Miracle-dz/UniFAR-FTUs huggingface dataset:
SVM Based Intrusion Detection Using Nonlinear Scaling Scheme

Intrusion is one of major security issues of internet with the rapid growth in smart and Internet of Thing (IoT) devices, and it becomes important to detect attacks and set out alarm. In this paper, Support Vector Machine (SVM) with nonlinear input data scaling scheme is proposed to detect attacks, which is different than the existing linear scaling based machine learning methods. Experiments on the NSL-KDD dataset show that the performances of the proposed method are compared favorably with existing works. The detection rate from the new method is 82.2% for binary-classification, compared to 81.2% by existing Artificial Neural Networks (ANN) based works. For multi-classification, the proposed method shows similar performances of ANN. Further more, the detection rate of Denial of Service (DoS) is 86.5%, compared to 77.7% by existing ANN based works.
Performance Evaluation of Advanced Classification Models on Spatial Location Based Shimmer2 Sensor Data Sets

Physical activity recognition is an emerging research field of machine learning and recognition in computer vision. The main theme of human physical activity recognition is to identify different daily activities of human being using wearable sensor. As the relevant features at the surface of raw data play significant role in the accuracies of classifiers, feature engineering method called Dimensionality Reduction(DR) method, Principal Component Analysis(PCA)proposed. In this paper Principal Component Analysis(PCA) reduces the size of the data to improve the classification accuracy of the classifiers under consideration. Advanced classifiers, Support vector machine (SVM), Naive Bayes (NB), J48 and Random Forest Tree (RFT) classifiers were deployed and tested on spatial location based Shimmer2 sensor mHealth raw data, generated time domain features, and reduced feature data obtained after PCA. It is found that these methods relatively performs well on the reduced feature data sets. Random Forest (RF) classifier outperforms well on the reduced data sets.
An X-Band High-Gain Low-Profile Dipole Antenna Based on Electromagnetic Band-Gap

This paper presents a broadband high-gain dipole antenna operating in X-band. The antenna consists of a printed dipole antenna and a resonant cavity formed by a broadband Electromagnetic Band-Gap structure and a metal ground. It is worth noting that the EBG structure consists of multilayer dielectric unprinted slabs with different dielectric constant, thickness and interlayer spacing. Moreover the antenna obtains high-gain performance maintaining the impedance bandwidth. The peak Gain of the antenna is 8.2dB, the 3-dB gain bandwidth is 38% and the footprint is $0. 49\lambda\times 1.46\lambda\times 0.16\lambda$,
Joint Computation Offloading and Routing Optimization for UAV-Edge-Cloud Computing Environments

Computation offloading is significant to the UAV swarms by migrating computational tasks from the UAV swarms to the edge or cloud computing infrastructure. However, when studying the computation offloading problem, existing approaches do not fully consider the characteristics of UAV swarms and do not differentiate the characteristics of cloud computing and edge computing well. In this paper, we study a joint computation offloading and routing optimization problem for UAV swarms under an UAV-Edge-Cloud computing architecture. Our contributions can be summarized as the following three aspects. First, to fully optimize the characteristics of UAV swarms, we jointly consider the computation offloading and routing for UAV swarms. Then, to highlight the characteristics of cloud computing and edge computing, we propose a novel three-layer computing model joint computation offloading and routing problem. Finally, we design a polynomial near-optimal approximation algorithm to solve the joint optimization problem using the Markov approximation technique. Finally, our simulation results demonstrate the high efficiency of our proposed algorithm.
Profiling health prevention population for hypertension screening and ECG test rationing

This paper addresses the question of whether ECG test is needed for hypertension screening for all subjects of preventive health checkup. For this purpose, we propose a decision tree approach for subject profiling depending on their characteristics and results of medical exams. The population of hypertension subjects being too small with 1% of the whole, learning sets with higher hypertension population are proposed to enhance the decision tree approach. The decision tree allows identifying subject groups for which ECG is needed. Numerical experiments with historical data from CES-Saint Etienne show a correct classification probability of 96% of hypertension subjects and a drastic reduction of 98% ECG tests. Last but not the least, the resulting decision tree is implementable in practice.
A Modular Soft-Switched Silicon-Carbide Step-Up Converter with a Dual Coupled LCL Resonant Networks and a Primary-Hybrid Variable Frequency/Phase-Shift-Control

A modular silicon carbide based step-up converter with dual-active-bridge (DAB) circuit and a dual coupled LCL resonant circuit structure with complete soft-witching operation is presented. In particular, each module in the proposed converter employs a hybrid frequency/phase-shifted control. A simple auxiliary circuit is also included in both the input and output port of each module of the converter to assist soft-switching operation when phase-shift control is utilized in the control scheme. The dual LCL resonant circuit networks in the proposed converter allow zero voltage switching (ZVS) turn-on and zero current switching (ZCS) turn-off for all the primary-side switches and ZCS turn-on and off in all the secondary-side switches over a wide load range. Results are first provided on a 150kW, 300V/10kV system to verify with the theoretical analysis of the proposed circuit. Experimental results confirm that at a full-load efficiency of 96% is achieved on a laboratory-scale SiC-based converter system.
Analysis of Pulse Train Controlled PCCM Boost Converter With Low Frequency Oscillation Suppression

For pulse train (PT) controlled switching converter, when it operates in continuous conduction mode (CCM) and discontinuous conduction mode (DCM), there exist disadvantages of low frequency oscillation and narrow load range, respectively. To avoid these disadvantages of PT controlled switching converter in CCM and DCM, a PT controlled boost converter operating in pseudo continuous conduction mode (PCCM) is taken as the research object in this paper, its working principle has been analyzed in detail. Based on the analysis of output voltage ripple during one switching cycle, the combination of high- and low-power control pulses in a repetition cycle is analyzed and the expression of output voltage ripple is derived for different load conditions. Finally, simulation and experiment are performed to verify the correctness of the control pulse combinations and the output voltage ripple analysis. Results show that compared with DCM and CCM boost converters, the PT controlled PCCM boost converter not only has wider load range, but also can suppress low frequency oscillation.
Rocket Detection Using Passive Radar - Challenges and Solutions

In the paper selected aspects of rocket detection with the use of passive radar are analyzed. The bistatic radar cross-section of a rocket is calculated using electromagnetic simulator. Next, the radar equation for passive radar is analyzed taking into consideration the radiation patterns of the transmitter antenna. Limitations of integration time are also discussed. The theoretical analysis is supplemented with measurement results - the processing results of a rocket observed with a DVB- T based passive radar are shown and analyzed.
Machine Learning-based RF Jamming Detection in Wireless Networks

Due to the open and shared nature of wireless medium, wireless networks are vulnerable to Radio Frequency (RF) jamming attacks since an attacker can easily emit an interference signal to prevent legitimate access to the medium or disrupt the reception of signal. An attacker may utilize different jamming strategies by exploiting the vulnerabilities of wireless protocols at different layers. Therefore, the detection and classification of jamming attacks is important to take proper countermeasures. In this paper, we firstly discuss some classic jamming strategies and some performance metrics for jamming detection. We then implement the jamming attack modules based on the NS-3 simulator, and study the effects of different types of jamming strategies. We propose some jamming detection schemes based on a variety of machine learning algorithms. The effectiveness of the proposed jamming detection schemes are valuated and optimized based on the collected data for different jamming attacks.
Wirtinger's Calculus for the Load Flow in Power Distribution Grids

This paper shows a Wirtinger's-Calculus based loadflow methodology for power distribution grids. This approach allows to obtain an algorithm which works directly on the complex domain maintaining some useful symmetries and a compact representation. The paper aims to introduce Wirtinger's Calculus as a suitable tool for power systems analysis; therefore, it is presented as a tutorial, playing especial attention on the implementation in modern scrip languages such as Matlab/Oc-tave, which allow easy representation and fast complex-array calculations. A Newton's-based method is proposed in which the Jacobian is replaced by Wirtinger's derivatives obtaining a compact representation. Simulation results complement the analysis. Despite being a mature theory, Wirtinger's-Calculus has not been applied before in this type of problems.
A Ka-Band Wideband Circularly Polarized High Gain Slotted Cavity Antenna Array Using High-Order Modes and Sequential Rotation Technique

A wideband and high gain circularly polarized (CP) $4\times 4$ slotted cavity antenna array at Ka-band is presented in this paper. The array employs a substrate integrated waveguide based slotted cavity antenna using high order cavity modes as the basic element. A two stage sequential-phase feed network is utilized to guarantee good CP performances. A prototype of the full array is simulated, and the simulated results show that a wide 3-dB gain bandwidth of 18.3% and peak gain of 25 dBic are achieved. Within the bandwidth, the $\vert \text{S}_{11}\vert$ is smaller than −10dB, and axial ratio at boresight is smaller than 0.3dB.
Probabilistic Pose Estimation of Deformable Linear Objects

This paper presents a probabilistic framework for online tracking of nodes along deformable linear objects. The proposed framework does not require an a-priori model; instead, a Bayesian Committee Machine, starting as a tabula rasa, accumulates knowledge over time. The key benefits of this approach are a lack of reliance upon extensive pre-training data, which can be difficult to obtain in sufficiently large quantities, and the ability for robust estimation of nodes subject to occlusion. Another benefit is that the uncertainties obtained during inference from the underlying Gaussian Processes can be beneficial towards subsequent handling tasks. Comparisons of the non-time series framework were conducted against conventional regression models to measure the efficacy of the proposed framework.
Low-Cost Strategy to Mitigate the Impact of Aging on Latches’ Robustness

Analyses recently presented in the literature have shown that the Bias Temperature Instability (BTI) ageing phenomenon may increase significantly the susceptibility to soft errors (SEs) of robust latches. Particularly, this is the case of low-cost robust latches, whose robustness is obtained by increasing the critical charge of their most susceptible node, that is the node most contributing to the latch soft error rate (SER). Therefore, in applications mandating the use of low-cost robust latches, designers will have to face the problem of such latches’ robustness degradation during the IC operation. In order to cope with this problem, we here propose a strategy to reduce the impact of BTI on the SER of standard and low-cost robust latches. It will be proven that our approach enables to reduce by approximately the 50 percent the SER increase due to BTI during circuit lifetime with respect to original latches, at limited increase in terms of area overhead, latch setup time and power consumption, and with no impact on the latch input-output delay.
Dynamic Assessment of COTS Converters-Based DC Integrated Power Systems in Electric Ships

Maritime applications have found in the integration of the electric power system a way to further improve efficiency and reduce the weight of new electric ships. This movement has led scientists to integrate smart management systems to optimize the overall behavior of the grid. In this context, power electronics play a key role in linking the different elements of the power architecture. Moreover, the transition toward a dc distribution, which has already been established in other applications, is being regarded as a promising alternative to ease the integration of renewable sources, batteries, and the ever increasing number of dc loads. In this paper, blackbox models are proposed as a tool to foresee the effect of these complex interactions, overcoming the lack of detailed information about the power converters. Large-signal strategies are proposed in order to consider nonlinearities in the static and dynamic behavior of the converters. An accurate model of the physical layer is essential to allow intelligent systems to take the most out of the system performance. This approach offers the opportunity to study the dynamic response of complex interconnected systems, tune the system-level controllers, design protections, or assess the compliance of the system dynamics with the standards. Experimental results are included in order to validate the proposed method.
Class-Cone Based Nonnegative Matrix Factorization for Face Recognition

Nonnegative matrix factorization (NMF) is an effectively parts-based feature representation approach and has achieved good performance in different tasks such as computer vision, clustering and so on. To enhance the discriminative power of NMF in nonnegative feature space, this paper proposes a novel supervised matrix decomposition method, called Class-Cone based Nonnegative Matrix Factorization (CCNMF). We establish a loss function with class-cone regularization which contains the volumes of class-cones and the quantity of between class-cones. To minimize the objective function will leads to small class-cones and large distance between class-cones. This good property is beneficial to the performance of NMF algorithm. We solve the optimization problem using KKT conditions and obtain the updating rules of CCNMF. Our approach is experimentally shown to be convergence and successfully applied to face recognition. Experimental results demonstrate the effectiveness of the proposed CCNMF algorithm.
Research on Basic Support Capacity of Resources & Environment in China's Silk Road Economic Belt Based on GIS

With the China’ s Silk Road Economic Belt as a space carrier, GIS is used to analyze the basis for resources and environment of the Silk Road Economic Belt: Guangxi, Chongqing and Sichuan are the areas with high abundance of the combination of resources and environment; Yunnan, Shaanxi, and Ningxia are the areas with medium abundance of the medium combination of resources and environment; Gansu, Xinjiang, and Qinghai are the areas that with general abundance of the combination of resources and environment. Qinghai and Xinjiang are the areas with high degree of guarantee for resources and environment; Yunnan, Sichuan, Guangxi, and Ningxia are the areas with average degree of guarantee for resources and environment, and Gansu, Shaanxi, and Chongqing are the areas with general degree of guarantee for resources and environment. Qinghai and Xinjiang are the areas with high degree of matching between economic and social resources and environment; Gansu, Yunnan and Ningxia are the areas with average degree of matching between economic and social resources and environment; Sichuan, Guangxi, Shaanxi and Chongqing are the areas with general degree of matching between economic and social resources and environment.
Harmonic-Suppressed LTCC Bandpass Filter Using a New Feeding Scheme

This paper introduces a new harmonic-suppressed bandpass filter (BPF) using quarter guided-wavelength stepped-impedance resonator (SIR) based on the low temperature co-fired ceramic (LTCC) technology. The voltage-null point of the third harmonic is found on the miniaturized SIR (by choosing the reasonable ratios of impedance and electric length). It can be chosen as the exciting location of the feed line in the BPF design so that the third harmonic of the resultant BPF cannot be excited and is suppressed successfully, while the passband of the BPF operating at the fundamental resonant frequency of the resonator can be optimized and obtained by tuning other parameters. For demonstration, a LTCC BPF is designed and fabricated, and good agreement is observed between the measured and simulated results.
An Indoor Floor Positioning Method Based on Smartphone’s Barometer

For the problem of low availability and high environmental dependence of the traditional floor positioning technology, this paper proposes an indoor floor positioning method based on the smartphone’s barometer. First, one can get an initial floor location through the "entering" detection algorithm. Second, the user’s going upstairs or going downstairs activities are identified through the characteristics of air pressure and the altitude. Then we can get the latest floor position.In order to solve the problem of the floor misjudgment from different mobile phone’s barometer having different performance, this paper puts forward a pressure sensor relative error calibration method based on the neighbors. It can be used to calibrate the pressure data from the different cell phones, and effectively reduce the errors of the air pressure estimation elevation that is caused by the heterogeneity of the mobile phones. Finally, the experiment results show that the rate accuracy of the floor identification can reach more than 85% while reducing environmental dependence and improving availability.
The Perceptual Quality of the Oculus Rift for Immersive Virtual Reality

The recent release of the Oculus Rift, originally developed for entertainment applications, has reignited the interest of researchers and clinicians toward the use of head-mounted-displays in basic behavioral research and physical and psychological rehabilitation. However, careful evaluation of the Oculus Rift is necessary to determine whether it can be effectively used in these novel applications. In this article we address two issues concerning the perceptual quality of the Oculus Rift. (a) Is the Oculus able to generate an acceptable degree of immersivity? In particular, is it possible to elicit the sensation of presence via the virtual stimuli rendered by the device? (b) Does the Virtual Reality experienced through the Oculus Rift induce physical discomfort? To answer these questions, we employed four virtual scenarios in three separate experiments and evaluated performance with objective and subjective outcomes. In Experiment 1 we monitored observers’ heart rate and asked them to rate their Virtual Reality experience via a custom questionnaire. In Experiment 2 we monitored observers’ head movements in reaction to virtual obstacles and asked them to fill out the Simulator Sickness Questionnaire (Kennedy et al., 1993) both before and after experiencing Virtual Reality. In Experiment 3 we compared the Oculus Rift against two other low-cost devices used in immersive Virtual Reality: the Google cardboard and a standard 3DTV monitor. Observers’ heart rate increased during exposure to Virtual Reality, and they subjectively reported the experience to be immersive and realistic. We found a strong relationship between observers’ fear of heights and vertigo experienced during one of the virtual scenarios involving heights, suggesting that observers felt a strong sensation of presence within the virtual worlds. Subjects reacted to virtual obstacles by moving to avoid them, suggesting that the obstacles were perceived as real threats. Observers did not experience simulator sickness when the exposure to Virtual Reality was short and did not induce excessive amounts of vection. Compared to the other devices the Oculus Rift elicited a greater degree of immersivity. Thus our investigation suggests that the Oculus Rift head-mounted-display is a potentially powerful tool for a wide array of basic research and clinical applications.
Saliency-Enhanced Spoke-Type Rotor Geometry for Permanent Magnet Volume Reduction in Hybrid and Electric Vehicle Motors

Today's Hybrid and Electric Vehicles (HEV) market share is growing fast and the main car manufacturers are planning substantial investments to develop performant, reliable and affordable electric powertrains. Industry and research agree considering Interior Permanent Magnet (IPM) motors to be one of the most suitable technologies for automotive traction applications. However, these motors typically make use of expensive rare-earth permanent magnets and, as the emerging economies will increase the demand for these materials, prices will rise further making rare-earth-based solutions less competitive. Researchers have proposed different solutions: synchronous reluctance motors, replace rare-earth PM with ferrite, but probably the most immediate option is the design optimization to reduce the PM mass. In this paper a saliency enhanced spoke-type rotor geometry is proposed in order to replace the 2010 Toyota Prius rotor with a 28% reduction in the PM mass. This approach is studied with a novel analytic model and extensive Finite Element simulations.
Deep Pyramidal Residual Networks for Plankton Image Classification

In this paper, we explore a new deep learning algorithm called Deep Pyramidal Residual Networks (PyramidNet) to solve plankton image classification problem. It is well known that plankton image classification is playing an increasingly important role in the research of plankton organisms. Nowadays, scientists tend to use image-based technologies to study marine plankton. However, manual classification of the plankton images is a time-consuming and labor-intensive work. So it is urgent to find methods that can do automatic classification using few samples labeled by experts and have good performance. In recent years, Deep Convolutional Neural Networks (DCNNs) have shown remarkable performance in image classification tasks. The recently proposed algorithm PyramidNet even performed better than all previous state-of-the-art DCNNs in image classification experiments using some image classification benchmark datasets. So we explore the PyramidNet to solve our plankton image classification problem and it shows superior generalization ability, gaining higher accuracies. Another meaningful point of our experiment is the improved F1 score compared to other benchmark methods. We use the WHOI-Plankton dataset in our experiment. Although some DCNNs have achieved high accuracies on this dataset, it doesn't mean that the classifiers are really accurate about the minority classes because of the low F1 score which is also another important measurement for image classification. In this paper, we choose the accuracy and F1 score as our evaluating indicators and it ultimately shows that the PyramidNet outperforms the benchmark methods not only on the accuracy but also on the F1 score.