Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Acoustic Reverse Time Migration With an Attenuated and Reversible Random Boundary

Pre-stack reverse time migration (RTM) based on the two-way wave equation has been proved to be the most accurate seismic migration method theoretically. However, it requires reverse-order access to the wavefield calculated in forward time. In recursion computing, such out-of-order access requires that most of the recursion history should be stored on the hard disk. For massive amounts of seismic data, loading the saved wavefield data from the disk during imaging has been the bottleneck of RTM, restricting its wide application. To solve this problem, the wavefield in forward time must be reconstructed in reverse order. Although the random boundary can avoid the disk requirement by creating random velocity around the computational domain when propagate the source function. However, the random wavefield reflected from the boundary can generate unwanted artifacts in the final images. In this paper, we develop an attenuated and reversible random boundary condition which is implemented by mixing the reversible attenuation and random boundary conditions. Similar to the random boundary scheme, the proposed method just needs to save the last one or two wavefield snapshots into the memory in forward process. It then reconstructs the source wavefield in reverse order, while greatly reduces the disk input and output (I/O) requirements. Taking the attenuated property into consideration, the artificial events reflected from the boundary can be eliminated. Thus, our method can improve the imaging quality largely compared with the random boundary scheme. Numerical results demonstrate that the RTM images with our proposed attenuated and reversible random boundary condition can not only eliminate the unwanted artifacts, but also improve the computational efficiency greatly.
UCA-Based Sub-Connected Hybrid Precoding for mmWave Multi-User Systems

In millimeter-wave communications, full-dimension multiple-input multiple-output has been widely utilized to generate both horizontal and vertical beams by equipping uniform planar array (UPA) at the base station. However, considering the antenna radiation pattern for each sector, UPA offers nonuniform coverage in horizontal domain, where UEs on the boundary of each sector possess lower beamforming gains and hence the loss of spectral efficiency compared to the UEs at the center. In this paper, uniform cylindrical array (UCA) based hybrid precoding is proposed to improve the uniformity of beam coverage in horizontal domain and the fairness of spectral efficiency for UEs at different locations. Firstly, the UCA-based codebook is designed to generate uniform beams in horizontal domain. Next, the sector-free sub- connection transmitter architecture is designed to reduce the hardware complexity. Meanwhile, to serve multi-user transmission, the corresponding cyclic UE scheduling along with grouped digital precoding are introduced to suppress the inter-user interference. Theoretical analyses and simulation results demonstrate that the proposed scheme can improve the fairness of spectral efficiency compared to its conventional UPA counterpart.
Designing a Speech Interface for Voice activated MAV Ground Control Station

Flying Micro Aerial Vehicle (MAV) through speech command helps untrained professionals to conveniently control the flight. Such a control is achieved through Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) technologies. This paper describes the development of MAV Ground Control Station (GCS) with a speech interface to enable issuing of Voice Commands. The paper also presents the performance of the speech interface during a flight trial.
Pedestrian Detection and Tracking Based on 2D Lidar

In order to make the service robot carry out dynamic path planning according to the movement state of pedestrians and improve the ability of collaboration with humans, this paper proposes a pedestrian detection and tracking algorithm based on 2D lidar. Pedestrian detection based on 2D lidar is mainly done by detecting the legs of pedestrians. In the laser data preprocessing stage, in order to reduce the influence of lidar point cloud noise, Gaussian filtering is used to filter the original point cloud data. In order to reduce the influence of the environment itself on the detection accuracy, an algorithm for projecting the laser data with the static map is proposed to remove the static background data. In the clustering segmentation stage, this paper uses Euclidean clustering to segment the laser point cloud clustering. In the feature extraction stage, in order to improve the detection accuracy of pedestrians' legs, the number of statistical features is increased on the basis of extracting geometric features; In order to improve the generalization ability of the model, and to solve the problem of scale inconsistency of pedestrian leg feature description, the extracted pedestrian features are normalized. This article uses SVM to build a human leg classifier. In the pedestrian tracking phase, this paper proposes a multi-person tracking algorithm based on Kalman filtering. The experimental results show that the proposed scheme can obtain better pedestrian detection and tracking effects, and the real-time performance is higher.
Design and Experimental Evaluation of a 12 kW Large Synchronous Reluctance Motor and Control System for Elevator Traction

Recently, a new type of motor, synchronous reluctance motor (SRM), has attracted wide attention from academia and industry because of its potential applications in fans, pumps, and elevator traction systems. Compared with traditional motors, these motors have lower eddy-current loss, less torque ripple, reduced noise, smaller moment of inertia, and faster dynamic response, and they provide a greater operating efficiency and safety and are simpler and easier to maintain. However, the ontology design and operation control of SRMs continue to be significant hurdles that must be overcome prior to practical implementation. In order to facilitate the practical application of SRMs in industry, at the invitation of an elevator company, we designed a large SRM for elevator traction. Herein, we describe the design of the proposed system and present a theoretical analysis of the system. Furthermore, we fabricate a real prototype and the corresponding control system and perform an experimental test under the rated operating conditions and $1.5\times $ overload conditions in order to verify the SRM’s performance. The results of the experimental testing were satisfactory and consistent with the theoretical calculations. At present, we have entered the stage of small-batch trial production and we expect to ultimately implement this novel design. Further, the approach to ontology design and operation control in this study can be used to inform the future development of novel SRMs.
Anomaly based Mitigation of Volumetric DDoS Attack Using Client Puzzle as Proof-of-Work

Increasing use of Internet has made its users vulnerable to various types of attacks like Distributed Denial of Service (DDoS) attack which makes the resources unavailable to the benign user. Many mechanisms exist against DDoS attack for its detection, prevention, response and mitigation. One such technique is use of client puzzle which has main motive to prevent the attackers from flooding the Internet Service Provider (ISP) network by checking the incoming packets for the sending rights in the form of client puzzle solution. In this paper, we present a combination of anomaly and volume based approaches to safeguard the victim network from DDoS attack by checking the sender for having sending rights which are granted against a challenge puzzle generated by client puzzle module and diverting the attack traffic to dynamic provisioning module when the flooding traffic is becoming cumbersome to be handled by the victim. This technique is dynamic in nature which is activated on the basis of volume of traffic being flooded towards the victim. NS2 network simulator is used to simulate the proposed approach. The proposed approach limits various limitations of existing approaches, i.e. it reduces the collateral damage by distinguishing packets having Proof of Work (PoW). The simulation results depict high malicious packet drop rate and less benign packet drop rate.
Acoustic Characteristics of Normal and Pathological Voices Analysis and Recognition

In this paper, the acoustic parameters of voice are studied, and the differences between normal voice and pathological voice are analyzed. 53 samples of normal voice and 173 samples of pathological voice are selected from the MEEL database of Kay Company for experiments. Bayesian network, support vector machine, K-nearest neighbor, C4.5 decision tree and random forest are used to recognize the pathological voices. The experimental results show that the acoustic characteristic parameters have distinguish ability, and the average recognition rate is over 86%. The pathological voice recognition rate based on random forest classifier is the best, reaching the rate of 90.27%.
An Automatic Data Cleaning Method for GPS Trajectory Data on Didi Chuxing GAIA Open Dataset Using Machine Learning Algorithms

In this paper, a new auto cleaning method for the GPS trajectory data on Didi Chuxing GAIA Open Dataset is developed. The support vector machine (SVM) and Decision Trees are employed to identify invalid, weak, and normal data of the Didi Chuxing GAIA Open Dataset raw data. To implement the proposed method, the Pandas Python library and scikit-learn Python library are used to read and process the data. Finally, an auto cleaning example is presented to illustrate the effectiveness of this method.
Multiply-by-n Switched Capacitor Amplifier Insensitive to Capacitance Mismatch

Precision multiply-by-2 amplifiers have been researched intensively due to their use in pipelined analog-to-digital converters. For integer gains greater than 2, not much work has been reported. This work presents a multiply-by-n switched capacitor amplifier, for n greater than 2, whose gain is insensitive to capacitor mismatch. Simulation results show that the variation in gain due to capacitance mismatch is much lesser than conventional architectures.
Flexible Expected Shortfall Estimation Using Parametric & Non-Parametric Methods with Applications in Finance, Insurance & Climatology

Techniques employing Data fusion concepts are regularly being used in Quantitative Risk Management (QRM) for robust analysis. In our previous work, we studied the most commonly used risk metric of interest, Value-at-Risk $(\mathbf{VaR})$. While VaR is a commonly used risk metric, an alternative risk metric, Expected Shortfall (ES) is well known to have better theoretical properties than VaR. We extend our previous work on studying VaR to include estimating the ES also known as Conditional Value-at-Risk (CVaR). The standard approach of estimating CVaR involves using Monte Carlo simulation (MCS) approach (denoted henceforth as classical approach). This approach involves breaking down the losses into loss severity and loss frequency assuming independence among them. In practice, this assumption may not always hold. To overcome this limitation and handle cases with both light & heavy-tail data, we propose using both a parametric & non-parametric approach. We implement Data-driven Partitioning of Frequency and Severity (DPFS) using K-means Clustering, and Copula-based Parametric modeling of Frequency and Severity (CPFS). These two approaches are verified using simulation experiments on synthetic data and validated on five publicly available datasets from diverse domains. The classical approach estimates CVaR inaccurately for 80% of the simulated data sets and for 60% of the real-world data sets studied in this work. Both the DPFS and the CPFS methodologies attain CVaR estimates within 99% historical bootstrap confidence interval bounds for both simulated and realworld data. Overall, we find that the CPFS method performs better in CVaR estimation for real-world datasets than our previous studies for VaR estimation.
A Hybrid Two-Stage 3D Object Recognition from Orthogonal Projections

The availability of large data-sets of 3D scans of objects promoted the potential of applying deep learning techniques to learn how to categorize objects based on their 3D representations automatically. However, a straightforward application of deep learning classifiers to 3D data requires a prohibitive amount of processing power, and therefore, it is not as effective as the classification of objects represented in the 2D domain. This paper address this limitation by mapping the 3D data into 2D representation and applying a hybrid deep and shallow configuration of neural networks to classify different objects. The proposed approach simplifies the network architecture and reduces the computational cost required to train the classifier. The 3D data is mapped into 2D representation using orthogonal projections made from multiple viewpoints. This approach has two additional benefits. Firstly, it increases the number of 2D data samples representing a given object. Secondly, the projected data reduces the occlusion, illumination, and noise problems usually associated with the 3D representation. Given the 2D object representation, the high classification accuracy is achieved through a hybrid, 2-stage classification system. In the first stage, a pre-trained Convolutional Neural Network (CNN) classifier (AlexNet) is fine-tuned to generate vectors of soft labels representing class probabilities. These vectors (or the first-stage CNN features) are passed to a shallow neural network trained to generate the final class labels. While the first stage classifier is trained directly on the physical data, the second stage classifier is trained on the probability vectors (or/and features) of the first stage classifier, and therefore, the classification outcomes of the first stage are in a way corrected by the second stage assessment. Validation experiments confirmed that the second stage classification significantly improves classification outcomes given by the first stage classifier.
Automated Optical Inspection of Soldering Connections in Power Electronics Production Using Convolutional Neural Networks

Automatic optical inspection (AOI) of solder joints is a common testing process in electronics production. Especially in power electronics production for electric drive systems, such inspection systems are employed for quality control of selective soldering processes for through-hole devices. Up to now, commercial systems rely on rule-based programming for the determination of soldering quality. However, this approach demands expert knowledge for setup and is very susceptible to changes in input data. To avoid error slip, thresholds are often defined very strictly, resulting in a high pseudo-error rate. Improvement is only possible through extensive expert input. As power electronics production is often characterized by a high variant and only medium quantities, this manual effort is critical. In this contribution, we benchmark a commercial AOI system with an adaptive approach utilizing convolutional neural networks based on a pre-trained VGG-16 algorithm with custom fully connected layers. Supervised learning is employed for each static region of interest with refined labeled data from the existing AOI system. To overcome the extremely unbalanced dataset, we employ data augmentation and data filtering. Our results show significant improvement in precision over the commercial system regarding the total recall. In addition, the adaptive system is also able to learn from pseudo-error classifications. We also show that our approach can not only output a binary classification but also identify process deviations that may still yield acceptable quality. Hence, this output might be used for an online control of process parameters in further research.
A New Concept for Multi-Beam Phased Array

We present a new concept for constructing a compact linear 1 × $n$ multi-beam phased array transceiver system that can be scaled to operate from radio to millimetre wavelength. In this design, all the components required to form the phased array are fabricated on a single printed circuit board, where both the input antenna array and the readout arrays are integrated on the same platform. This is made possible by using a novel planar power splitter/combiners technology, which allows the input signal from each antenna to be split simultaneously through these components arranged in horizontal, and re-combined vertically to form individual beam. This compact single-platform phased array system could be important for many applications that required compact and light-weight design such as 4G/5G wireless telecommunications, inter-satellite (CubeSat) or satellite-base station communication links, space-based remote sensing, vehicle transceiver system and astronomical receivers.
A Damped Newton Algorithm for Nonnegative Matrix Factorization Based on Alpha-Divergence

A novel Newton-type algorithm for nonnegative matrix factorization based on $\alpha$ -divergence is proposed in this paper. The proposed algorithm is a cyclic coordinate descent algorithm that decreases the objective function value along one coordinate direction at a time by using a damped Newton method for monotone equations. It is proved that the proposed algorithm has the global convergence property in the sense of Zangwill. It is also shown experimentally that the proposed algorithm is fast, independent of the value of $\alpha$ while conventional algorithms become very slow for some values of $\alpha$.
Inertial Motion Capture Using Adaptive Sensor Fusion and Joint Angle Drift Correction

The ambulatory motion capture and gait analysis using wearable MEMS based magnetic-inertial measurement units (MIMUs) is challenging despite multisensor fusion and effective anatomical (sensor-to-segment) calibration. The MEMS based sensors show degraded performance when run for long time, especially indoors. This is due to the fact that assumption of no acceleration except gravity and homogenous magnetic field no longer holds, when the motion is being performed. The rate gyro is used to complement the accelerometer/ magnetometer for orientation estimation, but integration of its residual biases as well as noise eventually causes the sensor fusion estimates to drift. The errors in heading angle or yaw are particular significant due to persistent nature of magnetic inhomogeneity in the environment. This ultimately results in inaccurate and drifting joint angle estimates between body segments that would require some means of correction. In present work, we propose a new adaptive covariance based EKF for sensor fusion which makes it effectively robust to both dynamic body accelerations as well as inhomogeneous magnetic field. The adaptive covariance method penalizes the bad accelerometer and magnetometer measurements and intelligently updates the gyro biases online using only undisturbed readings of accelerometer/magnetometer. Our sensor fusion algorithm provides accurate orientation estimates for each MIMU node over time. In order to account for any residual drift of joint angles, we propose a novel correction term in our anatomical formulation that performs online correction of drift in individual joint angles and updates it as an orientation offset. This offset correction for joint angle is performed automatically when the limb or extended torso are in neutral quasi-static pose and this condition is judged using accelerometers. Overall our approach achieves precise orientation estimates in highly dynamic conditions and avoids drift or error accumulation due to inhomogeneous magnetic fields during inertial motion capture.
A 10 Gb/s, 150 mA Laser Diode Driver with Active Back-Termination in 0.13-µm SOI CMOS Technology

The design of a 10 Gb/s laser diode driver (LDD) in 0.13-µm SOI CMOS technology is described. The driver utilizes cross-coupled neutralization capacitors and integrates a tunable negative capacitance circuit to extend the bandwidth. Loading reflections due to the imperfect matching at the laser side are absorbed by an active back termination (ABT) circuit. Compared to a driver with a resistive termination, the proposed driver reduces the power dissipation by 40%. The LDD can deliver a modulation current ranges from 10 to 150 mA to 25–Ω lasers with a preserved dynamic performance due to the usage of a segmented output driver scheme. Post-layout simulation results show that the proposed driver has a bandwidth of 7 GHz, and the return loss is better than 8.6 dB in the frequency band of interest. The driver supports DC and AC coupling to 25–Ω lasers with a total power dissipation of 1.06 and 0.72 W, respectively at maximum output current. The physical layout occupies an area of 0.84 × 0.9 mm2,
A Fractional Order PI (FOPI) Controller Design For Shunt Active Power Filter To Improve The Power Quality

In this article, a Fractional Order Proportional Integral (FOPI) controller based shunt active power filter (SAPF) is proposed. The FOPI controller is designed to have a constant voltage of the DC-Link of the SAPF which is used for the elimination of harmonics and there by to improve the power quality. To design the FOPI controller, phase margin, gain crossover frequency and velocity error constant are taken as desired specifications. For harmonic current extraction and gate signal generation, instantaneous active and reactive power (P −Q) approach and pulse width modulation technique (PWM) have been used. In order to show the effectiveness of the proposed FOPI controller in maintaining the a constant voltage and in eliminating harmonics thereby improving the power quality of the system, SAPF model is developed in MATLAB/SIMULINK and the effectiveness of the designed FOPI controller is analyzed. The results obtained with FOPI controller illustrates that the FOPI makes the system to have a the constant voltage of the DC-Link and compensates the harmonic components present in the system.
A Literature Survey of Optimization Technique of Unit Commitment Implementation in Microgrid Electricity System With Renewable Energy Sources

In recent years, efforts to build electricity supplies for islands and remote areas have been carried out by developing renewable energy sources (RES) and integrated into existing electrical systems. For this reason, it is necessary to apply a method of regulating electricity supply. Unit Commitment (UC) is daily scheduling of power generation units to achieve optimization goals while still meeting various operational, environmental, and technical constraints. With a significant increase of the operation of the RES intermittent power plant in the power system, the development of the UC method becomes more necessary, to maintain system stability and resilience, and minimum operating costs. Therefore, new optimization methods need to be developed to be applied to the UC scheme for microgrid electric power systems.This paper discusses alternative optimization methods to optimize UC solutions in microgrid power systems with intermittent RES plant. In this paper, a comparison of the strengths and weaknesses of each optimization technique is discussed, with the intent and purpose of finding the most appropriate optimization technique to utilize. The problem in the UC scheme has been studied by experts using numbers of methods. The combination of mathematical techniques and stochastic / meta-heuristic techniques is proven to provide better optimization solutions. This study is aimed at optimizing UC in electric power systems with intermittent RES power plants. In particular, the power system under study is a developing electricity system, for islands or remote areas. The proposed method is the enrichment of existing optimization techniques, with additional innovations in analytical methods to improve the performance of the UC schemes applied to the microgrid system.