results suggest that Amazonian floodplain forests play a major role in the persistence and community dynamics of the large-bodied forest birds. Pouco se sabe sobre a contribuição de diferentes tipos de florestas aos padrões de beta-diversidade, abundância e biomassa da avifauna na região amazônica. Este artigo apresenta dados à respeito das aves de médio a grande porte em florestas de terra firme e florestas adjacentes sujeitas a inundação sazonal (várzea e igapó) na região do baixo Rio Purús da Amazonia centro-ocidental. Nossa análise, baseada num total de 2,044 observações obtidas durante censos ao longo de transectos, foi direcionada a 23 espécies de aves de médio a grande porte pertencentes a 10 famílias. As aves mais abundantes em florestas de terra firme e igapó foram os tucanos, e as araras as mais abundantes em florestas de várzea. A densidade de população agregada de todas as espécies na terra firme foi ligeiramente mais elevada do que na várzea. A floresta de igapó sustentaram as mais baixas densidades populacionais. Terra firme e várzea diferem consideravelmente na composição e abundância de espécies, visto que a avifauna do igapó é intermediária entre a da terra firme e a da várzea. Os resultados sugerem que as florestas inundáveis tem uma papel muito importante na manutenção das comunidades de aves de grande porte na Amazônia, e pricipalmente aquelas espécies que usam grandes mosaicos de floresta.
A Unified Method for Vision Aided Navigation of Autonomous Systems

In this paper we present a unified method for vision-aided Navigation applied to autonomous systems. Traditionally, vision aided navigation methods can be broadly classified under two approaches: (a) methods that relies only on features available in the scene, and (b) methods that uses registered models of the environments. The first approach employs features tracking that provides only partial navigation aiding. In order to achieve full localization, the second approach that uses registered models of the environment is needed. These environment models could be derived from man-made fiducial or structures of natural terrain. The main drawback of the disparate approaches is due to the lack of navigation solution that is scalable under different conditions.
Machine Vision System for Autonomous Agricultural Vehicle

The problem of the vehicle motion control for automated driving is considered. The robotic chassis is designed for agriculture and should work in the absence of roads. The general structural diagram of the chassis’ machine vision system is given. A structural obstacle separation method for constructing obstacle maps on the ground is proposed. The method uses lidars to detect obstacles. The implementation of the method is based on the assumption that the terrain within the area no significant differences in elevation (gullies, dips) and water obstacles. For solving the detecting obstacle problem in these conditions, it is enough to detect points which height exceeds a certain threshold, and to identify the relationship between these points to assess the obstacle size. The clustering of points in each layer by the Euclidean distance is performed. Then, the coordinates of the cluster centers are recalculated into the global coordinate system, which allows transferring obstacles to an area map, taking into account the dimensions that determine the degree of the detected obstacle danger. An algorithm for implementing the proposed method is described. The report also provides information on the composition of the software for the robotic chassis machine vision. Simulation results of the proposed method of the allocation of obstacles are presented. The method has low computational complexity, which reduces the requirements for the robotic chassis on-board computer.
Cascaded Multi-Task Learning of Head Segmentation and Density Regression for RGBD Crowd Counting

In this paper we propose a novel regression based RGBD crowd counting method. Compared with previous RGBD crowd counting methods which mainly exploit depth cue to facilitate person/head detection, our approach adopts density map regression and is more robust to severe occlusion under dense crowded scenarios. We develop a cascaded depth-aware counting network that jointly performs head segmentation and density map regression. Our network explicitly feeds depth map at each stage so that depth cues are sufficiently exploited. The multi-task strategy allows the network to explicitly attent to foreground regions of a crowd scene and improve density regression. To generate the ground truth of head segmentation and density map, we propose a head scale estimation method according to the basic geometric assumption and camera projection function. Experiments on two public RGBD crowd counting benchmarks, ShanghaiTechRGBD dataset and MICC dataset show that the proposed method achieves new state-of-the-art on both datasets. Further, our method can be easily extended to RGB datasets and achieves comparable performances on WorldExpo’10 dataset and UCF-QNRF dataset.
A Novel Flow Control Mechanism to Avoid Multi-Point Progressive Blocking in Hard Real-Time Priority-Preemptive NoCs

The recently uncovered problem of multi-point progressive blocking (MPB) has significantly increased the complexity of schedulability analysis of priority-preemptive wormhole networks-on-chip. While state-of-the-art analysis is currently deemed safe, there is still significant inherent pessimism when it comes to considering backpressure issues caused by downstream indirect interference. In this paper, we attempt to simplify the problem by considering a novel flow control protocol that can avoid backpressure issues, enabling simpler schedulability analysis approaches to be used. Rather than construct the analysis to fit the protocol, we modify the protocol so that effective analysis applies. We describe the changes to a baseline wormhole router in order to implement the proposed protocol, and comment on the impact on hardware overheads. We also examine the number of routers that actually require these changes. Comparative analysis of FPGA implementations show that the hardware overheads of the proposed NoC router are comparable or lower than those of the baseline, while analytical comparison shows that the proposed approach can guarantee schedulability in up to 77% more cases.