Miracle-dz/UniFAR-FTUs huggingface dataset:
An Image Orientation Estimation Method Based on Gradient Histogram and Point Matching

In recent years, the application of UAV has been more and more widespread. Attitude estimation as the main research direction of the control system has attracted much attention from industrial and academic area. The performance of traditional sensor fusion method is degraded significantly when the system is placed in high noise environments. Vision based estimation method, which depends on image geometry, has high robustness in complex environment. In this paper, we propose an image orientation estimation method based on gradient orientation histogram and point matching. In order to illustrate the performance of the proposed method, several simulations have been carried out. The first experiment is orientation estimation based on different point matching methods. The results are feasible, but contain a big error gap between different feature descriptor methods. The real-time algorithm has a large error because of the bad matching samples. Therefore a measurement method based on gradient orientation histogram is proposed to correct the results of real-time point matching orientation estimation method. This method use the output of gradient orientation histogram filter the results of pure point matching orientation estimation. The experiment results show that the proposed method can effectively shrink the error of real-time point matching orientation estimation.
Hp-adaptive Pseudospectral Convex optimization for Rocket Powered Landing Trajectory Planning

Rocket powered landing trajectory planning is a typical non-convex and non-smooth optimization problem. To solve this problem, a convex optimization method based on hp-adaptive pseudospectral method is proposed. Combining the advantages of these two methods, the proposed method can quickly and accurately obtain the optimal trajectory. Firstly, the original non-convex optimal control problem is transformed into a convex optimal problem by lossless convexification and change of variables convexifying the non-convex thrust constraint and nonlinear dynamics respectively. Then, t Then, the hp adaptive pseudospectral method is used to discretize the time-continuous problem, in which the position of the discrete points can be adaptively adjusted to the control discontinuities according to the curvature of the state variable. Finally, the original non-convex problem can be transformed into a second-order cone programming problem, and the highly efficient interior point method can be used to quickly solve the problem. Numerical simulation results show that the proposed method can quickly solve the non-smooth trajectory optimization problem and accurately capture the control discontinuities.
Jamming-Assisted Legitimate Surveillance of Suspicious Interference Networks With Successive Interference Cancellation

This letter investigates legitimate surveillance of a suspicious interference network in the presence of a full-duplex monitor. To improve the surveillance performance, jamming from the monitor is applied to interfere with the suspicious users and successive interference cancellation (SIC) decoder is equipped at the monitor to better decode signals from multiple suspicious users. To maximize the number of successfully eavesdropped suspicious users, the jamming power as well as the SIC decoding order at the monitor are optimized. By exploring the particular problem structure, high-complexity optimal and low-complexity suboptimal algorithms are proposed. It is shown that the suboptimal algorithm is practically optimal and the proposed algorithms outperform the benchmark algorithms with conventional SIC decoding order.
A Very Robust and Reliable 2.7GHz +31dBm Si RFSOI Transistor for Power Amplifier Solutions

A robust and low cost Si RFSOI Power Transistor which can deliver +31dBm output power with 74% of Power Added Efficiency (PAE) and 18dB of Gain has been optimized for 4G & 5G sub-6GHz Power Amplifier (PA). By means of innovative characterizations combining RF aging tests and modeling, it is proved that this great performance can be achieved while maintaining a very high level of reliability of the PA transistor.
Security Vulnerability Assessment of SCADA System for Evidence-Based Reasoning and Bayesian Network

To assess the information security vulnerability of the wind farm SCADA system quantitatively, this paper proposes an evaluation method based on evidence-based reasoning and Bayesian network. Firstly, Analyze the wind farm SCADA system and establish an evidence-based reasoning model for system vulnerability. Secondly, based on the Bayesian theorem, the link model is used to link the function to construct the fragile Bayesian network of each path to the inference model. Then, using AHP and Bayesian inference to quantify the nodes in the inference model, the conditional probability between nodes and the posterior probability of the nodes is calculated. The hypothesis probability is used as the criterion for vulnerability assessment, and the vulnerabilities of node, path, and whole system are obtained. Finally, the simulation results prove that this method helps managers to assess the information security vulnerability of SCADA system, and then take effective defense measures.
Educational Robotics and Computational Thinking Development in Preschool

Programming robots playing with the environment is an ongoing research project aimed at developing a robot and intervention program capable of enhancing young children's cognitive abilities and computational thinking skills. Using a robot sensitive to color and distance, children are able to use everyday objects in order to interact with it and program its movements. An eleven session intervention program using this robot was designed with the objective of creating playful learning instances were children would practice abilities associated with what is generally thought of as computational thinking. We'll assess children's computational thinking, fluid intelligence, working memory, planning, sequencing, number recognition and magnitude comparison, vocabulary, mental rotation, inhibitory control and cognitive flexibility to test for cognitive effects of the intervention. An active control in which children interact with the robot by remotely controlling it was used.
From Wireless Personal Area Network (WPAN) to Long Range (LoRa) Technology

In the wireless communication, with the increasing demand of internet, video and data sharing, higher communication speed and capacity communication technologies have developed. In this stage, the fact that the frequency band is not an infinite size source is an important problem. Problems arise when sharing this limited resource by different communication technologies. As a result of different distance needs of the users in wireless communication, the development of short and long range technologies has become inevitable. This study is based on technical features and advantages and disadvantages of Long Range technology according to Wireless Personal Area Networks (WPAN). It examines the two technologies comparatively and presents their characteristics.
Conversion of Artificial Neural Network to Spiking Neural Network for Hardware Implementation

Spiking neural networks (SNNs) are potentially an efficient way to reduce the computation load as well as the power consumption on edge devices because of the sparsely activated neurons and event-driven behavior. In this paper, a continuous-valued artificial neural network (ANN) with fully connections is equivalently converted into spiking operations and the parameters are quantized to low resolution. With the proposed method, data bandwidth can be reduced and the algorithm is proved to be more useful and hardware-amenable on FPGAs. From the simulation results, the ANN with 8- and 4-bit weights received accuracy drop of 0.3% and 0.6%, respectively. The conversion of the quantized ANN to SNN received acceptable error drop within 0.15%.
Study on Unsteady Aerodynamic Characteristics of Bionic Flapping Wing Aircraft

This paper studies the aerodynamic characteristics of the composite motion of the wing during the flight of the bionic flapping wing. The dynamic hybrid mesh technique is used to solve the Nave Stokes (N-S) equation, and the changes of the lift resistance generated by the wing under different flapping amplitude, flapping frequency and chord torsion angle parameters are analyzed. By analyzing the aerodynamic efficiency of the wing, the optimal motion parameters of the flapping wing are obtained, which provides a theoretical basis for the design of the flapping bionic aircraft.
Centralized Voltage Signal-Based Fault Detection and Classification for Islanded AC Microgrid

Insignificant fault current in inverter-based islanded AC microgrids makes fault detection challenging. This paper introduces a voltage signal-based fault assessment method for islanded AC microgrids. It considers a new set of features such as instantaneous jumps in amplitude, phase angle and frequency of voltage signal for fault detection and classification. Hilbert transform is used to extract the above features in the time domain. The proposed method can detect any kind of disturbance occurring in the system including step changes in loads and generations and faults, within a cycle. It can also identify whether the detected disturbance is a fault or a non-fault disturbance, by considering the rate of change of frequency calculated over a cycle in addition to the instantaneous jumps in voltage. Fault type classification is performed with the introduced instantaneous features, where support vector machine and artificial neural network classifiers are trained separately, and their performances are tested with unknown test data. The proposed method is validated with balanced as well as unbalanced islanded AC microgrid systems. Further, the performance of the proposed method is verified with both clean and noisy data.
Brain Tumor Recognition Based on Data Augmentation and Convolutional Neural Network

The brain tumor is one of the most dangerous diseases at present. Accurate diagnosis of brain tumors can contribute to improving the prognosis conditions of patients. Existing methods have some shortcomings, such as manual extraction of features and insufficient amount of data. Since the convolutional neural network (CNN) can extract features automatically, we propose a deep Convolutional Neural Network to diagnose the brain tumors. In this paper, an automatic system based on CNN is proposed to classify three categories of brain Magnetic Resonance Images, including normal images, brain images with meningiomas and brain images with gliomas. Several steps, including image preprocessing, data augmentation and image classification, are applied to the original brain images. And the experiments show that the accuracy of the proposed system on testing set can reach 93.33%, which indicates that our model can achieve a comparable classification result.
Learning Sparse Support Vector Machine with Relaxation and Rounding

A sparse representation of Support Vector Machines (sparse SVMs) is desirable for many applications. However, for large-scale problems with high-dimensional features solving sparse SVMs remains a challenging problem, and most of the existing work are heuristic in that there are no performance guarantees and can't effectively control the trade-off between the sparsity and the accuracy of the decision hyperplane. To address this issue, we propose a new method for via relaxation and rounding, which obtains (ε,δ-approximate solution in Õ(n/εδ) time with probability at least 1-δ. Such regularization explicitly penalizes parameters different from zero with no further restrictions. We first show that learning sparse SVMs with ℓ_0 norm can be reformulated as an exactly Boolean program by introducing Boolean variables to each parameter. With dual and Boolean relaxation, this Boolean problem can be relaxed as a convex programming. For the ε-approximate solution of this convex programming, we get a feasible solution of the original problem without loss accuracy by a determined rounding. We analyze the proposed method in details and give a provable guarantee which is missing from the previous work. Experimental results on both synthetic data and real world data support our theoretical results and verify the validity of the proposed method.
New Concept of Multi-State Diplexers Implemented on Planar and Cavity Circuits

This paper presents our recently proposed multi-state diplexers (MSDs) implemented on different structures. The proposed MSDs have inherent property of multiple frequency channels, two of which can be used for duplexing mechanism by exciting one port as the input in each state. The MSDs have an attractive application in frequency hopping communication systems with miniaturization in their overall sizes. Then, three approaches to design the MSDs using different structures (planar triple-mode resonator, triple-mode cavity resonator and uniform/stepped impedance stub-loaded resonators) are presented. All the MSDs are finally fabricated and measured, the measurement are in good agreement with the simulation.
Optimizing SDN Control Plane Scalability by Efficient Switch to Controller Migration

Switch to multi controller migration is still a hot topic in SDN research field which requires a lot of factors to be considered. Among those factors is SDN controller scalability, migration stability and load balancing rate across all controllers. The focus of the paper is to present a new approach for optimizing SDN control plane scalability and controllers load balancing rate through efficient switch to controller migration by using self-balancing binary search trees.
Intelligent Aiming Method Based on Target Trajectory Prediction

The gun intelligent aiming system can calculate the aiming point real-timely for the stationary target, but for the moving target, the shooter still needs to estimate the preaiming position by himself. In order to realize the unmanned intelligent gun system, a smart aiming method based on target trajectory prediction was proposed in this paper. Firstly, we measure the target through sensors and video images, obtain the target motion trajectory and preprocess it to obtain the target space-time trajectory sequence. Secondly, we build an ARIMA model to predict the target path, decompose the trajectory into simple sub-trajectory, which are classified by moving state and stationary state. Finally, we use the prediction algorithm based on Kalman filter to predict the trajectory position of the target at the future moment in the sub-trajectory segment combined with the target motion trend estimation. We carried out simulation experiments to simulate different motion modes of the target and constructed time-space trajectory sequences for prediction. Experimental results demonstrated that the method proposed in this paper has high prediction accuracy.
Implementation of Chinese Reader Aid for Visually-Impaired by Using Neural Network and Text Summarization Technologies

This paper proposes the intelligent system to assist the visually-impaired people in reading printed Chinese document. As the document may consist of the combination of text and photos, the proposed system first applies the text frame detection scheme to identify the text parts of the document, and utilizes the Optical Character Recognition (OCR) to convert the text image into pure text file. The aim of this paper is to implement the automatic text summarization algorithm to get the brief meaning of the whole text. And the visually-impaired people can listen to the summary of the document through the existing text to voice tool. In the system, the neural network model is adopted to find out text blocks before recognizing text, and to optimize the Chinese automatic text summarization system. The performance of the proposed scheme is also evaluated by the TextRank with Word2Vec. The proposed system can assist the visually-impaired people especially in daily life when it is applied in the wearable devices.
A Neural Attention Model for Real-Time Network Intrusion Detection

The diversity and ever-evolving nature of network intrusion attacks has made defense a real challenge for security practitioners. Recent research in the domain of Network-based Intrusion Detection System has mainly focused on adopting a flow-based approach when extracting features from raw packets. One drawback of this is that attack detection can only be carried out after the flow has ended. In this work, we present a new technique based on the neural attention mechanism; unlike many existing solutions, our technique can be applied for real-time attack detection since it uses time slot-based features. The proposed solution is a modified version of the transformer model which has been proposed and used in the language translation domain. We conduct experiments on a dataset extracted from a recent repository network traffic containing several kinds of network attack. We use the "bidirectional LSTM" and "conditional random fields" models as baseline for comparison and our performance results demonstrate that the proposed solution significantly outperforms the two baselines in terms of precision, recall, and false positive rates. In addition, we show that our solution is more computationally efficient than the bidirectional LSTM model as a result of the removal of recurrent layers.
Design of a Compact Microstrip Triplexer-Power Divider With E-shaped Resonator

A compact microstrip triplexer-power divider (PD) using E-shaped dual-mode resonator is presented in this paper. This design is directly composed of three independently designed dual-mode filter power dividers. And the common port does not need additional matching network, which brings good performance and compact size. Compared with other reports, the advantage of this circuit is that each dual-mode resonator can generate one passband and independently regulate the band-width. To validate this design concept, a dual-mode triplex PD prototype working at 1.64, 2.35, 3.03GHz has been implemented. The performance of the newly designed triplexer PD is demonstrated by simulation results.
A Novel Routing Protocol Based on Congruent Gravity Value for Underwater Wireless Sensor Networks

Reliability of data is severely affected due to high attenuation, high noise, limitation of the acoustic medium, and dynamic topology. The limitation of the acoustic medium, attenuation, and noise, much work was conducted by using forward error correction process, retransmission, attenuation model and Signal to Noise Ratio (SNR) to make data reliable. Many routing protocols are introduced to increase the lifetime of the network. However, there are also many improvements which need to be done to enhance the lifetime of the network. In this research work, we introduced a Congruent Gravity Value (CGV) routing protocol to enhance the lifetime of the network. Two types of packets will be transmitted in order to find out the relay node. One type of packet is hello packet, which will contain the information of source node and sink. The second type of packet is a response packet, which contains the information of the nearest neighbor node. Hello, the packet will always transmit by source node while the response packet will be transmitted either by the nearest neighbor node or by source node based upon condition set. That condition will save energy and enhance the network lifetime whenever it is feasible. Gravity function plays a preeminent role in the selection of the relay node. In order to tackle the Congruent gravity value, the cluster head selection technique is used. Network Simulator has been used to evaluate the performance of CGV, and its results are compared with ER2PR, VBF, and UFCA. These protocols utilized a large area in order to select a relay node. To overcome this problem, CGV used just those area which is best for the selection of the relay node and response packet transmission area is also minimize whenever it is feasible by the condition.
An Electrical Accommodation in Digital Grid System Using DC Sub-grid

This paper presents a DC sub-grid for electrical accommodation in a digital grid system. The sub-grid is composed of a positive DC voltage line and a negative DC voltage line, which are connected to a couple of inverters in the digital grid router. The adaptive hysteresis current control technique is applied to control the sub-grid lines maintaining the voltage at a constant value while to accommodating the given power. The simulation results show that the proposed control algorithm yields stable responses and enable the DGRs to accommodate the given power almost exactly.
A Band-limited Digital Predistortion Method for Hybrid MIMO Transmitters

This article proposes a novel band-limited processing method to achieve digital predistortion (DPD) linearization for hybrid beamforming large-scale antenna transmitters. The proposed approach aims to implement DPD that cancels distortion over a wide bandwidth using band-limited observation path. The theoretical analysis has shown that this method is feasible to linearize the massive multiple-input multiple-output (MIMO) system with hybrid precoding operation, and the numerical experiment results confirmed that the new approach can produce excellent performance with relatively lower rate analog-to-digital converters (ADCs).
Research on anti-noise performance of Digital Phase Sensitive Detection Algorithm in Magnetic Nanoparticle Thermometer

In a magnetic nanoparticle-mediated hyperthermia, the temperature is a key factor for thermal therapy to treat cancer. A harmonic amplitude detection algorithm determines the measurement accuracy of magnetic nanoparticle thermometer. In this paper, we introduce a harmonic detection algorithm, digital phase sensitive detection (DPSD), study DPSD how to affect the measurement accuracy of magnetic nanoparticle thermometer, and analyze the anti-noise performance of DPSD, including the static magnetic noise, industrial frequency magnetic noise, higher frequency magnetic noise, and Gauss noise. The optimum system parameters are proposed, and verified in numerical simulation experiments by use of MATLAB. The simulation results show that the DPSD with optimum system parameter can attach a high measurement accuracy of magnetic nanoparticle thermometer.
Design of Patch Antennas with Symmetry by a Hybrid Topology Optimization Method

A hybrid topology optimization (HTO) method is used for the design of patch antennas with symmetry. The scalar isotropic material with penalization (SIMP) and the level-set method (LSM) are combined together to form the HTO. By applying the hybrid scheme, a conical beam patch antenna and a two-element MIMO antenna are optimized. For the conical beam antenna, the topology of the patch and the existence and location of the vias are optimized simultaneously. For the MIMO antenna, only the isolation structure is optimized. In order to decrease the optimization complexity, the symmetry of the antennas are utilized and only parts of the structures are optimized. The performances of all the antennas are verified by the simulation and measurement.
Millimeter-wave End-Fire Circular-Polarized ME Dipole Antenna

This paper presents a three-dimension (3D) wideband 45 GHz circular-polarized ME (Magneto-Electric) dipole antenna with end-fire radiation property. It consists of a circular-polarized ME dipole radiator and a feeding SIW (Substrate Integrated Wave-guide). In addition, these two components are mutually vertical in space and the open end of the feeding SIW is employed to work as a coupling window for electromagnetic energy. Besides, two parallel metal baffles are loaded onto this coupling window to adjust the impedance matching between the ME dipole radiator and the feeding SIW. Therefore, the simulated impedance band-width of the proposed antenna for $\vert \mathrm{S}11\vert < -10\text{dB}$ can reach up to 22.2% from 40GHz to 50GHz. Furthermore, a stable LHCP gain which variation range is $8.5+0.25\ \text{dBic}$ over the operating band and a 3 dB AR (Axial Ratio) bandwidth of 16.7% are achieved.