Miracle-dz/UniFAR-FTUs huggingface dataset:
Crowdsourcing with Heterogeneous Workers in Social Networks

Many online social networking platforms are leveraging crowdsourcing to enhance the user experience. These platforms seek to incentivize heterogeneous workers to exert efforts to complete tasks (e.g., moderation of posts and articles) and truthfully report their solutions. Output agreement mechanism (e.g., majority voting) is a common approach to this end. In an output agreement mechanism, a worker is rewarded according to whether his solution matches those of his peers. However, prior related work has not studied the workers' heterogeneous solution accuracy and how this heterogeneity affects the platform's payoff. We fill this void by modeling and analyzing the interactions between the platform and workers as a two-stage Stackelberg game. In Stage I, the platform chooses the reward level for the majority voting to maximize its payoff. In Stage II, the workers decide their effort levels and reporting strategies to maximize their payoffs. We show that as a worker's solution accuracy increases, he is more likely to exert effort and truthfully report his solution under the equilibrium reward mechanism. However, given a fixed total worker population, it is surprising that the platform's overall payoff does not monotonically increase in the number of high-accuracy workers. This is because a larger number of high-accuracy workers brings marginally decreasing benefit to the platform, but the rewards required to incentivize them may significantly grow. Moreover, we show that as the solutions of the high-accuracy workers become more accurate, the platform needs a smaller number of such workers to achieve the maximum payoff.
Development and Testing of a Wearable Vibrotactile Haptic Feedback System for Proprioceptive Rehabilitation

The human sense of touch is an integral part of daily life. For tasks involving grasping and manipulation of objects, force feedback is a key requirement. Most of the systems give contact point or complete grasping force feedback; for precision grasping and other physical interactions, finger awareness and force feedback from independent fingers is essential. In this study a novel, wearable proprioceptive rehabilitation system is designed which restores the ability of identifying and distinguishing between individual fingers of a prosthetic hand or an exoskeleton in a non-invasive manner. Moreover, it provides different levels of force feedback from every finger as well, which enables the user to distinguish and control force in precision grasping activities. For testing the system accuracy, classical psychophysical methods were used on a group of 14 voluntary disabled subjects. The tests were conducted in both, ideal and real-world conditions i.e. without and with distractions and accuracies were calculated accordingly. A p-test was also conducted to observe significance between the samples of with and without distraction datasets. The system performed with an overall accuracy of 82.04% which was well above the min. performance measure of 60%. Vi-HaB is standalone system and can be mounted on any upper limb rehabilitation (prosthesis, exoskeleton) system for finger awareness and force feedback.
Evaluation of Soft Magnetic Ferrosilicon FeSi 6.5 for Laser Beam Melting

The electromagnetic properties of soft magnetic materials are of decisive importance for the efficiency, running characteristics and performance of electric motors. To fulfil the target criteria as favorably as possible, high requirements are placed on the processing techniques of soft magnetic materials with regard to accuracy, reproducibility and quality. Through continuous improvements, novel additive manufacturing processes are also able to meet these requirements. They can already be a pragmatic alternative, where conventional processes reach their technological or economical limits. Laser beam melting of metals in a powder bed is one of the processes in which a workpiece is created from shapeless metal powder. Due to its generative character, new design elements can be utilized to build lightweight motors or to manufacture them economically in small quantities. However, the process does not allow the production of a laminated soft magnetic part, which leads to increased eddy current losses. To counteract these, a material with a high specific electrical resistance has to be used. This publication presents the selection of a material suitable for this application, taking into account the state of the art and the procedure for qualifying the material in the laser beam melting process. This enables potential users of additive manufacturing to estimate the effort for the qualification of a tailor-made alloy and to use it for their own applications.
Discrete Monotonic Optimization Based Sensor Selection for TDOA Localization

This paper investigates the sensor selection problem for time difference of arrival (TDOA) localization in wireless sensor networks. Specifically, a multi-objective optimization problem is formulated in which a Boolean vector is involved to find the best tradeoff between the localization accuracy and the energy consumption. The ε- constraints method is introduced to convert the original multi- objective optimization problem to a tractable single-objective problem. To solve the converted sensor selection problem, we propose the polyblock outer approximation (POA) algorithm based on discrete monotonic optimization (DMO) in order to find the global optimal solution, which however can not be obtained by the traditional semidefinite relaxation (SDR) approach. Further, for the sake of practical implementation, we propose another two suboptimal algorithms, namely, POA-based accelerated cutting (POA-AC) algorithm and POA-based monotonic cutting (POA-MC) algorithm. Simulation results validate that the localization accuracy for sensors selected by the POA-AC algorithm and POA-MC algorithm is greater than the semidefinite relaxation (SDR) solution and achieves the same results as that by the exhaustive search method.
Analytical Method for Magnetic Field of Eccentric Magnetic Harmonic Gear

Eccentric magnetic harmonic gears (EMHG) can achieve high speed ratio transmission and large torque output by modulating the air gap length between permanent magnets (PM). The analytical method is based on the boundary perturbation method. The vector magnetic potential perturbation equation in the air gap region is established, and the general solution is obtained using the boundary conditions. According to the superposition principle, the air gap flux density of stator and rotor PMs acting alone is synthesized, and the electromagnetic torque and unbalanced magnetic pull are calculated. Last, we compare magnetic field distributions, electromagnetic torque and unbalanced magnetic pull computed by the analytical method with those obtained from finite-element method (FEM).
Interference Alignment for MIMO Downlink Heterogeneous Networks

In this paper, we investigate the interference alignment schemes for the downlink of a heterogeneous network (HetNet), where the macro base station (BS) can serve arbitrary number of macro users. Furthermore, the number of data streams for macro users and pico users can be different. Firstly, we propose a basic grouping method aided interference alignment (IA) scheme and derive the transmit and receiver beamforming matrices in a closed-form expression. Furthermore, for the case where the number of antennas at pico BS is equal to the number of antennas at each user, we propose an advanced generalized eigenvalue decomposition (GEVD) based grouping method aided IA scheme which can reduce the number of transmit antennas required at the pico BS compared to the basic grouping method aided IA scheme. Moreover, we also derive the maximum total achievable degrees of freedom (DoF) for each scheme. In addition, it can be proved that the maximum total achievable DoF of the GEVD based grouping method aided IA scheme is higher than or equal to that of the basic grouping method aided IA scheme for the same configuration.
3D-CLDNN: An Effective Architecture on Deep Neural Network for sEMG-Based Lower Limb Abnormal Recognition

In recent years, the application of surface electromyography (sEMG) has increasingly more prominent, while the development of deep learning algorithms cannot be ignored. In this paper, a deep neural network that combined 3D-convolutional layers and a long short-term memory layer (LSTM) is proposed. Meanwhile, we propose data augmentation methods and a transfer learning algorithm to improve our performance. This proposed method is shown to outperform the other networks in sEMG-based recognition of lower limb abnormality. The experiments with 94.12% accuracy show that our method is effective for recognition of lower limb abnormality.
Comparative Analysis of Dipole Antenna Configuration in Industrial Wireless Sensor Networks Application

To ensure the reliability of communication process, an appropriate RF technology is required in implementing Industrial Wireless Sensor Networks (IWSN). IWSN uses antenna to transmit information through radio wave. One of the most used antennas in IWSN is half-wave dipole antenna. To understand the characteristics of radio wave propagation in IWSN, this paper aims to perform comparative analysis of dipole antenna configuration in IWSN application. The proposed method was to simulate several configurations of half-wave dipole antenna 2.4 GHz in 3D model of an industrial site. The characteristic from each configuration was analyzed and compared from its gain, far field, S-parameter, and directivity. The results show that each configuration results in different value of gain, far field, S-parameter, and directivity.
Unsupervised Framework for the Identification of Visual Evoked Potential in MEG Measurements

Visual Evoked Potential (VEP) is used for the diagnostics of seizure disorders, such as epilepsy. In order to analyze the VEP, variations of the neural electric tensions on the area of visual cortex in the occiput are measured by Electroencephalography (EEG) and Magnetoencephalography (MEG). Traditionally identification of VEP depends on the visual inspection of components by an expert. Currently supervised machine learning techniques have been applied to replace or to complement the visual inspection performed by the expert. In this paper, we propose an unsupervised framework for the identification of VEP in MEG measurements. In order to identify the VEP, we separate the measurement into two groups: the measurement without VEP and the measurements with VEP. Next, we compare the components from both groups in order to identify the components with VEP in the measurements with VEP. We validate our results using measurements from the Jena University Hospital.
Security Issues of Cognitive Radio Network

Security is a critical issue in Cognitive Radio Networks CRN. And in order to implement appropriate security mechanisms, various possible attacks on (CRN) must be identified. This paper clarifies a literature review of different CRN surveys, security features of CRN, attacks on CRN and lastly attacks and their corresponding countermeasures are tabulated in order to identify researchers about the last updates in CRN's attacks and countermeasures.
High-Speed Cavity Enhanced Silicon Photodetector on SiN-SOI Platform for Short Reach Optical Datacom

We demonstrate on-chip integrated, high-speed cavity-enhanced Silicon MSM photodetector on SiN-SOI platform for short-reach optical datacom at 850 nm wavelength. Silicon MSM photodetector is integrated inside a SiN ring resonator cavity. Compare to the conventional waveguide integrated photodetector the proposed device achieves 10X fold enhancement in responsivity at 850 nm wavelength. High-speed characterization of the proposed device is performed by capturing the impulse response of the device with illumination of femtosecond duration pulse laser at 850 nm wavelength. We measure 3dB bandwidth of 6.1 GHz which can be further be improved suitable for datacoms and supercomputers.
A Comparison of WiFi-based Indoor Positioning Methods

With the popularity of smart phones and wireless network, location-based services (LBSs) attract extensive attention. The demands on indoor positioning systems (IPS) for the WiFi deployed environments are especially high. In this paper, we categorize the WiFi-based IPSs into two types, namely Received signal strength (RSS)-based and Channel state information (CSI)-based, and compare the practical performances of several WiFi-based IPSs. For the RSS-based methods, we select fingerprinting, trilateration method, sequence-based localization (SBL), and multidimensional scaling (MDS)-based methods for comparison. For the CSI-based method, we choose the time-reversal (TR) algorithm and propose a new experimental scheme to measure its performance. The pros and cons of each algorithm are also discussed.
A Simple Identification Method for Differentiating Between Ambient and Target Speech

The practical use of a system that supports table conversations, such as in restaurants and community rooms, requires the separation of target conversations near microphones from ambient acoustic noise in the background. Based on acoustic analysis, we confirmed that the difference between a conversation near a microphone and ambient sounds is represented in the standard deviation (SD) of the power levels of each utterance. In the case of noisy ambient conversation in a restaurant, we calculated the boundary between the SD of power values of target utterances near a microphone and that of ambient conversation using the Linear Discriminant Analysis (LDA) method. Using the calculated boundaries, we evaluated the performance of the system in distinguishing target conversations near microphones from ambient acoustic noise caused by extraneous speakers. Experimental results using four speakers demonstrated an average identification rate of 83.5%.
Three-Phase Grid Connected SPV Inverter with Flexible Control of DC Link Voltage

Growing concerns on climatic changes and rapid depletion of fossil fuels results in immediate action to accelerate the clean energy generation using renewable energy resources. Alternative input from solar energy for daily needs is one such platform which has immense potential and is universally accessible with least environmental effects. The proposed system deals with a solar powered source connected to utility with a grid interfaced inverter using adaptive DC link technology which reduces the overall power loss in the whole system. This methodology stands over the constant DC link technology. This project operates with a three-phase grid-coupled solar photovoltaic (SPV) system which includes a two-stage operation. The first stage deals with a boost converter which accomplishes the maximum power point tracking (MPPT) using Incremental Conductance (InC) technique which is more easier and accurate. The output of the boost then feeds the DC link capacitor which is made adaptable to grid voltage variations. The second stage consists of a voltage source converter (VSC) which enact as a PV inverter and delivers the power to the grid via boost converter. The proposed strategy was designed for a 20 kW SPV system and was simulated in MATLAB Simulink 2017b.
A High Phase Accuracy Multiphase DLL with Optimized VCDL Structure

In this paper, a 4-phase delay-locked loop (DLL) with high phase accuracy, low power consumption and small area is realized for time-to-digital converter (TDC). The proposed DLL adopts a new voltage-controlled delay line (VCDL) structure, which reduces the output phase error down to 2.32%. A simple digital auxiliary pulse width regulator is used to adjust the width of output pulse for further reduction of the duty cycle error. Designed with 0.18 µm CMOS process, the DLL with 4-phase outputs occupies an active area of 102 µm × 100 µm. With a supply voltage of 1.8 V, the power and peak-to-peak jitter of this proposed DLL is 2.27 mW and 12.9 ps, respectively.
Delay-Phase Precoding for THz Massive MIMO with Beam Split

Benefiting from tens of GHz bandwidth, Terahertz (THz) communications has been considered as one of the promising technologies for the future 6G wireless communications. To compensate the serious attenuation in THz band and avoid huge power consumption, massive multiple input multiple output (MIMO) with hybrid precoding is widely considered. However, the traditional phase-shifter (PS) based hybrid precoding architecture cannot cope with the effect of beam split in THz communications, which means that the path components of THz channel split into different spatial directions at different subcarrier frequencies, leading serious array gain loss. In this paper, we first point out the seriousness of beam split effect in THz massive MIMO by analyzing the array gain loss caused by the beam split effect. To compensate this array gain loss, we propose a new hybrid precoding architecture called delay-phase precoding (DPP). In the proposed DPP, a time delay (TD) network is introduced between radio- frequency chains and the traditional PS network, which converts phase-controlled analog precoding into delay-phase controlled analog precoding. When carrying out precoding, the time delays in the TD network are dedicatedly designed to generate frequency-dependent beams which are aligned with the spatial directions over the whole bandwidth. Thanks to the joint control of delay and phase, the proposed DPP can significantly alleviate the beam split effect. Simulation results reveal that the proposed DPP can generate beams with the near- optimal array gain over the whole bandwidth, and achieve the near-optimal achievable rate performance.
GF-3 PolSAR Marine Aquaculture Recognition Based on Complex Convolutional Neural Networks

Marine floating raft aquaculture is widely distributed along the coast in China. Polarimetric synthetic aperture radar (PoISAR) images can distinguish marine aquaculture targets from sea water background, but optical satellite remote sensing images cannot detect these effectively and completely. In this paper, considering the complex character of PoISAR data, a complex-value convolutional neural network is utilized for marine aquaculture recognition, which makes the most of phase information implicit in original complex data to improve detection accuracy. Experiments on actual GF-3 PoISAR images substantiate the effectiveness of the proposed approach.
Joint Data Offloading and Resource Allocation for Multi-Cloud Heterogeneous Mobile Edge Computing Using Multi-Agent Reinforcement Learning

In this work, we consider a heterogeneous multi-cloud mobile edge computing (Het-MEC) network, where multiple independent cloud centers (CCs) publish tasks to edge devices (EDs) and MEC servers, and compete for their computing and transmission resources. To minimize the system latency, we propose a distributed scheme for each CC to determine its data offloading and resource allocation strategy independently. Competition among multiple CCs in this distributed scheme is depicted by our designed multi-agent reinforcement learning (MARL) based algorithm, where each CC is self-motivated to learn the explicit models of other CCs and adjusts their behaviors. Simulation results indicate that multiple clouds are self-organized to take full advantage of computing and transmission resources to minimize their own task latency, while a lower system latency can be achieved compared with the cloud computing and local computing schemes.
Variable Action Period Predictive Flux Control Strategy for Permanent Magnet Synchronous Machines

This article introduces a method to achieve the variable action period (VAP) of voltage vector in the finite control set model predictive control (FCS-MPC). In the conventional FCS-MPC, the control behavior is limited since the action period of a voltage vector is normally fixed to the sampling period. This article aims to enhance the control performances by means of regulating the action period of selected voltage vectors, together with the selection of an optimal control action. A VAP-based predictive flux control strategy is thus proposed. The optimal voltage vector and its optimal action period are both obtained by means of a two-step cost function minimization. In this manner, the future states of the control objectives are taken into account in the construction of cost function. The action period for the selected voltage vectors is no longer fixed to the sampling period, which provides an additional control freedom to obtain improved reference tracking behaviors. Experimental validations and comparison studies demonstrate that enhanced steady-state performances as well as excellent dynamic behaviors can both be obtained with the proposed strategy adopted.
Effect of Loading on Optimum Weight of Planer Trusses Using Genetic Algorithms

Selection a proper layout of a structure can be considered as a challenge decision that facing the structural designer, this layout should withstand the applied loads based on different provisions, such as the allowable stress, the serviceability limit state and etc. The challenges will be increases when the cost and the duration of the construction are of vital importance. This paper demonstrates the use of an optimization tool based on a genetic algorithm in order to examine the effect of different loading values on a trusses response. Three design categories had been adopted in the evaluation; the size, the shape, and the topology that satisfies the relevant constraints given in the AISC-ASD-89 standard. For this reason, a traditional program under the name of GS-USA frame was employed. The program is judged based on the analysis of a benchmark truss, and by comparing the optimized a three well known trusses found in the literature that adopted different optimization techniques. The evaluation results showed that the genetic algorithm introduced a good optimization technique compared to other techniques. Although the GS-USA program was efficient in optimizing the weight, and the tensile stresses, however, the program stucked in compression stress. Analysis results showed that the weight, volume, and the members' stresses can be efficiently optimized for the suggested planer truss compared to Howe and Baltimore Trusses of the same span and bays, where the weight increments were slightly with increasing the load, and 57% of materials were saved, moreover the truss members were effectively stressed up to 74% of the allowable stress.
Rate and Reliability Trade-Off for mmWave Communication via Multi-Point Connectivity

The fundamental challenge of mmWave frequency band is the sensitivity of the radio channel to blockage, which gives rise to unstable connectivity and also impacts the reliability of a system. In this paper, we explore the viability of using Coordinated Multi-point (CoMP) connectivity for robust and resilient downlink communication. We provide a novel iterative algorithm for Weighted Sum-Rate Maximization (WSRM) problem leveraging Successive Convex Approximation (SCA) with low computational complexity, which admits closed-form solution via iterative evaluation of optimal Karush-Kuhn- Tucker (KKT) conditions. Unlike in the conventional CoMP schemes, the propose precoder design has, per-iteration, computational complexity in the order of base-station (BS) antennas instead of system-wide joint transmit antennas. This is achieved by splitting the beamformer design to be parallel across the coordinating BSs. For the downlink precoder design, a conservative estimate of the user specific rate is obtained by considering possible combinations of potentially blocked links. In the presence of random blockages, the effective throughput and outage performance of the proposed method significantly outperform several baseline scenarios, hence providing more reliable connectivity.