Miracle-dz/UniFAR-FTUs huggingface dataset:
Single- and Dual-Band Bandpass Filters Using a Single Perturbed SIW Circular Cavity

In this letter, single- and dual-band bandpass filters using a single perturbed substrate integrated waveguide (SIW) circular cavity are presented. The perturbed TM010, TM110 (a pair of degenerate modes), and TM210 resonant modes of the SIW circular cavity are used to design the proposed filters. The metallic vias are used to perturb the resonant modes. In addition, slot perturbations are introduced to the cavity to independently tune the center frequencies (CFs) of the passbands. Moreover, desired CFs and bandwidths of the passbands can be obtained by adjusting the perturbation elements. The designed filters are fabricated and measured to validate the present approach.
A Probabilistic HARQ Protocol for Demodulate-and-Forward Relaying Networks

This paper considers wireless communication protocols implementing both cooperation and hybrid automatic repeat request. Based on a simple example (one source, one relay, and one destination), we propose the use of probabilistic protocols as an alternative to classical, deterministic ones. Starting initially with the finite state Markov chain (FSMC) analysis of a deterministic protocol, the idea of probabilistic protocol comes as an association with the simplified FSMC of the deterministic protocol. This FSMC contains two parameters that can be optimized for finding the best performance. So far, probabilistic protocols have been proposed at higher layers of communication systems, while we consider here the physical and MAC layers. In our analysis, the relay works in the demodulate-and-forward mode, and we demonstrate that: 1) the FSMC analysis predicts accurately the performance of the actual system and 2) the performance of the probabilistic protocol (when the two parameters are carefully tuned) outperforms the performance of a reference deterministic protocol. The analysis is checked upon Monte Carlo simulations.
An Improved Pose Estimation Method Based on Projection Vector With Noise Error Uncertainty

Aiming at the problem of anomalous and non-independent distribution of the image errors in the feature-based visual pose estimation, a method of monocular visual pose estimation based on the uncertainty of noise error established by projection vector is proposed. First, by using the covariance matrix to describe the uncertainty of the feature point direction and integrating the uncertainty of the feature point direction into the pose estimation, characteristic point measurement error with different degrees of directional uncertainty can be adapted that can makes the algorithm robust. Then, by introducing the projection vector and combining the depth information of each feature point to represent the collinearity error, the model nonlinear problem caused by the camera perspective projection can be eliminated that can make the algorithm have global convergence. Finally, we use global convergence theorem to prove the global convergence of the proposed algorithm. The results show that the proposed method has good robustness and convergence while adapting to different degrees of error uncertainty, which can meet practical engineering applications.
A Quadruple-Polarization Reconfigurable Feeding Network for UAV RF Sensing Antenna

A quadruple-polarization reconfigurable feeding network for an unmanned aerial vehicle (UAV) RF sensing antenna operating at 915 MHz is presented. It consists of one single-pole four-throw (SP4T) switch, one 90° hybrid coupler, two 180° hybrid couplers, and two single-pole double-throw (SPDT) switches. By controlling the SP4T and SPDT switches, quadruple polarizations with two linear polarizations (vertical and horizontal) and two circular polarizations (right-hand and left-hand) were obtained. The proposed reconfigurable feeding network with quadruple polarization modes in the 902–928-MHz range has an insertion loss of approximately 2.5 dB including switch loss, and it achieves amplitude and phase variations of up to approximately 0.5 dB without divided power or switch loss, and 3°, respectively. Using the proposed feeding network, a circular low-profile UAV RF sensing antenna with quadruple polarizations was designed. Its electrical size, 10-dB impedance bandwidths, and a peak gain are approximately $0.57\lambda _{0} \times 0.57\lambda _{0} \times 0.08\lambda _{0}$ at 915 MHz, 16%, and 6 dBi(c), respectively.
Analysis on Vibration Performance of Slit-Resonant Beam Based on BP Neural Network

Influence of different slit sizes on performance like the natural frequency of slit-resonant beam in a MEMS resonant pressure sensor were investigated. The neural network model is established and trained by sufficient data obtained by Finite Element Method, and is used to obtain effects of different slit sizes on the performance of slit- resonant beam, which verified the results obtained by the theoretical vibration model of double-clamped resonant beam with slit structure. The results reveal the different influences of slit size, which can be used as reference for design and optimization of the slit structure and sensor performances.
Survey of Directed Fuzzy Technology

The fuzzy testing technology can effectively detect vulnerabilities. Based on Directed Symbolic Execution (DSE) fuzzing and Directed Grey Box Fuzzing (DGF), which can reach the specified target locations and scan the vulnerability quickly and efficiently. This paper introduces the theoretical knowledge of directed fuzzy testing technology, and several state-of-the-art fuzzy testing tools to elaborate the principle, advantages, disadvantages and the prospect of directed fuzzy technology.
Dual Discriminator Generative Adversarial Network for Single Image Super-Resolution

Recently, several algorithms have been proposed to achieve the single image super-resolution by using deep convolutional neural networks. In this study, we present a dual discrimination generative adversarial network (D2GAN) for single image super-resolution (SISR). The proposed model has better stability to complete the reconstruction of super-resolution images for ×4 scale factor. The improved residual network and perceptual loss function are applied in the proposed algorithm which demonstrates a superior performance over state-of-the-art restoration quality. Meanwhile, the proposed reconstruction network has a faster training and convergence speed compared with other super-resolution methods. The proposed approach is evaluated on standard datasets and gets improved performance than previous works that based on deep convolutional neural networks.
Proposing Camera Calibration Method Using PPO (Proximal Policy Optimization) for Improving Camera Pose Estimations

This paper highlights camera orientation estimation accuracy and precision, as well as proposing a new camera calibration technique using a reinforcement learning method named PPO (Proximal Policy Optimization) in offline mode. The offline mode is used just for extracting the camera geometry parameters that are used for improving accuracy in real-time camera pose estimation techniques. We experiment and compare two popular techniques using 2D vision feedbacks and evaluate their accuracy beside other considerations related to real applications such as disturbance cases from surrounding environment and pose data stability. First, we use feature points detection ORB (Oriented FAST and Rotated BRIEF) and BF (Brute-Force) matcher to detect and match points in different frames, respectively. Second, we use FAST (Features from Accelerated Segment Test) corners and LK (Lucas-Kanade) optical flow methods to detect corners and track their flow in different frames. Those points and corners are then used for the pose estimation through optimization process with the: (a) calibration method of Zhang using chessboard pattern and (b) our proposed method using PPO. The results using our proposed calibration method show significant accuracy improvements and easier deployment for end-user compared to the pre-used methods.
Anomaly Detection for Satellite Telemetry Series with Prediction Interval Optimization

Anomaly detection is an essential task enabling condition-based maintenance (CBM), fault diagnostics and system prognostics of complex satellite systems. With the advantages of requiring no labels and strong online detecting performance, some prognostics methods have been applied to detect anomalies among satellite telemetry series. Especially, the probability prediction models, e.g., Gaussian Process Regression and Relevance Vector Machine, are more suitable to identify these discords with the ability of providing uncertainty presentation. However, the prediction interval (PI) is derived by the optimization on the prediction-related index which is not constructed for the application of anomaly detection, and its performance will be discounted in this case. Thus, this paper introduces the detection-related index, i.e., coverage-width-based criterion (CWC), to optimize the model parameters which focus on the width of PI, as well as the coverage probability of PI. And the experiments on different types of telemetry series validate the improvement of our proposed framework on PI. Finally, the experiment on actual satellite telemetry series with abnormal points validate the effectiveness and applicability of our proposed method.
KF-Based Active Modeling for a Quadrotor Slung Load System

The control of quadrotor slung load (QSL) systems is a challenging task as a result of some distinguishing features like the multiple nonlinearities and time-varying load swing. To design an effective control scheme, the top priority is to establish an excellent model. In this paper, a practical active modeling method based on the Kalman Filter (KF) for a QSL system is developed to compensate uncertainties in system dynamics. First, a simplified quadrotor model is formulated as the reference model. Second, modeling errors are introduced and the KF is adopted to actively estimate the errors involved in the reference model. Finally, some experiments are conducted to validate the remarkable performance of the proposed modeling strategy in terms of uncertainties as well as disturbances in the dynamic model.
Geometry and Material Optimization of a Soft Pneumatic Gripper for Handling Deformable Object

Handling deformable object by a soft robotic gripper presents a challenging task due to the complex interaction between the gripper and the object. This paper focuses on investigating optimal design of a soft pneumatic gripper for handling deformable object. Two geometrical parameters of the soft finger were investigated and minimizing the deformation of the grasping target was considered as the objective. Finite element (FE) simulation was iterated until the optimal parameters were found. The optimized geometry of the soft finger was then used to investigate the optimal material property following the same process. Soft fingers with different designs and different materials were fabricated and experimentally tested to validate the optimization results. Finally, results suggested that optimal design could reduce the deformation on the target and softer material is more preferable and energy efficient for handling deformable objects.
INDUCTION OF HYPERTENSION BY VARIOUS ANIMAL MODELS

Blood pressure is the pressure of the blood flowing through our blood vessels against vessel walls. It depends on the blood pumped from the heart and elasticity of the blood vessels and varies with age and activity. Hypertension is defined as a diastolic blood pressure of 90mm Hg or higher and systolic blood pressure of 140mm hg or higher. Experimental models of human disease are used to study pathophysiological factors involved in hypertension and assess antihypertensive agents. Today different strains of rats with genetic hypertension are available and in most laboratories, therapeutic studies on hypertension are carried out on these models.
A Novel Unsymmetrical Coupling Structure Based on Concentrated Magnetic Flux for High-Misalignment IPT Applications

The capability of misalignment tolerance is vital for an inductive power transfer system. This paper proposes a novel unsymmetrical coupling structure that can remarkably enhance the output voltage stability with respect to the horizontal, vertical, and angular displacements. The core of this structure is concentrated magnetic field. The optimization method to derive the optimal loosely coupled transformer providing the strongest tolerance to misalignment and fulfilling the requirement of the minimum coupling coefficient is presented. The proposed structure is compared with planar circular and square coils and double-D (DD) coupler from the perspectives of magnetic coupling and anti-misalignment capability. A 200-W prototype is finally fabricated, where the primary series, secondary series-parallel compensation topology is employed to further improve the system's tolerance to misalignment. A maximum output voltage ripple factor of 2.83% and a minimum system efficiency of 91.9% are achieved when the horizontal displacement is as high as one-third of the coupling coil.
A Parallel Community Detection in Multi-Modal Social Network With Apache Spark

A constrained latent space model (CLSM) infers community membership based on two modalities in multi-modal social network data: network topology and node attributes. In this paper, we extend our previous model, CLSM in two ways. First, we introduce the Spark implementation of CLSM for parallel computation by fitting the inference algorithm into the map-reduce framework. Second, we consider user reputation besides the network homophily, which also affects social interactions between users. We test CLSM and its extension on two real-world problems: understanding link and user attributes in the location-based social network, and a review-trust network. Our proposed models in Spark can be easily deployed on commercial cloud services, such as Google cloud or Amazon web service and find latent community membership in large-scale datasets. We perform extensive experiments on real-world datasets and show how CLSM extension improves our previous model. We also share meaningful insights we discovered with the datasets.
Tunable Branchline Coupler Using Microfluidic Channels

A branchline coupler is presented that uses 12 microfluidic channels (3 per branch) to tune its center frequency. A lumped-element equivalent circuit model (ECM) is extracted for the fluidic channels and is used to predict the response of the coupler prior to fabrication. The circuit is realized on a 1.524-mm-thick Rogers 4003C substrate with <inline-formula> <tex-math notation="LaTeX">$\epsilon _{r} = 3.55$ </tex-math></inline-formula>. The microfluidic channels are milled through the ground backplane and are filled with ethyl acetate <inline-formula> <tex-math notation="LaTeX">$(\mathrm {C_{4}H_{8}O_{2}})$ </tex-math></inline-formula>, for which <inline-formula> <tex-math notation="LaTeX">$\epsilon _{r}=6.00$ </tex-math></inline-formula>. Experiments with the coupler show that starting with empty (i.e., air-filled) channels and progressively filling them with the fluid, the center frequency of the coupler can be tuned from 2.19 to 1.80 GHz, yielding a tuning range of 19.5%. A comparison between the modeled and measured results shows that the ECM predicted the coupler tuning range with an error below 2.8%.
RAIS - Remotely Accessible Irrigation System

In the past centennial agriculture has been a crucial aspect of living. As the world's population is increasing more food production is required than ever before. But current methods that are used for agriculture are not sustainable to match up with the escalating demands. Advancement in methodologies is a big need. Hence as automation is done in different sectors, it can be applied to agriculture as well to provide a better tool for producing crop more proficiently. This paper is trying to give an efficacious solution which reduces human efforts, enhances quality and precision in farming and related areas. We are developing a remotely accessible irrigation system using with which any person can know the status of his plantations/crops from anywhere. High charts used as medium of information representation gives a much clearer picture of the crop conditions directly to the user.
Control of Energy Storage System Integrating Electrochemical Batteries and Supercapacitors for Grid-Connected Applications

The implementation of ancillary services in renewable energy based generation systems requires controlling bidirectional power flow. For such applications, integrated energy storage systems (ESSs) in such generation platforms have emerged as a promising solution. However, a large variety of ESS solutions are available in the market, and even hybrid systems are proposed to optimize the overall performance. In this regard, the integration of supercapacitors (SCs) and electrochemical batteries is an attractive and feasible solution, as it takes the most of the combination of the large storage capacity of batteries with the high dynamic performance of SCs. This combination enables the implementation of energy and power services, such as inertia emulation, black start, or power oscillation damping among others. This solution avoids oversizing ESS and enlarges the battery lifetime, since the SCs absorve the high dynamic response meanwhile the battery feeds an attenuated power profile. This paper presents a strategy to manage mixed energy storage technologies, composed by a direct connection of a battery and an SC bank interfaced through a dc–dc converter. A power control loop distributes the power flow through each element in order to achieve an optimized performance, meanwhile, it permits to provide grid-frequency support and minimizes the number of cycles applied to the battery. The analysis made and the initial sections are endorsed by simulation and experimental results collected in a 50-kW test-bench.
Toward a Biologically Plausible Model of LGN-V1 Pathways Based on Efficient Coding

Increasing evidence supports the hypothesis that the visual system employs a sparse code to represent visual stimuli, where information is encoded in an efficient way by a small population of cells that respond to sensory input at a given time. This includes simple cells in primary visual cortex (V1), which are defined by their linear spatial integration of visual stimuli. Various models of sparse coding have been proposed to explain physiological phenomena observed in simple cells. However, these models have usually made the simplifying assumption that inputs to simple cells already incorporate linear spatial summation. This overlooks the fact that these inputs are known to have strong non-linearities such the separation of ON and OFF pathways, or separation of excitatory and inhibitory neurons. Consequently these models ignore a range of important experimental phenomena that are related to the emergence of linear spatial summation from non-linear inputs, such as segregation of ON and OFF sub-regions of simple cell receptive fields, the push-pull effect of excitation and inhibition, and phase-reversed cortico-thalamic feedback. Here, we demonstrate that a two-layer model of the visual pathway from the lateral geniculate nucleus to V1 that incorporates these biological constraints on the neural circuits and is based on sparse coding can account for the emergence of these experimental phenomena, diverse shapes of receptive fields and contrast invariance of orientation tuning of simple cells when the model is trained on natural images. The model suggests that sparse coding can be implemented by the V1 simple cells using neural circuits with a simple biologically plausible architecture.
Intelligent Fault Diagnosis of Rotating Machinery Based on Deep Recurrent Neural Network

Intelligent fault diagnosis methods of rotating machinery have attracted much attention in recent years. In this paper, an intelligent deep learning based method named deep recurrent neural network (DRNN) is proposed. Firstly, frequency spectrum sequences are adopted as inputs to reduce the input size. Then DRNN is constructed by the stacks of the recurrent hidden layer to automatically extract the features from the input spectrum sequences. Finally, softmax classifier is applied for fault recognition. The proposed method is verified with the experimental data, and the results confirm that the proposed method is more effective than traditional intelligent fault diagnosis methods.
Robust Global Localization by Using Global Visual Features and Range Finders Data

Global localization is a challenging problem of using sensor data to estimate the pose of a robot in an environment when the starting pose is unknowm. The conventional probabilistic algorithms called Monte Carlo Positioning (MCL) is one of the most popular methods to solve this problem. MCL algorithms use a set of weighted particles to approximate the distribution probability of where the robot is located and it requires a wandering motion to converge to a single, high likelihood pose during global localization. Sometimes this wandering motion is not allowed in actual industrial applications. This paper presents a framework which incorporates image-based localization module into a conventional MCL algorithm. The core module in our proposed approach is called Double Re-localization Decision Process (DRDP) by performing two selection of relocation decisions before and after the pose update process with two different sensor sources. A compact global descriptor is used for fast image association and a scan matching using vanilla ICP (Iterative Closest Point) of Point-to-line metric is applied to obtain further pose of the proposal candidate. Several experiments are designed to verify the effectiveness of the our approach in indoor environment.