Miracle-dz/UniFAR-FTUs huggingface dataset:
Secure Wireless Powered Cooperative Communication Networks With Finite Energy Storage

This paper investigates secure transmission in a wireless powered cooperative communication network (WPCCN) over Nakagami-$m$ fading channels, where multiple intermediate energy harvesting (EH) nodes with finite energy storage are deployed to assist the secure transmission between source and destination in the presence of an eavesdropper. Specifically, the intermediate EH nodes first harvest and accumulate energy from the source's signal and then forward the confidential signal or emit jamming signal to interfere the eavesdropper according to actual requirements. In order to exploit the intermediate EH nodes for secure improvement, considering both energy storage status and channel gains, three relay-and-jammer selection schemes at the intermediate nodes are proposed, namely: energy threshold based best-relay and random-jammer (ETBR), energy threshold based random-relay and best-jammer (ETRB), and energy threshold based best-relay and best-jammer (ETBB). For understanding the impact of critical parameters on the secrecy performance, we first capture the evolution of the EH nodes’ storages using finite-state Markov chain (FSMC) and then derive the analytical expressions for hybrid outage probability (HOP) and secrecy throughput (ST). Furthermore, numerical results demonstrate that our proposed selection schemes outperform the existing schemes (bR-rJ, rR-bJ, bR-bJ) due to the consideration of energy accumulation and storage status. In addition, there is a suitable value of the energy threshold for each proposed scheme to achieve the optimal secrecy performance.
Dual-band Omnidirectional Antenna for WiFi Applications

In this paper, the design of a dual-band omnidirectional patch antenna with stable ellipticity of less than 1.8dB is introduced. This antenna is designed for the WiFi application operations at 2.4 GHz and 5.0 GHz. It has an advantage of a much smaller and simpler structure compared with traditional dual-band WiFi antennas. Details of design, simulated results of this dual-band omnidirectional antenna are presented and discussed. The results confirm the validity of this design, which meet the requirement of WiFi application.
A ±45° Dual-Polarized Antenna for 5G mmWave Applications Based on Gap Waveguide Technology

A ±45° dual-polarized antenna is designed based on the gap waveguide technology which offers a low loss and easy-to-manufacture solution at the mmWave frequencies. Dual-polarized antennas help to increase the capacity and coverage of a wireless system and also decrease the overall cost. Two parallel arrays of 8 slots each are employed to create the orthogonal polarizations. The antenna operates in the 24.25 − 27.25 GHz frequency band. The simulated average gain of each polarization is 14.5 dBi while achieving a cross-polarization isolation of at least 15 dB.
An Intrusion Detection System Based on Convolutional Neural Network for Imbalanced Network Traffic

As the Internet integrates with social life closely, various cyber threats pose a huge challenge to Intrusion Detection Systems (IDS). The performance of IDS based on traditional machine learning did not meet our expectations. In this paper, we propose an intrusion detection model based on Convolutional Neural Network (CNN). Before CNN training, Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbors (SMOTE-ENN) algorithm is applied to balance the network traffic. We use NSL-KDD dataset to evaluate the model. The proposed SMOTE-ENN-based CNN IDS model achieves an accuracy of 83.31%. Furthermore, the detection rates of User to Root (U2R) and Remote to Local (R2L) attacks are significantly improved. Results show that SMOTE-ENN-based CNN IDS outperforms the previous IDS model.
A 15 – 34 GHz Robust GaN based Low-Noise Amplifier with 0.8dB Minimum Noise Figure

In this report, a broad band (15 GHz to 34 GHz) low noise amplifier using 100 nm GaN on silicon technology is designed and fabricated. The LNA shows an extremely low noise figure of 1.2 dB and small signal gain of 18.5±1.5 dB across the bandwidth. The LNA can work in the drain bias from 3.5 V to 8 V without increasing the noise figure. The chip size is 2 mm × 1.3 mm. The robustness of the LNA is also tested by stressing the working LNA with a continuous-wave input power, no significant degradation is observed.
Robot Awareness of Human Behaviors and Interacting with Humans by A Two-Step Identifying Approach combining LSTM and SSD

Robot cooperating with humans is a hot research topic recently. And the first challenge is how to specifically identify humans’ intention. In this paper, we propose a two-step approach based on LSTM and SSD for identifying humans’ specific intention. A LSTM with attention mechanism is firstly presented for recognizing humans’ actions based 3D skeleton data; then, the hand-held objects are detected by a SSD neural network. Finally, a human-robot interaction framework are constructed based on the proposed intention identification model and DMP model. The experiments show that the proposed method can effectively identifying humans’ intention. And the robot accomplishes to interact with the human with the built motion database and DMP.
A Miniaturized Base Station Antenna

In this paper, a miniaturized dual-polarized base station antenna is proposed. The antenna radiator consists of the conventional crossed loop dipoles. The radiator of the proposed antenna is miniaturized by loading an angular metal structure at the end of the dipole arm and an annular parasitic structure below the radiator. The size of the radiator is reduced from 52.5 × 52.5mm2 (0.39λ0×0.39λ0, where λ0 is the free-space wavelength at 2.2GHz) to 41.5×41.5mm2 (0.3λ0×0.3λ0), so the edge length of the radiator is decreased by nearly 21%. Simulation results show that the antenna achieves an impedance bandwidth of 45% with S11 ＜-15dB (1.7-2.7 GHz), and S21<-25 dB. Within the operating bandwidth, the average gain is close to 8dBi and the half-power beamwidth is about 72±3°.
Millimeter-Wave Capacitively Coupled Microstrip Patch Array Antenna for 79 GHz Radar Sensors

In this paper, a novel capacitively coupled microstrip patch array antenna is proposed for 79 GHz radar sensors. The proposed antenna has advantages of low profile, low cost and ease of fabrication by implementing it on a millimeter- wave substrate. Two types of capacitively coupled patch arrays are designed and developed; one is 1 × 18 array for the short- range radar (SRR) sensor and the other is 4 × 18 array for the mid-range radar (MRR) sensor. To confirm the feasibility of the proposed antenna, the prototypes of the designed antennas are fabricated and measured. The simulated and measured reflection coefficients are in good agreement and less than -10 dB in a wide range of frequencies including 79 GHz. The measured maximum antenna gains are 13.94 dBi and 18.03 dBi for the 1 × 18 array and 4 × 18 array in the boresight direction at 79 GHz.
Strong Converse Using Change of Measure Arguments

The strong converse for a coding theorem shows that the optimal asymptotic rate possible with vanishing error cannot be improved by allowing a fixed error. Building on a method introduced by Gu and Effros for centralized coding problems, we develop a general and simple recipe for proving strong converse that is applicable for distributed problems as well. Heuristically, our proof of strong converse mimics the standard steps for proving a weak converse, except that we apply those steps to a modified distribution obtained by conditioning the original distribution on the event that no error occurs. A key component of our recipe is the replacement of the hard Markov constraints implied by the distributed nature of the problem with a soft information cost using a variational formula introduced by Oohama. We illustrate our method by providing a short proof of the strong converse for the Wyner-Ziv problem and strong converse theorems for interactive function computation, common randomness and secret key agreement, and the wiretap channel; the latter three strong converse problems were open prior to this work.
Research on Constraining Strategies of Underwater Glider's Trajectory Under the Influence of Ocean Currents Based on DQN Algorithm

This paper presents a novel approach about constraining trajectories of underwater gliders based on Deep Q Network(DQN) and the dynamic model of gliders. In terms of dynamics modeling, we choose the angle of attack and roll angle as the input of the glider' movement simulation, the profile motions in the vertical plane is manipulated by the angle of attack, and the gyratory motion is accomplished by controlling the roll angle. Since different roll angles allow the glider to turn with different radius, it can be used to counteract the influence of current on the motion state of the glider. And the determination of the proper roll angle to make the glider's trajectory consistent with the preset route is a sequential decision problem. We use the glider's motion space discretization and apply the deep reinforcement learning theory to acquire the optimal roll angle, thus finally achieve the goal that improves the accuracy of the glider's trajectory. The simulation results show that the proposed method has better performance and advantage to deploy compared with the conventional PID method.
Generation of 1.8-W Average Power From a Diode-Pumped Femtosecond Rotary Nd:Glass Disk Laser

We demonstrate 1.8 W of average output power with 350 fs of pulse duration from a diode-pumped mode-locked rotary Nd:glass disk laser. In this laser with rotating Nd:glass disk structure, we use a Nd:glass disk with a diameter of 50 mm, which disperses heat to a wide area, to extend the upper limit of pumping power of Nd:glass for high output average power. We achieve passively soliton mode-locking for obtaining femtosecond pulses based on a semiconductor saturable absorber mirror. To our best knowledge, this is the highest average power reported from a diode-pumped femtosecond Nd:glass laser oscillator. The results are significant for power scaling of femtosecond Nd:glass lasers.
Sparse Multi-Graph Ranking towards Social Image Retrieval†

Graph ranking is one of popular and successful technique for information retrieval. However, conventional graph ranking has two shortcomings when deployed for social image search. First, as social tags are noisy and incomplete, using that, the initial ranked list of images is inaccurate and impacts the following visual re-ranking. Another tough issue is how to conduct query-sensitive image re-ranking when multiple visual feature sets are available. In this work, we propose a sparse multigraph ranking framework, in which multiple graphs built on different visual features are integrated to simultaneously encode the image ranking. In particular, a sparse constraint is imposed on the fusion of different features, hoping to select a compact yet informative combination of features for different queries. To deal with the highly noisy issue inherent in social tags, a tag refinement solution along with word embedding is utilized to derive the more accurate initial ranking list, which services as the supervision signal for the proposed graph ranking framework. Extensive experimental analyses and evaluations on NUS-WIDE dataset demonstrate the proposed method can achieve state-of-the-art performance.
Image Super-Resolution Using Capsule Neural Networks

Convolutional neural networks (CNNs) have been widely applied in super-resolution (SR) and other image restoration tasks. Recently, Hinton et al. proposed capsule neural networks to resolve the problem of viewpoint variations in image classification tasks. Each capsule is represented as either a vector or a matrix to encode more object information, such as position, size, direction, etc. Instead of detecting specific features, these capsule neural networks search for the most relevant features using an iterative process. Therefore, capsule neural networks require fewer parameters compared to traditional neural networks. Inspired by these advances, we make use of a capsule neural network to exploit more potential features for image SR. In this paper, we develope two frameworks: the Capsule Image Restoration Neural Network (CIRNN) and the Capsule Attention and Reconstruction Neural Network (CARNN), to incorporate capsules into image SR convolutional neural networks. The CIRNN takes advantage of the rich information encoded in the capsules to reconstruct accurate high-resolution images. The CARNN generates SR attention features by utilizing the robust segmentation capability of the capsules. Our experiments show that both frameworks can enhance SR for most testing datasets. The CIRNN performs better than the CARNN and can achieve better performance than other traditional CNN methods with a similar amount of parameters.
Grip Force Perception Based on dAENN for Minimally Invasive Surgery Robot

Although robot assisted minimally invasive surgery brings the gospel to patients, force perception is gone. Among all the contact forces during surgery, the instrument grip force plays the most important role. In this paper, a grip force perception method based on denoising AutoEncoder Neural Network (dAENN) is proposed. The method utilizes sensor data including encoder readings and motor current over a time window as the input of dAENN for sufficient information. An Artificial Neural Network (ANN) is then introduced as a machine learning tool to learn the nonlinear mapping between the compact features and grip force labels. Feature extraction is first introduced into grip force perception problem in this paper. Experiment results shows adequate expressive capability of the extracted coding as well as the superior grip force perception performance over several popular data-based methods under the same dataset.
Prediction of TBM Tunneling Parameters through an LSTM Neural Network

With the wide application of tunnel boring machines (TBMs) in tunnel construction, the adaptive tuning of the TBM tunneling parameters has become a research focus. Nowadays, since complicated geological conditions are still challenging to predict, the fine-tuning of tunneling parameters mainly relies on operational experience. Artificial intelligence provides a convenient solution for predicting tunneling parameters through data mining and machine learning. Based on in-situ data, this work proposed a novel method for segmenting the original data in tunneling cycles and analyzing the parameter correlation for data size reduction. Subsequently, a model was established based on an LSTM to predict tunneling parameters in the steady phase based on the data in the rising phase. The results demonstrated that the model is capable of predicting torque and thrust accurately. This makes it possible to adjust the TBM tunneling parameters according to current geological conditions in real time. The present study is of great significance for the tunneling efficiency and construction safety in the actual TBM construction, since it can improve its scientific and intelligent level.
A Motion Free Image Based TRF Reader for Quantitative Immunoassay

Time resolved acquisition of fluorescence provides advantages of reduced background reflection and reduced analyte autofluorescence, considerably improving the signal to background ratio. Europium chelate with its relatively longer fluorescence decay in milliseconds can be analyzed with inexpensive detectors with simpler timing considerations. In this work we present a Time Resolved Fluorescence (TRF) reader using a monochrome camera and its associated exposure trigger circuit to capture TRF from Europium chelate striped on nitrocellulose membrane. The reader is imaging based and does not require complex mechanical moving parts to read the test strip under investigation. Ten different concentrations of Europium between 100 ng/ml and 1000 ng/ml were imaged and the output of the developed TRF reader was found to be linear (R2=0.9873) over the range of concentrations. An improvement of 70% was observed in the signal to background ratio using TRF technique.
Control of Space Flexible Manipulator Using Soft Actor-Critic and Random Network Distillation

Due to the excessive degree of freedom of the space flexible manipulator, we can hardly obtain its accurate dynamic model for its motion planning. In this work, we formulate the precise motion control of the free-floating space piecewise constant curvature (FSPCC) continuum manipulator (i.e. space flexible manipulator) as a sparse reward problem in reinforcement learning, and use the Soft Actor-Critic (SAC) algorithm along with the Random Network Distillation (RND) method to train the optimal policies. Firstly, we use the RND method to jointly train a predictive network and a fixed network. The discrepancy between the output values of the two networks is served as an internal reward for the environment. Secondly, the SAC algorithm aims to maximize the expected return and the entropy of the policy. Policies with high entropy will successfully complete the task while acting as randomly as possible. Finally, the internal rewards tend to incentivize the agent to explore more widely for faster convergence of the algorithm. We applied this method to the FSPCC continuum manipulator simulation model and the results demonstrate that the SAC algorithm together with RND method can control the FSPCC continuum manipulator to catch the target quickly, even in the presence of sparse reward.
A Social Economic Analysis of the Impact of GDPR on Security and Privacy Practices

The General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have been presented by many policymakers as fundamental, welfare enhancing policies. While individuals value privacy, these policies require significant up front and ongoing investment by firms. For example, an analysis commissioned by the California Department of Justice's Office of the Attorney General estimates 14:1 cost to benefit ratio. No such analysis could be found from EU authorities for the GDPR. Sweeping regulatory regimes can create unintended consequences. This paper offers a brief introduction to the new cybersecurity challenges created by the GDPR and CCPA within firms and in the larger Internet ecosystem. As a result of the regulation, firms face many challenges to comply with costly and complex rules, broad definitions of personally identifiable information (PII), and increased risk of fee and/or lawsuit for violations, vulnerabilities, and lack of compliance. Since the promulgation of the GDPR, important security side effects have reported including the blocking of public information in the WHOIS internet protocol database, identity theft through the hacking of the Right to Access provision (Article 15) and other provisions, and the proliferation of network equipment with security and privacy vulnerabilities. The paper also offers a brief overview of the Gordon-Loeb (GL) model used for calculating the optimal investment in cybersecurity. [1] A preliminary data set is offered to examine the difficulty of estimating the cost of cybersecurity investment in light of the GDPR. Notably, the value of the European Union's data economy was estimated to be €300 billion in 2016 [2]. The given GL model would suggest that the optimal investment to protect data would be €13.2 billion. The actual European cyber spend was some €15 billion in 2015, [3] a slightly higher number which covers the EU plus additional European countries, suggesting that the GL model some applicability. There are limited GL type models and tools to guide data protection or privacy investments, and given the emergence of new data protection expectations, it is worth investigating how and whether firms can deliver both sets of expenditures and to what degree. The low level of GDPR compliance suggests that a workable equation of data protection is still not clear for most firms.
Towards Formal Methods of IoT Application Layer Protocols

This paper provides an overview of the application of formal methods for two most commonly used application layer protocols in IoT domain, MQTT and CoAP. Formal methods give the possibility to improve security and are even able to provide security guarantees with respect to a given model. Our research shows, that we can distinguish three formal verification fields for the two protocols, namely qualitative and quantitative analysis, implementations and security properties. A formal verification review of selected protocols is provided in accordance with these fields, used tools and considered properties. Based on that research, a short summary and open challenges are given.
Analysis and Optimization of Radial Force of Permanent-Magnet Synchronous Hub Motors

Permanent-magnet synchronous hub motors (PMSHMs) have been investigated for electric vehicles. However, there are some challenges such as effective cooling and radial force. As PMSHMs are installed on the wheels, the radial force will directly affect the ride comfort of the vehicle. This article presents the analysis and optimization of the radial force for PMSHMs. The radial force densities of the symmetry and asymmetry PMSHMs are analyzed first. It is found that the symmetry PMSHMs have balanced force, while the asymmetry PMSHMs normally have big unbalanced radial force and vibration. To reduce the radial force of the asymmetry PMSHMs, an improved sequential Taguchi optimization method (ISTOM) with mixed orthogonal array is presented for an asymmetry PMSHM in this article. It can be found that the proposed method is efficient and can significantly reduce the radial force of the hub motor.
A Highly Mobile Crawling Robot Inspired by Hexapod Insects*

Rescue robots are usually used in security and disaster investigating missions. Yet the complex control and actuation systems make them very heavy and expensive. This paper proposed a novel crawling robot named "HIbot" inspired by hexapod insect with low cost, light weight and simple control. Both movable body and legged-wheel were adapted to achieve fixable movement of the robots with one DC motor and no extra control. A series of experiments demonstrated that the HIbot can crawl in various outdoor environments such as gravel, grassland, etc. It can cross obstacles that are as high as 2.8 times of the radius of the legged-wheel. Such characteristics give the robot a great potential in applications.
Influence of Geometric Errors and Measuring Errors on Positioning Accuracy of an Industrial Robot

This paper discusses the influence of geometric error parameters and measuring error parameters on the positioning accuracy of an industrial robot. Firstly, the kinematic model of a 6-DOF industrial robot based on the modified Denavit-Hartenberg (D-H) parameter method was established. Secondly, the influence of the geometric error parameters on the positioning accuracy of the robot’s end-effector is analyzed by simulation experiments in detail, including the link length errors, link twist errors, link offset errors and joint angle errors. And then the influence of measuring error parameters is also discussed. The results show that under individual effect of the linear errors, the length of positioning error of the robot’s end-effector is equal to the value of the corresponding error parameter. However, the angle errors have remarkable amplification effects on the accuracy of the robot. Moreover, it should be noted that the influence of measuring error parameters on the positioning accuracy of the robot is on the same order of magnitude as that of the geometric error parameters. Therefore, the measuring errors parameters should be taken into account during kinematic calibration.