Miracle-dz/UniFAR-FTUs huggingface dataset:
Large Signal Behavioral Modeling of Power Transistor from Active Load-pull Systems

Large signal behavioral modeling for RF power transistor has been developed rapidly in the past decade, this paper compares and contrasts recent nonlinear large-signal behavioral modeling techniques designed for power transistors. These techniques include traditional function based behavioral modeling techniques, e.g. X-parameters, Padé model, Cardiff model, load-pull X-parameters; and artificial neural network (ANN) and machine learning (ML) technique based methods, e.g. real value feed-forward neural network (RVFFNN) model, Bayesian inference model, support vector regression (SVR) based model, in the frequency domains. Besides, approaches to generating the data for modeling from active load-pull measurements are reviewed as well, including closed loop, feed-forward, and open loop active load pull system.
Multi-Modal Sensor Medical Image Fusion Based on Multiple Salient Features With Guided Image Filter

In this paper, we propose an efficient image fusion algorithm using multiple salient features with guided image filter to prevent the problem of low contrast detail. First, we employ the guided image filter to decompose the input images into a series of smoothed and detailed images at different scales. Second, the salient features are extracted from the decomposed smoothed images and detailed images using two different algorithms: the spectral residual (SR) algorithm for extracting mainframe information and the graph-based visual saliency (GBVS) model for extracting gradient saliency information to construct the fusion rules. In addition, generalized intensity-hue-saturation (GIHS) is adopted to combine the decomposition coefficients. Finally, the fused image is reconstructed by the fused smoothed and fused detailed images. The experimental results demonstrate that the proposed algorithm can achieve better performance than other fusion methods in the domains of MRI-PET and MRI-SPECT fusion.
Event-triggered Sliding Mode Control Based Trajectory Tracking of Robotic Manipulators in a Cyber-Robotic Space

This paper presents trajectory tracking control of robotic manipulators using an event-triggered sliding mode control (ET-SMC) strategy. Sliding mode control is an efficient tool to control complex nonlinear systems, due to its low sensitivity to disturbances / uncertainties. In an event-triggered strategy, the control signal is updated only when a particular event condition is violated, thus avoiding consistent periodic execution of control tasks. Here, the ET-SMC based trajectory control achieves robust stability with the desired tracking performance and reduced number of control calculations. Simulation results are given to illustrate the theoretical predictions.
SRAM cell with better read and write stability with Minimum area

This paper describes a novel SRAM architecture to improve read and write stability. Read stability is enhanced by increasing $\beta$ ratio to 4 and write stability is enhanced by storage node charging and discharging through two transistors. The proposed 8T SRAM architecture is compared with Conventional 6T, decoupled 8T SRAM cell and 10T SRAM cell. 6T SRAM cell required 584.1mV word line voltage to write data into a cell at 1.2V supply voltage whereas the proposed SRAM cell requires only 512.8mV, leakage power is close to leakage power of 6T SRAM cell.
Rascal, 10 Years Later

When we designed the first version of Rascal in 2009, we jokingly promised ourselves to only write a single paper on the language itself, and see it as vehicle for research from then on,—that one paper became the SCAM 2009 article, now awarded with the SCAM most influential paper award. Since then, Rascal has evolved significantly, and has been successfully applied in research, education, and industry. This extended abstract gives an overview of the impact of Rascal over the last 10 years, and looks at current and future developments.
Fish Detection and Tracking in Pisciculture Environment using Deep Instance Segmentation

This study presents a novel approach in detecting and tracking of fish in pisciculture. Pisciculture in general involves challenging tasks of counting and monitoring fish in natural or nature like, man-made habitats such as inland fisheries for breeding, feeding and sorting purposes. These are presently achieved using conventional methods that are inefficient when implemented in large-scale commercial productions. To overcome such difficulties and improve the efficiency of the processes, images of fish and fish seeds are captured in natural murky water habitats through a vision sensor on board an unmanned aerial vehicle (UAV). In this research paper, a deep instance segmentation algorithm called Mask R-CNN along with GOTURN tracking algorithm is employed for real time fish detection and tracking. A comparison study is also carried out (i) fish detection on high resolution images (ii) fish detection on high resolution image multi-region parallel processing (iii) fish detection on high resolution image multi-region parallel processing with tracking. The results are found to be accurate with image multi-region parallel processing along with tracking, with an F1 score of 0.91 at 16 frames per seconds on in-land fishes environment.
Automatic Detection of Cardiac Arrhythmias Using Ensemble Learning

Objectives: Electrocardiogram (ECG) is an important clinical tool for diagnosis of cardiac abnormalities. Physicians make diagnoses by visual examination of ECGs. Analysing huge amounts of ECGs however, can be very time consuming and cumbersome. Hence, developing analytic software is of great importance to automatically analyse these ECG signals to detect efficiently the common cardiac arrhythmias. Methods: Proposed an ensemble learning approach for automatic processing of ECG signals and classification of arrhythmias. Twenty six features (based on wavelets, heartbeat intervals, and RR-intervals) are extracted and three algorithms, namely, Random Forest (RF), Adaptive Boosting (AdaBoost) and Artificial Neural Network (ANN) are utilized for classification. Results: The proposed method is evaluated on ECG signals from 44 recordings of the MIT-BIH arrhythmia database. The overall classification accuracy of the RF, AdaBoost, and ANN are 96.16%, 96.16% and 94.49%, respectively. Additionally, the overall classification accuracy of the ensemble model is improved to 96.18%. Conclusion: Experimental results show that the performance of the ensemble model for ECG heartbeat classification improves the overall accuracy. Significance: This paper proposes an accurate and easy to use approach to classify heartbeats into one of the five classes recommended by ANSI/AAMI standard, which can be used in real-time within a tele-health monitoring framework.
Bit Error Probability Analysis of UFMC Systems with Carrier Frequency Offset

One of the crucial requirements of next generation wireless communication systems is relaxed synchronization due to large number of connected devices with wide range of capabilities. Universal filtered multicarrier (UFMC), one of the most promising waveforms among the recently proposed waveform types for the next generation wireless communication system, that can support this demand to a great extent. In this paper, the bit error rate (BER) performance of UFMC communication system in the presence of carrier frequency offset (CFO), which is useful for feasibility studies, is analyzed. This is done by deriving the symbol error probability of gray coded quadrature phase shift keying (QPSK) based UFMC system with CFO under additive white Gaussian noise (AWGN) channel conditions. The accuracy of the expression thus derived is validated through simulation studies. Also, a comparative study is made on the sensitivity of orthogonal frequency division multiplexing (OFDM) and UFMC to CFO.
A Continuous Estimation Model of Upper Limb Joint Angles by Using Surface Electromyography and Deep Learning Method

The continuous control of rehabilitation robots based on surface electromyography (sEMG) is a natural control strategy that can ensure human safety and ease the discomfort of human-machine coupling. However, current models for estimating movement of the upper limb focus on two dimensions movement, and models of three dimensions movement are too complex. In this paper, a simple-structure temporal information-based model for upper limb motion was proposed. An experiment of the multijoint motion of the upper limb was carried out. We studied the touching motion and the compound task motion of the upper limbs. The touch motor task consists of three tasks, namely, shoulder abduction, shoulder forward bend and finger-nose. The compound tasks include driving and fetching objects. Three-dimensional upper limb movement data and sEMG signals of seven upper limb muscles were recorded from seven healthy subjects. Model training was carried out after data preprocessing and feature extraction. Then, 120 s of upper limb motion data was used to verify the performance of the model proposed in this paper. The estimated accuracy of the model for the touch tasks was 0.9171, and it was 0.8109 for compound tasks. Compared to the multilayer perception (MLP) model, a 13.57% reduction in the root-mean-square error (RSME) was observed. The results show that the model has good accuracy for estimating the angular motion of the upper limb and that it has the potential to be applied for three-dimensional motion control in an upper limb mirror-image therapy rehabilitation robot.
A behavior electro-thermal model of the IGBT using Matlab/Simulink

SIMULINK users are constantly faced with the inability to analyze the dynamic behavior of electronic circuits that contain IGBT devices, using the semiconductor models of the SIMULINK library. These models represent only the static behavior. In this paper, a complete Electro-thermal model of the IGBT component for the SIMULINK simulator has been developed. The model based on a behavioral analysis, present the optimum compromise between accuracy and the capability to perform fairly complex simulation. The model parameter's can be quickly extracted from standard measurements or from data sheets.
Modeling and Control of a Wind Turbine Based On a Doubly Fed Induction Generator

In this paper, the (DFIG) for wind energy conversion system is studied so as to control the rotor speed and the reactive power produced by the (DFIG). To achieve these objectives, a decoupling control scheme has been applied utilizing PI controllers. In the DFIG topology, the stator is specifically associated with the grid and the rotor winding is associated with the grid through an AC/DC/AC converter. First, we propose the mathematical model in Park's coordinates of the whole system. Next, a field oriented control for decoupling the torque and the reactive power is applied. Finally, the DC Link voltage is regulated at a well defined constant value. This work is developed and programmed using Matlab/Simulink environment. The obtained simulation outcomes demonstrate the effectiveness of the suggested controller.
Dual-Band MIMO Antenna Structures with High Isolation

This paper presents three printed dual band MIMO antenna structures. Each antenna structures consist of longer and shorter rectangular arms forming L-shape, F-shape and bent F-shape MIMO antenna. High isolation is achieved by employing slots in ground and a T-shaped ground stub. $\mathbf{S}11 < -9.5\mathbf{dB}$ is obtained over 2.3-2.7GHz and 5.1-6.0GHz for Wi-Fi and Bluetooth (2.4-2.5GHz). , 4G Wi-MAX (2.3-2.4GHz and 2.6-2.7GHz), WLAN (5.15-5.35GHz and 5.725-5.875GHz) applications. $\mathbf{S}12 < -15\mathbf{dB}$ and $\mathbf{S}12 < -20\mathbf{dB}$ over the lower and higher frequency band is achieved respectively for the three proposed antennas. The antenna features nearly omnidirectional radiation patterns with stable gain and acceptable MIMO performance parameters.
Complementary Characterization of Laser-Induced Plasmas by Optical Emission Spectroscopy and Triple Langmuir Probe

In this article, we study the temporal evolution of the electron density and temperature of carbon laser-produced plasmas (LPP) using a combination of optical emission spectroscopy (OES) and the triple Langmuir probe (TLP) method. OES has been widely used to characterize LPPs but it is limited to distances relatively close to the target. On the other hand, the main advantage of TLP over the single-probe method is that it allows to obtain simultaneous measurements of electron temperature (<inline-formula> <tex-math notation="LaTeX">$T_{e}$ </tex-math></inline-formula>) and density (<inline-formula> <tex-math notation="LaTeX">$N_{e}$ </tex-math></inline-formula>), without any voltage sweeping. In this article, we compare both techniques; measurements by OES were performed at distances from the target <1 cm, whereas the triple probe was employed for distances >5 cm. The plasma was generated by focusing nanosecond laser pulses onto a high purity graphite target placed inside a vacuum chamber. Our results show that the values of <inline-formula> <tex-math notation="LaTeX">$T_{e}$ </tex-math></inline-formula> measured using both techniques predict decreasing exponential behavior. <inline-formula> <tex-math notation="LaTeX">$N_{e}$ </tex-math></inline-formula> determined by the spectral line Stark broadening in OES (<1 cm) was two orders of magnitude larger than that measured by the TLP (>5 cm). This difference in the electron density can be explained by radiative recombination and the presence of different kinds of plasma species detected by the TLP technique. The results show that both methods are complementary and their combination can be used to characterize LPP in a wide range of distances and timescales.
Automatic Blood Glucometer-Tonometer to Control Diabetes Mellitus

The control of diabetes mellitus is an actual problem of modern medicine and should be accompanied by the monitoring of the glycemic level in a continuous mode. We have proposed a non-invasive computational method for the study of glycemia, based on modeling the correlation relationship of hemodynamic and biochemical blood parameters. Mathematical empirical formulas for calculating blood glucose levels by blood pressure indicators during the day, which allow us to create a database for monitoring indicators, were obtained. A new automatic analyzer has been developed. It measures blood pressure and non-invasively calculates blood glucose using an application program using empirical formulas. The analyzer's control microcontroller, mounted on the base of the ATmega85 8-bit programmable microcontroller, contains an application program for the UART universal asynchronous transceiver - an interface that allows us to connect the device to the computer using a 6-pin USB-UART converter. Using application programs and signaling devices, the device allows blood pressure measurement, non-invasive calculation of blood glucose levels and real-time transfer of the database to a computer with the ability to analyze the results. The device can be used in medical institutions in the diagnosis of arterial hypertension and diabetes mellitus, as well as for self-control.
MHNet: Multiscale Hierarchical Network for 3D Point Cloud Semantic Segmentation

Point cloud semantic segmentation is a challenging task in 3D understanding due to its disorder, unstructured and nonuniform density. Currently, most methods focus on network design and feature extraction. However, it is difficult to capture the point cloud features of complex objects comprehensively and accurately. In this paper, we propose a multiscale hierarchical network (MHNet) for 3D point cloud semantic segmentation. First, a hierarchical point cloud feature extraction structure is constructed to learn multiscale local region features. Then, these local features are subjected to feature propagation to obtain the features of the entire point set for pointwise label prediction. To take fully advantage of the correlations of propagated information between the different scale coarse layers and the original points, the local features of each scale are characterized by feature propagation to obtain the features of the original point clouds at the corresponding scale. The global features propagated from different scales are integrated to constitute the final features of the input point clouds. The concatenated multiscale hierarchical features, including both local features and global features, can better predict the segmentation probability of each point cloud. Finally, the predicted segmentation results are optimized using the conditional random filed (CRF) with a spatial consistency constraint. The efficiency of MHNet is evaluated on two 3D datasets (S3DIS and ScanNet), and the results show performance comparable or superior to the state-of-the-art on both datasets.
Deep CNN-based Feature Extractor for Target Recognition in Thermal Images

Target recognition in thermal infrared images is challenging due to high variability of target IR signature and competing background IR signature due to a number of environmental and target parameters. Traditional hand-crafted feature extractors are limited by these challenges. Recently, deep learning has shown promising success for a number of computer vision works. In this paper, deep CNN-based feature extraction is explored for target recognition in thermal images. In this study, two pre-trained CNNs, AlexNet and VGG19 are considered. A number of deep CNN-based feature extractors are evaluated by extracting features from different layers of the network. The results indicate the robustness of the deep CNN-based feature extractor. The VGG19_fc6 architecture has demonstrated superior performance with 6% improvement in the classification accuracy against the WignerMSER based state of the art target recognition on two class FLIR thermal infrared dataset.
SVPWM Based Generalized Switching Schemes for Seven Level DCMLI Including Over Modulation Operation - FPGA Implementation

This paper presents space vector pulse width modulation based generalized switching schemes including over modulation region for multilevel inverters. The main feature is that, it substantially minimizes determination of space vectors by generating all switching patterns without lookup tables in turn promotes the design and implementation. A new switching scheme is formed based on the nature of triangles of the space vector diagram, where in the patterns are arranged for reduction of device frequency and enhancement of harmonic production by eliminating further switching commutations. The method is analyzed through simulations and executed experimentally through SPARTAN FPGA processor on seven level diode clamped inverter. The outcomes prove the performance of the presented method.
Performance Evaluation of Quad-Ridged Horn Antenna in Variation of Its Ridge Profile

The performance evaluation of quad-ridged horn antenna in comparison with double-ridged with varying a ridge profile is presented. Four types of ridge profile utilized by the antennas are proposed for the performance evaluation, namely linear, quadratic, exponential and Gaussian ridges. The performance evaluation is carried out by characterizing the influence of ridge shape toward the antenna parameters in terms of bandwidth, beamwidth, gain, sidelobe level, and isolation. From the results, it can be inferred that each type of ridge profile produces different frequency and performance responses both for quad-ridged and double-ridged horn antenna. The quadratic ridge profile yields the widest antenna bandwidth and lowest sidelobe level (SLL) for both horn antennas. While in terms of gain and beamwidth, optimum performances are achieved by the antennas with linear and quadratic ridge profiles, respectively.
A Dense U-Net Architecture for Multiple Sclerosis Lesion Segmentation

Lesion segmentation is pivotal for the identification and cure of Multiple Sclerosis (MS). It is the need of the hour to highlight the neural networks with fewer layers of convolution, less training data samples while maintaining high accuracy and less processing speed to achieve real-time lesion segmentation. This paper evaluates a network with neural blocks comprising two jump points which are responsible for forward feeding of input data within the block. The downsampling path of the network architectures ensures transition invariance with reducing parameters while the upsampling path results in accurate localization. Data augmentation in the preprocessing part of the architecture helps achieving desired result with limited annotated data samples. We argue that the proposed net has numerous advantages: ReLu, relatively fewer layers and input output merging helps rectify vanishing gradient problem while decreasing parameters helps increase performance. The presented model has achieved promising results which signifies the efficacy in terms of some of the well known quality matrices. The dice score, sensitivity and specificity of the segmented lesions is computed on MICCAI MSSEG 2016 dataset. The method is entirely automated, efficient and fairly precise. Supplementary improvements in the model could provide a more robust and cost efficient approach to traditional medical image segmentation techniques.
Performance Analysis of Adaptive Modulation in MIMO-MRC Systems with Transmit Beamforming over Rayleigh Product Channels

Prior works on the performance analysis of multi-antenna adaptive modulation systems have focused on the rich scattering environments. However, in practice, the transmitted signal may be subject to insufficient scattering or keyhole effect. A multiple-input multiple-output (MIMO) channel model that captures the effect of the double scattering is the Rayleigh product channel. In this paper, we present an analytic study of the adaptive modulation scheme for transmit beamforming (TBF) systems in Rayleigh product MIMO channels. In order to adjust the modulation constellation size, the signal-to-noise ratio (SNR) at the maximum ratio combining (MRC) receiver output is sent back to the transmitter. The modulation order is then fixed with respect to the channel conditions. Analytical expressions of system performance, such as average spectral efficiency (SE), bit error rate (BER), and outage probability (OP), are derived and confirmed by Monte Carlo simulations.
Adiabatic Coupler With Design-Intended Splitting Ratio

We demonstrate adiabatic couplers (ADCs) with design-intended splitting ratios (SRs) for the silicon-on-insulator (SOI) platform. The operational principle, numerical simulations, and experimental results on the designed ADC are presented. In the operational principle, we derive the analytical relationship between the SR and the effective index-difference, based on which we calculate the corresponding SRs under various output waveguide width-differences at a central wavelength of 1310 nm. For the simulation process, a segmented optimization method is employed to analyze the adiabatic transition process through the ADC. From the experiment results, broadband ADCs with design-intended power SRs, from 7%/93% to 50%/50%, are fabricated and then tested over a bandwidth of 100 nm, i.e., from 1260 to 1360 nm for the fundamental transverse electric (TE) mode. Furthermore, the thermal stability of the ADC is investigated. When increasing the stage temperature from 20 $^\circ$C to 50 $^\circ$C, the variances of the SRs for different wavelengths across O band are within 1.3%, which makes the design a potential candidate for performance robustness against temperature variations.