Miracle-dz/UniFAR-FTUs huggingface dataset:
Amplitude-Preserving Imaging Condition for Scattering-Based RTM in Acoustic VTI Media

Conventional seismic imaging methods rely on reflected waves to obtain the interfaces of geologic variations and the normal-incidence reflectivity at the interfaces. However, the scattering phenomena affect the imaging quality, especially in anisotropic media. Most of the current techniques that neglect the scattering effects are insufficient to acquire accurate images. To correct such scattering effects and reduce the image artifacts, we have derived a new, scattering-based, amplitude-preserving imaging condition with anisotropic background assumption in acoustic vertical transverse isotropic (VTI) media. In addition, we present a correlation-type representation for this imaging condition to avoid the problem of being divided by small numbers during reverse-time migration (RTM). Based on this proposed imaging condition, the amplitude of imaging result has the specific physical meaning of the velocity perturbation and can be retrieved by RTM accurately. Numerical experiments on synthetic data show that our inverse-scattering imaging condition can produce more accurate amplitudes on images than cross correlation imaging condition.
Classification of Fake News on Facebook a Novel Social Network with K-Means Clustering Approach for Against Principal Component Analysis Method for Better Accuracy

To classify the fake news on Facebook using machine learning algorithms with improving accuracy. Materials and Methods: The Fake news classification implemented in the dataset is used to detect the exact real meaning of the content. In this research study the dataset is labelled as title, text, subject and date, these data are applied on the machine learning algorithms such as K-Means (KM) Clustering taken as group-1 and compared with Principal Component Analysis (PCA) algorithm taken as group-2 used for 80 percent of g power value and dataset used for this using 0.05 significant value and 95 percent of confidence interval also the standard deviation and error value are used. Results: The novel social network used for this research study with K-Means clustering and PCA algorithms and predicted best algorithm for better accuracy.
Data-driven Reconstruction of Fingerprints from Minutiae Maps

In this paper we explore the power of conditional generative adversarial networks and in particular of the pix2pix network to reconstruct realistic fingerprint patterns from minutiae maps. In our considerations a minutiae map is a grayscale image that encodes minutiae locations and orientations as these are presented in a minutiae template. We propose a novel approach for minutiae encoding in a minutiae map and study to which degree the reconstruction may be successful if trained with a low number of samples. Moreover, we explore the generalization ability of the trained models in cross-dataset and cross-sensor experiments. Reconstruction from pseudo-random minutiae enables synthesis of anonymous fingerprints as well as controlling the diversity of generated samples including synthesis of mated fingerprints which is vital for compilation of large-scale public evaluation datasets.
Estimation Of The Lower Heating Value Of Solid Recovered Fuel Based On Swir Hyper-Spectral Images And Machine Learning

In this work, we apply Machine Learning techniques to Hyper-Spectral Images acquired by a Short Wave Infra-Red (SWIR) Camera, to classify the materials composing the Solid Recovered Fuel (SRF). This classification, enabled by data pre-processing techniques, is used to estimate the Lower Heat Value (LHV) of SRF samples, building on models of the literature. The accurate and timely estimates of SRF LHVs yield significant benefits to SRF consumers.
Noise Analysis of Current Sensor for Medium Voltage Power Converter Enabled by Silicon-Carbide MOSFETs

New semiconductor devices based on wide bandgap materials are emerging in medium voltage power electronic converter applications, presenting new opportunities to the industry relying on semiconductor devices. SiC MOSFETs with blocking voltages of 10 kV (and above) is a promising technology, however, their fast switching transitions result in increased output voltage slew rate (dv/dt), which poses challenges to the applicability of the SiC MOSFET technology. This paper examines the impact of the increased dv/dt on the applicability of an off-the-shelf closed loop hall-effect current sensor when utilized in a medium voltage SiC MOSFET based power electronics converter. Analysis of the capacitive couplings in the current sensor is presented along with an experimental determination of the parasitic capacitance between its primary conductor and secondary winding. Experimental measurements have identified two distinct noise components in the current sensor measurement path due to: 1) capacitive coupling between the primary conductor and secondary winding, and 2) an inferred capacitive coupling into the active circuitry of the current sensor.
Research on a Segmentation and Location Algorithm Based on Mask RCNN for Agaricus Bisporus

This paper proposes a recognition and localization algorithm based on Mask RCNN(Mask Region-based Convolutional Neural Network) for Agaricus bisporus to address the problems caused by the poor environmental adaptability of traditional vision methods, such as high mushroom unrecognized rate especially for small mushrooms and sticky mushrooms, poor robustness of recognization, low accuracy of centroid localization, etc. Firstly, the algorithm uses Mask RCNN to segment Agaricus bisporuses and obtain the masks of each Agaricus bisporuses. Next, the Agaricus bisporus mask edges are extracted, then the ellipse is used to reconstruct the contour depending on the data of the mask edges, and the center point of the ellipse is taken as the center point of the Agaricus bisporus. The experiments show that the proposed algorithm in this paper has stronger robustness, it greatly improves the success rate of recognition of Agaricus bisporus under different light intensities and complex growth environments, and the generated contours much more fit with the actual contours, the size of the recognized Agaricus bisporus and the localization accuracy are higher, with the segmentation accuracy AP50 reaching 95.061%, which can better meet the requirements of Agaricus bisporus picking and its growth status monitoring.
Impact of proton radiation on dark current of InAs/GaSb type-2 superlattice longwave infrared photodetector

In this communication, we present the electrical characterizations of InAs/GaSb type-2 superlattice (T2SL) long wavelength infrared (LWIR) barrier photodetectors under proton radiation. The set of irradiated LWIR detectors exhibited a 50% cut-off wavelength from 11 to 13 µm at 80 K and a p-type absorbing zone thickness from 1.5 to 5.5 µm. The irradiations were performed with 60 MeV protons and fluences up to 8x1011 H+/cm2. Dark current measurements were done before and after each irradiation step. We observed an increase of the dark current with increasing the displacement damage doses and such degradation of electrical performance depends neither on the cut-off wavelength nor on the thickness of the absorbing zone or on the number of T2SL periods.
A Novel Attention Ensemble Traffic Flow Prediction Model based on Spatio-Temporal big data

The emergence of intelligent transportation systems has put forward higher requirements for the problem of traffic flow prediction. Since the topology of the traffic road network changes dynamically, the traditional convolutional neural network that extracts the features of graph structure data cannot capture such dynamic features. In this paper we propose a traffic flow prediction model based on spatiotemporal characteristics to predict the traffic speed in the traffic road network. The dynamic topology structure in the traffic road network is obtained through the graph attention network (GAT), and the temporal characteristics of the data are processed by the long short-term memory network (LSTM), and the periodic characteristics of the traffic flow are captured. In this paper, the prediction model is mainly divided into space module and time module, which can capture the characteristics of traffic data in an all-round way, reduce the loss function value, and improve the prediction accuracy of the model.
Analysis of Formations and Game Styles in Soccer

An appropriate tactic will affect the situation of a soccer game, so corresponding tactical analysis is very important. The analysis of formations and the analysis of game styles are two significant aspects. In this paper, we find the most suitable method of team formation extraction and design a game style analysis network. Specifically, based on the method of minimum entropy data partitioning, we first conduct a series of experiments to identify the formations utilized by different teams and find an effective way to identify them through the comparison of experimental results. Then, we utilize the extracted formation information and a role-based passing network to design a novel team style analysis network. The experimental results show that our method has a good effect on game style analysis.
Deep Learning based Mobile Traffic Flow Prediction Model in 5G Cellular Networks

Due to drastic development of smart phone devices, there is a considerable increase in mobile traffic flow that is provided difficulty on the fifth generation (5G) cellular networks. The growing quantity of communication data along with the need for maximum peak data rate, enhanced reliability and minimum delay in 5G network poses several challenging issues. At the same time mobile traffic flow prediction in 5G network is a challenging topic. In this aspect, this paper proposed a novel DL based mobile traffic flow prediction (DLMTFP) model in 5G networks. The goal of the DLMTFP technique is to differentiate the user’s mobile traffic, application usage and traffic patterns. In addition, the DLMTFP technique involves the modelling of bidirectional long short term memory (BiLS TM) model for the estimation of the mobile traffic flow. Moreover is employed for the hyper parameter optimization of the BiLSTM model. The exploitation of the hyper parameter optimization process helps to significantly boost the predictive outcome of the BiLSTM model. The DLMTFP technique is able to predict the traffic flow in peak range. For validating the improved predictive performance of the proposed DLMTFP technique a comprehensive simulation analysis takes place over the other state of the techniques.
MRI Brain Tumor Classification and Uncertainty Estimation using Hybrid CNN-ELM Model

Medical image processing is an enthralling area of study. This article proposed a novel brain tumor identification and verification method that uses MR brain imaging to help clinicians diagnose and characterize cancer with high accuracy and consistency. The hybrid method presented has the advantage of being novel because it is being used for the first time; the novel technique is based on a Hybrid CNN-ELM Algorithm structure. The method is divided into six stages: the first stage requires the input of MRI images, the second stage involves filter preprocessing, the third stage involves image enhancement using the Gray-level spatial dependence matrix (GLSDM), the fourth stage involves applying the Hybrid CNN- RM Algorithm, and the fifth stage involves method performance. The hybrid CNN-ELM model, which was novel at the time, generated the best findings and the best performance, with an accuracy of approximately 97.14 percent. When MRI pictures are used to identify cancer, the hybrid technique outperforms existing models such as C-SVM and ELM. Based on the proposed hybrid approach’s increased performance, this aids in developing machine approaches for the early detection of brain cancers and helps physicians identify patients more accurately. To include uncertain information, they additionally use test-time dropout and data augmentation. In addition, a hybrid technique is proposed to help increase classification accuracy.
Single-Sensor Localization of Moving Sources Using Diffusion Kernels and Brownian Motion Model

Recently, we introduced a single-sensor method for estimating the location and velocity of moving sources. The obtained state-of-the-art results are valid to slow sources whose velocities are gradually changing through time. In this paper, we challenge our algorithm's fundamental assumption. We apply the algorithm to sources that have rapid and random fluctuations in their velocity. The proposed algorithm is based on a supervised learning approach, using diffusion maps with a Euclidean distance-based diffusion kernel. Experimental results demonstrate the benefits of the proposed single-sensor localization method for sources with a Brownian motion model of randomly fluctuating movements.
PID controller for tracking of autonomous mobile robot combined with Artificial Potential field and Iterative learning control

A path tracking and obstacle avoidance system along with iterative control learning for an autonomous mobile robot is investigated. The location of static obstacles is known, and a path planning algorithm is implemented while considering the robot’s kinematic limitations (such as size, weight, and steering angles). The first contribution of this work is the implementation of an artificial potential field algorithm for trajectory generation while avoiding static obstacles. A multi-constrained Proportional-Integral-Derivative controller is used for trajectory tracking. The second contribution is the implementation of an iterative learning control for minimum time iterative control tasks. The system’s distance coordinates, and trajectory is stored for each lap and used to develop recursive feasibility and time efficient performance cost for each iteration. Simulation results show the effectiveness of the proposed control logic.
GNS-3 Emulation Platform to Study Wide Area Network Performance in Contexts Close to Reality

This work describes an access core network emulated by means of the GNS3 code; the reliability of such a platform was verified by comparing some traffic treatments used in a real test bed. This GNS3 platform is here used to investigate the performance of Very High Capacity Networks in the presence of the impairments described in ITU-T Y1540 and in particular to evaluate the effective TCP throughput. This platform is also tested to interact with the external world and in particular with a website and with a real NG-PON2.
Decoupling of Dual-Band Dual-Polarized Base Station Array Antenna

Dual-band dual-polarized arrays (DBDPA) using interleaved configuration have drawn industry attention as a promising way to improve channel capacity in a package that has a minimum width. By integrating high-performance filtering elements, a novel interleaved DBDPA for base station use is presented in this paper. For the first time, a radiation null is introduced into the low-band (LB) antenna using the intrinsic suppression mode of folded dipole. Then, to enhance the gain-suppression level in out-of-band to 16 dB, four rectangular loops are introduced under the radiator. To achieve high selectivity for high-band (HB) antenna, a meander line loop is introduced and analyzed for the first time in this paper. Simulations indicate that the presented HB antenna achieves 17 dB of out-of-band rejection and 243 dB/GHz of roll-off rate. The combined use of these two filtering elements finally enables the realization of a DBDPA with low mutual coupling and low cross-band scattering. According to the measurements, this antenna covers two wide frequency ranges of 2.26–2.73 GHz and 2.98–4.3 GHz with a 25 dB cross-band isolation level.
Review of Non-Invasive Blood Pressure Estimation via Modern Approaches

Hyperpiesia has become a most serious issue in this modern world. More than a 100 million people are now affected by blood pressure. Both high and low level of blood pressure may lead to severe problem in the human body. Measurement of blood pressure requires modern equipment in hospital environment which provides systolic and diastolic blood pressure. The blood pressure values provide a great support for assessing cardiovascular diseases. The present cuff-based method is not suitable for continuous monitoring and has many drawbacks. This article presents a systematic review on estimating blood pressure through biomedical signals via noninvasive techniques. The different scientific articles have been collected and reviewed for human blood pressure measurement. This paper also offers an overview of the technologies available for measuring blood pressure and highlight their limitations.
Signal Integrity and Power Leakage Optimization for 3D X-Point Memory Operation using Reinforcement Learning

As the signal integrity (SI) issues become critical with high bandwidth and density applications, the SI analysis and optimization are necessary. The SI optimization loop including design, modeling, simulation, analysis and revision is repetitive and confined to specific applications. To overcome the recurrent issues, we proposed reinforcement learning (RL) model for SI and power leakage optimization in 3D X-Point memory operation. We defined the MDP components to reflect the optimization problem and the RL model shows learning convergence. The optimal design shows 6.2 % of crosstalk, 17.7 % of IR drop and 25.3 % of power leakage improvement than original design.
Lidar-Visual-Inertial Odometry Using Point and Line Features

We present a robust real-time Lidar-Visual-Inertial Odometry method that introduces visual line features. Compared with the traditional multi-sensor fusion scheme based on point features, our approach introduces visual line features for pose estimation, utilizes more environmental features, and provides additional constraints on the scene structure. In our method, we first use an improved LSD algorithm for line feature extraction to achieve its real-time requirement, then extract the depth information of the point-line features by LIDAR. The extracted depth information serves as an a priori constraint for point-line bundle adjustment, and we finally use the computed pose estimation as an a priori estimation for the laser odometry to calculate the ultimate pose estimation, thus realizing a laser vision inertial odometry method using point-line features. This method can improve the accuracy of attitude estimation to some extent. Our evaluation of the M2DGR dataset shows that our method achieves higher accuracy than most open source frameworks.
OA-STGCN: An Output Anchoring-based Graph Convolutional Network for Human Trajectory Prediction

Human trajectory prediction is an important task for urban self-driving. It can provide the key input information for the planning decision module or used for collision avoidance system. However, pedestrian trajectories are influenced by both the intend of pedestrian itself and the interaction with the surrounding environment, which makes it a challenging task. The existing methods usually modeled these interactions with some methods integrating observed pedestrians’ states. In this paper, we present an Output Anchoring based Spatio-Temporal Graph Convolution Network (OA-STGCN) for human trajectory prediction, which considers the subjective fac-tors of pedestrian generating target trajectory, includes the Time-Extrapolator Convolutional Neural Network (TXPCNN), and followed by a simple and effective Attention Anchoring Block (AAB) composed of SE-block and CBAM-block. By experiments with two open benchmark datasets ETH and UCY show that, the proposed method outperforms the existing SOTA algorithm.
Multi-modal Human-machine Conversation System for Real Physical World

Enabling machines to process multi-modal information and understand the real physical world is an important step to achieving free human-machine conversation. However, previous human-machine conversation systems are mostly limited to single-modal (e.g., chat robot), single-round (e.g., visual Q & A), and static visual information (e.g., visual dialogue). To address the above problem, we develop a multi-modal human-machine conversation system for specific application scenarios. The system includes two modules: (i) an interactive visual grounding module that can actively disambiguate user's queries, and (ii) an interactive fine-grained recognition module that can model objects in the 3D environment and actively ask for missing visual information. A video demo of our system under the automobile sales scenario can be found here11https://drive.google. com/file/d/1IfBsMKq55ryLOZchIT6G-R5CyWnC3Skiew? usp=sharing.
Virtual Gradiometer-Based Noise Reduction Method for Wearable Magnetoencephalography

Optically Pumped Magnetometer (OPM) can be utilized to realize a flexible and wearable Magnetoencephalography (MEG) system that improves the tracking accuracy and detected signal-to-noise ratio (SNR). However, the OPM-MEG is highly sensitive to environmental magnetic-field interference and thus noise reduction is critical for high-quality signal detection. In this work, we demonstrated a virtual gradiometer-based noise reduction method for wearable OPM-MEG, where three reference OPM sensors are configured as the basis set to map the environmental interference. The reference sensors were placed on the support and the subject's scalp respectively and the head model, alpha-rhythm and auditory evoked response experiments were performed. The results show that the proposed method can remarkably reduce the common-mode magnetic-field noise in the whole detection frequency range. Even though the applied white magnetic-field noise reaches up to 1 pT/Hz1/2, the noise-floor of the denoised data can still be maintained near the limited sensitivity of the OPM sensors. Meanwhile, high SNR alpha-rhythm and auditory evoked response signals for the actual subjects can be measured under the noise correction by the proposed method.
Stable Toffoli Gate on Fixed-Frequency Superconducting Qutrits

Entangling gates in superconducting quantum computers suffer from imperfection of control and relaxation of qubits. The Toffoli gate, essential to various quantum algorithms and to certain fast error correction schemes, represents a particular challenge, as it is decomposed into at least six two-qubit gates, whose errors accrue on top of the increased probability of qubit relaxation due to the long gate time. While it is possible to significantly reduce the depth of the Toffoli gate by exploiting three-level, i.e., qutrit states internally, existing methods are susceptible to errors arising from frequency drifts due to charge fluctuation effects in the second excited state of the transmon. In this poster, we propose a novel qutrit-based implementation of the Toffoli gate that is inherently robust against such errors. The method is extensively validated through a pulse-level simulation and experiments performed on IBM Quantum machines. On one device, where the total Toffoli gate time is 2. 510μs, we obtained average gate fidelity values of 0.928 ± 0.007 and 0.896 ± 0.036 from multiple measurements performed during the first one and twenty-four hours after calibrations, respectively, where the errors represent the standard deviations of the measurement results.