Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Guidelines to Avoid Bifurcation in a Series–Series Compensated Inductive Power Transfer System

Resonant inductive power transfer (RIPT) is gaining in popularity for wireless charging applications of future electric transportation. A fundamental impediment to the efficient operation of an RIPT system is the existence of bifurcation phenomenon in a doubly tuned circuit. Bifurcation can be avoided by either selecting complicated control strategies or by proper design strategies. Many control strategies have been presented in the literature for avoiding bifurcation. However, systematic design strategies for avoiding bifurcation are still unknown. This paper presents a simplified, and easy to follow set of design guidelines for series–series compensated RIPT (SS-RIPT) systems. The design guidelines avoid bifurcation by calculating the parameters for a given load profile in a systematic and easy to follow approach. Following the design guidelines, a 3.6-kW setup has been fabricated as an example and verified using a finite element analysis as well as experimental testing. Selection of a resonant frequency and output voltage for 3.6 kW was influenced by the availability of testing equipment available in the lab. However, the design guidelines are generalized, and by following it, an SS-RIPT system of any frequency and output voltage rating, applicable for EV charging, can be designed.
Data Augmentation Using Conditional GANs for Facial Emotion Recognition

In the area of computer vision, deep learning has shown better results than traditional methods. But training a deep network requires a large amount of data and it is often very expensive to collect a huge high-quality labeled dataset, especially in real applications. This is the case in the task of facial emotion recognition, where there are not many examples for training and the distribution of emotion examples can be quite imbalance. In this paper, we use conditional generative adversarial networks to augment our training set. To improve the quality of generated images, we use deep convolution network as the body architecture of generator and discriminator. Batchnorm layer is added after every convolution layer. LeakyReLU activation function is used for all layers except for the final layer of generator, which uses Tanh activation function to squeeze the output to a range between −1 and 1. We also apply Wasserstein distance as the loss function to improve the stability of our network. Using our designed conditional GANs, we first increase the number of samples in the minority classes to handle the problem of imbalanced data. Then we increase the number of samples in all classes to extend the whole training set to solve the problem of inadequate data for training. We then test our conditional GANs on a public facial emotion dataset FER2013. Experiments with and without GANs augmentation are presented. Results show that we can gain accuracy increase (4% absolute and 7% relative) after we adopt the augmentation pipelines, indicating the usefulness of our proposed methods.
A Novel Reconfigurable Liquid Metal Antenna Controlled by an Impressed Voltage

To realize frequency reconfigurable of antenna, a new design for continuously tuning frequency using liquid metal is proposed in this paper. The antenna consists of liquid eutectic gallium and indium (EGaIn)within the microfluidic channel, which is controlled by an impressed voltage. According to a new electrochemical method that Electrochemical deposition (or removal)of a surface oxide on the EGaIn significantly lowers (or increases)its interfacial tension, we can change the length of EGaIn in the channel without pump controlling. By the coaxial cable, the five EGaIn monopolar antennas are connected to form a bending structure array, which can not only change the size of the liquid antenna, but also increase the range of reconfigurable frequency. Simulation in HFSS shows that antenna provides a frequency tuning range from 1.3 GHz to 11.3 GHz, and the adjustable range ratio is approximately 8.7: 1. The maximum realized gain is 4.09 dBi and the maximum total efficiency is 82%.
Design of Iterative Hybrid Beamforming for Multi-User mmWave Massive MIMO Systems

Hybrid beamforming that combines analog phase shift and digital processing is a promising solution to alleviate the implementation cost and power consumption in millimeter wave (mmWave) massive MIMO systems. To achieve sum rate optimization, an iterative scheme that considers both the analog and digital precoders in the optimization metric is proposed. The digital precoder is mainly used to remove multiple access interference for enhancing the multiplexing gain. Then, the coordinated beamforming procedure between the base station and mobile stations is performed to exploit the array gain. Although the iterative scheme is used, only few iterations are required. This scheme can also be extended to the systems using precoding and combining vectors from quantized codebooks. Furthermore, with the channel reciprocity property or the two-phase training strategy, the iterative scheme can obtain the required channel information. From the simulation results, the sum rate is improved, especially when quantized codebooks are used.
Servo Control Based on Pupil Detection Eye Tracking

The proposed article presents a proof of concept developed for the evaluation of eye tracking algorithms based on pupil detection, built around a servo-based pan/tilt platform controlled by pupil movement. An image of the eye is captured using a head mounted infrared video camera, and after the pupil center position is detected, its coordinates are mapped to control the pan and tilt of a servo motor-controlled miniature platform. The system allows efficient real-world evaluation of different eye tracking algorithms and it can have a wide range of application scenarios depending on the type of accessory mounted on the platform.
Science and Technology Service Industry Agglomeration and Regional Innovation: Does Human Capital Matter?

By improving the traditional knowledge production function, this paper establishes a theoretical link among the agglomeration of science and technology service industry, human capital and regional innovation. Based on the data from the year 1985 to 2016 in Beijing, this paper finds that after the introduction of industrial agglomeration, human capital and their interaction item, the adjusted knowledge production function has a better fit than the traditional one, and it can report a better quantitative relationship between regional innovation and its determinants; besides, the level of human capital will strongly adjust the relationship between the agglomeration of the industry and the innovation capability within the region, specifically, only when the level of human capital is higher than a certain threshold, will industrial agglomeration effectively enhance regional innovation.
A Cascade Calibration of Near-Field Source Gain and Phase Errors

Based on instrumental sensors, a method of cascade estimation of the near-field signal source using the symmetric uniform linear array is proposed. The method realizes the separation, decoupling and estimation of parameters by reconstructing the virtual array and steering vector transformation. The method has no loss of aperture in reconstructing the virtual array, avoids the multi-dimensional spectrum search and is more practical. Compared with the traditional methods, the method is less computationally intensive, the gain and phase error calibration accuracy is higher, and the parameter estimation accuracy is higher. Simultaneously, cascading estimation enables real-time estimation of the azimuth, range, and error parameters, and simulation experiments show the performance of the proposed algorithm in this paper.
Bio-inspired Approach for Inverse Kinematics of 6-DOF Robot Manipulator with Obstacle Avoidance

This paper presents a bio-inspired approach for the inverse kinematics problem of 6-DOF serial manipulator. More specifically, we are addressing the trajectory planning and motion planning of the end-effector using the PSO algorithm. Hence, a multi-objective PSO has been investigated to plan the optimal trajectory of the end-effector by avoiding the obstacles, as well as we have introduced the virtual shortest path concept. On the other hand, we have proposed a combination of the modified PSO and the Tabu search algorithm to select the optimal configuration of the manipulator joints. Moreover, the energy consumed by the robot has been taken into consideration.In order to assess the effectiveness of our proposal, it has been evaluated under different simulation scenarios, where the results figured out that our proposal is promising and can be applied to both robot manipulators and mobile robots.
Identification of Traditional Motifs Using Convolutional Neural Networks

This paper presents a design for identifying and classifying the Romanian traditional motifs found on 4 different categories (clothes, ceramics, carpets and painted eggs) by training a Convolutional Neural Network (CNN) model derived from the Residual Network (ResNet-50) architecture. We also implemented a system which can detect and identify through a webcam if the object in front of it contains a learned motif. Experimental results show that our neural network has an overall accuracy of 99.4% and a reduced webcam processing time.
An Iterative Reputation Ranking Method via the Beta Probability Distribution

Ranking user reputation and object quality has drawn increasing attention for online rating systems. By introducing an iterative reputation-allocation process, in this paper, we present an iterative reputation ranking algorithm in terms of the beta probability distribution (IBeta), where the user reputation is calculated as the probability that the user will give fair ratings to objects and the high reputation users’ ratings have larger weights in dominating the corresponding quantity of fair/unfair ratings. User reputation is reallocated based on their ratings and the previous reputations. The user reputation and users’ quantities of fair/unfair ratings are iteratively updated until they become stable. The experimental results for the synthetic networks show that both the AUC values and Kendall’s tau $\tau $ of the IBeta algorithm are larger than those generated by the RBPD method with different fractions of random ratings. Moreover, the results for the empirical networks indicate that the presented algorithm is more accurate and robust than the RBPD method when the rating systems are under spamming attacks. This paper provides a further understanding on the role of the probability for the online user reputation identification.
Chinese Ink Stylized Drawing for Digital Images

Chinese ink stylized drawing for digital images combined the traditional Chinese ink painting techniques with computer non-photorealistic rendering technique, which can simulate the Ink style works of the painter’s. This algorithm presented an automatic simulation algorithm for Chinese ink style rendering based on two-dimensional digital images. It used a digital image as input, combined digital image processing technique and non-photorealistic rendering technique, through image diffusion, decolorization, abstraction and Xuan paper simulation, finally got the image with Chinese ink painting style. The innovation of this algorithm is that the saliency map is used in the simulation process. It could enhance the saliency area which was weakened after the image extraction, and make the edge of the image have more obvious effect of Chinese ink painting. Experimental results showed that the algorithm could approximate the traditional Chinese ink painting simulation, with high operational efficiency.
Non-Local Means with Steer Search Window and Adaptive Parameter

The non-local means(NLM) algorithm is an effective image denoising method, and many variants have been proposed in NLM. However, the search window in most variants is always square which is highly likely to introduce irrelevant sample pixels in denoising. To this end, motivated by steering kernel, an elliptical search window that is defined along the local edge structure is adopted, as well as the strategy of adaptive patch size and smoothing parameter method to improve the ability of noise suppression and detail preservation. Experimental results show the effectiveness and better performance of the proposed method when comparing with other methods.
Cost-Based and Effective Human-Machine Based Data Deduplication Model in Entity Reconciliation

In real world, databases often have several records representing the same entity and these duplicates have no common key, thus making deduplication difficult. Machine-based and crowdsourcing techniques were disjointly used in improving quality in data deduplication. Crowdsourcing were used for solving tasks that the machine-based algorithms were not good at. Though, the crowds, compared with machines, provided relatively more accurate results, both platforms were slow in execution and hence expensive to implement. In this paper, a hybrid human-machine system was proposed where machines were firstly used on the data set before the humans were further used to identify potential duplicates. We performed experiments using three benchmark datasets; paper, restaurant and product datasets. Our algorithm was compared with some existing techniques and our approach outperformed some methods by achieving a high accuracy of deduplication and good deduplication efficiency while incurring low crowdsourcing costs.
A Design of Cross-Realm Authentication Scheme in Openstack Based on Declaration

Aiming at the issue how users in Windows domain cross-realm access cloud computing resources, a cross-realm authentication scheme based on federated identity was proposed. Based on the idea of the declaration, the scheme uses the federated identity provider to replace the gateway in the traditional gateway-based cross-realm authentication model, so as to realize the users in Windows domain access the cloud resources without re-authentication. The scheme uses SAML protocol to exchange user identity information between different domains, which ensures versatility and security of the system and realizes seamlessly secure communication between different security domains. Finally, based on claim provider, federated identity provider and application service provider, we give the design of the key components of the three modules, then the feasibility of the scheme is verified with the popular cloud platform OpenStack.
Environmentally Friendly Functionalization of Porous Carbon Electrodes for Aqueous-Based Electrochemical Capacitors

A green chemical method is reported for the synthesis and functionalization of high surface area carbon-based materials for applications as electrodes in short-term energy storage devices, such as supercapacitors. The proposed method is focused on sustainable chemistry and engineering. Our results evidenced an excellent performance of electrochemical capacitor devices with a high lifespan over a wide working voltage with low equivalent series resistance. Great enhancement of electrode wettability and charge transfer at the electrode/electrolyte interface using oxygenated surface functional groups was obtained using the present method. Actually, the present findings are quite promising thus encouraging further investigations of this green-functionalization method for all other carbon-based materials, such as graphene, carbon nanotubes, doped-diamond, and so on.
An Efficient Face Recognition Algorithm Based on Deep Learning for Unmanned Supermarket

In recent years, technologies related to face recognition have achieved rapid development. Face recognition Algorithms using Convolutional Neural Network (CNN) in the field of deep learning have been improved compared with traditional ones in terms of recognition accuracy, anti-interference ability and the speediness of identification. The mature face recognition technology now plays a decisive role in many areas, and also one of the key technologies in the facilitation of unmanned supermarkets. However, most algorithms nowadays just use the face database in unrestricted scenes to train, verify and evaluate. Their performance in practical applications is not satisfactory enough. In this paper, we eager to find a face recognition algorithm and system that are more suitable for unmanned supermarket scenes. After in-depth analysis of the scene, we find out that motion blur and large-scale recognition are two important reasons why current algorithms do not perform well. Aim to solve these problems, we summarize and propose new algorithms based on the existing ones. The experimental results show that the algorithm has effective face recognition ability in unmanned supermarket.
On Detecting Manifestation of Adversary Characteristics

Adversaries are conducting attack campaigns with increasing levels of sophistication. Additionally, with the prevalence of out-of-the-box toolkits that simplify attack operations during different stages of an attack campaign, multiple new adversaries and attack groups have appeared over the past decade. Characterizing the behavior and the modus operandi of different adversaries is critical in identifying the appropriate security maneuver to detect and mitigate the impact of an ongoing attack. To this end, in this paper, we study two characteristics of an adversary: Risk-averseness and Experience level. Risk-averse adversaries are more cautious during their campaign while fledgling adversaries do not wait to develop adequate expertise and knowledge before launching attack campaigns. One manifestation of these characteristics is through the adversary's choice and usage of attack tools. To detect these characteristics, we present multi-level machine learning (ML) models that use network data generated while under attack by different attack tools and usage patterns. In particular, for risk-averseness, we considered different configurations for scanning tools and trained the models in a testbed environment. The resulting model was used to predict the cautiousness of different red teams that participated in the Cyber Shield ‘16 exercise. The predictions matched the expected behavior of the red teams. For Experience level, we considered publicly-available remote access tools and usage patterns. We developed a Markov model to simulate usage patterns of attackers with different levels of expertise and through experiments on CyberVAN, we showed that the ML model has a high accuracy.
An 8-Channels FPGA-Based Reconfigurable Chirp Generator for Multi-Band Full Polarimetric Airborne/Spaceborne CP-SAR

This paper outlines the development work of a new Field Programmable Gate Array (FPGA) based chirp generator from which the new chirp generator is capable of synthesizing 4 pairs of I & Q chirp signals. The paper first proposes a multi-channel Direct Digital Synthesis (DDS) based reconfigurable chirp generator architecture whereby the chirp signal properties for each channel are independently reconfigurable in real-time. In addition, a reconfigurable Timing and Control Unit (TCU) is embedded into the system to generate the important timing synchronization signals which are essential for SAR hardware system. The proposed chirp generator is implemented on a custom-designed FPGA-based hardware platform with 8 on-board independent channels of Digital-to-Analog Converter (DAC). In future, the system will be used in Josaphat Microwave Remote Sensing Laboratory (JMRSL) airborne and spaceborne dual-band (C/X) full polarimetric Circularly Polarized Synthetic Aperture Radar (CP-SAR) system.
Ship Recognition Method based on Visual Knowledge Base

In order to ensure the navigational safety, a ship recognition model based on visual knowledge base (SRVKB) is presented. In SRVKB, CNN model is combined with Itti model and multi-feature extraction method. Moreover, SRVKB can yield a visual knowledge base of ship, which simulates the process from visual attention to object recognition in human visual system. Firstly, carry out de-noising process and contour extraction on the object area of the image. Secondly, by adding three different channels to CNN model, multi-features can be obtained. Finally, visual knowledge base of ship yielded via SRVKB can be used as a guide in ship recognition. The experiment results show that the proposed method can not only locate the ships’ positions. Meanwhile, compared with single feature method, SRVKB with multi-features can better the recognition effective.
A Novel Oscillator Based Signal Conditioning Circuit for Online Measurement of Capacitive Sensors

A novel and simple oscillator based interface circuit for the medium range capacitive sensor ($1{\mu}\mathbf{F}$ to $100{\mu}\mathbf{F}$) is presented. Op-amp based Hartley oscillator circuit is used to convert a change in capacitance to change in frequency. As the output of the signal conditioning circuit is frequency, so it is very easy to use this frequency directly for transmission in communication systems. In this manner, the presented circuit can be used directly for acquiring the reading from the remotely placed sensors. An IoT based circuit system is also presented here for online measuring the capacitance and display it on the local server. The described circuit is simple to build and as it is op-amp based circuit, so losses are very less and there is negligible loading effect while connected to the sensor. The output frequency from the oscillator circuit is measured, and then it is converted into an equivalent voltage, which then measured by Arduino and Raspberry-pi sends this measured values to the Wi-Fi based server for online display.
Performances of Microwave Photonic Notch Filter Based on Microring Resonator With Dual-Drive Modulator

A tunable microwave photonic notch filter with ultrahigh radio frequency (RF) rejection ratio is achieved by using a dual-drive Mach–Zender modulator and a microring resonator. In addition, we dedicated to clarify the mechanisms of the microwave photonic notch filters based on a microring resonator under both overcoupled and undercoupled states and the performance differences between these two situations are investigated in detail. Furthermore, the experimental results show that the cancellation microwave photonic notch filter with ultrahigh RF rejection ratio can be achieved with both undercoupled and overcoupled microring resonators. Besides, an RF rejection ratio exceeding 50 dB was achieved for a microring resonator with an optical extinction ratio of only 4 dB, where a 46-dB enhancement from optical to RF response is obtained. Also, the bandwidth of the microwave photonic filter can tuned from 0.65 to 2.2 GHz by adjusting the coupling ratio of the ring resonator. Meanwhile, by tuning the wavelength of the optical carrier, the filtering frequency can be tuned and a tuning range of about 25 GHz was achieved. The implementation is much simpler and more economical compared with previous reports and has great potential in monolithic integration of microwave photonic filters on chip.