Miracle-dz/UniFAR-FTUs huggingface dataset:
Broadband Dual-Polarized Waveguide Slot Filtenna Array With Low Cross Polarization and High Efficiency

A broadband waveguide slot filtenna array with low cross polarization and high efficiency is presented for dual-polarization applications. The vertical polarization radiation is achieved by offsetting longitudinal slots on the ridged waveguide, while the horizontal polarization radiation is achieved by V-shaped slots on the ridged waveguide. Two polarization ridged waveguide linear arrays combine to obtain dual-polarized radiation with cross-polarization levels under −43 dB in wide angles. To extend the bandwidth of the array, each <inline-formula> <tex-math notation="LaTeX">$1 \times 16$ </tex-math></inline-formula>-element linear array is separated into two <inline-formula> <tex-math notation="LaTeX">$1 \times 8$ </tex-math></inline-formula>-element arrays, which are fed by a two-way ridged waveguide divider via H-shaped slot couplings. The evanescent-mode ridged waveguide filters are integrated into a two-way ridged waveguide divider that suppresses the out-of-band signal interference. The signals of the interference bands are suppressed effectively, which are −25 dB lower than that of the operation band. Furthermore, 16 pairs of dual-polarized linear arrays are designed, fabricated, and measured. The results show that the array has perfect properties such as bandwidth, polarization purity, efficiency, and immunity to interference.
Cifar-10 Image Classification with Convolutional Neural Networks for Embedded Systems

Convolutional Neural Networks (CNN) have been successfully applied to image classification problems. Although powerful, they require a large amount of memory. The purpose of this paper is to perform image classification using CNNs on the embedded systems, where only a limited amount of memory is available. Our experimental analysis shows that 85.9% image classification accuracy is obtained by our framework while requiring 2GB memory only, making our framework ideal to be used in embedded systems.
Sonar-to-Satellite Translation using Deep Learning

Sonar images pose hindrances when being elucidated for applications such as underwater navigation and localization. On the other hand, satellite images are simpler to be interpreted, but require GPS that is unavailable underwater due to absorption phenomena. Thus, we propose a neural network capable of translating an acoustic image acquired underwater to a textured image. We called the process sonar-to-satellite translation. We adopted a state-of-the-art neural architecture on a dataset comprised of sonar data and their respective satellite images. The experimental results show our method can extract interesting features from acoustic images and generate an informative texture image.
Flexible Selecting of Style to Content Ratio in Neural Style Transfer

Humans have created many pioneers of art from the beginning of time. There are not many notable achievements by an artificial intelligence to create something visually captivating in the field of art. However, some breakthroughs were made in the past few years by learning the differences between the content and style of an image using convolution neural networks and texture synthesis. But most of the approaches have the limitations on either processing time, choosing a certain style image or altering the weight ratio of style image. Therefore, we are to address these restrictions and provide a system which allows any style image selection with a user defined style weight ratio in minimum time possible.
Distributed Optimization with Dynamic Event-Triggered Mechanisms

In this paper, we consider the distributed optimization problem, whose objective is to minimize the global objective function, which is the sum of local convex objective functions, by using local information exchange. To avoid continuous communication among the agents, we propose a distributed algorithm with a dynamic event-triggered communication mechanism. We show that the distributed algorithm with the dynamic event-triggered communication scheme converges to the global minimizer exponentially, if the underlying communication graph is undirected and connected. Moreover, we show that the event-triggered algorithm is free of Zeno behavior. For a particular case, we also explicitly characterize the lower bound for inter-event times. The theoretical results are illustrated by numerical simulations.
A Filtering Patch Antenna With Reconfigurable Frequency and Bandwidth Using F-Shaped Probe

A wideband filtering patch antenna is investigated in this paper. The patch antenna is fed by an F-shaped probe, which consists of one upper and two lower horizontal arms, as well as a vertical arm that connects them. Owing to the novel excitation scheme, two probe modes along with a patch mode are simultaneously excited within the passband, producing a wide bandwidth of 21.3% with stable antenna gains and radiation patterns. Meanwhile, cross-coupling is constructed in the antenna, generating two symmetrical radiation nulls right at the two sides of the passband. Consequently, a compact filtering antenna with bandpass response and high selectivity is obtained, without utilizing any extra filtering circuit. This design is also extended to realize a reconfigurable filtering antenna. Two varactor diodes are embedded in the F-shaped probe to continuously tune the frequency of the operating band from 2.05 to 2.52 GHz. The antenna bandwidth can be flexibly tuned from 2.2% to 21.3%, and simultaneously good filtering performance is kept during the tuning of different states.
Reliability Testing and Stress Measurement of QFN Packages Encapsulated by an Open-Ended Microwave Curing System

In this paper, the influence of microwave curing on the reliability of a representative electronic package is examined by reliability testing and measurement of residual stresses. A LM358 voltage regulator die was mounted to an open quad flat no-leads (QFN) package for reliability testing. For the stress measurement, a specifically designed stress measurement die was mounted to the QFN package. The chips were encapsulated with Hysol EO1080 thermosetting polymer material. Curing was performed using an open-ended microwave oven system equipped with in situ temperature control. Three different temperature profiles for microwave curing were selected according to the requested degree of cure and chemical composition of the cured material. A convection cure profile was selected for the control group samples. Temperature cycle tests and highly accelerated stress tests (HAST) were performed on a total number of 80 chips. Ninety-five QFN packages with stress measurement chips were also manufactured. The increased lifetime expectancy of the microwave-cured packaged chips was experimentally demonstrated and measured between the 62% and 149% increased lifetime expectancies after temperature cycle test (TCT), and between 63% and 331% after a HAST and TCT compared to conventionally cured packages. The analysis of specifically designed stress test chips showed significantly lower residual stresses ranging from 26 to 58.3 MPa within the microwave-cured packages compared to conventionally cured packaged chips, which displayed residual stresses ranging from 54 to 80.5 MPa. Therefore, this paper provides additional confidence in the industrial relevance of the microwave curing system and its advantages compared to the traditional convection oven systems.
Research of Single-Event Burnout and Hardening of AlGaN/GaN-Based MISFET

This brief first time presents single-event burnout (SEB) simulation results for conventional AlGaN/ GaN gate field plate MISFET (GFP-C MISFET), simultaneously, a hardened MISFET with electrode connected doped plugs in the buffer (EC-DP MISFET) is proposed for the first time. The SEB triggering mechanisms contain the back-channel effect and following impact ionization dominated by electron in the high field region. By comparing the simulation results from the GFP-C MISFET and proposed hardened EC-DP MISFET, the carriers induced by heavy ion can be quickly absorbed to drain and source electrode through EC-DP, so that the proposed EC-DP MISFET can achieve better SEB performance than conventional one. With a heavy ion having the linear energy transfer value of 0.6-pC/ $\mu \text{m}$ striking vertically, SEB threshold voltage obtained in GFP-C MISFET and hardened EC-DP MISFET is 280 and 338 V, respectively.
Predictive Direct Torque Control Based on New Formulation and Fuzzy Logic for Permanent Magnet Synchronous Machine

This paper presents an alternative control strategy of permanent magnet synchronous motor (PMSM) drive, so called Model Predictive Direct Torque Control (MPDTC), its principle based on model system, prediction components and optimization problem. Then thus actuate the inverter. Conventional (MPDTC) both electromagnetic torque and stator flux are evaluated in the cost function. However, due to computation stator flux reference and prediction of stator flux in the next cycle, the control system has become more complicated. In this study the control algorithm successfully reduced by selected electromagnetic torque and daxis current as control variables. In addition, to improve the performance of speed tracking and increase the robustness against disturbance and uncertainties a PI-fuzzy logic controller is adopted in external speed loop where the conventional PI controller suffers from the parameters’ variation. The obtained results proved the effectiveness of this strategy with encouraging performance which is manifested clearly when the suggested controller employed.
Virtual Reality meets Degas: an immersive framework for art exploration and learning

The recent increase in the spreading of Virtual Reality allows a large number of consumers to experience this technology in several fields, from gaming to learning. In this paper we propose a framework that allows the user to freely move and interact in a virtual museum implemented with the HTC Vive. The goals of this work are: to investigate the possibility of using this approach for providing the user with a personalized and educative exploration of an artistic content, and to measure the immersivity and usability of the proposed system by means of a subjective experiment.
Flocking-Inspired Transmission Power Control for Fair Resource Allocation in Vehicle-Mounted Mobile Relay Networks

The Cucker–Smale flocking model is designed to analyze the velocity and position-control mechanisms in a group of living individuals, such as a flock of birds. In this model, each agent (a bird or fish) adjusts its velocity according to its own velocity and the weighted mean of the relative velocities of the other members of the flock. We herein focus on the simple, distributed, and autonomous nature of the flocking model and apply its operating principles to fair resource allocation for vehicle-mounted mobile relays (VMRs) by considering their need for distributed fair resource allocation in various network topologies. By adaptively adjusting the VMR transmission power, the proposed method effectively alleviates the interference between users in vehicles and users directly connected to the base station. We evaluate the performance of the proposed method in the context of the IEEE 802.16j based system. The achievable bandwidth performance of the proposed method is evaluated relative to that of both the fixed power allocation (FPA) method and the signal-to-interference plus noise ratio (SINR)-based transmit power control method. The result shows that the proposed method adjusts to various network topologies and provides better achievable bandwidth than both the SINR-based method and the FPA method.
Stability and optimality of multi-scale transportation networks with distributed dynamic tolls

We study transportation networks controlled by dynamical feedback tolls. We consider a multiscale transportation network model whereby the dynamics of the traffic flows are intertwined with those of the drivers' route choices. The latter are influenced by the congestion status on the whole network as well as dynamic tolls set by the system operator. Our main result shows that a broad class of decentralized congestion-dependent tolls globally stabilise the transportation network around a Wardrop equilibrium. Moreover, using dynamic marginal cost tolls, stability of the transportation network can be guaranteed around the social optimum traffic assignment. This is particularly remarkable as the considered decentralized feedback toll policies do not require any global information about the network structure or the exogenous traffic load on the network or state and can be computed in a fully local way. We also evaluate the performance of these feedback toll policies both in the asymptotic and during the transient regime, through numerical simulations.
Towards Designing Optimized Low Power Reversible Demultiplexer for Emerging Nanocircuits

Reversible Logic plays a vital role in the field of nanotechnology and quantum computing. One of the most challenging issues in circuit design is power consumption. Reversible logic is one of the ways to optimize power consumption. This paper presents an efficient approach for designing optimized reversible demultiplexer which consumes less power. A generalized approach is proposed to design 1-to -n reversible demultiplexer. Theorem on the numbers of gates, garbage outputs and quantum cost has been presented. The comparative results show that the proposed design is much better than the existing designs in terms of garbage outputs, gate counts and quantum cost. The proposed reversible demultiplexer also requires less power and less area than existing demultiplexers. The proposed design is simulated in DSCH 3.5 software which verifies the correctness of the proposed circuit.
Maximizing D2D-Based Offloading Efficiency With Throughput Guarantee and Buffer Constraint

Device-to-device (D2D) based offloading stands out as a promising approach to mitigate the pressure over cellular networks. From the perspectives of both operators and users, an ideal solution is expected to maximize the offloading efficiency (ratio) while guaranteeing the quality-of-service (QoS) of users. However, existing approaches seldom jointly take into consideration offloading efficiency optimization and QoS guarantee. In this paper, we study how to maximize the percentage of total offloaded traffic of mobile content downloading via D2D, while guaranteeing the user minimum throughput under limited buffer size. The major challenge is how to incorporate all potential D2D opportunities to maximize the offloading performance. We propose a spatial–temporal dynamic graph (STDG) to capture all possible D2D opportunities in D2D-based traffic offloading. Based on STDG, we mathematically formulate an optimization problem of maximizing the offloading efficiency, subject to throughput requirement and buffer size constraint. The formulated problem is a linear-fractional problem and can be transformed into an equivalent linear program, whose solution reveals the maximum offloading ability that D2D achieves under QoS satisfaction. Trace-based simulations validate the superiority of our approach over three benchmark approaches.
Robust Constrained Interpolating Control of Interconnected Systems

This paper presents a decentralised interpolating control scheme for the robust constrained control of uncertain linear discrete-time interconnected systems with local state and control constraints. The control law of each distinct subsystem relies on the gentle interpolation between a local high-gain controller with a global low-gain controller. Both controllers benefit from the computation of separable robust invariant sets for local control design, which overcomes the computational burden of large-scale systems. Another advantage is that for each subsystem both low-and high-gain controllers can be efficiently determined off-line, while the inexpensive interpolation between them is performed on-line. For the interpolation, a new low-dimensional linear programming problem is solved at each time instant. Proofs of recursive feasibility and robust asymptotic stability of the proposed control are provided. A numerical example demonstrates the robustness of decentralised interpolating control against model uncertainty and disturbances. The proposed robust control is computationally inexpensive, and thus it is well suited for large-scale applications.
Closed-Circuit Versus Open-Circuit Characterization of Hard Magnets

Two effects must be considered for the purpose of comparing hysteresis loops of hard magnetic materials (e.g., Nd–Fe–B magnets), measured in open circuit (e.g., vibration sample magnetometer or extraction magnetometer) to those measured in closed circuit (i.e., in a hysteresigraph). The first of these is related to the influence of magnetic viscosity during magnetization reversal, which reduces coercivity. Due to longer measurement times with magnetometers compared to hysteresigraphs (typically 100 s versus 1 s), the coercive field is reduced more in open-circuit measurements than in closed-circuit ones, by typically 20 kA/m. The second effect to be considered is due to differences in various contributions to the demagnetizing field. The slope of the magnetization variation in open-circuit loops is different to that due to the usually considered demagnetizing effects (applicable to soft materials). A reduction in the open-circuit coercive field value compared to the closed-circuit one is found, which is of the order of 25 kA/m for the systems discussed here. This is attributed to the fact that the reversal process itself is affected by how the measurement is made. Reversal has less collective character during open-circuit than during closed-circuit measurements.
Neural Network-Based AILC for Nonlinear Discrete-Time System with Iteration Varying Initial Error and Reference Trajectory

Based on the neural network approximator, an online adaptive iterative learning control (AILC) algorithm is proposed for a class of nonlinear discrete-time system. Here, the random iteration initial error and trajectory reference are considered. The nonlinear system is transformed to a predictor form, and the desired control signal is thus achieved by implicit function theorem. A neural network using only a norm adaptation law is utilized to approximate this desired control signal iteratively. By using Lyapunov analysis, it is proven that all the system signals are bounded and the system tracking error converges to a neighborhood around zero as iteration number goes to infinity. In contrast to the existing AILC results, the most advantage of the proposed neural network-based AILC is that the number of the adjustable parameters is highly reduced.
Event- Triggered Consensus of Multi-Agent Systems on Strongly Connected Graphs

This paper studies consensus control of linear multi-agent systems (MASs) over directed graphs. The communication topology is assumed to be strongly connected. A distributed event-triggered state-feedback protocol is proposed for achieving consensus, which monitors the relative state information between neighboring agents and triggers a sampling event when some state-related inequalities are violated. An existence condition for the protocol is derived, which needs to solve an algebraic Riccati equation and find some scalar parameters related to the graph Laplacian. It is proved that the protocol excludes both singular triggering behavior and Zeno behavior if some scalars are properly assigned. Compared with the existing results, the proposed method can deal with general linear MASs on strongly connected graphs but does not require the absolute state information of agents. The effectiveness of the proposed method is illustrated by a numerical example.
Fast Optical Flow Extraction From Compressed Video

We propose the fast optical flow extractor, a filtering method that recovers artifact-free optical flow fields from HEVC-compressed video. To extract accurate optical flow fields, we form a regularized optimization problem that considers the smoothness of the solution and the pixelwise confidence weights of an artifact- ridden HEVC motion field. Solving such an optimization problem is slow, so we first convert the problem into a confidence-weighted filtering task. By leveraging the already-available HEVC motion parameters, we achieve a 100-fold speed-up in the running times compared to similar methods, while producing subpixel-accurate flow estimates. The fast optical flow extractor is useful when video frames are already available in coded formats. Our method is not specific to a coder, and works with motion fields from video coders such as H.264/AVC and HEVC.
Extrinsic Noise Suppression in Micro RNA mediated Incoherent Feedforward Loops

MicroRNA mediated incoherent feed forward loops (IFFLs) are recurrent network motifs in mammalian cells and have been a topic of study for their noise rejection and buffering properties. Previous work showed that IFFLs can adapt to varying promoter activity and are less prone to noise than similar circuits without the feed forward loop. Furthermore, it has been shown that microRNAs are better at rejecting extrinsic noise than intrinsic noise. This work studies the biological mechanisms that lead to extrinsic noise rejection for microRNA mediated feed forward network motifs. Specifically, we compare the effects of microRNA-induced mRNA degradation and translational inhibition on extrinsic noise rejection, and identify the parameter regimes where noise is most efficiently rejected. In the case of static extrinsic noise, we find that translational inhibition can expand the regime of extrinsic noise rejection. We then analyze rejection of dynamic extrinsic noise in the case of a single-gene feed forward loop (sgFFL), a special case of the IFFL motif where the microRNA and target mRNA are co-expressed. For this special case, we demonstrate that depending on the time-scale of fluctuations in the extrinsic variable compared to the mRNA and microRNA decay rates, the feed forward loop can both buffer or amplify fluctuations in gene product copy numbers.
Hybrid 3D Hand Articulations Tracking Guided by Classification and Search Space Adaptation

We propose a novel method for model-based 3D tracking of hand articulations that is effective even for fastmoving hand postures in depth images. A large number of augmented reality (AR) and virtual reality (VR) studies have used model-based approaches for estimating hand postures and tracking movements. However, these approaches exhibit limitations if the hand moves rapidly or into the camera's field of view. To overcome these problems, researchers attempted a hybrid strategy that uses multiple initializations for 3D tracking of articulations. However, this strategy also exhibits limitations. For example, in genetic optimization, the hypotheses generated from the previous solution may search for a solution in an incorrect search space in a fast-moving hand gesture. This problem also occurs if the search space selected from the results of a trained model does not cover the true solution although the tracked hand moves slowly. Our proposed method estimates the hand pose based on model-based tracking guided by classification and search space adaptation. From the classification by a convolutional neural network (CNN), a data-driven prior is included in the objective function and additional hypotheses are generated in particle swarm optimization (PSO). In addition, the search spaces of the two sets of the hypotheses, generated by the data-driven prior and the previous solution, are adaptively updated using the distribution of each set of the hypotheses. We demonstrated the effectiveness of the proposed method by applying it to an American Sign Language (ASL) dataset consisting of fast-moving hand postures. The experimental results demonstrate that the proposed algorithm exhibits more accurate tracking results compared to other state-of-the-art tracking algorithms.