Miracle-dz/UniFAR-FTUs huggingface dataset:
Location-aware Power Adaptation Scheme for UAV Communications

In this paper, we propose an optimal power allocation scheme for multiple unmanned aerial vehicles (UAVs) communications that maximizes the sum rates at the UAVs. The proposed scheme exploits the location information of multiple UAVs and adapts the transmit power based on the UAV distance from the transmitting base station (BS). The location information enables the BS to dynamically adapt the transmit power over the UAVs entire flight time to maximize the average sum rate. The average sum rate and power allocated to the UAVs during the flight time are used as the performance metrics. In addition, the proposed scheme is compared with the fixed power allocation scheme, and the results give useful insight into UAV location-aware communication. The simulation results show that at low transmit power, a sum rate improvement can be achieved when the adaptive power transmission scheme is applied as compared to a fixed transmit power schemes.
3D Reconstruction of Underground Tunnel Using Kinect Camera

The underground structure of coal mine is intricate for non-uniform illumination. Establish a three dimensional model for underground environment has received significant attention and concern during the recent years. There is a high requirements for the quality of underground monitoring images in coal mine to ensure safe and efficient production. In this paper, the RGB image and depth image are obtained by Kinect camera. The acquired RGB and depth maps are filtered for removing noise point or outliers. The feature points of the depth image are extracted to realize the precise matching between the RGB image and the depth image. Point clouds are obtained and registered into a single position using improved Iterative Closest Point algorithm. The experimental results show the effectiveness and practicability of the proposed method. Accurate 3D reconstruction of the object has been achieved with dense point cloud.
Ground Object Recognition from Aerial Image-based 3D Point Cloud

Recently, several attempts have been made to grasp 3D ground shape from a 3D point cloud generated by aerial vehicles, which help to fast situation recognition. For example, in case of earthquake disasters, we may detect building collapse and inclination by comparing the height of buildings in 3D models before/after the disasters. However, identifying such objects on the ground like buildings, vehicles and trees from a 3D point cloud, which consists of 3D coordinates and color information, is not straightforward due to the gap between the low-level point information (coordinates) and high level context information (objects). In this paper, we propose a ground object recognition method from a 3D point cloud that captures the heights of ground surface. Basically, we rely on some existing tools to generate such a 3D point cloud from aerial images, and our method tries to give semantics to each set of clustered points. In the proposed method, firstly, such points that correspond to the ground surface are eliminated using the elevation data from Geographical Survey Institute. Next, we apply an inter-point distance-based clustering and noise filtering method according to the point density of each cluster. Then such clusters that share some regions are merged to correctly identify a point cluster that corresponds to a single object. Finally, a filtering method is applied based on the knowledge on the sizes of objects. We have evaluated our method in several experiments conducted in real fields. We have confirmed that our method can remove the ground surface within 5% error, and can recognize most of the objects.
Development of simulation model of offshore wind power generation with MMC for HILS application

Because there exist disadvantages of transmission capacity and distance constraints in AC transmission in medium and high voltage transmission, power transmission by HVDC (High voltage direct current) is actively used nowadays. Where, MMC(Modular Multilevel Converter) is widely used for voltage type HVDC. This paper describes a model applied to real-time simulation tool such as RT-Lab in order to test the control performance of offshore wind power system using HVDC with MMC.
K-Means Clustering with Feature Selection for Stream Data

K-means clustering is popular for its efficiency and is often chosen for analyzing large-scale data. However, it is hard to deal with high-dimensional data, which often contain lots of redundant features. In addition, in real-world applications, we usually confront with massive data streams, such as transport system and social media, which are often periodically generated in high-dimensional space. Although existing K-means extensions have achieved great success on high-dimensional data by integrating with dimension reduction methods, they are limited to off-line data. To solve these problems, we propose a streaming Kmeans clustering with feature selection. The proposed algorithm divides the traditional clustering procedure into several related multiple clustering tasks and selects the representative features by the group sparsity regularization technique. Besides, within such framework, the shared information among neighbor streams can be properly explored. Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed model.
Investigating into Saliency Priority of Bottom-up Attributes in 2D Videos Without Cognitive Bias

Saliency in an image or video is a region of interest that stands out relative to its neighbors and consequently attracts more human attention. A key factor in designing an algorithm to measure the importance and distinctiveness (i.e. saliency) of different regions of a frame is to understand how different visual cues affect the human perceptual and visual system. To this end, we investigated bottom-up features including color, texture, and motion in 2D video sequences for both one-by-one and combined scenarios to provide a ranking system stating the most dominant circumstances for each feature individually and in combination with other features as well. In this work, we mostly considered the feature combination scenarios under conditions in which we had no cognitive bias. Human cognition refers to a systematic pattern of perceptual and rational judgements and decision-making actions. Since computers do not typically have this ability, we tried to minimize this bias in the design of our experiment. First, we modelled our test data as 2D images and videos in a virtual environment to avoid any cognitive bias. Then, we performed an experiment using human subjects to determine which colors, textures, motion directions, and motion speeds attract human attention more. The proposed ranking system of salient visual attention stimuli was achieved using an eye-tracking procedure. This work provides a benchmark to specify the most salient stimulus with comprehensive information for both static and dynamic scenes. The main goal of this work is to create the ability of assigning a ranking of saliency for the entirety of an image/video frame rather than simply extracting a salient object/area which is widely performed in the state-of-the-art.
Modeling the Automatic Voltage Regulator (AVR) Using Artificial Neural Network

The artificial neural network (ANN) has been successfully applied to many systems’ models with the objective of improving their performance. This study presents the ANN application to the automated voltage regulator (AVR) model of the synchronous machine. The studied system consists of the single-machine infinite bus (SMIB) power system. Data was extracted over a diverse range of conditions. Different fault types were also considered for collecting a large number of data to avoid overfitting. The multilayer feedforward neural network (MLFFNN) approach was applied to design the paradigm and system equations were solved using the MATLAB Simulink software.
Place Recognition for SLAM Using Texture-independent Features and DCGAN

In order to make an accurate 3D map in visual SLAM, it is important to check whether the current place is already visited or not because we should close the loop at the correct position. If it is not visited before, it is very hard to register the 3D map. In this paper, we propose a method for revisited place detection based on unsupervised learning, and a method for high-quality 3D map rendering based on root finding optimization, both of which are faster than previous works. In order to detect revisited places, we generate local patches based on the superpixel and learn them using unsupervised learning based neural networks. Experimental results show that the superpixel-based feature extraction method detects image features that cover both textured and texture-less regions, unlike previous feature extraction methods. Thus, we can utilize the proposed method not only for outside scenes, but also for indoor scenes. Moreover, when the place recognition process is performed using the unsupervised learning and extracted image features, we can improve the average precision over previous methods.
Short-Term Wind Power Prediction Based on Improved Chicken Algorithm and Support Vector Machine

This study contributes to proposing the improved chicken swarm algorithm optimization support vector machine (ICSO-SVM) model to predict the short-term wind power. In order to improve the prediction accuracy of the model, the following work has been done. Firstly, in order to avoid the traditional chicken swarm algorithm (CSO) getting into the local optimal solution, an improved chicken swarm algorithm is proposed. The position update formulas of hens and chicks are modified in ICSO, which makes the algorithm have better global convergence and convergence accuracy. Secondly, the wind speed prediction model of ICSO-SVM is established to predict short-term wind speed in wind farms. Finally, the wind power is predicted by the power curve conversion method, which is based on the predicted wind speed data. The predict data is compared with the real data, which shows that the novel model had a higher prediction accuracy. The experimental results show that the prediction accuracy of the proposed model meets the actual engineering requirements.
Development of an Affordable Customisable Upper Extremities Pneumatically-Powered Exoskeleton

Affordability of biomechanical devices have been subjected to numerous discussion amongst users that requires such products. Therefore, this paper will explore on the possible affordable technologies that would enable the end-users to have access to the upper-extremities pneumatically-powered exoskeleton in daily life. Such devices will enable rehabilitation as well as assistances to individual that would require such assistive technology in their daily living. The customizable exoskeleton features adjustable arm-length for proper fitting to users. This exoskeleton is powered by McKibben pneumatic muscles mimicking muscles in the upper extremities. Moreover, the system enables remote monitoring via cloud or IoT platform. Future development of this prototype will be incorporation of more telerehabilitation features to improve adoption in user-friendliness of this enabling technology.
Feasibility and Reliability Analysis of LCC DC Grids and LCC/VSC Hybrid DC Grids

Power system interconnections using high-voltage direct-current (HVDC) technologies between different areas can be an effective solution to enhance system efficiency and reliability. Particularly, the multi-terminal dc grids that could balance and ensure resource adequacy increase asset utilization and reduce costs. In this paper, the technical feasibility of building dc grids using the line-commutated converter-based (LCC) and voltage source converter-based (VSC) HVDC technologies is discussed. Apart from presenting the technical challenges of building LCC dc grids and LCC/VSC hybrid dc grids, the reliability modeling and analysis of theseDCgrids are also presented. First, the detailed reliability model of the modular multi-level converters (MMCs) with series-connected high-voltage and low-voltage bridges is developed. The active mode of redundancy design is considered for the reliability model. To this end, a comprehensive whole system reliability model of the studied systems is developed. The reliability model of each subsystem is modeled in detail. Various reliability indices are calculated using this whole system reliability model. The impacts of the redundancy design of the MMCs on these indices are presented. The studies of this paper provide useful guidance for dc grid design and reliability analysis.
PERSON RE-IDENTIFICATION BY REFINED ATTRIBUTE PREDICTION AND WEIGHTED MULTI-PART CONSTRAINTS

Person re-identification (re-id) aims to match person images captured in non-overlapping camera views. Convolutional Neural Network (CNN) has been verified to be powerful in pedestrian feature extraction. However, the CNN features focus more on global visual information, which are sensitive to environmental variations. In comparison, attribute features contain semantic information and prove to be more stable to cross-view appearance changes. In this paper, we present a novel network which leverages high-level semantic attributes to enhance pedestrian descriptors. By introducing hand-crafted multi-colorspaces and texture information to refine CNN features, we acquire a more invariant and reliable feature representation for attribute prediction. The attribute-based stream is further embedded into a part-based CNN branch for re-id. This part-based CNN is trained with a weighted integration of multi-part identification losses. Experiments on two public datasets demonstrate significant performance improvements of our method over state of the arts.
Delay Tolerant Network model based on D2D communication

Heterogeneous networks is the basis for 5G mobile communication networks evolution, one of the major challenges is the interaction between the devices in Het-Nets without the base station. This article discusses perspectives, challenges and services 5G networks, as well as direct device-to-device communication - technology, which can provide low power, high data rate and low latency services between end-users. In this paper, we consider only the interaction process of a network node with a mobile node. Data delivering process over the network is the subject of further research.The simulation results showed that the interaction process of ad hoc network nodes in the presence of mobile nodes is random, and data transfer probability from fixed to mobile nodes depends on the mobile node moving speed.
Disturbance Rejection of IPMSM Drives by Simplified Taylor Series-Based Near Optimal Control Scheme in Wide Speed Range

This paper proposes a simplified near optimal control (NOC) scheme to reject the disturbances of interior permanent magnet synchronous motor (IPMSM) drives by minimizing the complex calculations in the Taylor series method required for solving the state-dependent Riccati equation (SDRE). Since the speed tracking performance of an IPMSM can be significantly degraded in the presence of the disturbances owing to the practical factors (e.g., external load torque, parameter uncertainties, and so on), an SDRE-based near optimal disturbance observer (NODO) is designed to accurately estimate and reject the disturbances associated with the speed and current in the feedforward control. The state-dependent coefficient matrices of the IPMSM are rearranged in the controller and observer to minimize the complex offline calculations of the Taylor series method used to solve the SDRE. By significantly simplifying the calculations, the proposed SDRE-based NOC scheme can be efficiently applied to the IPMSM drives. To verify the precise and robust control performance of the proposed NODO-based near optimal speed controller, experiments are conducted via a prototype IPMSM drive system with a TI-TMS320F28335 DSP under different disturbances. Finally, the comparative experimental results with the conventional NOC are presented under load change and wide speed range conditions.
Direction of Arrival Estimation Using Distributed Circular Microphone Arrays

This paper considers a new method for estimating Direction of Arrival (DOA) of plane waves using distributed circular microphone arrays. Sound field produced by incident waves is decomposed into circular harmonics functions, and then we can obtain an expression of the circular harmonics coefficients. The expression is associated with position in the sound field. This formulation enables us to obtain a virtual uniform circular array from the distributed microphone array. Then we use Estimating Signal Parameter via Rotational techniques (ESPRIT) to estimate the source DOA based on a recursive relationship of Bessel functions. Simulations demonstrate that the proposed method can get higher accuracy than conventional method for distributed microphone array.
Integrating Electric Vehicles in Microgrids: Overview on Hosting Capacity and New Controls

This article reports an overview of main issues related to hosting capacity and harmonic disturbances caused by electric vehicle (EV) penetration in a smart grid, taking into account renewable energy sources and energy storage systems as well as nonlinear loads. A new mixed deterministic and probabilistic method based on algorithms and new control strategies, able to perform EVs charging controls, is presented. The methodology shows that it is possible to achieve high levels of power quality, thanks to a pre-evaluation of harmonic disturbance. Studies on real networks are reported in order to carry out indices able to define a preliminary assessment of the network hosting capacity. Suggestions and guidelines about the management of EVs charging stations are proposed and discussed in order to properly integrate them in a smart grid concept.
High-Power Density, 900-W LLC Converters for Servers Using GaN FETs: Toward Greater Efficiency and Power Density in 48 V to 6\/12 V Converters

The LLC resonant converter can achieve soft switching over a wide operating range, making it a suitable candidate for high-efficiency dc-dc conversion, even at a high output current. We applied enhancement-mode gallium nitride (eGaN) fieldeffect transistor (FET) technology to a 1-MHz, 900-W LLC resonant converter in a fixed 4:1-ratio dc-dc transformer application operating with a 48-12-V conversion suitable for data-center applications. The converter achieves power efficiency and density density exceeding 98% and 1,500 W/in<sup>3</sup>, respectively, with excellent thermal performance.
Drone Detection Based on FD-HOG Descriptor

The rapid development of Unmanned Aerial Vehicle (UAV) technology, also known as drones, has made people benefit in many ways, but it also brings privacy and security issues, such as appeared at private place, airports, prisons, etc. Therefore, the detection of drones in a specific area is crucial. Video detection is an effective method with various advantages. In this paper, we have used the background subtraction method to detect the moving object in the video sequence which recorded by static cameras, then extracted the global Fourier descriptors and the local HOG features of the moving object images. Finally, the FD+HOG features have been sent to the SVM classifier for classification and recognition. Our algorithm is simple and efficient, an overall with 98% accuracy was obtained in our data set.
Design and Characterization of Two Different Structure of Junctionless Nanowire Transistor Considering Quantum Ballistic Transport Model

In this work, the performance of two different structure of double gate junctionless nanowire transistor (DG-JLNT) has been analyzed. The conventional junctionless double gate (JLDG) and the modified flared up source and drain with submerged gate electrode structure are analyzed using Non-Equilibrium Green’s Function (NEGF) approach by TCAD Silvaco Atlas. As the NEGF approach shows better accuracy than other approaches in quantum modeling simulation for smaller channel length, this work was conducted depending on this approach. Performance parameter named Drain Current (ID), Threshold Voltage (Vth), Subthreshold Swing (SS), on state and off state current ratio (Ion/Ioff) for the mentioned structures of n-channel JLDG transistor are analyzed. Simulation has been performed by varying channel length (Lg) and oxide thickness (Tox) to observe the electrical characteristics. In order to observe Short Channel Effects (SCEs), the above parameters were investigated. In essence, flared up JLNW transistor showed better SCEs performance in current on state and off state (Ion/Ioff) ratio and lesser Threshold Voltage Roll Off (TVRO) over channel length variation. But in other SCEs parameters like SS and Drain induced barrier lowering (DIBL), conventional JLDG is pictured as the better candidate.
Gold Passivated Cu-Cu Bonding At 140°C For 3D IC Packaging And Heterogeneous Integration Applications.

In the present modern era of electronic industry has motivated for high performance integration by vertically stacked three dimensional integrated circuits (3D ICs). Electronic interconnections at packaging and die levels, Pbfree solder micro bumps are intended to replace conventional Pb-containing solder joints due to increasing awareness of an environmental conservation, and processing at low thermal budgets. The better alternative for solder is copper, due to its high electrical and thermal properties. But the surface oxidation was the major bottleneck. In this work, we have demonstrated low temperature and low-pressure copper to copper interconnect bonding using optimized thin gold passivation layer. Here the passivation layer over the copper surface was optimized to a thickness of 3nm there by helps in preventing Cu surface oxidation and makes lower surface RMS roughness. High-density surface plane orientations that have been studied using XRD helped in faster diffusion through an interface. Majorly in this work, we have discussed the time taken for copper atoms to diffuse over the ultra-thin passivation layer of gold using Fick’s second law approximation. These conditions have been used while bonding. Bonded samples were subjected to various reliability studies in order to confirm the efficacy of the proposed Au passivation based bonded structure. Also, we have observed the Interface quality using TEM, and C-SAM (mode C-Scanning acoustic microscopy) imaging resulting in good quality of bonding. The diffusion of copper atomic species movement across the interface is confirmed by EDS analysis. Low and stable specific contact resistance ($\sim$1.43 $\times$ 10-8 $\Omega$ cm2) at robust conditions are confirmed to be effective and front runner for low temperature, low pressure Cu-Cu bonding for 3D IC packaging and heterogeneous integration.
Walk this way: To be useful around people, robots need to learn how to move like we do

Robots have walked on legs for decades. Today's most advanced humanoid robots can tramp along flat and inclined surfaces, climb up and down stairs, and slog through rough terrain. Some can even jump. But despite the progress, legged robots still can't begin to match the agility, efficiency, and robustness of humans and animals. • Existing walking robots hog power and spend too much time in the shop. All too often, they fail, they fall, and they break. For the robotic helpers we've long dreamed of to become a reality, these machines will have to learn to walk as we do. • We must build robots with legs because our world is designed for legs. We step through narrow spaces, we navigate around obstacles, we go up and down steps. Robots on wheels or tracks can't easily move around the spaces we've optimized for our own bodies.