Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization-Oriented Resource Allocation Management for Vehicular Fog Computing

Vehicular fog computing, which extends the mobile cloud paradigm, is usually composed of stable infrastructures, a large volume of vehicles, portable devices, and robust networks. As a service-providing platform, it is significant to quickly obtain the required service with the aim to correctly save the energy of the corresponding nodes and effectively improve the network survivability. However, the limited capacity of components makes such a situation more complicated. This paper aims to reduce serving time by allocating the available bandwidth to four kinds of services. A utility model is built according to the above-mentioned serving methods and is solved through a two-step approach. For the first step, all the sub-optimal solutions are provided based on a Lagrangian algorithm. For the second step, an optimal solution selection process is presented and analyzed. A numerical simulation is executed to illustrate the allocation results and the optimal utility model while optimizing the survivability.
A Multi-Features Fusion Method Based on Convolutional Neural Network for Vehicle Recognition

Vehicle recognition is a vital part of the advanced driver assistance system (ADAS). In this paper, we propose a multi-features fusion (MFF) method combined with convolutional neural network (CNN) for vehicle recognition, which can enhance the vehicle recognition relative to the single feature and improve the accuracy of vehicle recognition. Our method firstly trains the CNN using different convolution kernels, network layers and feature maps to obtain the optimal CNN model, and extracts all feature vectors of the hidden layer in an image, namely CNN features. At the same time, the histograms of oriented gradients (HOG) features and the principal component analysis (PCA) features of the vehicle are also extracted from the image separately. Then we fuse these features with different normalization methods to obtain a more distinguishable comprehensive feature. Finally, we can recognize vehicle in the image by support vector machine (SVM). Our experimental result shows that the proposed MFF method is superior to CNN+HOG+SVM, HOG+PCA+SVM and PCA+CNN+SVM feature fusion methods, and the accuracy is further improved from 96.63% to 98.00%.
A Method of Identifying Shift Switch of Expected Failure Isolation

After setting expected failure during power grid operation risk assessment, failure isolation contains two steps, one is to identify the switches that should be disconnected, which are called shift switches, the other is to disconnect the shift switches. This paper proposes a method of identifying shift switch of expected failure isolation, where substation topology and complex wiring are considered. First, from the viewpoint of failure isolating effect, failure elements are classified into independent elements and element groups. Second, element group and its composition are obtained according to its structural features. Then, the method of identifying shift switches of failure isolations for all kinds of failure elements is established. Finally, the proposed method is validated by applying it to classical failures of a practical power grid.
Lifetime Control of Modular Smart Transformers Considering the Maintenance Schedule

Smart Transformers (ST) are power electronic transformers capable of providing grid services and DC connectivity compared to conventional low-frequency transformers. However, failure of the components and consequent maintenance is one of the major challenges compared to it's counterpart, limiting its application. By controlling the power flowing through the cells in a modular ST architecture, the remaining lifetime of these cells can be controlled. The idea is to schedule the maintenance by actively controlling the lifetime of the individual cells using an optimization algorithm. The proposed algorithm has the ability to schedule the maintenances according to predefined maintenance intervals. A potential ST architecture consisting of Cascaded H-bridge (CHB) and Dual-Active Bridge (DAB) converters are analyzed with simulations and experiments to validate the proposed power routing algorithm and its impact on the lifetime and maintenance scheduling.
A Novel, Interdisciplinary, Approach for Community Detection Based on Remote File Requests

Community structures are formed in many real-world networks, e.g., biological or medical groups, student groups, and so on. Communities are perhaps the most important feature of today’s networks, since the majority of people who join a network also tend to join one or more communities. Therefore, several researchers find that the detection of hidden communities is a very interesting and challenging research field. Communities are represented as the groups of nodes on a graph, corresponding to users with similar interests. This paper introduces a novel, interdisciplinary, approach for community detection, combining social networks and distributed systems, where remote access to shared files is offered in a networked environment. A new metric, based on data requests, is introduced and used as a measure of the belonging degree of a node in a certain formed community. Two sets of simulations are used to verify our scheme: simulation results on synthetic networks and results derived from real data.
A Novel Approach for Studying Privacy Behavior in Social Media

Social Networking Sites achieved a breakthrough over the past few years since they allow individuals to be in touch with family and friends, but also raised privacy concerns when it comes to disclosing personal information with others. This study investigates users' behavior in terms of how they choose their privacy settings on Facebook. It examines the impact of faces and tags existence on user's privacy. Moreover, we extend the work on YourPrivacyProtector, a system for monitoring and recommending privacy settings in Facebook, and enhance the recommendation system by examining photos with faces and tags. We use machine learning techniques to understand privacy settings of different users and recommend them a stronger setting. We also evaluate our algorithms with a case study.
Complete Genome Sequence of Avian Coronavirus Strain D274

Avian coronavirus, the causative agent of avian infectious bronchitis, occurs as multiple genotypes and lineages, and full genomes are not available for the majority of them. This paper reports the (previously unknown) complete genome sequence of strain D274 of this virus (27,599 nucleotides), isolated from chickens in The Netherlands in 1979. ABSTRACT Avian coronavirus, the causative agent of avian infectious bronchitis, occurs as multiple genotypes and lineages, and full genomes are not available for the majority of them. This paper reports the (previously unknown) complete genome sequence of strain D274 of this virus (27,599 nucleotides), isolated from chickens in The Netherlands in 1979.
Design of a Low Noise Clock Generator Based on TSMC65nm Process

This paper designed a low noise 14 GHz phase-locked loop (PLL) for high-speed serial interface. We use a differential structure with a feedback charge pump to reduce the system's common-mode noise and current mismatch. To further reduce the phase noise, a differential structure of the third-order filter is introduced, which can also decrease the chip area. By using a differential control capacitor, the nonlinear of the VCO can be alleviated. The PLL adopts TSMC 65nm CMOS technology, and the working voltage of the system is 1.1v. Simulation results show that the PLL locking time is 800ns and the phase noise of the VCO is 107dBc/Hz at 1MHz.
A 10-bit 16-MS/s Ultra Low Power SAR ADC for IoT Applications

This paper presents a 10-bit 16MS/s ultra-low-power Successive Approximation Register (SAR) ADC with capacitor array combining the split-capacitor array and monotonic switching procedure. To achieve the ultra-low power requirement, bootstrapped switch, low power capacitor array, dynamic two-stage comparator without any calibration are employed. The chip area is 0.0288mm2 based on 55nm CMOS process. At 1MHz input signal and 16MS/s sample rate, the measurement results show that we get 55dB SNDR and 71dB SFDR. The power consumption of ADC core is only about 150uW, resulting in a FOM of 20.3 fJ/conversion-step.
FLUIDS: A First-Order Lightweight Urban Intersection Driving Simulator

To facilitate automation of urban driving, we present an efficient, lightweight, open-source, first-order simulator with associated graphical display and algorithmic supervisors. FLUIDS can efficiently simulate traffic intersections with varying state configurations for the training and evaluation of learning algorithms. FLUIDS supports an image-based birdseye state space and a lower dimensional quasi-LIDAR representation. FLUIDS additionally provides algorithmic supervisors for simulating realistic behavior of pedestrians and cars in the environment. FLUIDS generates data in parallel at 4000 state-action pairs per minute and evaluates in parallel an imitation learned policy at 20K evaluations per minute. A velocity controller for avoiding collisions and obeying traffic laws using imitation learning was learned from demonstration. We additionally demonstrate the flexibility of FLUIDS by reporting an extensive sensitivity analysis of the learned model to simulation parameters. FLUIDS 1.0 is available at https://berkeleyautomation.github. io/Urban_Driving_Simulator/.
Detection and Identification of Power Switch Failures Using Discrete Fourier Transform for Fault-Tolerant Operation of Flying Capacitor Multilevel Converters

Prompt detection of power switch failure is crucial for fault-tolerant operation of multilevel converters. The paper presents a new technique of detecting, identifying, and locating a device fault in a flying capacitor multilevel converter (FCMC). This is realized by continuously analyzing the output voltage harmonics for failure detection and subsequently performing a diagnosis algorithm to identify failure type and location. The harmonics analysis is performed by the existing FPGA controller using discrete Fourier transform. This new approach uses only existing output voltage sensors without adding any new hardware, and can identify the location and fault type with good accuracy and speed. Both simulation and experimental results validate the proposed concept on a 5-level GaN FET based FCMC prototype. Furthermore, fault tolerant operation of the FCMC is experimentally demonstrated after a switch short fault is detected by the new technique.
Negative Emotions, Positive Experience: What Are We Doing Wrong When Evaluating the UX?

Since its idealization more than 20 years ago, much research has been carried out on the User eXperience (UX) field, with several evaluation methods being proposed. However, studies have been pointing out conflicting results when evaluating the UX. Users frequently evaluate their UX as positive, even when experiencing many negative emotions when interacting with a product. Moreover, variables such as the peak-end effect and the memory-experience gap may also have been influencing the results, leading to misinterpretations about the product's quality. In this context, this paper presents our work-in-progress research on the following research question: "What are we doing wrong when evaluating the UX? ". We discuss about different variables that may have been influencing users' perceptions about their experiences in previous studies and highlight research opportunities. With this work, we expect to shed light on and bring reflection to current practices on UX evaluations in order to progress the research in the UX field.
Modelling AC Winding Losses in a PMSM with High Frequency and Torque Density

This paper investigates proximity and skin effects winding losses in a high performance and high frequency brushless permanent magnet machine designed for electric sport racing application, where such losses have a relevant impact. A detailed finite element analysis (FEA), with all conductors per slot modelled, is performed for various winding configurations, with single and multiple strands-in-hand, considering random conductors placement within the stator slots. A new hybrid analytical-FEA method is proposed for speeding up the losses calculation and then validated against pure FEA method. The proposed method can be further used in accurate and fast estimation of the motor performance and in optimization procedures, avoiding heavy computational burden of full FEA simulations.
QCM Mass Sensitivity Analysis of Modified Electrode Structures Using Finite Element Method

This paper study the quartz crystal microbalance (QCM) mass sensitivity distribution of modified electrode structure using Finite Element Method(FEM). QCM can achieve nanogram level of quality detection, so it is widely used in surface science, electrochemistry, biomedical science and so on. However, the results of QCM detection are less reproducible and consistent. After theoretical deduction and simulation found that these deficiencies are closely related to mass sensitivity. The mass sensitivity is depended on Sauerbrey theory and Bessel equation. Also, the Energy trap theory affected on mass sensitivity. We considered that these theory and modified electrode structure design to optimize mass sensitivity based on Finite Element Method. This modified electrode structure simulation shows that it has better mass sensitivity distribution and improve the QCM detection consistent.
Analytical Modeling of a Segmented-Pole Synchronous Reluctance Machine with CRGO Laminations

This paper proposes a modified version of an analytical model used for the analysis, design, and characterization of a segmented-pole synchronous reluctance machine made of grain-oriented steel laminations. The feasibility of grain-oriented steel laminations to improve the saliency ratio of synchronous reluctance machines is initially discussed. The magnetic features of the grain-oriented steel laminations are compared with the characteristics of non-grain-oriented steel laminations. Based on that a mathematical approach to include the superior magnetic properties of the steel in the analytical model of the synchronous reluctance machine is presented. The proposed model is validated through FEA and experiments. It is observed that for a full utilization of the superior magnetic features of the grain-oriented steel, saturation of the rotor core must be avoided.
Facial Recognition via Transfer Learning: Fine-Tuning Keras_vggface

The challenge of developing facial recognition systems has been the focus of many research efforts in recent years and has numerous applications in areas such as security, entertainment, and biometrics. Recently, most progress in this field has come from training very deep neural networks on massive datasets. Here, we use a pre-trained face recognition model and perform transfer learning to produce a network that is capable of making accurate predictions on a much smaller dataset. We also compare our results with results produced by a selection of classical algorithms on the same dataset.
Multi-Receiver Modulation Classification for Non-Cooperative Underwater Acoustic Communications

In this paper, we design a non-cooperative underwater acoustic communications system employing automatic modulation classification (AMC) and blind channel estimation (BCE). The proposed system is based on the multi-receiver and the blind channel estimation exploits cross-relation between the receivers and the sparsity structure of the underwater acoustic channels. Based on the estimated channel impulse response, the classification ability of the featured-based AMC algorithm and the performance of the blind demodulator in multipath sparse channels is investigated. The simulation results show that the proposed system provides a good classification and demodulation performance while exhibiting significantly accurate blind channel estimation.
The Challenges of Creating Engaging Content: Results from a Focus Group Study of a Popular News Media Organization

The process of content creation for distribution via social media platforms is not a trivial one for social media editors as the goal of creating both serious and engaging content is challenging, with no clear or differing guidelines or rules across and between platforms. For creators of serious content, such as news organizations, advertisers, or educational institutions, engagement has a deeper meaning beyond likes, shares, etc. that is aimed at the audience actually processing the underlying content associated with a social media post. In this research, we report findings from a group study that aimed to understand the process and challenges of creating engaging content across three social media platforms in a major news organization. The findings from the study indicate that creating engaging content is effort- and time-consuming, and they highlight the need to support the process of creating engaging content across multiple social media platforms. Our longer-term goal is to develop a system design to support social media editors' creation of engaging content with which they can select engaging passages from news articles and select platforms on which to publish the content.
An approach to the design and implementation of the hierarchical control system of FMS, combining structured knowledge representation formalisms and high-level Petri nets

A presentation is made of the methodology adopted in the design of the coordination subsystem and the real-time scheduling subsystem for the simulation and the real-time control of flexible manufacturing systems (FMSs). This particular approach, combining structured, object-oriented paradigms and a particular extension of high-level Petri nets, is presented and discussed. The focus is on the following issues: (1) the modeling of the FMS control system in a structured knowledge base (frame-based multiple-inheritance semantic networks); (2) the automatic synthesis of Petri net models of the coordination subsystem, starting from such a knowledge base; (3) the dynamic behavior of the coordination subsystem; and (4) the structure of the real-time scheduling subsystem, based on particular heuristic suboptimal multicriterion algorithms. The interaction mechanisms between the coordination and scheduling subsystems are defined. A particular case study involving a class of problems on the concurrent scheduling of multiple projects is taken as an example. <<ETX>>
Temperature Dependent Carrier Mobility of Epoxy/BN/Graphene Composites for HTS Cable Joint

The flashover at the solid-liquid interface is the main dielectric failure in the cryogenic insulation system, which has great relation with the carrier mobility. In this paper, epoxy$/\mathrm {B}\mathrm {N}$(boron nitride)/Graphene composites with different contents were prepared and the surface potential decay (SPD) method was employed to analyze the transit time and the carrier mobility. The experimental results indicate that the behaviors of surface potential and carrier mobility are affected by specimen temperature. Obtained results show that BN and Graphene particles can affect the carrier mobility and modulate the discharge speed, which may further influence the flashover voltage at solid-liquid interface at cryogenic temperature.
Research on Monostatic Radar Cross Section Simulation of Small Unmanned Aerial Vehicles

With the increasing applications of small civil Unmanned Aerial Vehicles (UAV), the detection technology of small VAV radar has become a hot research. The primary prerequisite for radar detection is the efficient radar cross-section (RCS). This paper focus on the RCS of civil VAV-DJI Phantom 3 Advanced Vision. Frist of all, each component part of this VAV is drawn by Soilworks2014. And then the VAV is formed by the assembly of each part. At last, the monostatic RCS of each parts is simulated using FEKO7.0, and the RCS of VAV is simulated using CST2017. The carrier frequency of radar is set 10GHz. Simulational results show that the main parts that affect the RCS are separately battery, control circuit board, cloud terrace and camera. The typical RCS of DJI Phantom 3 Advanced Vision is between 0.01sm to 2.34sm. The RCS fluctuation between different angles reaches 20dB.
Classification and Detection of Demagnetization and Inter-Turn Short Circuit Faults in IPMSMs by Using Convolutional Neural Networks

This paper represents classification and detection algorithm for inter-turn short circuit faults (ISCF), demagnetization faults (DF), and hybrid faults (a combination of ISCF and DF) in an interior permanent magnet synchronous machines (IPMSMs). We confirm the characteristic of normal and various fault types by using the fast Fourier transform patterns of the phase currents in finite element analysis model. Based on the characteristic, we propose a classification and detection method using a convolutional neural networks model. The training and test data were extracted using three-phase currents obtained from the experiments. The test results verify the effectiveness of the proposed method.
Maritime Moving Target Long Time Integration for GNSS-Based Passive Bistatic Radar

This paper addresses the exploitation of Global Navigation Satellite Systems (GNSS) as transmitters of opportunity in passive bistatic radar systems for maritime surveillance. The main limitation of this technology is the restricted power budget provided by navigation satellites, which makes it necessary to define innovative moving target detection techniques specifically tailored for the system under consideration. To this aim, this paper puts forward long integration time techniques able to collect the signal energy over long time intervals (tens of seconds), allowing the retrieval of suitable levels of signal-to-disturbance ratios for detection purposes. A local plane based technique is first considered, providing target detection in a plane that represents the section of maritime area covered by the radar antenna. As a suboptimum solution in terms of achievable integration gain, but more efficient from a computational point of view, a second technique is considered working in the conventional bistatic range and Doppler plane (basic plane based). Results against synthetic and experimental datasets show the effectiveness of the proposed techniques.
A High Frequency Microstrip Patch Antenna Integrated with FSS Using Substrate Integrated Suspended-Line Technology

A square patch microstrip antenna was initially studied; its frequency and bandwidth characteristics were noted. The frequency selective surface (FSS) was then employed based on the studied characteristics to enhance the impedance bandwidth and antenna gain. With embedded air lacunas and low loss dielectric properties of the substrate integrated suspended line (SISL) technology, an antenna radiation efficiency of 91.6% is seen. The size of the antenna is relatively smaller and can be fabricated on a standard PCB technology at low cost.
An Electromagnetic Cantilever Generator with High-Permeability Magnetic Materials

This paper presented an electromagnetic cantilever generator (EMCG) of high energy density with high-permeability magnetic materials for harvesting ambient vibrational energy. We utilized a flexible cantilever connecting with a pair of magnets on the free end, and the coils with high-permeability magnetic materials are set right in front of the magnets. The electrical power is produced from coils based on electromagnetic mechanism. The high-permeability magnetic materials amplify the magnetic flux change, making it appropriate for vibrational energy harvesting at a resonant frequency. The gap between magnets and coils is discussed since it will influence the output of the system. Under the resonant frequency of 21 Hz, a peak-to-peak open-circuit voltage of 4.5 V is obtained at an acceleration of $1g$ with the gap of 2 mm. And a maximum value of 160 mW is measured at a load resistance of $4\ \Omega$, this translate into a power density of 32 mW/cm3, which can be a promising way to solve the power supply problem.