Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-Flux-Quantum Multiplier Circuits for Synthesizing Gigahertz Waveforms With Quantum-Based Accuracy

We designed, simulated, and experimentally demonstrated components for a microwave-frequency digital-to-analog converter based on single flux quantum (SFQ) circuits and an amplifier based on superconducting-quantum-interference-device (SQUID) stacks. These are key components for a self-calibrated programmable waveform reference for communications metrology capable of synthesizing high-frequency signals with quantum-based output accuracy. The amplifier is an SFQ voltage multiplier circuit that consists of a network of SFQ-splitters and SQUID transformers that provides output signals consisting of quantized pulses. The circuits were fabricated using our Nb/Nb$_{x}$Si$_{1-x}$/Nb Josephson-junction (JJ) fabrication process, which produces self-shunted JJs with Nb-doped silicon barriers. In order to demonstrate quantum-based reproducibility, stability and performance at 4 K, we synthesized single-tone and multitone waveforms at gigahertz frequencies and demonstrated their operation over a range of synthesizer output and experimental bias parameters. We also propose circuit designs for achieving higher synthesis frequencies and higher output power with improved power accuracy and spectral purity, and discuss the potential limitations of these circuits.
Integral Sliding Mode Control With Power Exponential Reaching Law for DFIG

Variable wind speed, electromagnetic induction, heating and aging can subject the Doubly fed induction generator (DFIG) to parameter variation and uncertainties. As such, an integral sliding mode controller (ISMC) has been designed to suppress any disturbance that may arise in the system. ISMCs cause chattering phenomena in systems. A novel power exponential reaching law has been proposed to mitigates the chattering effects and achieve very fast convergence.
Extrinsic Calibration of Multiple LiDARs of Small FoV in Targetless Environments

The integration of multiple solid state LiDARs could achieve similar performance as a spinning LiDAR, by focusing on the dedicated area of interests. However, due to their small FoV settings, it is either required to rely on external sensors or form FoV overlaps to calibrate the extrinsic parameters between multiple LiDAR units. To overcome such limitations, we develop a targetless calibration method, which creates FoV overlaps (hence co-visible features) through movements and constructs a factor graph to resolve the constraints between LiDAR poses and extrinsic parameters. By solving the formulated problem with graph optimization, our proposed method could calibrate the extrinsic of LiDARs with few or even no overlapped FoVs, meanwhile, produce a globally consistent point cloud map. Experiments on different sensor setups and scenes have demonstrated the accuracy and robustness of our proposed approach.
Survival Multipath Energy-Aware Resource Allocation in SDM-EONs During Fluctuating Traffic

Energy consumption efficiency during resource allocation is crucial for large scale networks. The evolution of space division multiplexing elastic optical networks (SDM-EONs) has presented tremendous spatial dimensions over single-mode networks to manage transmission of largetraffic volumes, during peak load of the fluctuating traffic. However, due to large capacitytransmission rates, the resource allocation challenges have led to incremental energy consumption. This is due to the impact of crosstalk (XT), which occurs between active adjacent cores, during core switching, and increasing service disruptions caused by rampant link failures. To address the above issues, this article proposes a distance-adaptive energy-aware resource allocation (DERA) algorithm using a survival multipath scheme. The DERA algorithm implements the concept of the survival multipath scheme with Spectrum Compactness (SC) to minimize dynamic XT effects in SDM-EONs, during resource allocation. The objective is to improve spectral efficiency to save energy consumption, when the load of the fluctuating traffic peaks. During Routing, Modulation, Spectrum, and Core Allocation (RMSCA), the DERA algorithm can efficiently allocate resources in a survival multipath scheme. The performance of the DERA-RMSCA is compared with the two Multipath Protection (MPP) schemes, PRIME-RMSCA, and CMDE-RMSCA. The results demonstrate that DERA-RMSCA can achieve a better performance compared with PRIME-RMSCA, and CMDE-RMSCA.
Joint Beam and Polarization Forming of Intelligent Reflecting Surfaces for Wireless Communications

In this paper, we propose a novel intelligent reflecting surface (IRS) structure, which is capable of jointly forming desirable beam and polarization of a reflected wave with the aid of massive reflecting elements. More specifically, the proposed IRS is composed of unit cells, each having multiple variable capacitors. Active tuning of capacitances of the variable capacitors in each unit cell provides the full coverage of $360^\circ$ in the reflection phase. Furthermore, polarization is switched from an incident wave to the reflected wave through the excitation of two orthogonal modes, enabling arbitrary phase distribution over the IRS for the desired polarization. As a result, the proposed IRS allows the increase of the signal-to-noise ratio (SNR) at the receiver in a communication scenario, by adjusting the reflection phase shift of each IRS element. Moreover, the reflection phase shifts of each IRS element are optimized with the aid of the Newton method, such that the received signal power of a receiver is maximized. In our simulations, it is demonstrated that our proposed IRS successfully maximizes the SNR at the receiver, both in the single-user and the broadcast multi-user scenarios, where multi-user interference is absent.
Surface-enhanced Raman spectroscopy differences of saliva between lung cancer patients and normal people

Surface enhanced Raman spectroscopy (SERS) has shown the advantage of detecting low concentration biofluids presently. Saliva SERS of 21 lung cancer patients and 22 normal people were measured and differentiated in this paper. Intensities of most peaks of lung cancer patients are weaker than that of normal people, some are stronger but with a small change rate. Those peaks were assigned to proteins and nucleic acids which indicate a corresponding decrease of substance in saliva. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to deduce and discriminate the two groups of data, resulted in accuracy, sensitivity, and specificity being 84%, 94%, and 81%, respectively. In conclusion, SERS of saliva has the ability of predicting lung cancer.
Modulation Considerations for Modular Multilevel Converter Operating with Parallel Branches and Phase-Shifted Carriers

The branch paralleling represents one of the available options with regards to the modular multilevel converter power extension. In such a case, the main technical challenge resides in the control domain, whereas a new set of possibilities from the voltage modulation standpoint becomes available along the way. This paper provides a thorough mathematical analysis of the modular multilevel converter operating with parallel branches and utilizing phase-shifted carrier modulation to synthesize the desired voltage references. The influence of the adjacent branches’ carriers shift on the voltages across the terminals of the converter leg is presented in a comprehensive manner. Detailed simulations confirm the derived mathematical predictions and allow for the performance guided choice of relevant modulator parameters.
Performance Evaluation of Polarization Effects on 5G Clustered Delay Line Channels

The majority of the vast literature using state-of-art MIMO transmission schemes present evaluations on simplified propagation models using simplified antenna arrays. However, such a model neglects channel polarization effect, which could result in severe signal degradation. In this paper, we quantify the impact of channel polarization for arrays with co-polarized and cross-polarized antennas. We focus on the usage of the recently standardized 3GPP 38.901 clustered delay line channel for 5G New Radio. Evaluations have shown that while traditional co-polarized array can lead to better performance, in a realistic scenario of mutual transmitter-receiver, tilting channel polarization can cause severe degradation. This has motivated the use of cross-polarized antennas that are invariant to these tilts. Extensive simulations show that polarization causes 2-6dB degradation for traditional co-polarized arrays, while for cross-polarized arrays, this value is 1-3dB.
Efficient fault detection and diagnosis of wind energy converter systems

Fault detection and diagnosis for modern wind turbines converter (WTC) systems have been received an important measure for improving the operation of these systems, in such a way to increase their reliability, availability and required safety. Therefore, this paper deals with the problem of fault detection and diagnosis (FDD) in WTC systems. The developed FDD approach uses feature extraction and selection, and fault classification tools for monitoring WTC system under different operating conditions. The developed FDD approach is addressed such that, the principal component analysis (PCA) technique is used for feature extraction purposes and the machine learning (ML) classifiers are applied for fault diagnosis. In the proposed FDD approach, an efficient features in PCA subspace that extract and select the most informative features from WTC data are provided. Besides, their statistical characteristics are also included. The ML classifiers are applied to the extracted and selected features to perform the fault diagnosis problem. The effectiveness and higher classification accuracy of the developed approach are demonstrated using simulated data extracted from different operating conditions of the wind turbine.
Artifact Detection in Chronically Recorded Local Field Potentials using Long-Short Term Memory Neural Network

The process of recording local fields potentials (LFP) can be contaminated by different internal and external sources of noise. To successfully use these recordings, noise must be removed, for which an automatic detection tool is needed to speed up the detection process. This work presents the use of a specific configuration of the recurrent neural network based machine learning approach, known as the long-short term memory (LSTM), in two different settings to identify artifacts and compares the obtained results to a feed forward neural network both in terms of classification performance and computational time. Using spontaneous LFP signals recorded chronically by multisite neuronal probes in behaving rats, our results show that the LSTM model with and without drop out can achieve an accuracy of 87.1%.
An Improved Two-Vector Predictive Direct Power Control Using Extended Reactive Power

This paper proposes an improved predictive direct power control (P-DPC) of the pulse-width modulated (PWM) rectifier. Using extended reactive power as the mathematical framework, the proposed P-DPC has a simple structure and enhanced performance as compared with the classic counterpart. The proposed P-DPC uses two nonzero vectors to achieve improved tracking accuracy. To obtain a constant switching frequency, the duty cycle optimisation is introduced based on the principle of power error minimisation. A "2+2" voltage-vectors' sequence is further proposed to reduce the power ripple and enhance the current quality. No complicated sequence detectors, phase lock loop (PLL), and modulators are necessary to keep the high simplicity of the proposed P-DPC. Comparative studies via experimental and simulation results demonstrate the good performance of the proposed P-DPC.
Enhancing RF Sensing with Deep Learning: A Layered Approach

In recent years, radio frequency (RF) sensing has gained increasing popularity due to its pervasiveness, low cost, non-intrusiveness, and privacy preservation. However, realizing the promises of RF sensing is highly nontrivial, given typical challenges such as multipath and interference. One potential solution leverages deep learning to build direct mappings from the RF domain to target domains, hence avoiding complex RF physical modeling. While earlier solutions exploit only simple feature extraction and classification modules, an emerging trend adds functional layers on top of elementary modules for more powerful generalizability and flexible applicability. To better understand this potential, this article takes a layered approach to summarize RF sensing enabled by deep learning. Essentially, we present a four-layer framework: physical, backbone, generalization, and application. While this layered framework provides readers a systematic methodology for designing deep interpreted RF sensing, it also facilitates making improvement proposals and hints at future research opportunities.
Cefpodoxime Proxetil Floating Microspheres: Formulation and In Vitro Evaluation

The objective of the present study was to develop floating microspheres of cefpodoxime proxetil in order to achieve an extended retention in the upper GIT, which may result in enhanced absorption and thereby improved bioavailability. The microspheres were prepared by non-aqueous solvent evaporation method using polymers such as hydroxyl propyl methyl cellulose (HPMC K 15 M), ethyl cellulose in different ratios and cefpodoxime proxetil in each formulation. In vitro drug release were performed by USP apparatus type I and the microspheres were characterized by polymer compatibility by using FT-IR. The yield, particle size, Buoyancy percentage, drug entrapment efficiency, and in vitro drug release were studied. The result showed that microspheres yielded 50.5-72.2%. The particle size was distributed between75-600 μm, drug entrapment efficiency was 14.128. 2%, and Buoyancy percentage was 70.1-88.3%. The best drug release profiles were seen with formulation 2 at the ratio of drug to polymer of 1:2.
Transient Current Constraint of DAB Converter Based on Model Predictive Control

The dual active bridge (DAB) converter is a popular topology with the ability of galvanic isolation and bidirectional power flow. In the practical application, there could be the significant transient current overshoot between the different operating conditions or in the start-up phase of the DAB converter. Therefore, a discrete-time model based model predictive control method is proposed in this paper to constrain the transient inductor current overshoot. An experimental prototype is also built to validate the proposed method. The experimental results are consistent with simulation waveforms and the transient current overshoot can also be effectively constrained to mitigate its stress on the switch devices.
Hybrid Energy Storage Strategy for Suppressing Power Grid Fluctuation of Photovoltaic Power Generation System

Aiming at the problem that the grid-connected power fluctuation of the PV power system affects the stability of power grid operation, a hybrid energy storage strategy based on supercapacitor-battery is proposed to suppress the grid-connected power fluctuation of a photovoltaic power system. The low-pass filtering method is used to determine the suppressed goal of the supercapacitor, the high-frequency power is stabilized by the supercapacitor, and the low-frequency power is stabilized by the battery. Thereby achieve the purpose of rational allocation power deviation between different energy storage mediums. Secondly, a small-signal model of Buck/Boost bidirectional converter based on complementary PWM control is established for the situation that the DC bus voltage has greatly fluctuated when the double closed-loop control is to suppress. The battery adopts layered control, and the supercapacitor adopts a combination between voltage and current double closed-loop control and power feedforward to suppress DC bus voltage fluctuation. Finally, the Simulink simulation verifies the effectiveness of the proposed control strategy to suppress grid- connected power fluctuations.
Assessing Container Network Interface Plugins: Functionality, Performance, and Scalability

Kubernetes, an open-source container orchestration platform, has been widely adopted by cloud service providers (CSPs) for its advantages in simplifying container deployment, scalability, and scheduling. Networking is one of the central components of Kubernetes, providing connectivity between different Pods (a group of containers) both within the same host and across hosts. To bootstrap Kubernetes networking, the Container Network Interface (CNI) provides a unified interface for the interaction between container runtimes. There are several CNI implementations, available as open-source ‘CNI plugins’. While they differ in functionality and performance, it is a challenge for a cloud provider to differentiate and choose the appropriate plugin for their environment. In this article, we compare the various open-source CNI plugins available from the community, qualitatively, and through detailed quantitative measurements. With our experimental evaluation, we analyze the overheads and bottlenecks for each CNI plugin, especially because of the interaction with the datapath/iptables as well as the host network stack. Overlay tunnel offload support in the network interface card plays a significant role in achieving the good performance of CNIs that use overlay tunnels for inter-host Pod-to-Pod communication. We also study scalability with an increasing number of Pods, as well as with HTTP workloads, and briefly evaluate Pod startup latency. Our measurement results inform the outline of an ideal CNI environment for Kubernetes.
A Large Signal Stabilizing Algorithm for Interleaved Floating Dual Boost Converter fed DC Microgrid with Controlled Power Electronic Loads

Tightly regulated power electronic loads are penetrating in DC microgrid. They tend to draw constant power and thus, introduce negative incremental impedance destabilizing effect to DC microgrid. In practice, the stability condition of DC microgrid can be further exacerbated when large signal load changes occur. To ensure the large signal stability of DC microgrid with such loads, this paper proposes a new stabilizing control algorithm using nonlinear disturbance observer. The proposed method is applied to regulate the interleaved floating dual boost converter (IFDBC), which is widely recommended to feed DC microgrid by interfacing solar photovoltaic (PV) and energy storage systems. It has the merits of large voltage gain, high current-carrying capacity and low current ripples. Simulation and experimental results are provided to verify the efficacy of the proposed method.
Joint Mechanical Design and Flight Control Optimization of a Nature-Inspired Unmanned Aerial Vehicle via Collaborative Co-Evolution

In this work, a collaborative co-evolution approach is adopted to solve a joint physical design and feedback control optimization problem of a nature-inspired Unmanned Aerial Vehicle (UAV). Unlike traditional multirotors and fixed-wing aircraft, lift is achieved by spinning its entire body with attached aerofoils around a central axis and positional control is attained through regulation of 2 sets of independent aerodynamic surfaces and thrusters. The collaborative co-evolution process consists of 2 ‘species,’ the first consisting of the mechanical design variables and the second consisting of Proportional-Integral-Derivative (PID) and central pattern generator (CPG) controller variables. Each species have their own respective individual Evolutionary Algorithm (EA) solvers, Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES) and Parameter Exploring Policy Gradients (PEPG). In each optimization iteration, the parameters of one species is combined with representatives with the highest fitness from the other species and fed into a shared model for fitness evaluation, with each species taking turns to send a representative. Detailed performance comparison in trajectory tracking and power consumption between the proposed jointly optimized system against a design-only optimized, control-only optimized and unoptimized baseline were conducted. It was found that configurations with optimized designs would draw on average 18% less power than the non-optimized designs, and configurations with optimized controllers reduce error by 56% on average. The best performing configuration is the one with jointly optimized mechanical design and controller which outperforms all other configurations individually and collectively.
Initial Rotor Position Startup Process Emulation Based on Electric Motor Emulator

The electric motor emulator (EME) based on power-hardware-in-the-loop (PHiL) is aimed to imitate the motor’s behavior. Since initial rotor position information is essential while a motor is being driven, this paper discusses the initial rotor position startup process based on the PHiL testbench, in which a closed loop current controller and the motor model are implemented to emulate the operation of Permanent Magnet Synchronous Machine (PMSM) in the EME. In order to assess the performance of the PHiL testbench in initial rotor startup process, simulation results which focuses on rotor angle and three-phase stator current during initial rotor position startup is presented in this paper comparing to the conventional motor testbench. Hardware verification results are presented with three different kinds of initial rotor startup tests on the PHiL testbench.
Study on Systematic Ransomware Detection Techniques

Cyberattacks have been progressed in the fields of Internet of Things, and artificial intelligence technologies using the advanced persistent threat (APT) method recently. The damage caused by ransomware is rapidly spreading among APT attacks, and the range of the damages of individuals, corporations, public institutions, and even governments are increasing. The seriousness of the problem has increased because ransomware has been evolving into an intelligent ransomware attack that spreads over the network to infect multiple users simultaneously. This study used open source endpoint detection and response tools to build and test a framework environment that enables systematic ransomware detection at the network and system level. Experimental results demonstrate that the use of EDR tools can quickly extract ransomware attack features and respond to attacks.