Miracle-dz/UniFAR-FTUs huggingface dataset:
A game theoretic approach for hierarchical caching resource sharing in 5G networks with virtualization

Caching and virtualization have been considered as the promising techniques in 5G Networks. In 5G networks with virtualization, the caching resources deployed by infrastructure providers (InPs) can be abstracted into isolated slices and transparently shared by mobile virtual network operators (MVNOs). In this case, one of the most important issues is how the MVNOs to share the caching resource. To solve this issue, different from previous works, a hierarchical caching architecture that core network and radio access network (RAN) have the caching capability in 5G networks with virtualization is first considered in this paper. Then, we study the problem of hierarchical caching resource sharing for MVNOs, and a competitive game to maximize their expectation revenue based on the oligopoly market model is formulated. As it is a hard problem to find the optimal solution in the hierarchical caching resource sharing problem, we decompose the optimization problem into two independent caching resource sharing problems in RAN and core network, respectively. Then the local optimal solutions are solved and the global Nash equilibrium solution is achieved. Finally, simulation results are illustrated to demonstrate the performance of the proposed scheme.
Updated Typical Daily Load Profiles for LV Distribution Networks Customers

The evolution of power systems, especially at the distribution level for the increasing diffusion of new energy sources and new loads, makes urgent the need of updated models of the electrical behavior of LV customers. For this purpose, time series of consumption and generation have to be properly modeled to represent for instance, among the other goals, the impact of demand coincidence and of generation-load homotheticity. Despite the increasing diffusion of Smart Meters that could be used for producing more accurate and updated models, the current load profiling practices, neglect the actual variability among consumption habits of customers belonging to the same category. This paper proposes a method for clustering load profiles of LV customers, derived from two recent measurement campaigns in Italy, with the aim of finding updated typical load profile curves for different category of LV customers can be used in planning and operation studies. The comparison of the results obtained by applying the method to two available databases has been discussed.
A Community-Based IoT Service Platform to Locally Disseminate Socially-Valuable Data : Best effort local data sharing network with no conscious effort?

This paper describes the concept and techniques to construct a community-based cross-industrial IoT service platform by utilizing already-existing fixed and mobile facilities as wireless IoT gateways geo-distributed in a city, which will enable quick implementations of local IoT services to bring social and economical values. Our proposed concept follows the policy of "local data production for local data consumption" considering the fact that locally available but socially-valuable data can provide various kinds of IoT service without the need of any cellular phone network, or cloud/data center facility to process big data. A prototyping platform to demonstrate this concept was constructed in Tokyo, Japan by utilizing a soft-drink maker’s facilities, i.e., vending machines and beverage delivery trucks, and taxi service company’s facilities, i.e., taxis. The function of the platform as a data forwarding network to disseminate socially-valuable data is demonstrated, and the performance is evaluated.
A Comprehensive Review of Condition Based Prognostic Maintenance (CBPM) for Induction Motor

This paper presents condition monitoring aspects of induction motor, its present status with possible mitigation schemes and future maintenance challenges. The induction motors constitute the major portion of motors in domestic and industrial applications. These motors experience different types of failures and faults associated with insulation, bearing, stator, rotor, and eccentricity. As a matter of fact, these faults may subsequently enhance the probability of failures unless proper introspection is not accomplished. In order to reduce the failure time and operating cost, early detection is indispensable which necessitates condition-based approach on contrary to scheduled maintenance. The condition monitoring is a strong candidate to address the diagnosis of machinery failure problems and unreliability. In this context, a comprehensive analysis is reported in the literature with a focus on different methodologies being addressed for such objective. Utmost efforts are made to comprehensively analyze in the reported literature in a sequential manner citing the advantage and limitations in this paper. The authors hopefully described and illustrated the associated problems with possible mitigation in the context of condition monitoring which would be immensely helpful for future researchers working in these aspects and the future roadmap would be clearly reflected.
Cooperative Sensing and Analysis for a Smart Pothole Detection

The need for monitoring the quality of roads is undeniable. There are many different approaches for road quality controls. Among them, the one making use of smartphones has recently become very popular. Nevertheless, building a system that saves energy for smartphones and adapts to real conditions remains a big challenge. In this article, we introduce a lightweight architecture to sense and analyze potholes based on data collected with smartphones. In this model, we improve some algorithms for real-time road-anomaly detection using smartphones. The efficiency of these new algorithms has been verified by experiments with both cars and scooters.
Iterative Learning Control for a Rotary Flexible Joint Experiment

This paper deals with the Iterative Learning Control (ILC) of a Flexible Joint Experiment. The paper presents a short overview of the field of Iterative Learning Control and some basic ILC strategies (P – type and D – type) are highlighted. The design of ILC for linear systems is briefly presented. The ILC is implemented on a Flexible Joint experiment, working in repetitive manner, where the control goal is the trajectory tracking. Even the Flexible Joint is a nonlinear system, in order to tune the controller parameters a linear model is derived. The controller performances are evaluated using a real plant experiment. The main experimental results are presented and the performance of the control system is analyzed.
Fast Object Segmentation Pipeline for Point Clouds Using Robot Operating System

This paper presents a method to pipeline the segmentation process for point clouds using the Robot Operating System (ROS) and the Point Cloud Library (PCL). The pipeline’s objective is to optimize the run time of a conventional segmentation algorithm by working within the Robot Operating System framework. It can be implemented using any system and in conjunction with a GPU. It shows the greatest reduction in run time for the least downsampled clouds. Therefore, it can be used for real-time safety-critical applications especially in scenarios where the point cloud is sparse or of highly uneven spatial density and should thus not be downsampled. It was developed for Obstacle Avoidance for Autonomous Vehicles and Drones where segmentation is only the first step of a larger pipeline involving obstacle detection and tracking. It was observed to reduce run time up to 31.3% on the KITTI data set and up to 44.4% on data collected from a 16 channel Ouster lidar at the Indian Institute of Technology, Hyderabad.
MRI Reconstruction From Sparse K-Space Data Using Low Dimensional Manifold Model

Recent compressed sensing (CS) approaches to utilize the similarity and redundancy of magnetic resonance (MR) image patches to enable reconstruction from sparse k-space measurements. In this paper, the patches’ similarity and redundancy are exploited by applying the low-dimensional manifold model (LDMM). The basic assumption of the LDMM is that the image patches sample a manifold whose intrinsic dimensions are much lower than the high-dimensional ambient space. MR images also exhibit a low-dimensional patch-manifold structure. Based on this assumption, the dimension of the patch-manifold is used as a regularizer in a variational formulation. The MR image is then reconstructed by keeping the dimension of the patch manifold as small as possible. The proposed algorithm significantly increases the quality of the reconstructed images. The algorithm is evaluated on two datasets containing 100 MR images each. The reconstruction quality of the algorithm, gauged using three quality metrics: peak signal-to-noise ratio, structural similarity index measure, and normalized root-mean-square error, is better than the comparison methods.
Differential Privacy-Preserving Density Peaks Clustering Based on Shared Near Neighbors Similarity

Density peaks clustering is a novel and efficient density-based clustering algorithm. However, the problem of the sensitive information leakage and the associated security risk with the applications of clustering methods is rarely considered. To address the problem, we proposed differential privacy-preserving density peaks’ clustering based on the shared near neighbors similarity method in this paper. First, the Euclidean distance and the shared near neighbors similarity were combined to define the local density of a sample, and the Laplace noise was added to the local density and the shortest distance to protect privacy. Second, the process of cluster center selection was optimized to select the initial cluster centers based on the neighborhood information. Finally, each sample was assigned to the cluster as its nearest neighbor with higher local density. The experimental results on both the UCI and synthetic datasets show that compared with other algorithms, our method more effectively protects the data privacy and improves the quality of the clustering results.
Visual-Inertial Localization With Prior LiDAR Map Constraints

In this letter, we develop a low-cost stereo visual-inertial localization system, which leverages efficient multi-state constraint Kalman filter (MSCKF)-based visual-inertial odometry (VIO) while utilizing an a priori LiDAR map to provide bounded-error three-dimensional navigation. Besides the standard sparse visual feature measurements used in VIO, the global registrations of visual semi-dense clouds to the prior LiDAR map are also exploited in a tightly-coupled MSCKF update, thus correcting accumulated drift. This cross-modality constraint between visual and LiDAR pointclouds is particularly addressed. The proposed approach is validated on both Monte Carlo simulations and real-world experiments, showing that LiDAR map constraints between clouds created through different sensing modalities greatly improve the standard VIO and provide bounded-error performance.
3D Images of Pedestrians at 300GHz

This paper demonstrates the ability of an active 300GHz radar system to perform 3D imaging non-coherently with as few as three receivers. The large bandwidth of the radar system facilitated full 3D reconstruction using time of flight information alone, applying single-look direct backprojection at a series of azimuth positions.
mm-Wave Waveguide Traveling-Wave Power Combiner Design Using an Equivalent Circuit Model

The waveguide traveling-wave power combiner (WTWPC) falls into the category of asymmetrical non-isolated power combiners, and, therefore, the active load modulation occurring within the combiner was investigated. Proper manipulation of active load modulation gave rise to a two-step design method for the combiner. The first step formulated the specifications of the combiner in terms of active load impedance at its input ports, which determined the objective scattering parameters of the combiner, as well as the desired coherent excitation signals. The second step established a versatile equivalent circuit model to provide a mapping relation between the scattering parameters and the physical dimensions of the combiner. The proposed equivalent circuit method not only offered a close-to-reality description of the existing type of traveling-wave power combiners but also broke new ground for combiner variants that accommodate the more general driving amplifiers. A Q-band quasi-planar traveling-wave waveguide spatial combined amplifier was built to demonstrate the proposed method. The measured P−1dB bandwidth of the prototype was 6 GHz (33–39 GHz).
A Video Deblurring Algorithm Based on Motion Vector and An Encorder-Decoder Network

Camera shakes cause video motion blur. Video deblurring has been studied for years, and however, there are still unresolved problems, such as video frame alignment, frame selection, and frame ambiguity evaluation. We propose a video deblurring algorithm based on the motion vector and an encoder–decoder network. Our algorithm consists of four steps: first, the blurry image blocks in a video frame are located using a blurred image quality evaluation algorithm based on a response function of singular values. Second, the corresponding candidates of the blurry image block in the consecutive frames are searched using the motion vector, and the optimal candidate blocks are obtained using an objective function. Third, the blurry image block and the optimal candidate blocks are served as samples, which are inputted to an encoder–decoder network, so that the blurry image block is repaired. Finally, all blurry image blocks are replaced with the repaired ones, the boundary artifacts are eliminated, and the entire video frame is repaired. The experiments show that our algorithm yields sharper repair results, and the overall performance of our algorithm is better than other related algorithms.
Web tool for creating 3D visualizations with focus on real-time direct manipulation of mathematical objects' attributes

In this paper we describe a web-based tool for creating and publishing interactive presentations with 2D and 3D visualizations of mathematical objects. It provides ways for enhancing students ability to understand mathematical concepts by means of direct manipulation of functions attributes. Our prototype is realized using Javascript and WebGL and consists of some solved exercises which represent a learning unit. It offers an innovative look at one of possible ways of preparing better teaching material.
A Multidimensional Reputation Evaluation Model for Mobile Crowd Sensing

The participant’s reputation is vital to improve the quality of service for Mobile Crowd Sensing (MCS). A multidimensional reputation evaluation model was proposed in this paper to evaluate the participant’s reputation more objectively. Different from the existing strategies, the service delay and the count of the successful as well as the failed transactions were additionally utilized to evaluate the participant’s reputation. An algorithm based on Analytic Hierarchical Process (AHP) was presented to establish the reputation evaluation weight matrix. Besides, a fuzzy logic based mechanism was proposed to normalize the value of the four criteria and a dual-threshold mechanism was designed to achieve admission control more properly. Finally, extensive simulations were conducted and the simulation results confirmed the effectiveness of the reputation evaluation model.
WiWear: Wearable Sensing via Directional WiFi Energy Harvesting

Energy harvesting, from a diverse set of modes such as light or motion, has been viewed as the key to developing batteryless sensing devices. In this paper, we develop the nascent idea of harvesting RF energy from WiFi transmissions, applying it to power a prototype wearable device that captures and transmits accelerometer sensor data. Our solution, WiWear, has two key innovations: 1) beamforming WiFi transmissions to significantly boost the energy that a receiver can harvest ~2-3 meters away, and 2) smart zero-energy, triggering of inertial sensing, that allows intelligent duty-cycled operation of devices whose transient power consumption far exceeds what can be instantaneously harvested. We provide experimental validation, using both careful measurement studies as well as a controlled study with human participants, to show the viability of a custom-built WiWear-based wearable device, at least in office environments.
Simultaneous Localization and Calibration Employing Two Flying Cameras

A simultaneous localization and calibration system (SLAC) by using two flying cameras is proposed in this research. The main purpose of the proposed system is to calibrate the transformation between an active 3D camera and a mobile manipulator. The system can be applied to resolve the problem of fixed workspace and expand the workspace in automated picking and assembly systems for a variety of objects. The transformations among the mobile manipulator, the working platform, and the active 3D camera can be determined by using Extended Kalman Filtering - Simultaneous Localization and Calibration (EKF-SLAC) through estimation of positions of feature points in the world frame based on two flying cameras. After estimating the transformations, two mobile platforms, each with the manipulator and the 3D camera, respectively, are controlled to move around the working platform. Finally, experimental results are provided to validate the performance of the approach developed in this paper.
Annotation Framework for Hate Speech Identification in Tweets: Case Study of Tweets During Kenyan Elections

Considering the colossal amount of user-generated content on social media, it has become increasingly difficult to monitor hateful content being published on public online spaces, especially during the electioneering periods, particularly in Kenya. In this regard, it is crucial to automate the identification of hate speech in order to manage the volume, variety, veracity and velocity of this content. In this research, we postulate a supervised machine learning approach whereby annotation of the training data set is critical in determining the performance of the trained classifier. Therefore, we develop an annotation framework based on Sternberg’s (2003) hate theory and test its performance in classifying about 5k tweets using 3 human annotators per tweet. Preliminary results indicate an intercoder reliability score of 0.5027 based on Krippendorff’s alpha.
Investigation of Energy Characteristics of the Electromechanical System in Multi-motor Conveyors under Variation of Traction Load Level on the Belt

This paper deals with the investigation the energy characteristics of electric drive of the multi-motor belt conveyor with two drive motors in the head and one in the tail section at the change of freight traffic. Considered the typical variants of load changes within the technological cycle, which includes start of the conveyor in idle to the rated speed, gradual loading to nominal level of performance, operation at rated load and under the load fluctuations. It is proposed the voltage controller, which reduce energy losses in the electromechanical system at all stages of the technological cycle. The analysis of energy losses in case of one electric motor is presented, since such systems form the load distribution.
Adjacent Graph Based Vulnerability Assessment for Electrical Networks Considering Fault Adjacent Relationships Among Branches

Security issues related to vulnerability assessment in electrical networks are necessary for operators to identify the critical branches. At present, using complex network theory to assess the structural vulnerability of the electrical network is a popular method. However, the complex network theory cannot be comprehensively applicable to the operational vulnerability assessment of the electrical network because the network operation is closely dependent on the physical rules not only on the topological structure. To overcome the problem, an adjacent graph (AG) considering the topological, physical, and operational features of the electrical network is constructed to replace the original network. Through the AG, a branch importance index that considers both the importance of a branch and the fault adjacent relationships among branches is constructed to evaluate the electrical network vulnerability. The IEEE 118-bus system and the French grid are employed to validate the effectiveness of the proposed method.
Vibration Elimination for Quadrotor Slung System Based on Input Shaping and Double Closed-loop Control

Undesirable residual vibration in dynamical system always causes many problems in logistic industries. For instance, a quadrotor is now considered to be used to deliver cargos instead of road transportation. However, the swing of cargos during the transportation process always causes threat to its safety and stability. In this paper, the mathematical model of a quadrotor slung system is established, and a double closed-loop control strategy is proposed, together with an input shaping design, to reduce the swing angle of load and keep quadrotor stable simultaneously in the presence of system uncertainties. Some numerical simulations are presented and the results verify the effectiveness of the proposed method.
Propagation Model Evaluation for LoRaWAN: Planning Tool Versus Real Case Scenario

LoRa has emerged as a prominent technology for the Internet of Things (IoT), with LoRa Wide Area Network (LoRaWAN) emerging as a suitable connection solution for smart things. The choice of the best location for the installation of gateways, as well as a robust network server configuration, are key to the deployment of a LoRaWAN. In this paper, we present an evaluation of Received Signal Strength Indication (RSSI) values collected from the real-life LoRaWAN deployed in Skellefteå, Sweden, when compared with the values calculated by a Radio Frequency (RF) planning tool for the Irregular Terrain Model (ITM), Irregular Terrain with Obstructions Model (ITWOM) and Okumura-Hata propagation models. Five sensors are configured and deployed along a wooden bridge, with different Spreading Factors (SFs), such as SF 7, 10 and 12. Our results show that the RSSI values calculated using the RF planning tool for ITWOM are closest to the values obtained from the real-life LoRaWAN. Moreover, we also show evidence that the choice of a propagation model in an RF planning tool has to be made with care, mainly due to the terrain conditions of the area where the network and the sensors are deployed.
Stabilization of Magnetic Suspension System by using First-Order-Reset Element without Derivative Feedback

The stabilization of a magnetic suspension system is achieved by using only a nonlinear integrator without other element. The proportional-derivative (PD) feedback is the simplest control method for the magnetic suspension system. The proportional element gives restoring force, and the derivative element gives damping to the system. However, the derivative feedback element needs to be carefully designed because unnecessary high-frequency signals generate an adverse effect. Meanwhile, installing a first-order-reset element (FORE) has been proposed to reduce the vibration. The element is a first-order low-pass filter (LPF) with reset functions. This paper proposes to stabilize a magnetic suspension system with a FORE only. However, the magnetic suspension system cannot be stabilized by the basic FORE. Therefore, the FORE is modified as to the reset conditions. A magnetic suspension system is stabilized by the modified FORE. The characteristics of the stabilized magnetic suspension system are studied experimentally.