Miracle-dz/UniFAR-FTUs huggingface dataset:
A Two-step 2-D DOA Estimation Algorithm Via Underwater Acoustic Vector-Sensor Array

Direction of arrival (DOA) estimation based on underwater acoustic vector-sensor array has extensive applications in the oceanic field. To realize an accurate DOA estimation with lower complexity, this paper proposes a novel method to carry out the two-dimentional (2-D) DOA estimation. Firstly, we introduce the regularized multiple focal underdetermined system solver algorithm (M-FOCUSS) to solve the jointly sparse vectors reconstruction problem to determine the grid points of true DOAs accurately. Then, we utilize Taylor series expansion to precisely approximate the steering vectors of true DOAs and achieve an accurate estimation of the off-grid errors by solving an optimization problem. Simulation results show that our algorithm is of less computational complexity but leads to a high DOA estimation accuracy.
A Modified PAM Control with Automatic Angle Excitation for Experimental IPMSM in Consideration of SiC Inverter and Motor Core Loss

This study introduces a modified pulse-amplitude modulation (PAM) control technique for an interior permanent magnet synchronous motor (IPMSM) system using the silicon carbide (SiC) MOSFET inverter excitation. In more details, an automatic scheme based on a twelve-step switching pattern is designed for real-time searching the optimal excitation angle of the SiC inverter under the PAM control technique to minimize fluctuations of the d-axis motor current in the dq-reference frame. This automatic angle excitation scheme can reduce harmonics of motor current and voltage, from which it helps to decrease the motor core loss. Total harmonic distortion (THD) values of measured motor phase current and voltage in experiments are considered to validate the performance of the proposed PAM scheme in reducing the motor core and copper losses. The efficacy in lowering the loss of the SiC inverter under the designed PAM control technique with the optimal excitation angle is also examined in an additional test case.
Performance Analysis of Cascaded H-Bridge Multilevel Inverter with Variable Frequency ISPWM Technique

The multilevel inverter has applications in various areas. Now a days , multilevel inverters have wide scope in advancement of electric vehicles, static var compensators, photovoltaic purpose, interfacing with Renewable energy system.This paper proposes topology of multi level inverter with cascaded H-bridge which has several advantages over flying capacitor and neutral point clamped topology with the pulse width modulation (PWM) application. This topology can be used to increase number of level of output voltage by increasing number of input DC sources in appropriate manner. VFISPWM modulation method is proposed in this paper. In this switching method two signals are used with one is sinusoidal and other is inverted sine with variable frequency for reference and carrier signals respectively. The simulation results represents that the Total Harmonic Distortion (THD) for voltage is reduced with VFISPWM technique. The results of MATLAB simulation shows that fundamental voltage component gets improved with the significant reduction in THD as compared to Constant Frequency Inverted Sine PWM (CFISPWM)method.
Diffusion Approximated Kernel Least Mean P-Power Algorithm

Considering nonlinear and non-Gaussian environments that are frequently encountered in real-world scenarios, we thus propose the approximated kernel least mean p-power (AKLM-P) algorithm to address these tough factors for nonlinear distributed systems. In order to efficiently devise this algorithm, we use the property of shift-invariant kernel function that can be approximated by random Fourier features, and consider the impulsive noise characterized by the α-stable distribution. The stability in the mean of proposed distributed algorithm is then studied. Finally, the superiorities of proposed approach compared to diffusion kernelleast-mean-square (KLMS) algorithm are confirmed via the experimental results.
Discovering, Explaining and Summarizing Controversial Discussions in Community Q&A Sites

Developers often look for solutions to programming problems in community Q&A sites like Stack Overflow. Due to the crowdsourcing nature of these Q&A sites, many user-provided answers are wrong, less optimal or out-of-date. Relying on community-curated quality indicators (e.g., accepted answer, answer vote) cannot reliably identify these answer problems. Such problematic answers are often criticized by other users. However, these critiques are not readily discoverable when reading the posts. In this paper, we consider the answers being criticized and their critique posts as controversial discussions in community Q&A sites. To help developers notice such controversial discussions and make more informed choices of appropriate solutions, we design an automatic open information extraction approach for systematically discovering and summarizing the controversies in Stack Overflow and exploiting official API documentation to assist the understanding of the discovered controversies. We apply our approach to millions of java/android-tagged Stack overflow questions and answers and discover a large scale of controversial discussions in Stack Overflow. Our manual evaluation confirms that the extracted controversy information is of high accuracy. A user study with 18 developers demonstrates the usefulness of our generated controversy summaries in helping developers avoid the controversial answers and choose more appropriate solutions to programming questions.
Timespan-Aware Dynamic Knowledge Graph Embedding by Incorporating Temporal Evolution

Recently, Knowledge Graph Embedding (KGE) has attracted considerable research efforts, since it simplifies the manipulation while preserving the inherent structure of the KG. However to some extent, most existing KGE approaches ignore the historical changes of structural information involved in dynamic knowledge graphs (DKGs). To deal with this problem, this paper presents a Timespan-aware Dynamic knowledge Graph Embedding Evolution (TDG2E) method that considers temporal evolving process of DKGs. The major innovations of our paper are two-fold. Firstly, a Gated Recurrent Units (GRU) based model is utilized in TDG2E to deal with the dependency among sub-KGs that is inevitably involved in the learning process of the dynamic knowledge graph embedding. Furthermore, we incorporate an auxiliary loss to supervise the learning process of the next sub-KG by utilizing previous structural information (i.e., the hidden state of GRU). In contrast with existing approaches in the literature (e.g., HyTE and t-TransE), TDG2E preserves structural information of current sub-KG and the temporal evolving process of the DKG simultaneously. Secondly, to further deal with the time unbalance issue underlying the DKGs, a Timespan Gate is designed in GRU. It makes TDG2E possible to model the temporal evolving process of DKGs more effectively by incorporating the timespan between adjacent sub-KGs. Extensive experiments on two large temporal datasets (i.e., YAGO11k and Wikidata12k) extracted from real-world KGs validate that the proposed TDG2E significantly outperforms traditional KGE methods in terms of Mean Rank and Hit Rate.
High-Performance MEMS Pressure Sensor Fully-Integrated with a 3-Axis Accelerometer

The originality of the MEMS pressure sensor presented in this paper is due to the adjunction of a lever arm to sense the deformation of the diaphragm by suspended piezoresistive nanogauges. This transduction scheme can be easily transposed onto others inertial MEMS, and the paper illustrates this capability by the cointegration of a 10bar MEMS pressure sensor with a 3-axis MEMS accelerometers. The fabricated pressure transducer demonstrates high performances: the sensitivity is 6mV/V/bar and the pressure accuracy is estimated lower than 0,15% full scale for -35°C/+145°C temperature range.
Mismatch and Offset Calibration in Redundant SAR ADC

This paper presents a robust method to perform capacitor mismatch calibration in a redundant SAR ADCs correcting the effect of comparator static offset in the calibration process. Without proper handling of this effect, capacitor miscalibration can occur due to saturation of the redundancy intervals associated with implemented weights, eventually leading to a faulty behavior even with lower effective resolution than the case without calibration. To overcome this limitation, this work proposes a foreground technique which reuses the least-significant bit (LSB) capacitors in the array in a design with redundancy for offset compensation. The effectiveness of the method is demonstrated by realistic behavioral simulations in a 12-bit SAR ADC case study with 3 redundant bits.
Design and Measurement Considerations of Feeding Network Including Power Divider for Multi-Port Antenna Arrays

This paper describes a methodology concerning design and measurement of millimeter-wave (mm-Wave) feeding networks including power divider to achieve high performance modules in RF high-power electronics. The design approach considering multi-port measurement within compact mm-Wave array elements is introduced and the precision design method including power divider without any power imbalance is discussed. Despite high dielectric loss tangent of FR-4 substrates, low loss feeding network can be achieved for both beam steering capability and high gain. Additionally, the design guidelines of power divider are investigated to suppress mutual coupling causing power imbalance between each port. As another alternative, the feeding network including power divider is proposed by the realization method of using back-to-back transition structures without mutual coupling issue.
A 63 nW, 250 Hz, 70 dB-DR, Subthreshold CMOS Follower-Based LPF for ECG Detection

This paper presents a 3rd-order subthreshold CMOS lowpass filter for electrocardiography acquisition. The inherent local negative feedback of the well-known source follower filter and a vertical source-coupled-pair transconductor are adapted to enhance the signal swing of the proposed filter. Post-layout simulations using 0.35-µm CMOS verify that the proposed filter occupies an area of 0.152 mm2and consumes 63 nW from a 1.8 V supply. It achieves a cutoff frequency and input-referred noise of 250 Hz and 30.3 µVrms, respectively. For a 0.81% total harmonic distortion, the filter provides 70.3 dB SNR.
Analysis of Operational Characteristics of Traction Unit Combining Two Different Motors and Their Behaviors in Driving Cycle

This paper analyzes the characteristics of an electric vehicle traction motor unit combining an interior permanent magnet (PM) synchronous motor and PM-assisted synchronous reluctance motor. Their individual operational characteristics are first investigated. The effective combination of the two motors to achieve a wide constant power speed range (CPSR) is also evaluated. The Urban Dynamometer Driving Schedule driving cycles are used to evaluate the combined traction unit. Simulation results reveal that the control strategy would be different for the two types of motors to show the advantages of this combination. The designs to achieve a high CPSR are also suggested.
Incremental Estimation of Users’ Expertise Level

Estimating a user’s expertise level based on observations of their actions will result in better human-robot collaboration, by enabling the robot to adjust its behaviour and the assistance it provides according to the skills of the particular user it’s interacting with. This paper details an approach to incrementally and continually estimate the expertise of a user whose goal is to optimally complete a given task. The user’s expertise level, here represented as a scalar parameter, is estimated by evaluating how far their actions are from optimal. The proposed approach was tested using data from an online study where participants were asked to complete various instances of a simulated kitting task. An optimal planner was used to estimate the “goodness” of all available actions at any given task state. We found that our expertise level estimates correlate strongly with observed after-task performance metrics and that it is possible to differentiate novices from experts after observing, on average, 33% of the errors made by the novices.
Microstrip Dual-frequency Coupler Whose Dimensions are Reduced by the Use of Artificial Transmission Lines

Standard dual frequency directional couplers at low frequencies may not be practical, especially at low frequencies. For this reason, miniaturization of the traditional design was carried out using artificial transmission lines (ATL), which are equivalent to quarter-wave segments at the Central frequency. ATL elements are placed on the free space inside the branch. In order to maintain the efficiency of the device at two frequencies of 0.75 and 1.75 GHz, the connected open circuits stubs are bent in the form of a meander while maintaining its electrical length. To take into account the influence of boundary effects and the mutual influence of lines, the developed topology is modeled in the NI-AWR Design Environment program. Also, a model of the device was made and a full-scale experiment was conducted with the help of a vector network analyzer Rohde&Shwarz ZVA 24. The area of the compact coupler is 70.7% smaller than that of the traditional design. The negative factors of miniaturization are a reduction in bandwidth and an increase in imbalance.
Demonstration of a 2.34 Gbit/s Real-Time Single Silicon-Substrate Blue LED-Based Underwater VLC System

We develop a real-time discrete multi-tone (DMT) transceiver based on field programmable gate array (FPGA) chips for a single chip silicon-substrate light-emitting diode (LED) based underwater visible light communication (UVLC). On-chip resource usages are analyzed and discussed. To improve bit error rate (BER) performance, a novel channel estimation technique utilizing hybrid inter-symbol frequency-averaging (Inter-SFA) and intra-symbol frequency-averaging (Intra-SFA) is proposed and investigated. The real-time DMT transceiver is experimentally verified in a silicon substrate blue LED-based UVLC system with a 1.2 m underwater link. By using the enhanced channel estimation, a gross bit rate of 2.34 Gbit/s real-time DMT signal over 1.2 m underwater transmission can be achieved with the BER of 3.5 × 10−3. What's more, multiple-symbol interleaved Reed-Solomon (RS) codes are employed to further improve BER performance. The real-time measured post-FEC BER of the DMT-UVLC with multiple-symbol interleaved RS (255, 191) codes can be improved by more than six orders of magnitude. As a result, error-free (less than 1 × 10−9) transmission is observed in our real-time experiment. Furthermore, 1.485 Gbit/s 720p high-definition video underwater transmission is successfully demonstrated. To the best of our knowledge, it is the first time to demonstrate a real-time LED-based UVLC system with DMT modulation beyond Gbit/s.
An Online Identification Based Energy Management Strategy for a Fuel Cell Hybrid Electric Vehicle

This paper aims to present an accurate Energy Management Strategy (EMS) for a Fuel Cell Hybrid Electric Vehicle (FC-HEV). Batteries and FC performances are constantly changing with aging, temperature and so on. In order to ensure the accuracy of the EMS the parameters of those models are constantly updated thanks to an online identification. Adaptive filtering, based on Recursive Least Square (RLS) algorithm, is used to estimate the State of Health (SOH) and the State of Charge (SOC) of the battery in one hand. On the other hand The RLS algorithm estimates the polarization curve of the Fuel Cell. The impact of those estimated parameters on the EMS is shown through simulation.
Distributed Maintenance of a Spanning Tree of k-Connected Graphs

This work is devoted to the problem of spanning trees maintenance in the presence of crash failures in a distributed environment using only local knowledge. Using a pre-constructed spanning tree of a k-connected graph, we present a protocol to maintain a spanning tree in the presence of k-1 consecutive failures. The contribution of this paper is threefold. First, the problem is formalized as an occurrence of Menger's theorem in a distributed setting. The second result shows an implementation of the protocol which is composed of a set of modules encoded using a graph relabeling systems model. The last contribution is the implementation of this protocol in the asynchronous message passing model. For a given graph G =(V,E), where M is the number of its edges, N is the number of its nodes, and Δ is its degree; After each failure occurrence, our algorithms need the following requirements: The first one uses O(Δ × N) steps and O(Δ) bits per node. The second one uses O(N+M) messages and O(N) time and O(Δ) bits per node. In addition, we investigate the possible specification and verification of the presented algorithm using Alloy as a tooled formal language.
On-Orbit Measurement of the Effective Focal Length and Band-to-Band Registration of Satellite-Borne Whiskbroom Imaging Sensors

We have developed an approach for the measurement of the effective focal length (EFL) and band-to-band registration (BBR) of selected spectral bands of satellite-borne whiskbroom imaging sensors from on-orbit data. Our approach is based on simulating the coarser spatial resolution whiskbroom sensor data with finer spatial resolution Landsat 7 Enhanced Thematic Mapper Plus or Landsat 8 Operational Land Imager data using the geolocation (Earth location) information from each sensor and computing the correlation between the simulated and original data. For each scan of a selected spectral band of the whiskbroom dataset, various subsets of the data are examined to find the subset with the highest spatial correlation between the original and simulated data using the nominal geolocation information. Then, for this best subset, the focal length value and the spatial shift are varied to find the values that produce the highest spatial correlation between the original and simulated data. This best focal length value is taken to be the measured instrument EFL, and the best spatial shift is taken to be the registration of the whiskbroom data relative to the Landsat data, from which the BBR is inferred. Best results are obtained with cloud-free subsets with contrasting land features. This measurement is repeated over other scans with cloud-free subsets. We demonstrate our approach with on-orbit data from the Aqua and Terra MODIS instruments and SNPP and J1 VIIRS instruments.
Robust Tracking Control of Aerial Robots Via a Simple Learning Strategy-Based Feedback Linearization

To facilitate accurate tracking in unknown/uncertain environments, this paper proposes a simple learning (SL) strategy for feedback linearization control (FLC) of aerial robots subject to uncertainties. The SL strategy minimizes a cost function defined based on the closed-loop error dynamics of the nominal system via the gradient descent technique to find the adaptation rules for feedback controller gains and disturbance estimate in the feedback control law. In addition to the derivation of the SL adaptation rules, the closed-loop stability for a second-order uncertain nonlinear system is proven in this paper. Moreover, it is shown that the SL strategy can find the global optimum point, while the controller gains and disturbance estimate converge to a finite value which implies a bounded control action in the steady-state. Furthermore, utilizing a simulation study, it is shown that the simple learning-based FLC (SL-FLC) framework can ensure desired closed-loop error dynamics in the presence of disturbances and modeling uncertainties. Finally, to validate the SL-FLC framework in real-time, the trajectory tracking problem of a tilt-rotor tricopter unmanned aerial vehicle under uncertain conditions is studied via three case scenarios, wherein the disturbances in the form of mass variation, ground effect, and wind gust, are induced. The real-time results illustrate that the SL-FLC framework facilitates a better tracking performance than that of the traditional FLC method while maintaining the nominal control performance in the absence of modeling uncertainties and external disturbances, and exhibiting robust control performance in the presence of modeling uncertainties and external disturbances.
Real-Time State of Charge Estimation of Electrochemical Model for Lithium-Ion Battery

This paper proposes the real-time Kalman filter based observer for Lithium-ion concentration estimation for the electrochemical battery model. Since the computation limitation of real-time battery management system (BMS) micro-processor, the battery model which is utilized in observer has been further simplified. In this paper, the Kalman filter based observer is applied on a reduced order model of single particle model to reduce computational burden for real-time applications. Both solid phase surface lithium concentration and battery state of charge (SoC) can be estimated with real-time capability. Software simulation results and the availability comparison of observers in different Hardware-in- the-loop simulation setups demonstrate the performance of the proposed method in state estimation and real-time application.
A Deep Learning Approach to Dynamic Passive RTT Prediction Model for TCP

The Round-Trip Time (RTT) is a property of the path between a sender and a receiver communicating with Transmission Control Protocol (TCP) over an IP network and over the public Internet. The end-to-end RTT value influences significantly the dynamics and performance of TCP, which is by far the most used communication protocol. Thus, in communication networks, RTT is an important network performance variable. By measuring the traffic at an intermediate node, a network operator or service provider can estimate the RTT and use the estimation to study and troubleshoot the per-connection characteristics and performance. This paper aims at improving the accuracy and timeliness of the RTT estimation, to help network operators improving their analysis. We propose and evaluate a novel deep learning-based model capable of dynamically predicting at real-time the RTT between the sender and receiver with high accuracy based on passive measurements collected at an intermediate node, taking advantage of the commonly used TCP timestamps. We validate extensively our prediction methodology in a controlled experimental testbed and in a realistic scenario on the Google Cloud platform. We show that our model, which is based on classical deep learning algorithms, gives reasonably effective state-of-the-art performance results across multiple TCP congestion control variants. We also show that the model works well for transfer learning. Even though the RTT prediction model was trained on an emulated network, it performs well also when applied to a realistic scenario setting, as demonstrated in our experimental evaluation.
Novel X-band Loaded Circular Aperture Probe for Imaging of Multilayer Composites

Composite materials are being used in a plethora of applications these days due to advantageous features like low weight, high strength and stiffness. However, they are vulnerable to various anomalies, which require periodic inspection to prevent major catastrophes. Microwave near field imaging has demonstrated remarkable capability in detecting subsurface defects in composites. In this paper, a novel circular aperture probe loaded with a resonant iris, operating in X-band frequency range is proposed for near field imaging. To illustrate the effectiveness of the probe, its imaging performance for multilayer composites is compared to a rectangular waveguide. It is elucidated that the proposed probe produces images of high quality with relatively higher dynamic range.