Miracle-dz/UniFAR-FTUs huggingface dataset:
Circuit Modeling of a Wireless Power Transfer System by Eigenmode Analysis Based on the Impedance Expansion Method

The impedance expansion method (IEM), which has been previously proposed by the authors of this paper, is a circuit modeling technique for electrically very small devices. This paper describes a circuit modeling procedure that utilizes the IEM and eigenmode analysis for a wireless power transfer system. First, a multimode circuit model that can represent the higher order resonances in a broadband frequency range is presented. Subsequently, a single-mode circuit model—consisting only of passive elements—that can represent both the radiated and conduction loss powers at approximately its operating frequency band is presented.
Anisotropic Effect of Proton Irradiation on Pinning Properties of Fe(Se,Te) Thin Films

At the present day, iron-based superconductors (IBS) can be considered as competitors of cuprates for the fabrication of superconducting cables for high field application in the presence of particle irradiation. In particular, it has been demonstrated that 11-IBS are very robust against proton irradiation. In this paper, we analyze the effect of this kind of irradiation on the anisotropy of superconducting and pinning properties of Fe(Se,Te) thin films grown on CaF2. By electrical transport measurements, we evaluate the upper critical field as a function of the temperature and the pinning activation energy as function of the applied magnetic field, for different orientation between the material crystal structure and the field itself, in pristine and irradiated samples. The aim is to recognize if and how proton irradiation modifies the anisotropy of the material properties.
A Three-Dimensional Integrated Micro Calorimetric Flow Sensor in CMOS MEMS Technology

This article presents a 3-D integrated molybdenum (Mo) thermoresistive microcalorimetric flow sensor in a 0.18-μm CMOS MEMS technology. The sensor consists of a MEMS structure which is fabricated inside a sealed microchannel and a constant temperature control circuit implemented on the CMOS wafer. The MEMS structure and the CMOS circuit are 3-D integrated at the wafer level. For the N<sub>2</sub> gas flow, the proposed flow sensor achieves a high sensitivity of 0.71 mV/(m/s) and a wide bidirectional detection ability of −26–26 m/s. Moreover, an equivalent circuit model is proposed in this article, which depicts the nonlinear output/overheated temperature (<italic>V</italic><sub>out</sub>/Δ<italic>T</italic><sub>h</sub>) sensor response to the input gas flow. This model would be an efficient tool for the design and optimization of high-performance system-on-chip calorimetric flow sensors.
Iterative Loewner Matrix Macromodeling Approach for Noisy Frequency Responses

This paper presents a macromodeling technique for modeling distributed circuits characterized by noisy frequency-domain data. The proposed method is based on an iterative Loewner matrix (LM) approach. Using the LM approximation of previous iterations, the singular values and orthonormal matrices of the Loewner pencil are made to be more accurate. This approach is shown to minimize the biasing effect of the noisy data due to the nonlinearity of the singular value decomposition operation, resulting in more accurate poles and residues. Numerical examples illustrate that for noisy frequency-domain data, the proposed algorithm creates more accurate macromodels when compared with the traditional LM approach.
Systematic Study of the Contact Resistance Between REBCO Tapes: Pressure Dependence in the Case of No-Insulation, Metal Co-Winding and Metal-Insulation

No-insulation and partial-insulation are novel approaches for compact, high field rare-earth-Ba2Cu3O7-x-coated conductor magnets. They maximize the current density and overcome some of the coated conductors’ intrinsic challenges such as the low normal zone propagation velocity and the resulting difficulties to quench REBCO magnets safely. However, this comes at a reduction of the field quality and an increase of the charging time. The performance of this type of coils is mainly determined by the contact resistance between the different layers of the winding. In the case of partially-insulated coils, the contact resistance can be adjusted by a tailored choice of the material used for the metal co-winding or for the metallic coating of the REBCO tape. We present here a systematic study of the impact of pressure on the contact resistance in the cases of direct contact (no-insulation) as well as metal co-winding and metal coating (partial-insulation). Different “insulation” metals as well as various “insulation” to REBCO tape ratios are tested at 77 K in self-field varying the contact pressure in the range of 10–200 MPa. The goal is to provide coil manufacturers with the means of achieving a controlled contact resistance to optimize the coil performance.
Content-based File Type Identification

Data is encoded and stored in a wide variety of file types having different schema. As a result, File Type Identification (FTI) has become essential in data pre-processing and security. FTI traditionally involves interpretation of the key signature bytes found in the beginning of each file. However, when these signature bytes are corrupted, the contents of the file must be analysed to correctly identify the file type. Recently, studies leveraging machine learning algorithms have seen great success in content-based classification. In addition, when the signature bytes are not damaged, Commercial Off The Shelf (COTS) systems like Libmagic are preferred. We present an end-to-end framework combining signature and content-based file type classification. We utilize Libmagic in combination with machine learning algorithms, implemented in Spark, to accurately identify file types. The results show that our system can successfully identify the data schema and file types using a pool of workflow templates.
Photonics-Based Broadband Microwave Instantaneous Frequency Measurement by Frequency-to-Phase-Slope Mapping

A photonics-based broadband microwave instantaneous frequency measurement (IFM) method is proposed by monotonously mapping the frequency to the slope of the time-varying phase of the microwave signal under test. This frequency-to-phase-slope mapping approach is realized utilizing a variable photonic delay line and a microwave photonic in-phase/ quadrature (I/Q) mixer, where the I/Q mixer is composed of two phase modulators connected tail to tail within a fiber loop, an optical 90° hybrid, and a pair of low-speed balanced photodiodes. Thanks to the monotonous frequency-to-phase-slope mapping property, as well as the use of photonic delay line and microwave photonic I/Q mixing, the IFM system can achieve a wide-range frequency measurement without ambiguity. An experiment is performed. The established IFM system is able to operate from 5 to 67 GHz with a measurement error of less than 500 MHz, which achieves the widest frequency measurement range ever recorded by photonics-based IFM systems. In addition, the influences of the I/Q phase mismatch and the amplitude noise on the performance of the IFM system are also discussed. We believe that this photonics-based IFM system is a promising solution for wideband microwave frequency measurements.
Study on the Propagation Characteristics of Indoor Millimeter-wave at 37.2GHz by SBR Method

At present, the effective method for studying the characteristics of indoor millimeter-wave propagation is the SBR method. In this paper, received power and delay spread for indoor line-of-sight and non-line-of-sight propagation are studied by SBR method. The correctness of the results is verified by comparison with relevant measurement data.
Attention and Anticipation in Fast Visual-Inertial Navigation

We study a visual-inertial navigation (VIN) problem in which a robot needs to estimate its state using an on-board camera and an inertial sensor, without any prior knowledge of the external environment. We consider the case in which the robot can allocate limited resources to VIN, due to tight computational constraints. Therefore, we answer the following question: under limited resources, what are the most relevant visual cues to maximize the performance of VIN? Our approach has four key ingredients. First, it is task-driven, in that the selection of the visual cues is guided by a metric quantifying the VIN performance. Second, it exploits the notion of anticipation, since it uses a simplified model for forward-simulation of robot dynamics, predicting the utility of a set of visual cues over a future time horizon. Third, it is efficient and easy to implement, since it leads to a greedy algorithm for the selection of the most relevant visual cues. Fourth, it provides formal performance guarantees: we leverage submodularity to prove that the greedy selection cannot be far from the optimal (combinatorial) selection. Simulations and real experiments on agile drones show that our approach ensures state-of-the-art VIN performance while maintaining a lean processing time. In the easy scenarios, our approach outperforms appearance-based feature selection in terms of localization errors. In the most challenging scenarios, it enables accurate VIN while appearance-based feature selection fails to track robot's motion during aggressive maneuvers.
Oxidation of TCE by Combining Perovskite-Type Catalyst With DBD

A novel hybrid system constituting of La–Mn-based perovskite-type catalyst with nonthermal plasma (NTP) is developed and evaluated for the removal of trichloroethylene (TCE) from gas streams. Ce and Ni were used as promoter for modification LaMnO<sub>3</sub> because addition of Ce and Ni could enhance redox property and activity of catalyst. First, the activities of various perovskites including LaMnO<sub>3</sub>, La<sub>0.8</sub>Ce<sub>0.2</sub>MnO<sub>3</sub>, and La<sub>0.8</sub>Ce<sub>0.2</sub>Mn<sub>0.8</sub>Ni<sub>0.2</sub>O<sub>3</sub> were evaluated for TCE removal via catalysis, and the results indicate that La<sub>0.8</sub>Ce<sub>0.2</sub>Mn<sub>0.8</sub>Ni<sub>0.2</sub>O<sub>3</sub> has the highest activity for TCE removal among three perovskites prepared. The three perovskites are individually combined with NTP to form various plasma catalysis systems for TCE removal. Experimental results indicate that 100% TCE removal efficiency can be achieved with plasma catalysis (La<sub>0.8</sub>Ce<sub>0.2</sub>Mn<sub>0.8</sub>Ni<sub>0.2</sub>O<sub>3</sub>) when the system is operated with the applied voltage of 16 or 17 kV. On the other hand, the highest TCE removal efficiency achieved with NTP alone is 73.2% with the applied voltage of 17 kV. In addition, CO<sub>2</sub> mineralization efficiencies achieved with plasma catalysis combined with La<sub>0.8</sub>Ce<sub>0.2</sub>Mn<sub>0.8</sub>Ni<sub>0.2</sub>O<sub>3</sub> (31.1%–46.6%) are significantly higher than that of NTP alone (8.4%–13.1%). It is worth noting that concentrations of O<sub>3</sub> and NO<sub><italic>x</italic></sub> generated with plasma catalysis are significantly lower than that of NTP alone, implying that formation of O<sub>3</sub> and NO<sub><italic>x</italic></sub> could be effectively decomposed as plasma catalysis is applied for TCE removal. Overall, perovskite-type catalyst can be integrated with NTP to form plasma catalysis system for effective removal of TCE from gas streams.
An Achievable Rate Region for the Two-Way Channel with Common Output

In this paper, we derive a new achievable rate region for the two-way channel (TWC) with common output. The achievable rate region is based on two auxiliary directed graphs that represent the information that is used by each of the terminals. For any choice of the two auxiliary graphs, the achievable rate region is expressed with single-letter expressions. A degenerate choice of the auxiliary graphs, each with a single node, reduces the new achievable rate region to the Shannon’s inner bound. We also show that the achievable rate region outperforms the Shannon’s inner bound. This is shown with an explicit calculation of the achieved rate for the binary multiplying channel (BMC), using the inputs distribution from the Schalkwijk 82 scheme.
Sparse Representation Based Face Recognition Under Varying Illumination

Illumination with different lighting levels or angles is an important problem for classification of an individual in face recognition. To overcome this issue, generally classification algorithms are applied after pre-processing of the images to get rid of the low contrast regions to increase the accuracy of recognition. In this work, we use Steerable Gaussian Filter at the pre-processing step for all training and testing samples. In the classification step, we use the recently proposed “Classification via Sparse Reconstruction Vector (CSRV)“ algorithm. The performance of our approach is compared with that of the “Principal Component Analysis (PCA)” algorithm in terms of recognition rates (RR). Experiment results show that the CSRV algorithm has a better performance than that of the PCA algorithm with higher RR even for poorly illuminated images taken from Yale Database B.
Cognitive Radio Enabled Biomedical and Nonmedical Hospital Device Communication Protocols for CogMed

Owing to the recent advancement in IoT devices, the popularity of wireless devices in healthcare is growing rapidly. Thus it is anticipated that the healthcare services may face challenges such as medical spectrum scarcity, electromagnetic interference to bio-medical devices, medical data transmission reliability, etc. To overcome these issues, cognitive radio can be improvised and fine-tuned the wireless healthcare service system. However, contemporary researches on cognitive radio driven healthcare system have shown limited guidelines for medical emergency concerned network model and communication protocols. Thus this paper presents intelligent dynamic priority medical traffic transmission mechanism aware three Device to Device communication protocols for cognitve radio enabled hospital management system known as CogMed. To validate the performance of these protocols, several simulations are conducted, where it is observed that the CogMed transmission scheme performs better than other well established Methods. Moreover, the latency of majority hospital devices is within the threshold level of the Federal Drug and Food Administration prescribed standards for wireless medical devices.
Analyzing the Robustness of Deep Learning Against Adversarial Examples

Recent studies have shown the vulnerability of many deep learning algorithms to adversarial examples, which an attacker obtains by adding subtle perturbation to benign inputs in order to cause misbehavior of deep learning. For instance, an attacker can add carefully selected noise to a panda image so that the resulting image is still a panda to a human being but is predicted as a gibbon by the deep learning algorithm. As a first step to propose effective defense mechanisms against such adversarial examples, we analyze the robustness of deep learning against adversarial examples. Specifically, we prove a strict lower bound for the minimum $\ell_{p}$ distortion of a data point to obtain an adversarial example.
A Low-Profile, Dual-Polarized Patch Antenna for 5G MIMO Application

A dual-polarized patch antenna with an extremely low profile is proposed in this communication. It consists of a main radiator, an annulus, and a reflector. The main radiator contains two pairs of differentially driven feedlines and an irregular patch. These differentially driven feedlines are utilized to coupled feed the irregular patch through a small gap, which has distinguished feature compared with other designs. It turns out that a resonance at 3.55 GHz is generated. Then, an annulus is set above the main radiator to compensate the inductive component induced by the main radiator and generate the other resonant point. As a result, the dual-polarized antenna could work from 3.3 to 3.6 GHz with the extremely compact size of <inline-formula> <tex-math notation="LaTeX">$0.29\lambda _{\mathbf {0}} \times 0.29\lambda _{\mathbf {0}} \times 0.06\lambda _{\mathbf {0}}$ </tex-math></inline-formula>. (<inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> is the free-space wavelength at 3.45 GHz.) The simulated isolation and cross-polarization discrimination at 0° of the antenna element reach more than 35 and 44 dB. The simulated front-to-back ratio of co-polarization and cross-polarization is larger than 44 and 22 dB, separately. To verify the design, a prototype of the <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> array based on the element is fabricated and measured. The results show that the antenna has high potential in the fifth-generation (5G) massive multiple-input multiple-output application.
An Innovative Word Encoding Method For Text Classification Using Convolutional Neural Network

Text classification plays a vital role today especially with the intensive use of social networking media. Recently, different architectures of convolutional neural networks have been used for text classification in which one-hot vector, and word embedding methods are commonly used. This paper presents a new language independent word encoding method for text classification. The proposed model converts raw text data to low-level feature dimension with minimal or no preprocessing steps by using a new approach called binary unique number of word “BUNOW. ” BUNOW allows each unique word to have an integer ID in a dictionary that is represented as a k-dimensional vector of its binary equivalent. The output vector of this encoding is fed into a convolutional neural network (CNN) model for classification. Moreover, the proposed model reduces the neural network parameters, allows faster computation with few network layers, where a word is atomic representation the document as in word level, and decrease memory consumption for character level representation. The provided CNN model is able to work with other languages or multi-lingual text without the need for any changes in the encoding method. The model outperforms the character level and very deep character level CNNs models in terms of accuracy, network parameters, and memory consumption; the results show total classification accuracy 91.99% and error 8.01% using AG’s News dataset compared to the state of art methods that have total classification accuracy 91.45% and error 8.55%, in addition to the reduction in input feature vector and neural network parameters by 62% and 34%, respectively.
A Single Gate Driver Based Solid-State Circuit Breaker Using Series Connected SiC MOSFETs

Semiconductor devices based solid-state circuit breakers (SSCBs) are promising in the dc power distribution system as protective equipment for their ultrashort action time. This letter proposes a topology of SSCB using series connected silicon carbide (SiC) metal oxide semiconductor field effect transistors (mosfets), which only requires a single isolated gate driver. The SSCB has very low cost and high reliability because it only has 13 components including passive components and diodes apart from two SiC mosfets to achieve both balanced voltage distribution during short-circuit interruption duration and reliable positive gate voltage during on-state. The SSCB prototype is built and experimentally verified to interrupt 75 A short-circuit current under the dc-bus voltage of 1200 V within 1.5 μs.
The Implementation of a Virtual Desktop Infrastructure with GPU Accelerated on OpenStack

In recent years, Information industry trends gradually towards the clouds. Various clouds are related technologies and applications are growing with the popularity of cloud technology. While cloud virtual desktop is the first user access part of the cloud and the performance of cloud virtual desk of the network and GPU has been implicated. A smooth cloud virtual desktop is a very important issue in this environment. In this study, we proposed an integration of GPU on OpenStack and through PCI Pass-Through way to solve the problem of virtual machine display performance by allocating GPU resources for virtual machine. For the experimental, we will use different benchmarking software to measure the virtual machine. In the experiment, adjust the different number of vCPUs to measure the different cases performance of the FPS virtual machine (Frame per Second). Through this experimentation, we found out there are three different processing drivers for the virtual desktop.
Skipping the Replication Crisis in Visualization: Threats to Study Validity and How to Address Them : Position Paper

Replications are rare in visualization research, but if they were more common, it is not unreasonable to believe that they would show a similar rate of unreproducible results as in the psychological and social sciences. While a replication crisis in visualization research would be a helpful wake-up call, examining and correcting the underlying problems in many studies is ultimately more productive.In this paper, we survey the state of replication in visualization research. We examine six threats to the validity of studies in visualization and suggest ways to address them. Finally, we describe possible models for publishing replications that satisfy the novelty criterion that can keep replications from being accepted.
Dynamic Control of Parallel Robots Driven by Flexible Cables and Actuated by Position-Controlled Winches

An alternative approach to standard computed torque with feedback linearization is proposed in this paper to control cable-driven parallel robots (CDPRs) with highly flexible cables. Exteroceptive feedback is used to measure the end-effector Cartesian position at a high sampling rate. Stability is demonstrated using singular perturbation theory. The proposed control scheme is experimentally validated on a planar 3-degree-of-freedom CDPR and its efficiency is assessed by comparison with a simple kinematic control law.
Harmonic State-Space Based Small-Signal Impedance Modeling of a Modular Multilevel Converter With Consideration of Internal Harmonic Dynamics

The small-signal impedance modeling of a modular multilevel converter (MMC) is the key for analyzing resonance and stability of MMC-based power electronic systems. The MMC is a power converter with a multifrequency response due to its significant steady-state harmonic components in the arm currents and capacitor voltages. These internal harmonic dynamics may have great influence on the terminal characteristics of the MMC, which, therefore, are essential to be considered in the MMC impedance modeling. In this paper, the harmonic state-space (HSS) modeling approach is first introduced to characterize the multiharmonic coupling behavior of the MMC. On this basis, the small-signal impedance models of the MMC are then developed based on the proposed HSS model of the MMC, which are able to include all the internal harmonics within the MMC, leading to accurate impedance models. Besides, different control schemes for the MMC, such as open-loop control, ac voltage closed-loop control, and circulating current closed-loop control, have also been considered during the modeling process, which further reveals the impact of the MMC internal dynamics and control dynamics on the MMC impedance. Furthermore, an impedance-based stability analysis of the MMC-high-voltage direct current connected wind farm has been carried out to show how the HSS-based MMC impedance model can be used in practical system analysis. Finally, the proposed impedance models are validated by both simulation and experimental measurements.