Miracle-dz/UniFAR-FTUs huggingface dataset:
Interference-Aware Mode Selection for New Arriving Devices in D2D Communications

In this work, we consider mode selection mechanisms for device-to-device (D2D) communications underlaying a cellular network with multiple existing active D2D pairs. Focusing on the mode selection of the new arrival D2D pair with existing D2D pairs and cellular users, we study the mode selection rules based on the signal-to-interference plus noise ratio (SINR) of the new arrival user and the overall system capacity. For each selection rule, we utilize and discuss pros/cons of two different sets of precoders. In the first set of precoder choice, the block diagonalization (BD) is implemented among cooperating cellular users while zero forcing (ZF) is adopted for the D2D users. In the second set, the maximum signal-to-leakage-and-noise ratio (SLNR) precoder is employed for both cellular users and D2D pairs. The simulation results show that the underlay mode is the best option for the new arrival D2D pair.
Design of Wideband Radar Absorbing Structure Based on Lossy Frequency Selective Surface

This paper proposed a novel design of a metamaterial absorber with wideband absorption, wide incident angle and polarization insensitivity. Based on resistive film structure (lossy frequency selective surface), this metamaterial absorber unit consists of a nut ring shaped resistance film, a polymethacrylimide foam and a metallic ground. Simulated reflectivity and absorption rate show that the absorber has a strong absorption of over 90% for normal incident wave. The absorption band ranges from 4.05GHz to 17.96GHz, basically covering C, X and Ku band. Simultaneously, the absorber has no significant change for TE or TM wave at different polarization angle, showing good polarization insensitivity. The absorber also exhibits broad angle absorption characteristics of over 80% absorption with incident angle from 0–40°. The reason for the wideband absorption characteristic of the absorber is quantitatively explained from the perspective of multi-reflection interference theory. The calculated results agree well with simulated ones.
Three-Layered Incentive Framework for Mobile Crowdsourcing

The smartphone users have a lack of interest to participate in the crowdsourcing task. The primary causes behind this lack of interest are resource and security constraints. Resource constraints like the limitations of the battery, CPU, RAM of the smartphones causes an unwillingness to engage in crowdsourcing task. Moreover, security issues like disclosing confidential information stored in smartphones also inhibit the users to contribute in crowdsourcing task. We propose a three-layered incentive framework consisting of a resource management layer, privacy assurance layer and incentive layer. We address the challenges present in each layer, and the solution of these challenges will produce a standard incentive framework. This framework will alleviate all the issues created during user participation in a crowdsourcing task.
Tensor-based Spectral Analysis of Cascading Failures over Multilayer Complex Systems

Cascading failure in multilayer complex systems draws significant attentions from both industry and academia nowadays. In this paper, we propose a scalable tensor-based framework to represent the interdependent multilayer network, and use this framework to analyze the failure propagation based on a susceptible-infectious-susceptible (SIS) epidemic model. Specifically, the transition equations and transition tensor are derived to characterize the behavior of failure propagation. We show that the spectral radius of transition tensor is a failure indicator with an explicit failure threshold to measure the system reliability. Moreover, to make the failure indicator analytically tractable and computationally efficient, we derive its upper and lower bounds, as well as its approximated expressions in special cases as functions of the adjacency tensor and epidemic parameters. Our analytical results are evaluated by simulations in a set of multilayer networks generated by random graphs, which show that our results can achieve the desired performance compared with other benchmark approximation methods.
Novel  $W$ -Band LTCC Transition From Microstrip Line to Ridge Gap Waveguide and its Application in 77/79 GHz Antenna Array

An ultra-wideband transition from microstrip line to ridge gap waveguide (GWG) is proposed for W-band system integration with antenna array and a transceiver. Substrate integrated waveguide bridge is used in the proposed transition for electromagnet wave propagation between microstrip line and ridge GWG, which can realize impedance matching for a wideband frequency range. Back-to-back transition prototypes are fabricated by the low-temperature co-fired ceramic (LTCC) technique, and the probe measurement results show that the transition can work at W-band. Moreover, the application of the proposed transition in a LTCC slot antenna array with magnetic coupling ridge GWG feeding network operated at 77/79 GHz is studied comprehensively. The antenna performance measurements demonstrate that the proposed transition has a potential massive application in the GWG active antennas.
Contrasting Characteristics of Gas–Liquid Reactive Species Induced by Pulse-Modulated RF and kHz Sinusoidal Plasma Jets

The pulse-modulated radio frequency (RF) and kHz sinusoidal voltages are used for the excitation of two plasma jets, for which the reactive species in the plasma plumes and in the treated water are comparatively studied. With the same discharge power, it is found that concentrations of aqueous reactive species generated by the kHz sinusoidal plasma jet are higher than that of the pulse-modulated RF plasma jet, especially for the short-lived species OH. In addition, the comparison between the bacterial inactivation results is in accordance with that for the concentrations of aqueous reactive species for the two types of plasma jets. On the contrary, the emission spectra indicate that the gaseous reactive species such as OH(A) and O(3p5P) have much higher densities for the pulse-modulated RF plasma jet, which should ascribe to its higher electron density and electron temperature. Moreover, the concentrations of the aqueous reactive species and the emission intensities of the gaseous reactive species have similar variation trends for each of the plasma jets, as a function of the discharge power and the gap width. These results imply that the emission intensity could not reflect the relative concentration of an aqueous reactive species between these two plasma jets, but it could be used as an indicator of the concentration variation of an aqueous reactive species for each of the plasma jets. In addition, the phenomenon of electron trapping effect also indicates that the aqueous reactive species can be effectively generated only when more electrons act on the water sample.
Environmental Sound Classification Using Deep Convolutional Neural Networks and Data Augmentation

This work is about environmental sound classification by deep convolutional neural networks and data augmentation. Data augmentation is applied to increase the labeled training dataset. Data augmentation process improves the performance of audio classification. In this paper, first we present a strategy for generating a deep convolutional neural network (CNN) framework for environmental sound analysis with Urban-sound8K audio dataset. Secondly we analyze the performance of data augmentation methods on Urbansound8K audio dataset and compare the performance of CNN with different data augmentation methodologies. Data augmentation is basically a deformation technique. By this approach we can increase the number of dataset elements into its multiples. Here, compare the performance of different augmentation method to identify which one is the best augmentation technique for environmental sound analysis. Different types of data augmentations were applied to the dataset in the previous works. We introduce a new data augmentation method using LPCC feature.
Circular Antenna Array Synthesis Technique Including Mutual Coupling Using Unit-excitation Active Element Pattern

The unit-excitation active element pattern method used to solve the mutual coupling of the uniform circular antenna array is proposed in this paper. With the help of this method, the uniform circular antenna array's radiation pattern including the element mutual coupling and platform effect can be efficiently calculated. Two micorstrip circular antenna arrays with different radiation patterns are analyzed by this proposed method. Then the corresponding calculated results are compared with the full-wave simulation results from HFSS, and they agree well.
Automated Glaucoma Diagnosis Using Deep and Transfer Learning: Proposal of a System for Clinical Testing

We developed a deep learning algorithm for identifying glaucoma on optic nerve head (ONH) photographs. We applied transfer learning to overcome overfitting on the small training sample size that we employed. The transfer learning framework that was previously trained on large datasets such as ImageNet, uses the initial parameters and makes the approach applicable to small sample sizes. We then classified the input ONH photographs as “normal” or “glaucoma”. The proposed approach achieved a validation accuracy of 92.3% on a dataset of 277 ONH photographs from normal eyes and 170 ONH photographs from eyes with glaucoma. In order to re-test the accuracy and generalizability of the proposed approach, we re-tested the algorithm using an independent dataset of 30 ONH photographs. The re-test accuracy was 80.0% on average.
Stabilizing Phase-balanced or Phase-synchronized Trajectories of Van der Pol Oscillators in Uniform Electrical Networks

We study the dynamics of Van der Pol oscillators in a class of electrical networks with the goal of synthesizing feedback strategies to stabilize either phase-synchronized or phase-balanced motions. The electrical networks are composed of transmission lines with series R-L circuit models that are uniform, by which we mean that the R-to-L ratios of all lines are the same. The oscillators are coupled through linear filters on their output currents to nodes in the network. Our main results illustrate how the signs of the local feedback gains determine the stability of either phase-synchronized or phase-balanced trajectories. The results have implications on (and indeed, the paper is motivated by) decentralized control of power converters: synchronized solutions are of interest in parallel-connected dc-ac inverters and phase-balanced solutions are of interest in interleaving switching waveforms for multiphase dc-dc converters.
An Electricalthermal Coupling Numerical Method for Inhomogeneous Medium Problems

In this work, a FEM-based algorithm for calculating the electrothermal coupling process with inhomogeneous conductivity is presented. Firstly, electrothermal coupling problems are analyzed and corresponding coupling equations are presented. Secondly, iterative solution procedures are used to solve the coupling equations. Then, treatments for handling inhomogeneous problems are proposed. Finally, numerical experiments are presented to validate the efficiency of this algorithm.
Distributed Multi-Channel MAC Protocol for VANET: An Adaptive Frame Structure Scheme

The media access control (MAC) layer of the vehicular ad-hoc network (VANET) is designed 3 to provide fast and reliable vehicles to vehicles (V2V) or vehicles to infrastructures (V2I) access. With the development of autonomous driving and intelligent transportation, many sensor information in large-scale vehicles require efficient and reliable transmission with V2V and V2I technologies. In recent years, the demand for various services in VANETs is growing rapidly, which poses great challenges to the design of the MAC protocol. An adaptive scheme for channel frame structure can enhance channel access flexibility to cope with the changing network topologies in vehicular environments. In this paper, we adopt an adaptive frame scheme on the control channel and the service channels, which mainly improves two aspects of channel access performance: improved time slot utilization of the broadcasting period as much as possible while ensuring the vehicle can access the control channel quickly; and increasing the throughput of the service channels without generating excessive overhead. Analysis and simulation results demonstrate the performance of the proposed mechanism.
Reachability Confirmation of Statically Detected Defects Using Dynamic Analysis

Static and dynamic analysis of programs are well known approaches to the problem of automatic program behaviour analysis. Both methods have advantages and limitations. Static analysis has lack of precision for the sake of scalability. On the other hand, dynamic analysis has 100% precision while reaching defect point, but suffers from scalability issues. This paper describes an approach to confirmation of reachability of defects that were found by static analysis with help of dynamic analysis. The approach focuses on defects for which static analysis tool manages to generate a path which may contain branch conditions from the source of possible error to its sink (source-sink defects). The combination of different methods allows to circumvent limitations and multiply their advantages. Preliminary experiments on several open source projects show that real true positive defects can be confirmed to be reachable using the approach and some false positives can be proved.
Compact Dual-Channel Balanced Filter and Balun Filter Based on Quad-Mode Dielectric Resonator

In this paper, we propose a method for designing a dual-channel balanced filter and a dual-channel balun filter based on the quad-mode dielectric resonator (DR) for the first time. By sharing one common quad-mode DR, two balanced filters or two balun filters are integrated as one single-cavity configuration, featuring compact size and high integration. A cylindrical DR with two short ends is investigated to construct the quad-mode DR. By properly arranging input and output feeding probes, two modes of the DR are only excited by the feeding probes of one channel and the other two modes are excited by that of the other channel. Accordingly, signals cannot be transmitted between the two channels, resulting in high isolation. Moreover, the required out-of-phase characteristics of the balanced and balun filters can be obtained by the inherent electromagnetic field properties of the DR without adding additional circuits, featuring a simple structure. For demonstration, a dual-channel balanced filter and a dual-channel balun filter are designed and fabricated, showing excellent balanced or balun filter performance of each channel as well as high isolation between the two channels. As compared to the other reported DR balanced and balun filters, the proposed designs exhibit a significant size reduction, which are attractive in wireless systems.
Unbalanced Three-Phase $LLC$ Resonant Converters: Analysis and Trigonometric Current Balancing

Three-phase <inline-formula><tex-math notation="LaTeX">$LLC$</tex-math></inline-formula> resonant converters can handle very high power levels beyond the capabilities of half-bridge and full-bridge <inline-formula><tex-math notation="LaTeX">$LLC$</tex-math></inline-formula> topologies. Among other characteristics, three-phase <inline-formula><tex-math notation="LaTeX">$LLC$</tex-math></inline-formula> structures reduce output current ripple (small output filter), enable parallel power processing (low peak current), and provide good thermal distribution. However, all these key advantages can be severely compromised due to passive components tolerances, leading to undesired current balance issues in three-phase <inline-formula><tex-math notation="LaTeX">$LLC$</tex-math></inline-formula> resonant converters. Tolerances in resonant tank passive components are inevitable and lead to unequal peak currents between phases, uneven temperature distribution, and large output current ripple. This paper investigates the imbalances in three-phase <inline-formula><tex-math notation="LaTeX">$LLC$</tex-math></inline-formula> converters and proposes a novel trigonometric current balancing (TCB) technique using phasor analysis. In this strategy, the required input voltage phase angles are calculated to achieve balanced phase currents, even under severe unbalanced conditions. In some cases, the output filter current ripple is reduced to less than half. The methodology is verified with a 3-kW experimental prototype, which validates the analytical framework and effectiveness of TCB.
Research on Factors Affecting Sidelobe Level of Millimeter Wave Microstrip Array Antenna

In this paper, 77GHz vehicle-borne anti-collision radar antenna is taken as an example to study and analyze the influence factors of millimeter-wave microstrip array antenna sidelobe level. The proposed antenna includes waveguide microstrip conversion, microstrip transmission line, and 10-element unequal amplitude microstrip array antenna. In order to reduce the sidelobe level (SLL), the surface current and the space electric field of the microstrip transmission line are analyzed. The SLL of the antenna is effectively reduced by changing the structure of the microstrip transmission line. When the conventional microstrip transmission line is used, the E-plane SLL of the antenna is −11.43dB. With the improved transmission line form, the SLL and gain are −19.41 dB and 14.43dB, with are increased by 8dB and 0.6dB, respectively.
A New Game Theory Approach for Noise Reduction in Cognitive Radio Network

Noise reduction is one of the fundamental signal processing functions of the cognitive radio system, as noise affects data integrity and obscures the using of the unused channels in the licensed spectrum of Primary Users (PU). The majority of traditional filter methods handles only specific types of noises in a certain range of frequencies. Despite the intensive research in this area, building accurate noise reduction engines that equilibria between signal-to-noise ratio and computational cost remain a holy grail. In this paper, we made our initial effort towards this issue by building a novel game theory engine for effective noise reduction that incorporates the advantage of both adaptive filtering and wavelet denoising strategies. Adaptive filter solves the convergence problem and low mean square error rate. Wavelet transform as a nonstationary signal analysis can describe signal’s local features either spatially or spectrally and obtains the asymptotically optimal estimation of original signals. Game theory (GT) is adapted for optimal decision making under competition; so that it chooses the best strategies to achieve the best noise reduction equilibrium when others are affected by the decision of the individual player, as the experiments indicate that the proposed model on its ability to reduce noise significantly.
Improving Players' Profiles Clustering from Game Data Through Feature Extraction

The study of players profiles is an emerging area which commonly uses demographic characteristics as determinant features. However, there is the need for a deeper understanding that these characteristics alone do not provide. Another problem is the use of predefined profiles that might be oversimplified or too embracing. This paper investigates players profiles based on their gameplay data, besides demographics, using an educational math game as a testbed. Thus, problems such as noise, mixed data types and high dimensionality must be tackled. To this end, we investigated two feature extraction methods to mitigate these difficulties, Principal Component Analysis (PCA) and Features Agglomeration (FA). Then, two unsupervised learning algorithms were used to find the profiles in our experiments, showing that both PCA and FA improved clustering performance, wherein the best results indicated four profiles: advanced, skilled, beginners and intermediated. Our findings provide game designers with insights about playing styles, can be used to adapt the game in real-time and to assess how distinct players profiles perform in the educational subject, as well as their playing performance.
Multitrack Power Factor Correction Architecture

Single-phase universal-input ac–dc converters are needed in a wide range of applications. This paper presents a novel power factor correction (PFC) architecture that can achieve high-power density and high efficiency for grid-interface power electronics. The multitrack PFC architecture reduces the internal device voltage stress of the power converter subsystems, allowing PFC circuits to maintain zero-voltage-switching at high frequency (1 MHz–4 MHz) across universal input voltage range (85 <inline-formula><tex-math notation="LaTeX">$\rm V_{\text{ac}}$</tex-math></inline-formula>–265 <inline-formula><tex-math notation="LaTeX">$\rm V_{\text{ac}}$</tex-math></inline-formula>). The high performance of the power converter is enabled by delivering power in multiple stacked voltage domains and reconfiguring the power processing paths depending on the input voltage. This multitrack concept can be used together with many other design techniques for PFC systems to create mutual advantages in many function blocks. A prototype 150 W, universal ac input, 12 <inline-formula><tex-math notation="LaTeX">$\rm V_{\text{dc}}$</tex-math></inline-formula> output, isolated multitrack PFC system with a power density of 50 W/in<inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula>, and a peak end-to-end efficiency of 92% has been built and tested to verify the effectiveness of the multitrack PFC architecture.
Face Identification System by merging Reflectance and Illumination features

among the numerous biometric systems existing in the literature, face identification systems have received a considerable interest in latest years. This paper presents a novel approach to face-feature extraction based on the Adaptive Single scale Retinex algorithm (ASSR) and the Gabor filter-bank. The ASSR has been used to extract the illumination (I-image) and the reflectance images (R-image) from each original face image. NIimage (normalization illumination image) has been obtained by eliminating the uneven lighting from the I-image using morphological operations. Then, the Gabor filter bank is applied on the NI-image and the reflectance images to extract feature vectors of these images. These features have been concatenated to make a huge feature vector of every user. While in the next step PCA + LDA technique has been employed to reduce the dimensionality of these novel feature vectors of every user and to further improve its discriminatory power. Finally, the nearest neighbor classifier with cosine Mahalanobis distance has been used for matching and decision stages respectively. Experimental results demonstrate that the proposed system reaches better results than the existing in the state-of-the-art systems.