Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Accuracy of Navigation Measurements of Mobile Network Nodes

This article presents the results of modeling the possibility to improve the accuracy of navigation measurements for nodes of mobile networks located on moving objects. When placing mobile nodes on aircraft with a satellite navigation system, it is possible to improve the accuracy of their positioning by predicting the location of the aircraft at the time of subsequent navigation measurements. Increasing the accuracy of location of mobile nodes can increase their availability by optimizing the network topology. In addition, it is possible to convert a random network topology into a non–random topology based on navigation measurements of network nodes. The authors believe that the method of determining the location of a moving object in space by imposing constraints related to the dynamic properties of an object is promising. This method can be used to integrate satellite navigation aids and airborne instruments for measuring the parameters of the aircraft's movement. The authors present experimental results of the analysis of the possibility of increasing the accuracy in determining the location of a moving object with the help of this method.
Convolutional Neural Network-Based Land Cover Classification Using 2-D Spectral Reflectance Curve Graphs With Multitemporal Satellite Imagery

Researchers constantly seek more efficient detection techniques to better utilize enhanced image resolution in accurately detecting and monitoring land cover. Recently, convolutional neural networks (CNNs) have shown high performances comparable to or even better than widely used and adopted machine learning techniques. The aim of this study is to investigate the application of CNNs for land cover classification by using two-dimensional (2-D) spectral curve graphs from multispectral satellite images. The land cover classification was conducted in Concord, New Hampshire, USA, and South Korea by using multispectral images acquired from 30-m Landsat-8 and 500-m Geostationary Ocean Color Imager images. For the construction of input data specific to CNNs, two seasons (winter and summer) of multispectral bands were transformed into 2-D spectral curve graphs for each class. Land cover classification results of CNNs were compared with the results of support vector machines (SVMs) and random forest (RFs). The CNNs model showed higher performance than RFs and SVMs in both study sites. The examination of land cover classification maps demonstrates a good agreement with reference maps, Google Earth images, and existing global scale land cover map, especially for croplands. Using the spectral curve graph could incorporate the phenological cycles on classifying the land cover types. This study shows that the use of a new transformation of spectral bands into a 2-D form for application in CNNs can improve land cover classification performance.
Soft Biomimetic Prosthetic Hand: Design, Manufacturing and Preliminary Examination

The human hand is a complex structure. It is strong but precise. It consists of a very complex mechanical structure that enables the hand to adapt and efficiently handle objects of various shapes, weights and textures. Today's prosthetic devices, struggling to provide similar functions, become overly complex and expensive. They are composed of multiple, precise parts, including miniaturised actuators and sensors as well as complex control, to satisfy the manipulation tasks required. In this paper we propose a soft pneumatic hand that adapts passively to the handled object due to its mechanical compliance. It is pressure driven and enables individual fingers to be controlled independently for dexterity or in groups when a synergistic finger movement is needed. The hand has a truly anatomical shape, is easy to replace and cheap in production. The design can be easily adjusted in terms of shape and size in order to fit each individual user. The paper presents the design, manufacturing technology, current control system and preliminary tests of the hand's capabilities.
Research on the Real-Time Transfer Protocol for Harmonic and Interharmonic Synchrophasors

In modern power grid, the demand for broadband measurement and real-time transmission of harmonics and interharmonic phasors is increasingly urgent, but all the main communication protocols, including IEEE C37.118, IEC 61850 general communication protocol and IEC 61850-90-5 special phasor communication protocol, are difficult to meet the demand. In this paper, the demand of harmonic and interharmonic synchrophasor transmission is analyzed, the characteristics and shortcomings of several main protocols are analyzed and compared, and the extended protocol for harmonics and interharmonics communication based on the widely used C37.118 is proposed. The extended protocol has defined the harmonic and inter harmonic configuration frames, data frames and command frames, and described the communication process of harmonics and interharmonics data. This extended communication protocol supports the transmission of 1-50th harmonics and 10 frequency points of interharmonics, including amplitude, phase angle and frequency. The transmitted data is synchronously organized and sent to the control center with timestamps. According to this extended protocol, the data refresh period is controllable, and the data flow bandwidth is limited. This communication protocol supports independent transmission of fundamental, harmonic and inter harmonic data, also has a compact data model and high data transmission efficiency for harmonics and interharmonics data.
Zonal Formation for Multiple Microgrids using Load Flow Sensitivity Analysis

Essentially, consumers are mostly affected by interruptions in the distribution systems due to the direct connection between the distribution systems and the consumers. Hence, it would have been better to start the planning at the distribution stage. Reduction of power systems is advantageous to improve the computation efficiency and for fast and cost-efficient reliability analysis. In this paper, we propose a zonal formation approach via the sensitivity and reliability analysis methods. Sensitivity analysis is to identify the differential relationship among electric quantities in distribution systems, while reliability analysis focuses on the voltage variation during short circuit implementation. The modified IEEE 33 bus test system are used to illustrate the simplification of power system by implementation of proposed zonal formation.
Automatic Calibration of Multiple Cameras and Depth Sensors with a Spherical Target

In this work we present a novel approach for multi-sensor calibration that significantly outperforms current state-of-the-art. We introduce a new spherical calibration target which has major benefits over existing targets. Those are subresolution detection accuracy in both camera and depth sensor, view invariance and applicability to a wider range of sensor setups than current approaches. With our method a single person achieves high quality calibration in less than a minute. No preparations for setting up the environment for calibration is needed. Our method is fast, easy to use and fully automatic. We evaluate our method in simulation and show high accuracy with an error of less than 3mm in translation and 0.1 0 in rotation on real data.
SwarmTouch: Tactile Interaction of Human with Impedance Controlled Swarm of Nano-Quadrotors

We propose a novel interaction strategy for a human-swarm communication when a human operator guides a formation of quadrotors with impedance control and receives vibrotactile feedback. The presented approach takes into account the human hand velocity and changes the formation shape and dynamics accordingly using impedance interlinks simulated between quadrotors, which helps to achieve a life-like swarm behavior. Experimental results with Crazyflie 2.0 quadrotor platform validate the proposed control algorithm. The tactile patterns representing dynamics of the swarm (extension or contraction) are proposed. The user feels the state of the swarm at his fingertips and receives valuable information to improve the controllability of the complex life-like formation. The user study revealed the patterns with high recognition rates. Subjects stated that tactile sensation improves the ability to guide the drone formation and makes the human-swarm communication much more interactive. The proposed technology can potentially have a strong impact on the human-swarm interaction, providing a new level of intuitiveness and immersion into the swarm navigation.
Distributed Data Privacy Preservation in IoT Applications

Recently, the Internet of Things (IoT) has penetrated many aspects of the physical world to realize different applications. Through IoT, these applications generate, exchange, aggregate, and analyze a vast amount of security-critical and privacy- sensitive data, which makes them attractive targets of attacks. Therefore, it is rather necessary for IoT systems to be equipped with the ability to resist security and privacy risks when fulfilling the desired functional requirements and services. To achieve these goals, there are many new challenges for IoT to implement privacy preserving data manipulation. First, data analysts need to process privacy-sensitive data to extract the expected information without privacy disclosure. In addition, many privacy related factors, including privacy valuation and risk assessment, affect sensitive and private data trading between data owners and requesters. Moreover, the data owners' security behavior also plays an important role in privacy protection in IoT applications. Concerning these issues, this article introduces and surveys privacy preserving techniques in the processes of data aggregation, trading, and analysis: the balance between data analysis and privacy preservation from the data analysts' perspective, secure data trading from the perspective of data owners and requesters, and secure private data aggregation from the data owners' perspective.
Spectral Domain-Based Input Impedance Prediction of a CPW-Fed Notch Antenna

This letter deals with a general network model for predicting the input impedance of a coplanar-waveguide-fed notch antenna operating over the first two (anti-)resonant modes. The model parameters (radiation conductance, open-end capacitance, short-end inductance, coplanar waveguide line to slot transition characteristics) are theoretically derived using the spectral domain approach. Various antenna sets, including several substrates (FR4, Rogers AD6006/DiCLAD880) and width-to-height ratios, are considered for validation. The proposed model showed positive correlation with full-wave simulations.
Transfer Learning based Photovoltaic Module Defect Diagnosis using Aerial Images

The automated system inspection with defect detection in large-scale photovoltaic (PV) farms is an urgent problem to be addressed. This paper presents a transfer learning based solution for visible module defects diagnosis in these conditions. The solution mainly includes three parts: a normative, time-updated, sundry dataset; a pre-trained deep learning model; a transfer learning strategy. Image augmentation technology is applied in the dataset. The proposed transfer learning based solution is able to extract the defects’ features from local representation to global one and low level to high one with more robustness in comparison with the enhanced CNN based method. The proposed solution is evaluated through extensive experiments and the numerical results clearly demonstrate its effectiveness.
Scan Context: Egocentric Spatial Descriptor for Place Recognition Within 3D Point Cloud Map

Compared to diverse feature detectors and descriptors used for visual scenes, describing a place using structural information is relatively less reported. Recent advances in simultaneous localization and mapping (SLAM) provides dense 3D maps of the environment and the localization is proposed by diverse sensors. Toward the global localization based on the structural information, we propose Scan Context, a non-histogram-based global descriptor from 3D Light Detection and Ranging (LiDAR) scans. Unlike previously reported methods, the proposed approach directly records a 3D structure of a visible space from a sensor and does not rely on a histogram or on prior training. In addition, this approach proposes the use of a similarity score to calculate the distance between two scan contexts and also a two-phase search algorithm to efficiently detect a loop. Scan context and its search algorithm make loop-detection invariant to LiDAR viewpoint changes so that loops can be detected in places such as reverse revisit and corner. Scan context performance has been evaluated via various benchmark datasets of 3D LiDAR scans, and the proposed method shows a sufficiently improved performance.
Comparison of Multimodal Heading and Pointing Gestures for Co-Located Mixed Reality Human-Robot Interaction

Mixed reality (MR)opens up new vistas for human-robot interaction (HRI)scenarios in which a human operator can control and collaborate with co-located robots. For instance, when using a see-through head-mounted-display (HMD)such as the Microsoft HoloLens, the operator can see the real robots and additional virtual information can be superimposed over the real-world view to improve security, acceptability and predictability in HRI situations. In particular, previewing potential robot actions in-situ before they are executed has enormous potential to reduce the risks of damaging the system or injuring the human operator. In this paper, we introduce the concept and implementation of such an MR human-robot collaboration system in which a human can intuitively and naturally control a co-located industrial robot arm for pick-and-place tasks. In addition, we compared two different, multimodal HRI techniques to select the pick location on a target object using (i)head orientation (aka heading)or (ii)pointing, both in combination with speech. The results show that heading-based interaction techniques are more precise, require less time and are perceived as less physically, temporally and mentally demanding for MR-based pick-and-place scenarios. We confirmed these results in an additional usability study in a delivery-service task with a multi-robot system. The developed MR interface shows a preview of the current robot programming to the operator, e. g. , pick selection or trajectory. The findings provide important implications for the design of future MR setups.
Energy-Efficient Trajectory Generation for a Hexarotor with Dual- Tilting Propellers

In this paper, we consider a non-conventional hexarotor whose propellers can be simultaneously tilted about two orthogonal axes: in this way, its underactuation degree can be easily adapted to the task at hand. For a given tilt profile, the minimum-energy trajectory between two prescribed boundary states is explicitly determined by solving an optimal control problem with respect to the angular accelerations of the six brushless motors. We also perform, for the first time, a systematic study of the singularities of the control allocation matrix of the hexarotor, showing the presence of subtle singular configurations that should be carefully avoided in the design phase. Numerical experiments conducted with the FAST-Hex platform illustrate the theory and delineate the pros and cons of dual-tilting paradigm in terms of maneuverability and energy efficiency.
Application of the Kriging Method for an Electromagnetic-Transient-Simulation-Aided Optimization Technique

Electromagnetic transient (EMT) simulation programs are often used to obtain the optimal values of circuit and control parameters. In this paper, an EMT-simulation-aided optimization technique using the Kriging method is proposed. Then, it is applied to the design of the automatic reactive power regulator (AQR) controller of a static synchronous compensator (STATCOM). It is shown that the optimal solution of the AQR controller can be obtained efficiently by the proposed technique.
Towards Minimal Intervention Control with Competing Constraints

As many imitation learning algorithms focus on pure trajectory generation in either Cartesian space or joint space, the problem of considering competing trajectory constraints from both spaces still presents several challenges. In particular, when perturbations are applied to the robot, the underlying controller should take into account the importance of each space for the task execution, and compute the control effort accordingly. However, no such controller formulation exists. In this paper, we provide a minimal intervention control strategy that simultaneously addresses the problems of optimal control and competing constraints between Cartesian and joint spaces. In light of the inconsistency between Cartesian and joint constraints, we exploit the robot null space from an informationtheory perspective so as to reduce the corresponding conflict. An optimal solution to the aforementioned controller is derived and furthermore a connection to the classical finite horizon linear quadratic regulator (LQR) is provided. Finally, a writing task in a simulated robot verifies the effectiveness of our approach.
Planning Topological Navigation for Complex Indoor Environments

The ability to move around the environment is one of the most important capabilities of a mobile robot. Although navigation is considered an already achieved capacity, there is still much work to be done to integrate navigation with high level reasoning and acting. Navigate in indoor environments also involve complex actions, such as opening doors, use elevators, and many others. We propose a topological navigation system based on Artificial Intelligence (AI)Planning. Starting from a symbolic representation of the environment, navigation tasks are divided into phases, in which different actions are required. This approach has demonstrated to be very effective to plan the operations of a robot at indoor environments. The final result is method compact, efficient and scalable. Our system has been successfully tested at European Robotics League in the humanoid robot Pepper.
Unified and Integrated Circuit Antenna in Front End—A Proof of Concept

A concept of seamlessly colocated and cointegrated antennas and front-end chain circuits in the form of a unified and integrated design space is proposed, studied, and demonstrated in this paper, which strives to create future joint circuiting-radiating front-end analog functional blocks. To show the proposed concept, the building elements involving dual-function amplifier antenna are highlighted as an example and arranged in a series-connected array, each acting simultaneously as a matching circuit and transmitting antenna. In other words, a single geometrical space serves as both circuits and antennas, meaning a space overlap in which the circuit is an antenna and the antenna is a circuit. This reflects both the harmony and harmonious relationship of the circuit antenna. In this paper, the spacing between two neighboring array elements is utilized to accommodate active devices, such as transistors, in this example. As the first theoretical and experimental demonstration, this paper involves the design of patch antennas, the analysis of mutual coupling, and the placement of amplifying transistors in different operation modes. The modeling procedure for a <inline-formula> <tex-math notation="LaTeX">$1 \times N$ </tex-math></inline-formula> amplifier-antenna system is presented. The proposed concept is experimentally validated by prototyping a <inline-formula> <tex-math notation="LaTeX">$1 \times 2$ </tex-math></inline-formula> amplifier-antenna array at 5 GHz with rectangular patches and off-the-shell HJ-FET. An equivalent circuit network is established for the proposed dual function block. Measured results show that the experimental block has −10 dB matching bandwidth of 125 MHz at the steering angle of +30° with an amplifying gain of 11.5 dB. It is also experimentally illustrated that frequency tuning and beam steering can easily be enabled by varying the dc bias. The design of <inline-formula> <tex-math notation="LaTeX">$1 \times 4$ </tex-math></inline-formula> amplifier-antenna array at 5 GHz is also discussed to generalize the proposed theory and guide the design of higher order amplifier arrays.
Apple Counting using Convolutional Neural Networks

Estimating accurate and reliable fruit and vegetable counts from images in real-world settings, such as orchards, is a challenging problem that has received significant recent attention. Estimating fruit counts before harvest provides useful information for logistics planning. While considerable progress has been made toward fruit detection, estimating the actual counts remains challenging. In practice, fruits are often clustered together. Therefore, methods that only detect fruits fail to offer general solutions to estimate accurate fruit counts. Furthermore, in horticultural studies, rather than a single yield estimate, finer information such as the distribution of the number of apples per cluster is desirable. In this work, we formulate fruit counting from images as a multi-class classification problem and solve it by training a Convolutional Neural Network. We first evaluate the per-image accuracy of our method and compare it with a state of the art method based on Gaussian Mixture Models over four test datasets. Even though the parameters of the Gaussian Mixture Model based method are specifically tuned for each dataset, our network outperforms it in three out of four datasets with a maximum of 94% accuracy. Next, we use the method to estimate the yield for two datasets for which we have ground truth. Our method achieved 96-97% accuracies. For additional details please see our video here: https://www.youtube. com/watch? v=Le0mb5P-SYc.
Switch: A Battery Conservation Framework for Mobile Devices

Sophisticated mobile applications may require more resources than are readily available on mobile devices. Mobile device resources such as processing power and storage can be extended with cloud-based mobile augmentation. However, some resources, specifically battery life and bandwidth, cannot be augmented. This research identifies that it is important to be able to estimate the energy consumption of both offloaded and local tasks when making offloading decisions. Due to the fact that the energy consumption profile of mobile devices with diverse capabilities are not the same, this aspect needs to be considered. This research proposes the Switch framework to conserve the limited battery life on mobile devices using a device specific energy consumption profile. The evaluation of the framework suggests that Switch can successfully be used to conserve battery life on mobile devices by making intelligent offloading decisions.