Miracle-dz/UniFAR-FTUs huggingface dataset:
Wideband Monostatic Co-Polarized Co-Channel Simultaneous Transmit and Receive Broadside Circular Array Antenna

A novel wideband monostatic circularly co-polarized co-channel simultaneous transmit and receive (C-STAR) broadside circular array antenna with a partially shared beam-forming network (BFN) is proposed. The configuration consists of four sequentially rotated two-arm Archimedean spiral antennas, backed by a finite ground plane and driven by a BFN. Every two diagonal antenna pairs are fed with differential phases to obtain similar co-polarized broadside transmitting (TX) and receiving (RX) beams. Each individual element has single feeding port and operates simultaneously as TX and RX over the same frequency. The proposed C-STAR approach utilizes antenna orientation, geometric symmetry, and partially shared BFN to overcome self-interference. In the absence of asymmetry and BFN imbalances, complete wideband self-interference cancellation is achieved without relying on time-, frequency-, space-, pattern-, antenna/port-, or polarization-multiplexing. The fabricated prototype has measured TX/RX isolation >38 dB, axial ratio <3.7 dB, and realized gain between 3 and 8.8 dBic over 1.25–2.5 GHz. The TX and RX radiation patterns are similar with envelope correlation coefficient >0.93.
A New Single DC Source Six-Level Flying Capacitor Based Converter With Wide Operating Range

This paper presents a new six-level flying capacitor based (FC-based) multilevel converter with one dc source and the capability of operating in all power factors and modulation indexes. Multilevel converters with one dc voltage source are attractive in many applications as they do not need rather expensive and bulky multiwinding input transformer connection at the dc side. On the other hand, not all classic multilevel converters with one dc source can produce any desirable number of output voltage levels at all power factors and/or modulation indexes. In this paper, a hybrid structure is proposed in which six voltage levels can be realized at the ac terminals. The modulation technique and the control strategy for the FC voltage balancing are presented. To show the advantages of the proposed converter, different performance criteria, such as switch count and rating, the size of capacitors, switching frequency, and power losses, are compared with other existing six-level topologies. The results indicate that the proposed structure is superior to other six-level converters from different standpoints. Simulation results are used to further evaluate the performance of the proposed converter. A laboratory-type experimental setup is used to validate the theoretical results.
PMSM PI Current Controller Tuning Using Structurally Constrain LQR LMI Criteria for MIMO Plants

In this paper the convex optimization technique is proposed for parameter tuning of a PI controller. Presented non-iterative two step procedure is able to optimize the coefficients of a structured output feedback matrix using the LQR cost function. It is also shown that the matrix coefficients can be linked to PI controller parameters simply by extending the state space model of the plant. The capability of this method to optimize controllers for MIMO plants is demonstrated on current control of the real PMSM.
Breast Cancer Histopathology Image Classification with Deep Convolutional Neural Networks

This work addresses the problem of intra-class classification of Breast Histopathology images into Eight (8) classes of either Benign or Malignant Cell. Current manual features extraction and classification is fraught with inaccuracies leading to high rate false negatives with attendant mortality. Deep Convolutional Neural Networks (DCNN) have been shown to be effective in classification of Images. We adopted a DCNN architecture combined with Ensemble learning method using TensorFlow Framework with Backpropagation training and ReLU activation function to achieve accurate automated classification of these Images. We achieved inter-class classification accuracy of 91.5% with the BreakHis dataset.
Fine-grained Collaborative K-Means Clustering

We present a collaborative clustering algorithm of fine-grained images, where the subtle inter-class differences are represented using collaborative filters. Cluster centers are represented as optimal weighted collaboration of data points, and the optimal weight matrix is analytically obtained. This may be viewed as a generalization of the K-means clustering algorithm, where these weights would be unity. We also introduce a matrix inversion scheme that allows us to scale up collaborative representations by orders of magnitude and this allows us to apply the scheme when the number of data points is large. Collaborative clustering outperforms K-means and a few of its popular variants K-medians, K-modes and K-medoids. It also outperforms DBSCAN and its recent variation DSets-DBSCAN. We have chosen species recognition (bird and butterflies) as a representative fine-grained image categorization problem, though the proposed algorithm is a general approach applicable to other similar tasks. We also introduce a new benchmark fine-grained image dataset, that of Indian endemic butterfly species (Titli.vl), which is available through the corresponding author.
Fast Semantic Segmentation for Scene Perception

Semantic segmentation is a challenging problem in computer vision. Many applications, such as autonomous driving and robot navigation with urban road scene, need accurate and efficient segmentation. Most state-of-the-art methods focus on accuracy, rather than efficiency. In this paper, we propose a more efficient neural network architecture, which has fewer parameters, for semantic segmentation in the urban road scene. An asymmetric encoder–decoder structure based on ResNet is used in our model. In the first stage of encoder, we use continuous factorized block to extract low-level features. Continuous dilated block is applied in the second stage, which ensures that the model has a larger view field, while keeping the model small-scale and shallow. The down sampled features from encoder are up sampled with decoder to the same-size output as the input image and the details refined. Our model can achieve end-to-end and pixel-to-pixel training without pretraining from scratch. The parameters of our model are only <inline-formula><tex-math notation="LaTeX">$0.2M$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$100 \times$</tex-math></inline-formula> less than those of others such as SegNet, etc. Experiments are conducted on five public road scene datasets (CamVid, CityScapes, Gatech, KITTI Road Detection, and KITTI Semantic Segmentation), and the results demonstrate that our model can achieve better performance.
Diurnal Variation of Ionospheric Electron and Ion Temperatures with Altitude by QJISR

The ionospheric plasma temperatures in March and June measured by the Qujing incoherent scatter radar have been used to study the diurnal variation of electron and ion temperatures with altitude (193-513km). It is found that there exist the morning and sunset enhancement and the daytime constant valley in electron temperature in March. The features like morning overshoot, day time valley (about 1000K) and sunset enhancement (with the maximum 3500K) show larger values at higher altitudes. The electron temperature above 1000K decreases with an increase in density. The sunset peak occurs earlier at higher altitudes. These above characteristic isn't prominent for electron temperature in June and ion temperature in all seasons. Both the electron and ion temperature reach a peak at about 200km and then decreases. The possible mechanism is also presented.
Wind Speed Estimation MPPT Technique in Grid-Connected Wind Energy Conversion System

Due to the several problems with wind speed sensor such as high cost and maintenance, this paper suggests a wind speed estimation based a maximum power point tracking (MPPT) for a variable speed wind energy conversion system (VS-WECS) based on 1.5 MW PMSG. The Back-to-Back converter (BTBC) uses for delivering the electrical power extracted from the PMSG into the utility grid. Otherwise, the BTBC is consisting of two side, the first one is the machine side converter (MSC) uses for extracting the maximum power (MP) from the WECS under different wind speed variations and the second side is the grid side converter (GSC) uses to supply the utility grid with the active power from wind turbine(WT). In this study, the wind speed estimated based on the rotor speed only. The simulation results stated that the proposed MPPT technique can generate optimal rotor speed for extract the maximum output power.
Bandwidth Extension of Doherty Power Amplifier Using Complex Combining Load With Noninfinity Peaking Impedance

In this paper, a high-efficiency Doherty power amplifier (DPA) using the complex combining load (CCL) with noninfinity peaking impedance for broadband operation is presented. By applying the CCL, the load impedance of the carrier amplifier at saturation power level can be compensated at different operation frequencies. Considering the influences of the output impedance of the peaking PA on the carrier PA, a detailed analysis of the optimal noninfinity peaking output impedance with the CCL is carried out on the whole operation frequency range. Theoretical analysis of the output power and drain efficiency (DE) reveals that the CCL can offer a new degree of freedom for extending the bandwidth of the DPA. Based on the proposed theory, a modified DPA working over the frequency band of 1.1–2.4 GHz (bandwidth of 74%) is designed and fabricated based on GaN HEMT devices. Experimental results exhibit 6-dB output back-off drain efficiencies of 43.8%–54.9% and saturated drain efficiencies of 55.4%–68% can be obtained. The saturated output power of the DPA is 43.3–45.4 dBm with a gain of 9.5–11.1 dB across the whole operation band. When driven with long-term evolution modulated signals with 6.5-dB peak-to-average power ratio, the DPA exhibits an adjacent channel leakage ratio better than −49 dBc while maintaining a DE of 44% after digital predistortion linearization.
Network-Based Quantized Control for Fuzzy Singularly Perturbed Semi-Markov Jump Systems and its Application

This paper deals with the quantized control problem for nonlinear semi-Markov jump systems subject to singular perturbation under a network-based framework. The nonlinearity of the system is well solved by applying Takagi–Sugeno (T-S) fuzzy theory. The semi-Markov jump process with the memory matrix of transition probability is introduced, for which the obtained results are more reasonable and less limiting. In addition, the packet dropouts governed by a Bernoulli variable and the signal quantization associated with a logarithmic quantizer are deeply studied. The major goal is to devise a fuzzy controller, which not only assures the mean-square $\bar { \sigma }$ -error stability of the corresponding system but also allows a higher upper bound of the singularly perturbed parameter. Sufficient conditions are developed to make sure that the applicable controller could be found. The further examination to demonstrate the feasibility of the presented method is given by designing a controller of a series DC motor model.
The Role of Closed-Loop Hand Control in Handshaking Interactions

In this letter, we investigate the role of haptic feedback in human–robot handshaking by comparing different force controllers. The basic hypothesis is that in human handshaking force control there is a balance between an intrinsic (open-loop) and extrinsic (closed-loop) contributions. We use an underactuated anthropomorphic robotic hand, the Pisa/IIT hand, instrumented with a set of pressure sensors estimating the grip force applied by humans. In a first set of experiments, we ask subjects to mimic a given force profile applied by the robot hand, to understand how human perceive and are able to reproduce a handshaking force. Using the obtained results, we implement three different handshaking controllers, in which we varied the intrinsic and extrinsic contributions and in a second set of experiments, we ask participants to evaluate them in a user study. We show that a sensorimotor delay mimicking the reaction time of the central nervous system is beneficial for making interactions more human-like. Moreover, we demonstrate that humans exploit closed-loop control for handshaking. By varying the controller we show that we can change the perceived handshake quality and also influence personality traits attributed to the robot.
On Exploiting Active Redundancy of a Modular Multilevel Converter to Balance Reliability and Operational Flexibility

This paper presents a practical strategy for utilizing the submodule (SM) redundancy of a modular multilevel converter (MMC) for its fault tolerance. This strategy provides a systematic framework for balancing the tradeoff between two conventional methods for using the active redundancy and, thus, achieves operational flexibility. One of the existing methods improves SM reliability owing to less voltage stress on the SM components by employing all of the SMs to form the ac or dc voltages (voltage-sharing mode). The other avoids transients by keeping the average SM voltage constant at the cost of slightly increased stress on the SM components (fixed-level mode), which, however, can be controlled to provide the grid-adaptive operation by reserving the energy of the SMs not in service. We, thus, develop a new redundancy management scheme by integrating these two methods and exploiting their technical benefits to meet the PQ requirements and MMC control performance. This research provides a theoretical basis and a technical guide to determining the number of SMs, which can further increase the voltage steps as per the MMC and grid conditions. This paper also connects the remaining PQ capability of the MMC at a particular operating point with the SM redundancy concept by defining a potential redundancy, especially useful when the physical redundancy is exhausted. The theoretical findings and efficacy of the proposed strategy are validated through PSCAD/EMTDC time-domain simulations followed by experiments using a nine-level single-phase MMC system.
Motion Estimation From Surface Electromyogram Using Adaboost Regression and Average Feature Values

The method to estimate joint motion quickly and accurately from surface electromyogram (sEMG) has been explored by many researchers. However, the effect of different grabbing loads is ignored by most of them, which limits its clinical and daily applicability. In order to eliminate this effect in the course of motion estimation, an Adaboost-AF method based on Adaboost regression was proposed to identify the load information and estimate motion intention. This method is composed of three parts: the sEMG preprocessing part, the load identification part, and the motion intention estimation part. The average value of sEMG feature signal, which is obtained during sEMG preprocessing part, was used to identify the load information. Five features, root mean square (rms), waveform length, difference absolute standard deviation value, the integral signal of sEMG (IEMG), and low-pass filtered signal of sEMG (LPFEMG) similar to sEMG envelope were explored whether the average value of features signal can be used to identify the load under different speeds. After the load was identified, the Adaboost regression framework with the decision tree as the weak learner was applied to be the motion intention estimation part. Experimental results showed that the average rms difference between an actual angle and estimated angle by using the Adaboost-AF method was 0.1193 ± 0.0148, and the average execution time for a section of data with 17.58 s was 0.3932 ± 0.0610s. (Scikit-learn packet was adopted, and the version number was V0.21. The running software was Spyder, Python3.6.) This paper shows that the method based on the average value of sEMG feature signals can eliminate the influence of different loads without adding new sensors; IEMG and LPFEMG from the biceps brachii muscle show the optimal loads identification performance; the proposed Adaboost-AF method takes the influence of different loads into account and ensures the rapid and accurate estimation performance of motion intention.
Canny-VO: Visual Odometry With RGB-D Cameras Based on Geometric 3-D–2-D Edge Alignment

This paper reviews the classical problem of free-form curve registration and applies it to an efficient RGB-D visual odometry system called Canny-VO, as it efficiently tracks all Canny edge features extracted from the images. Two replacements for the distance transformation commonly used in edge registration are proposed: approximate nearest neighbor fields and oriented nearest neighbor fields. 3-D–2-D edge alignment benefits from these alternative formulations in terms of both efficiency and accuracy. It removes the need for the more computationally demanding paradigms of data-to-model registration, bilinear interpolation, and subgradient computation. To ensure robustness of the system in the presence of outliers and sensor noise, the registration is formulated as a maximum a posteriori problem and the resulting weighted least-squares objective is solved by the iteratively reweighted least-squares method. A variety of robust weight functions are investigated and the optimal choice is made based on the statistics of the residual errors. Efficiency is furthermore boosted by an adaptively sampled definition of the nearest neighbor fields. Extensive evaluations on public SLAM benchmark sequences demonstrate state-of-the-art performance and an advantage over classical Euclidean distance fields.
Power-Based Droop Control in DC Microgrids Enabling Seamless Disconnection From Upstream Grids

This paper proposes a local power-based droop controller for distributed energy resource converters in dc microgrids that are connected to upstream grids by grid-interface converters. During normal operation, the grid-interface converter imposes the microgrid bus voltage, and the proposed controller allows power flow regulation at distributed energy resource converters’ output. On the other hand, during abnormal operation of the grid-interface converter (e.g., due to faults in the upstream grid), the proposed controller allows bus voltage regulation by droop control. Notably, the controller can autonomously convert from power flow control to droop control, without any need of bus voltage variation detection schemes or communication with other microgrid components, which enables seamless transitions between these two modes of operation. Considering distributed energy resource converters employing the power-based droop control, the operation modes of a single converter and of the whole microgrid are defined and investigated herein. The controller design is also introduced. Furthermore, the power sharing performance of this control approach is analyzed and compared with that of classical droop control. The experimental results from a laboratory-scale dc microgrid prototype are reported to show the final performances of the proposed power-based droop control.
Correlation of Characteristic Ne SXR Signal Pulse With Computed Plasma Focus Dynamics in the Ne (97.5%)–Kr (2.5%) Admixtures of the INTI PF Machine at 12 kV

This paper shows the correlation of the characteristic Ne soft X-ray (SXR) signal pulse with computed dynamics of the plasma focus using doped neon in Ne (97.5%)/Kr (2.5%) admixtures. The Lee model code is coupled with a specifically designed correlation excel template. The results show that typically, the characteristic Ne SXR pulse signal starts after the start of the reflected shock (RS) and before the beginning of the pinch. Specifically, in the illustrated shot, the RS phase starts at <inline-formula> <tex-math notation="LaTeX">$3.375~\mu \text{s}$ </tex-math></inline-formula> while the pinch phase starts at <inline-formula> <tex-math notation="LaTeX">$3.384~\mu \text{s}$ </tex-math></inline-formula>. The characteristic Ne SXR signal pulse starts at <inline-formula> <tex-math notation="LaTeX">$3.377~\mu \text{s}$ </tex-math></inline-formula>, which is 2 ns after the start of the RS and 7 ns before the pinch starts. There is a consistent trend of Ne SXR correlation with the computed dynamics in the Ne doped with Kr admixtures up to a doping level of 2.5%.
Effect of Faults in Solar Panels on Production Rate and Efficiency

The economic life of Solar Power Plants (SPP) is accepted for more than 20 years and this point is taken into account in financial analysis. The cumulative effect of very small changes in the efficiency of a system that will generate energy for 20 years increases significantly over the years. SPP efficiency determines the losses in the generated energy. The failures in SPPs have a serious impact on the reliability of the system and the energy balance. Because of this, the energy losses produced by the SPP plant directly depend on two major factors. These factors are plant failures and production inefficiencies in photovoltaic (PV) panels. To know the negative impact of energy losses in PV plants, contributes to optimization in maintenance and design and increases efficiency in power production. In this study, probable losses in SPPs are classified and examined and the possible consequences of these losses and their effects on SPP performance are explained. In addition, by using the actual data in the operation and maintenance follow-up of many power plants, error rates grouped by the deficiencies in energy balance are achieved. According to these results, the process in the system under test, 67415 MW of power plant and 59211 MW of power plant has been obtained, resulting in a total 87.83 percent difference.
Method of Moments Simulation of Modulated Metasurface Antennas With a Set of Orthogonal Entire-Domain Basis Functions

A family of orthogonal and entire-domain basis functions (named Fourier–Bessel) is proposed for the analysis of circular modulated metasurface (MTS) antennas. In the structures at hand, the MTS is accounted for in the electric field integral equation (EFIE) as a sheet transition impedance boundary condition on the top of a grounded dielectric slab. The closed-form Hankel transform of the Fourier–Bessel basis functions (FBBFs) allows one to use a spectral domain formulation in the method-of-moments (MoM) solution of the EFIE. Moreover, these basis functions are fully orthogonal, which implies that they are able to represent the global evolution of the current distribution in a compact form. FBBFs also present a better filtering capability of their spectrum compared to other well-known orthogonal families such as the Zernike functions. The obtained MoM matrix is sparse and compact, and it is thus very well-conditioned and can be efficiently computed and inverted. The numerical results based on the proposed decomposition are presented and compared with those based on the use of the Gaussian ring basis functions and with the full-wave analysis of MTS antennas implemented with small printed elements. A very good agreement is observed.
Ultra-Low Complexity Block-Based Lane Detection and Departure Warning System

This paper proposes an ultra-low complexity block-based lane detection and departure warning system. Based on the distribution of the lane markings in the region close to the vehicle, a parameterized region of interest (ROI) is determined. The lane markings in the ROI are enhanced by increasing the pixel intensity for detection in various environmental conditions. To reduce the computational burden, the ROI is partitioned into non-overlapping blocks and two simplified masks are proposed to obtain the block gradients and block angles. The driving conditions are classified into four classes to simplify the lane detection process and the proposed lane departure warning system is based on the lane detection results. The experimental results reveal that the average lane detection rate and the departure warning rate are 96.12% and 98.60%, respectively. With a $1920\times 1080$ resolution, the average processing time is 4.28 ms per frame.