Miracle-dz/UniFAR-FTUs huggingface dataset:
SVPWM Strategy With Neutral Point Voltage Balance Capability for Electrolytic Capacitorless Vienna Rectifier

The neutral point (NP) voltage fluctuation is an inherent problem of the three-level converter, which is generally solved by increasing the capacitance value. However, electrolytic capacitors will cause problems such as short lifetime and low power density. Using film capacitors on the DC side of Vienna rectifiers instead of electrolytic capacitors can solve these problems. However, the reduction of the capacitance value will bring about the NP voltage fluctuation, which will further affect the quality of the grid-side current. In this paper, a modulation strategy based on SVPWM with redundant vector distribution is proposed, which can suppress NP voltage fluctuations and improve input current quality for Vienna rectifier. In order to reduce the NP voltage fluctuation, the influence of each vector on the NP current is analyzed. By assigning the action time of the redundant small vector, the NP current is zero in a switching cycle, which suppresses the fluctuation of the neutral point voltage and reduces the input current harmonics. Finally, the proposed modulation algorithm is compared with the traditional modulation algorithm on the electrolytic capacitorless Vienna rectifier platform, and the feasibility of the algorithm is verified.
Survey of Topology Morphing Control Techniques for Performance Enhancement of Galvanically Isolated DC-DC Converters

This article provides a systematic survey on the topology morphing control techniques used in the galvanically isolated dc-dc converters. Existing techniques are broadly categorized based on the part where a converter changes its topology: at the input side, at the output side, and the advanced techniques that cannot be categorized in the first two groups. The techniques span from the simple reconfiguration between the full- and half-bridge switching cells similar to the conventional power supplies with universal ac input to the advanced multitrack topology and reconfigurable resonant tanks. In addition, the described variety of application examples proves the wide application range and versatility of the topology morphing control. These include fault tolerance, efficiency curve flattening, input voltage range extension, and other advantages. Hence, this article provides a single comprehensive source for researchers and engineers willing to enter the topic of topology morphing control.
Automatic Segmentation of Fovea and Macula in Retinal Fundus Images

We propose an automated method to segment the fovea and macular region in retinal fundus images which is a precursor to assessing the severity of age-related macular degeneration. The fovea region has been localized using the zero-mean normalized cross-correlation technique. The segmentation of the fovea and the macular region has been achieved using the elliptical active disc technique. The elliptical active disc is a shape-specific active contour model and has five free parameters. To obtain the optimal active disc fit on the region of interest to be segmented, the active disc energy has been optimized with respect to five free parameters using the gradient ascent algorithm. Further computational savings has been achieved using Green's theorem. The experimental validation has been done on MESSIDOR, DIARETDB0, DIARETDB1, DRIVE, and IDRiD fundus image databases adding up to 508 images for segmentation of fovea. We attain an average Dice similarity index and fovea segmentation accuracy of 80.45% and 99.66%, respectively on varied fundus image data.
Design 4x1 Space-Time Conjugate Two-Path Full-Rate OFDM Systems

Secured and robust wireless communication systems are critical in rapidly changing mobile fading channels. Further developing the 2x1 conjugate cancellation (CC), we proposed a 4x1 space-time (ST) orthogonal frequency division multiplexing (OFDM) system in conjunction with CC as a block coded two-path transmission scheme. This full-rate 4x1 STCCOFDM system alleviates the effect of inter-carrier interference (ICI) in mobile channels with an outstanding BER performance due to the high diversity order and two-path CC block coding scheme which offers high signal-to-ICI ratio. Both Walsh–Hadamard transform (WHT) and Zadoff-Chu transform (ZCT) are used as the orthogonal pre-coder to further improve bit error rate (BER) performance. Employing the unique pre-coder at the transmitter, the security is achieved at the user’s receiver terminal since the user must perform the inverse operation via a prior known pre-coder. By employing the same order M-ary modulation in transmission, this 4x1 pre-coded full-rate STCCOFDM systems offer an outstanding BER than that of the 4x1 pre-coded half-rate ST OFDM system in mobile channels with the same bandwidth efficiency. By using a higher order M-ary modulation in transmission, the 4x1 full-rate STCCOFDM systems offer an outstanding BER over the full-rate 4x1 ST OFDM in mobile environments with the same bandwidth efficiency. Simulations prove that this full-rate 4x1 STCCOFDM systems are robust to mobile channels and the architecture can be generalized to multiple receiver antennas in the fifth generation (5G) systems.
Aircraft Recognition Using ISAR Image Based on Quadrangle-points Affine Transform

In radar automatic target recognition (RATR), inverse synthetic aperture radar (ISAR) image recognition shows its advantages. During ISAR image feature extraction, the feature points extraction is a classic method. However, the scatterer amplitude might appear to undulate, causing the failure of the matching pair, and affecting the result of recognition. To solve this problem, this paper proposes a quadrangle-points affine transform reconstruction (QATR) algorithm. Firstly, using the four structures of the aircraft nose, tail, and wings, the affine transform coefficients are calculated to reconstruct the aircraft as the top view. Then the template matching algorithm is adopted to recognize the target. The proposed method only needs few templates for each class and has robustness with attitudes sensitivity. The experiments based on real data demonstrate the effectiveness of this method.
Beamforming Design for Integrated Sensing and SWIPT System

In order to achieve high spectrum utilization, it is necessary to consider the employment of the Integrated Sensing and Communication for the future wireless communication systems. On the other hand, since the radio signal for wireless communication can carry energy at the same time, simultaneous wireless information and power transfer (SWIPT) becomes a popular and helpful technology to enhance the energy transmission efficiency. In order to further improve the spectrum efficiency, we propose to design an integrated sensing and SWIPT (iS2WIPT) system, where a base station transmits combined signals to perform downlink multiuser communication, multiuser energy harvesting and radar target sensing simultaneously. We further formulate the problem as designing the transmit beamforming to minimize the beampattern matching error subject to the given total transmit power budget and the quality-of-service (QoS) constraints at all users, i.e., the individual signal-to-interference-plus-noise ratio (SINR) requirements at information users (IUs) and energy harvesting requirements at energy users (EUs). So as to solve the intractable nonconvex problem, we provide a difference-of-convex-functions (DC) representation for the problem and further solve it with global convergence guarantees. Numerical results clearly show that our proposed DC algorithm outperforms the classical semidefinite relaxation algorithm significantly under different situations. What is more, the results show that there is a tradeoff between the radar sensing performance and the QoS requirements of downlink users (both IUs and EUs).
Ultrafast Spiking Membrane III-V Laser Neuron on Si

We demonstrate a photonic spiking neuron based on an excitable membrane laser on Si. Both excitatory and inhibitory inputs up to 25 GBaud produce excitatory outputs with clearly-defined thresholds. <11 ps spike pulse widths and ~100 ps refractory periods have been achieved at ~15.3 pJ/spike.
Classification of P300 signals from P300 spelling system based on ASK-CNN model

How to detect P300 signal efficiently and accurately is great importance to improve the performance of P300-speller system. In this paper, a convolutional neural network (CNN) classification with average-scale kernel size (ASK-CNN model) is proposed to extract the temporal features of P300 signals for different subjects in P300 spelling system, and further optimize the classification accuracy compare with traditional CNN for P300 EEG signals detection. The experimental results show that compared with the traditional CNN model for detecting P300 signals, the ASK-CNN model can significantly improve the classification accuracy of P300 signals, especially when the superposing times of stimulus flashing is less. In addition, our model training speed is also faster than the traditional CNN model.
Network Traffic Anomaly Prediction for Beyond 5G Networks

Network traffic anomalies can have a detrimental effect on end-to-end network performance and reliability, compromising severely (demanding) services and applications, such as those offered by the fifth-generation (5G) mobile networks and beyond. To prevent the network drift towards increasingly inefficient operating modes, such traffic anomalies need to be detected first, so that the proper actions be taken to avoid such undesirable network drift. This paper proposes a novel framework focusing on proactively detecting such traffic anomalies, that is, predicting upcoming network traffic anomalies before they actually occur. To this end, two mechanisms are developed: a mechanism aiming at identifying different network traffic behaviors in an automated manner; and a mechanism for predicting network traffic behaviors for the next time interval spanning several seconds. The automated network traffic anomaly identification is realized via the application of clustering and decision tree-based learning. A time series model (specifically, a Bidirectional Long Short Term Memory (BiLSTM) Autoencoder) is employed for the proactive detection of forthcoming network traffic behaviors. Evaluation results are derived showing a prediction accuracy of up to 90.02%, demonstrating the effectiveness and viability of the proposed framework, as well as its potential for higher effectiveness compared to a state-of-the-art solution.
A Flux Controlled MOS-Based Optimized High Frequency Meminductor Emulator

In this paper, we propose an optimized meminductor emulator for high frequency operations. For the first time, the proposed meminductor emulator is designed using only MOS transistors making it realizable using monolithic IC fabrication. The numerical analysis of the proposed meminductor is performed employing Cadence Virtuoso with TSMC <inline-formula> <tex-math notation="LaTeX">$180~nm$ </tex-math></inline-formula> PDKs, and its behavior is verified with respect to different parameters, such as voltage, frequency, and process corner variations. Post layout analysis and physical experiments are also conducted to validate the proposed design. It is found that the area utilization and power consumption of the proposed meminductor are <inline-formula> <tex-math notation="LaTeX">$2350.67~\mu m^{2}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$3.3~mW$ </tex-math></inline-formula>, respectively. Its maximum operating frequency is <inline-formula> <tex-math notation="LaTeX">$20~MHz$ </tex-math></inline-formula>, and it is <inline-formula> <tex-math notation="LaTeX">$3.42\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$258\times $ </tex-math></inline-formula> more efficient in terms of area utilization, maximum operating frequency, and power consumption, respectively, compared to its nearest contemporary meminductors. Further, the applicability of the proposed meminductor is also verified by designing a chaotic oscillator to exhibit its utility in a wide range of applications.
Towards Artificial Intelligence-enabled Medical Pre-operative Airway Assessment

For surgeries which require general anesthesia, airway management is imperative. Difficult airway, which inhibits proper intubation, can be fatal. As such, pre-operative airway assessments are conducted by clinicians to determine the ease of intubation as well as to identify patients with difficult airway. To improve the process, artificial intelligence (AI) methods can be employed to predict such difficult airway situations so that suitable preparations can be made beforehand. However, due to the need for explainability of AI models required by healthcare regulations, typical black box models which work best with most data-driven AI methods cannot be used. Therefore, in the current work, a machine learning model has been established to predict the specific medical facial landmarks that are currently used by clinicians. These include the eyes, mentum, thyroid notch, suprasternal notch, forehead, tragus and radix. The model is based on convolutional neural network and a practical facial landmark detector concept. Furthermore, k-fold cross-validation sampling and the Adabelief optimizer have been utilized. The model prediction results display accurate prediction of the features, with the testing loss exhibiting good stability and maintaining well below 0.01 throughout. Attributed to that, the current model can lead to meaningful diagnosis of difficult airway during airway assessments.
Comparative Design between Induction Motor and Synchronous Reluctance Motor used for Electric Vehicle Conversion

In this paper, an induction motor (IM) and a synchronous reluctance motor (SynRM) have been designed suitable for an electric vehicle (EV) conversion. For a safety reason of the EV conversion, a DC voltage is limited to 150 V. The specification of the designed motors is based on the New European Driving Cycle (NEDC) which has been applied for a passenger car. To achieve a high accuracy of a motor simulation model, a finite-element (FE) method is utilized. Both motors have been mainly compared in terms of the performance. Without a need of permanent magnet material, they have a great potential to be used for the EV conversion with the lower production cost.
AN OPERATIONAL TEST OF A NUMERICAL PREDICTION METHOD FOR HURRICANES

Abstract Seventeen forecasts of hurricane tracks, each up to 72 hours, were made by numerical methods under operational conditions as a test of Kasahara's [5] prediction model. Although the small size of the sample precludes making firm conclusions, the results here obtained compare unfavorably with the regularly issued subjective forecasts. In general, the forecast motion is too slow and to the right of the actual hurricane track.
Capacity of phase-sensitively preamplified optical links at low signal-to-noise ratio

We experimentally show that phase-sensitively preamplified links have higher spectral efficiency than using conventional amplifiers at low signal-to-noise r atios. At 10 Gbaud, 4QAM modulation provides the best spectral efficiency at received powers below -59.7 dBm and 16-QAM from -59.7 dBm to -55.2 dBm.
Radar target classification using multiple perspectives

The problem of radar target classification is examined for the case when more than one perspective or viewing angle of the target is available to the sensor. Using full-scale target signature measurements as the source data, it is shown how, for the first time, multiple perspectives enhance the classification performance. Indeed this is the case even if only one additional perspective is available for exploitation. Further, we explore the classification performance both as a function of the number of perspectives and of the signal to noise ratio. Three approaches to high range resolution profile multi-perspective classification have been implemented. This removes any possible bias that could be introduced by a single individual classifier. The results show, for all three, a consistent improvement in the classification performance, as the number of perspectives is increased. The techniques employed also provide considerable insight into the classification process highlighting the degree of complexity of this extremely challenging problem.
A Rectifying Circuit Realizing the Nonlinearity of an Absorbing Metasurface

Rectifying circuits are widely used in wireless power transfer applications that convert radio frequency (RF) signals into direct-current (DC) energy. In this paper, a rectifying circuit is proposed as the detecting component in a nonlinear absorbing metasurface for spatial microwaves. It is mainly composed of a matching network, a voltage-doubler rectifier, and an amplifying circuit, functioning to generate DC that changes with the microwave intensity. Loads of the circuit are the PIN diodes embedded in the meta-atoms of the metasurface. While microwaves are getting stronger, the enhanced DC reduces the resistances of the diodes, making the meta-atom array more absorptive. The performance of the proposed circuit is simulated using ADS, and its significant role in realizing the intensity-dependent absorption of the metasurface is demonstrated through field-circuit cosimulations.
Temperature Analysis of Permanent Magnet Synchronous Motor Based on Iterative Calculation of Boundary Heat Transfer

Accurate calculation of heat dissipation coefficient is an important prerequisite to improve the accuracy of motor temperature. Due to the influence of motor structure and ambient temperature, the heat dissipation coefficient is difficult to calculate accurately. In this paper, a method by calculating the boundary heat transfer conditions iteratively is studied to get the heat dissipation coefficient of the motor surface. The influence of different ambient temperatures on the heat dissipation coefficient is analyzed. The influence of the heat dissipation coefficient solved by the iterative method and the traditional empirical formula on the accuracy of the finite element temperature calculation model are compared. The validity of iterative method is verified. Finally, the accuracy of the iterative method to calculate the heat dissipation coefficient is verified by experiments.
Bioimpedance detection and analysis for the prefrontal functional area

It is of great significance that bioimpedance detection for the analysis of cognitive activity in brain functional area. And it is also important for brain science research and clinical diagnosis. In this paper, the cerebral blood flow signal is detected and studied by bioimpedance technique. Firstly, the detection method of brain bioimpedance signal is designed, and the bioimpedance signal of subjects is collected and denoised. Secondly, the characteristic parameters of CBF in bioimpedance and bioimpedance differential signals, including peak value, curve area and comprehensive parameters, are extracted to characterize the activity changes of brain functional areas. Finally, the effectiveness of the detection method is verified by the significant changes of brain bioimpedance characteristic parameters in the prefrontal functional area under the letter memory experiment. The changes of brain impedance characteristic parameters in different periods of memory task are further analyzed. The results show that the bioimpedance characteristic parameters of the prefrontal area are changed by memory task, and the peak bioimpedance and differential bioimpedance are most significantly changed. Therefore, cerebral blood flow bioimpedance technology can monitor brain activity in real time, and with the increase of memory load, cerebral blood flow in the prefrontal functional area will increase correspondingly, cerebral blood flow velocity will also increase correspondingly, and the activation degree of this functional area will be enhanced.
High-Efficiency Injection-Molded Waveguide Horn Antenna Array for 76-81 GHz Automotive Radar Applications

A high-efficiency waveguide horn antenna array for 76-81 GHz automotive medium-range MIMO radar is designed, fabricated, and tested. The antenna consists of a linear array with eight elements and is realized by assembling two injection-molded plastic layers metalized using physical vapor deposition (PVD) and electroplating. Excellent free-space performance is achieved in the 76-81 GHz band, with measured impedance bandwidth of 20.8% and a maximal measured realized gain of 17.1 dBi at 78.5 GHz. With a measured efficiency of 91% (-0.4 dB), the built prototype represents an attractive alternative to conventional PCB technology.
Layer Selection, Power Allocation and Modulation Analysis of LACO-OFDM

As a visible light communications (VLC) modulation method, the layered asymmetrically clipped optical orthogonal frequency division multiplexing (LACO-OFDM) is promising. The layers in LACO-OFDM provide a high flexibility for power allocation and modulation selection. In this paper, we analyze the impacts of LACO-OFDM layer selection, layer power allocation and modulation selection. Firstly, we compare the BER performance of different power allocation methods and investigate the impact of layers on BER and optical power. On this basis, we propose a joint layer selection and power allocation algorithm. Simulation results show that the proposed power allocation scheme has a better bit error ratio (BER) performance than the common schemes. Finally, we verify the layer modulation selection and propose a search method with low time complexity.
Utilization of Database Spatial Queries for Region of Interest Extraction in Stored Images

Image processing, specifically extracting regions of interest, being a costly operation would benefit from a method of storing its output region and input image data in a database for future querying so as to prevent re-execution of the processing operation. This study investigates the performance of using built-in spatial queries found in a couple of modern Database systems to store and retrieve specific regions of image data. After testing two widely used Database software: MongoDB and Postgresql, results have shown that MongoDB performs better in terms of average and tail latency as well as efficiency in disk space consumption.
Significance of Quadrature and In-Phase Components for Synthetic Spoofed Speech Detection

For synthetic and voice converted Spoofed Speech Detection (SSD), Instantaneous Frequency (IF)-based features either exploit Hilbert Transform (HT) or Teager-based Energy Separation Algorithm (ESA) to estimate IF. However, HT-based approach leads to poor resolution in time-domain, and ESA-based approach leads to the lack of relative phase information. To that effect, we propose CFCCIF-QESA feature set, which encompasses excellent time resolution as well as relative phase information. Hence, we illustrate the significance of incorporating quadrature-phase component along with in-phase component for SSD of synthetic and voice converted spoof. The proposed feature set is evaluated using various performance metrics, namely, EER, MCC, F-measure, J-statistic, Jaccard Index, and Hamming loss. CFCCIF-QESA achieves a relative decrease in Average EER (AEER) by 22.53% w.r. t. the performance of CFCIF-ESA, for all the attacks in the dataset. Furthermore, results w.r. t. S10 attack (i.e., MaryTTS using unit selection synthesis), which is the most difficult to detect attack, show that CFCCIF-QESA achieves a 29.16% relative decrease in EER as compared to the CFCCIF-ESA. Furthermore, using model-level measures such as, Kullback Leibler Divergence (KLD) and Jenson Shannon Divergence (JSD), we also show the better discriminative ability of our proposed feature set CFCCIF-QESA than the existing CFCCIF-ESA. Finally, the analysis of the latency period for CFCCIF-QESA and CFCCIF-ESA is presented, and it shows better suitability of CFCCIF-QESA w.r. t. deployment in practical SSD systems.