Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of algorithms for searching, analyzing and detecting fraudulent activities in the financial sphere

According to Digital Evolution Index 2017, Russia is included to the category of so-called "Break Out" countries. The major problem to be encountered at transfer to the digital economy is adaptation of new technologies â€“ such as Big Data, Blockchain, Internet of Things, Cryptocurrency, machine learning. No less important field is development of friendly informative environment facilitating international cooperation, cyber safety problems resolving, etc. This example provides the data of the report prototype of a system to detect suspicious transactions. This system shall read and analyze the transaction database and, in accordance with search algorithms, it detects suspicious transactions within the entire data base. The algorithm consists of several stages: development of a graph, selection of suspicious and trusted transactions, calculation of signs and machine learning. The methods of social connections analysis, parallel processing of graphs and mathematical apparatus of neural networks are used as the basis of this research.
Bio-Inspired Neurons Based on Novel Leaky-FeFET with Ultra-Low Hardware Cost and Advanced Functionality for All-Ferroelectric Neural Network

For the brain-inspired neuromorphic computing, various emerging memory devices, including FeFET, have been applied to develop the artificial synapses, while the artificial neurons are still mostly CMOS-implemented and suffer from high-hardware-cost issue, especially when expanding advanced functions. In this work, a novel leaky-FeFET (L-FeFET) based on partially crystallized $\text{Hf}_{05}\text{z}_{\text{r}05}\text{O}_{2}$ layer is designed to mimic biological neurons. For the first time, we propose and experimentally demonstrate a capacitor-less L-FeFET neuron for basic leaky-integrate-and-fire function with ultra-low hardware cost of only one transistor and one resistor. Furthermore, a new hybrid L-FeFET-CMOS neuron is implemented to expand advanced spike-frequency adaption with almost half of hardware cost compared with CMOS neuron. This work provides a highly-integrated and inherently-low-energy implementation for neuron and the possibility for all-ferroelectric neural networks.
Reduced-Cost Gradient-Based Optimization of Compact Microwave Components through Adaptive Broyden Updates

In this paper, electromagnetic (EM)-driven design optimization of miniaturized microwave components using expedited trust-region gradient search is presented. Our approach involves a combination of finite differences and a rank-one Broyden formula to update the circuit response Jacobian. The latter is performed for selected parameters depending on the alignment between the most recent design relocation and the coordinate system axes. The alignment threshold is adaptively adjusted based on the algorithm convergence status. Comprehensive numerical validation of the proposed algorithm involving a compact microstrip coupler as well as a three-section CMRC-based impedance transformer is provided demonstrating significant speedup of up to 48 percent as compared to the benchmark algorithm.
A QoS alert Scheduling based on Q-learning for Medical Wireless Body Area Network

The latest generation of personal area networks and, more apparently, wireless body area networks (WBANs) are the pivotal building blocks of future generation networks, and the Internet of Things as well. In this paper, we propose a quality of service (QoS) alert scheduling scheme based on Q-learning for internet of things (IoT) enabled medical WBAN. As an optimization problem, the scheduling intends to maintain a tradeoff between overall sum rate and response time of the network. This task is certainly a big challenge as a typical medical WBAN has no centralized coordinator. To overcome this, we formulate an analytical framework of medical WBAN message passing scenario using M/M/c/K queuing model and develop a Q-learning based scheduling algorithm. Through simulation analysis, we prove the efficiency of the proposed method over previous approaches.
Substrate Integrated Waveguide (SIW) Leaky-Wave Antenna With Sinusoidally Modulated Transverse Slots

Substrate integrated waveguide (SIW) leaky wave antenna is studied with sinusoidally varying width of the transverse slots. Width of the transverse slots is changed by a modulation factor to obtain the modulated reactive surface. As a result, beam-direction and beamwidth can be independently controlled by phase and attenuation constants respectively of the leaky-wave antenna.
A Simulation-based Feasibility Study of a Proprioception-inspired Sensing Framework for a Multi-DoF Shoulder Exosuit

The compliant nature of exosuits makes them ideal for providing assistance to complex joints like the shoulder. Exosuits require soft, compact and accurate sensing units for reliable feedback control. In this work, we introduce an OpenSim simulation-based prototype of a proprioception-inspired sensing framework for a multi-DoF shoulder exosuit. The prototype is used to study the feasibility of the sensing system concept to accurately track multiple degrees of freedom (DoFs) of the shoulder simultaneously. The sensing system fuses data from 4 custom string poten-tiometers (SPs), that work together to sense the joint angles at the shoulder. The tendon-routing of the SP modules in the exosuit is proprioception-inspired and based on the organization of the muscles influencing shoulder movement. The sensor fusion/mapping of the simulation data from multi-sensor space to joint space is a multivariate multiple regression problem and was solved using Multi-Layer Perceptron (MLP) & Long Short-Term Memory (LSTM) neural networks. A simulation of the framework in OpenSim on 200,000 random shoulder movements achieved a root mean square error (RMSE) of $\approx 0.2^{\mathrm{o}}$ when trained on 70,000 random movements and tested on 130,000 random movements in both DoFs simultaneously.
Image Inpainting using exemplar based technique with improvised data term

This paper urges on upgrading the image inpainting technique mainly for images involving edges and corner information. We have built our algorithm on the exemplar based method of image inpainting. Our algorithm uses fractional derivative and additional curvature finding methods for finding data term in patch priority. Patch matching is performed using the traditional methods. We aim at obtaining good results for edges and corners in the damages. The results obtained show that the suggested modification in inpainting algorithm can give better results in terms of SSIM and visual impact of originality.
Fractional Laplacian Viscoacoustic Wave Equation Low-Rank Temporal Extrapolation

Description of seismic wave attenuation is a hot topic in the geophysical area, and it is the basis of the attenuation-compensated seismic imaging technique, which aims to retrieve a high-resolution subsurface image for geological structure analysis and hydrocarbon reservoir prediction. The numerical simulation of viscoacoustic wave equation is an effective way to observe the seismic attenuation in lossy media. The existing study confirms that the seismic-quality-factor (Q) that is used to represent the strength of the viscous behavior of the earth is nearly independent of frequency, which is referred to as the constant-Q (CQ) model. The mathematical concept of fractional Laplacian is recently introduced to the geophysical area to form a compact CQ wave equation to describe seismic wave propagation. However, numerically solving the fractional Laplacian CQ wave equation by the traditional pseudospectral time-domain (PSTD) method suffers from a strict stability condition and great numerical dispersion due to a low-order temporal finite-difference (FD) approximation. To improve the temporal extrapolation accuracy, we derive the analytical wavenumber (k)-space domain propagators underlying the fractional Laplacian wave equation. We regard the k-space operators as mixed-domain matrices in the case of heterogeneous media and adopt a low-rank decomposition to approximate the matrices. With the low-rank approximation, an efficient time-marching formula is obtained for wavefield temporal extrapolation. We formulate the time-marching formula into a first-order equation system in terms of pressure and particle-velocity to welcome the perfectly matched layer (PML) absorbing boundary condition to eliminate the wraparound effects caused by the Fourier transform. A spatial-variable density is also incorporated to simulate more realistic amplitude variation. The numerical examples are carried out to verify the accuracy and stability of the viscoacoustic low-rank extrapolation.
Extensions to Two-Port Network Modeling Method and Analysis of Multiple-VSC-Based Systems

In the impedance-based stability analysis methods, the grid-connected voltage source converter (VSC) is described as a multiple-input multiple-output (MIMO) system, which is also a two-port network. In this paper, a stability analysis method based on the concepts of scattering parameters (S-parameters) and reflection coefficients in the two-port network theory is proposed. Through the use of reflection coefficients, the open-loop gains at each bus can be derived individually. Thus the system with multiple VSCs is characterized as connections of two-port networks with S-parameters. The stability can then be analyzed using the classical stability analysis tool and the relative stability is defined. Experimental results validate the theoretical findings.
Regression Genetic Algorithm (RGA) Based Approach For Optimizing Bank Deposit

Bank' service and interest rate play an important role to stimulus bank deposit fund. Those variables determine the increase of bank's income. Unfortunately, statistical analysis data application cannot prove it, especially the variable service. The aim of this study is to analyze the correlation between the service and the increase in bank deposit fund. This research used a genetic algorithm to discover correlation of those variables. In the implementation, regression and genetic algorithm (RGA) are combined to analyze the variable service by using population, crossover, and mutation approached. Compare to the regression method only, RGA can prove the correlation. Moreover, the good service greatly increases the bank deposit fund shows that in generation = 229, service = 0, 1181099, and deposit = 98873. It means that a good service toward customer may increase or optimize the amount of bank deposit.
End-to-end robot manipulation using demonstration-guided goal strategie

In deep reinforcement learning, finding the optimal manipulation policy of a multi–DOF manipulator in 3D space requires intricate reward shaping for the agent to find the optimal policy. However, reward shaping requires cumbersome optimization of the reward function based on prior knowledge on robotic tasks to achieve. This makes it desirable to learn various manipulation policies with a simple reward function.In this study, we propose a method that learns the manipulation policy of a manipulator in a sparse reward setting. To this end, Hindsight Experience Replay (HER) is combined with Twin Delayed DDPG (TD3) by applying the goal strategy that incorporates demonstrations for the policy. It is shown that the policy can estimate the joint control command of a 7-DoF manipulator from raw RGB video inputs in sparse reward setting in an end-to-end manner.
EEG Emotion Detection Using Multi-Model Classification

In the field of Human-Machine Interaction HMI identifying the human emotion is an important input for the machines to better enhance the communication. In this paper, we identifying 10 different emotions based on Valance, Arousal and Dominance using five different models and using new filters using Riemannian geometry to enhance classification. EEG signals are collected and passed to the proposed models, the accuracy of the detection was ranging from 50% to 70%. Two sessions have been conducted per subject to collect the data for training and for testing the models. The SAM assessment technique has been used to tag the training data.
A Frequency Reconfigurable Antenna with slotted ground plane for Multi-band Applications

A multi-band frequency reconfigurable slot antenna for wireless applications is proposed in this paper. The antenna consists of rectangular patch on the top. A U-shaped slot with short ends, L-shaped slot with open ends, and another U-shaped slot inside the L-slot are etched on the ground plane. To avoid cross polarization, another L-slot and U-slot inside the L-slot are introduced symmetrically. Three PIN diodes are inserted inside the U-slot and two L-slots to achieve the concept of reconfigurability in frequency. The proposed antenna can switch between two triple band modes (2.7/7/8.3 and 3/7.8/9.5 GHz), two dual band modes (3/6.8 and 6.2/8.4 GHz) and two single band modes (6.2 and 7.3GHz). The total size of the antenna is $27 \times 25$ mm2. A dipole like radiation pattern is observed at lower frequency bands in most of the states. The maximum gain of 6.5 dB is obtained.
A Generalized Segmentation Algorithm for Planar Resonant Structures with Discrete Components

A compact relation is developed based on the indefinite impedance matrix. The proposed technique generalizes the application of the segmentation method to arbitrarily shaped planar structures including the effect of lumped circuit components. The components are accounted for by modifying the previously used terminal relations in the development of the segmentation method. The proposed algorithm targets power integrity (PI) applications in printed circuits and integrated circuit packages. By direct computation of distributed circuit parameters of planar power delivery networks (PDN) including discrete components, the need for a second phase circuit simulation is eliminated. An application of the proposed method is demonstrated on a practical circuit and the results are validated in comparison to a numerical electromagnetic (EM) simulator.
Complexity Reduction on HEVC Intra Mode Decision with modified LeNet-5

The HEVC (H.265) standard was finalized in April 2013, currently being as the prevalent video coding standard. One key contributor to the performance gain over H.264 is the intra prediction that extended a large number of prediction directions on various sizes of prediction units (PUs), thus at a cost of very high computational complexity. When HEVC has been emerged, several fast Intra prediction and Coding Unit (CU) size decision algorithms are being developed for practical applications. Actually, these two components would cost around 60% to 70% encoding time in the all-intra HEVC encoding. In this paper, a novel CNN-based solution is proposed and evaluated. The main idea is to elect a smallest set of adequate intra directions using our modified LeNet-5 CNN model, thus reduce the computational complexity of (further) rate distortion optimization to a tolerable limit. Besides, two filters are employed: the edge strength extractor in [4] and the early terminated CU partition in [7] to skip most of the unlikely directions and to decrease the number of CUs, respectively. The experimental results demonstrate that the proposed method provides a decrease of up to 66.59% computation with a slightly increase in the bit-rate (1.1% on average) and a little reduction of picture quality (0.109% on average in PSNR) at most.
Radiation Characteristic Improvement of Ku-Band SIW Horn Antenna using Probe-Feeding Technique

In this paper, a coaxial to substrate integrated waveguide (SIW) transition is integrated with Ku-band SIW H-plane horn antenna to improve the radiation characteristic over the desired operating band. The probe-fed SIW horn is realized on a $\lambda_{0}/13 (\lambda_{0}$, being calculated at the center frequency) thin substrate and loaded with a multi-layer multi-dielectric impedance transformer (MLMDIT) at its aperture. The MLMDIT is basically a three-layer multi-dielectric structure which reduces the aperture mismatch over a wide operating band. The materials of high dielectric values are used to realize the top and bottom layers of the MLMDIT, while the substrate extended from horn aperture acts as its middle layer. The use of high dielectric constant material to realize the top and bottom layers of the MLMDIT facilitates the wideband operation while maintaining the overall thickness to $0.2\lambda_{0}$ at 15 GHz. The fabricated prototype is relatively simple, compact and thin. The experimental result show −10 dB reflection coefficient bandwidth of 34.73% (12.9–18 GHz). The observed end-fire radiation pattern is fairly stable and undistorted throughout the operating band with maximum gain of 7.5 dBi.
Design and Analysis of Planar Substrate Integrated Waveguide Horn Antenna

In this paper, a planar substrate-integrated waveguide (SIW) horn antenna is designed and analyzed. The broadwalls of the horn antenna are detached so that the substrate with only narrow walls acts as an impedance transformer between SIW and freespace. The substrate perforation has been done on the extended substrate to improve the radiation performance of antenna. The microstrip to SIW transition is designed so as to maintain its planar nature. Two feeding techniques (Elevated coplanar waveguide feeding and microstrip feeding) are considered and analyzed. SIW horn antenna with microstrip transition is fabricated and tested.
5G mm-Wave Link Range Estimation Based on Over-the-Air Measured System EVM Performance

Performance of 5G New Radio millimeter wave (mmW) systems are expected to be tested over-the-air (OTA). In this paper, we use OTA measured system error vector magnitude (EVM) with varying automatic gain control, to characterize the achievable link range with 5G NR waveform. The platform used in the measurements is one of the first public 5G mmW wireless backhaul Proof of Concept systems. We show that OTA system EVM measured in an Electro Magnetic Compatibility (EMC) chamber with varying AGC correlates well with line of sight outdoor measurements. Hence, the link range of the system can be approximated by attenuating OTA signal level either with gain controls of the radio units or with attenuation material between the radio units. The key advantage of link range estimation based on OTA measurements in the EMC chamber is better repeatability and protection from random radio interference which is present in real outdoor environment.
Quantitative Metabolomics Analysis of Depression based on PLS-DA Model

Application of metabolomics for the study of some diseases could include the identification of prognosis of a diagnostic markers for the disease. Depression is a disease that disrupts metabolism. Our study aimed to use metabolomics approaches to the investigation of stress-induced depression. For processing our data, which we have obtained by measuring concentrations of metabolites (Amino Acids, Biogenic Amines, Acylcarnitines, Lysophosphatidylcholines, Phosphatidylcholines, Sphingomyelins, and Hexoses) in the blood plasma of depressive rats, we have use mathematical method PLS DA. A Variance Importance in Projection (VIP) score was a measure of a features importance in the PLS-DA model. We have presented ten metabolites with VIP score higher than two from the whole analyzed metabolites file. We have found different metabolism between female and male rats and depressive groups, too. The study has shown which parameters in blood plasma could indicate changes in metabolism due to stress-induced depression in rats. However, these results require further analyzes and clinical trials to assess changes in metabolomics in depressed individuals.
Deep Learning in Liver Biopsies using Convolutional Neural Networks

Nonalcoholic fatty liver disease (NAFLD) presents a wide range of pathological conditions, varying from nonalcoholic steatohepatitis (NASH) to cirrhosis and hepatocellular carcinoma (HCC). Their prevalence is characterized by increased fat accumulation and hepatocellular ballooning. They have become a cause of concern among physicians and engineers, as significant implications tend to occur regarding their accurate diagnosis and treatment. Although magnetic resonance, ultrasonography and other noninvasive methods can reveal the presence of NAFLD, image quantitative interpretation through histology has become the gold standard in clinical examinations. The proposed work introduces a fully automated diagnostic tool, taking into account the high discrimination capability of histological findings in liver biopsy images. The developed methodology is based on deep supervised learning and image analysis techniques, with the determination of an efficient convolutional neural network (CNN) architecture, performing eventually a classification accuracy of 95%.
Gain and Aperture Efficiency Improvement using Uniplanar EBG Superstrate

In this paper, frequency selective surface (FSS) properties of a uniplanar electromagnetic band-gap (EBG) unit cell are used to improve the gain and aperture efficiency of a probe feed microstrip patch antenna. The unit cell shows the pass band characteristics at 7 GHz. An array of $9\times 9$ unit cell is used of dimension $38.88\times 38.88\ \mathbf{mm}^{2}$ to achieve gain improvement of 2.57 dB and aperture efficiency of 77.79%-86%.
A Novel Method for Synchronization with Unbalanced Grid: An Experimental Investigation

A major issue for the grid connected renewable energy system specifically during unbalance grid condition is the grid synchronization. Under unbalance grid condition, in order to implement stable operation, it is necessity to determine the grid parameter accurately and rapidly. Algorithms used for Grid synchronization plays an important role in the control and steady operation of grid-connected converters. To implement stable and steady control strategy under unbalance grid condition, the detection of grid parameter accurately and rapidly are also crucial. Synchronous Reference Frame Phase Locked Loop (SRF-PLL) works satisfactory for balance grid condition but its performance deteriorates with unbalance grid condition. However, Dual Second Order generalized Integrator (DSOGI)-PLL performs reasonably well in grid synchronization during unbalance grid condition. DSOGI consists of a low pass lter (LPF) and band pass lter to attenuate harmonic of the unbalance grid. In SRF-PLL, the angle is tracked in such a way that the maximum phase voltage is reflected on d-axis while component voltage along q-axis is zero. Hence taking the advantages of DSOGI and SRF-PLL, the DSOGI-PLL architecture has been proposed in this paper. The proposed DSOGI-PLL has better performance in grid synchronization under unbalance grid conditions.
Extremely Compact Integrate-and-Fire STT-MRAM Neuron: A Pathway toward All-Spin Artificial Deep Neural Network

This work reports the complete framework from device to architecture for deep learning acceleration in an all-spin artificial neural network (ANN) built by highly manufacturable STT-MRAM technology. The most compact analog integrate-and-fire neuron reported to date is developed based on the back-hopping oscillation in magnetic tunnel junctions. This novel device is unique because it performs numerous essential neural functions simultaneously, including current integration, voltage spike generation, state reset, and 4-bit precision. The device itself is also a stochastic binary synapse, and thus eases the implementation of the compact all-spin ANN with high accuracy for online training.