Miracle-dz/UniFAR-FTUs huggingface dataset:
Summated Singular Vectors Based Passive Bi-static RADAR Detection

Target detection is a fundamental problem in radar systems. One of the most common techniques used in passive radars for detection is Cross-correlation of data received from the surveillance channel and the reference channel. However, the optimality of this kind of detection gets shaky when the reference channel is noisy. Here in this research, singular vectors based detector has been proposed by using the combination of all the singular vectors present in the received data matrices. Furthermore, a simple closed-form expression is derived for the threshold of the detector and compared with the results obtained from computational simulation using the Monte-Carlo method. Moreover, as the proposed detector is a random matrix theory (RMT) inspired detector, so the performance of this detector which we will now call Summated Singular Vectors based Detector (SSVD) is also compared with other RMT based detectors. Our results show improved detection probability and hence an improved performance as compared to existing known methods.
PicPose: Using Picture Posing for Localization Service on IoT Devices

Device self-localization is an important capability for many IoT applications that require mobility in service capabilities. In our previous work, we have designed the ArPico method for robot indoor localization. By placing and recognizing pre-installed pictures on walls, robots can use low-cost cameras to identify their positions by referencing to pictures' precise locations. However, using ArPico, all pictures need to have clear rectangular borders for the pose computation. But some real-world pictures does not have clear thick borders. Moreover, some pictures may have odd shapes or are only partially visible. To address these problems, a new picture-based localization service PicPose is presented. PicPose relies on the feature points extracted from a camera-captured image and conducts feature point matching with the original wall picture to conduct pose calculation. Using PicPose, even partially visible pictures can be used for localization, which is impossible for ArPico and ArUco. We present our implementation and experiment results in this paper.
High-Mobility CoMP Massive MIMO Uplink Transmissions: Channel PSD Analysis and Doppler Spread Suppression

Coordinated Multi-Point transmission/reception (CoMP) is one of the promising techniques for high-mobility wireless communications systems to increase cell edge user throughput in both uplink and downlink transmission. In this paper, we consider the high-mobility CoMP uplink transmission, where a high-speed terminal (HST) simultaneously transmits signals to multiple base stations (BSs) with angle-domain Doppler shifts compensation. We derive the exact channel power spectrum density (PSD) for uplink CoMP transmissions. The antenna weighting technique is then proposed to suppress the Doppler spread, where an optimization problem is formulated to minimize the maximum Doppler spread among all the BSs. Numerical results are provided to corroborate both the PSD analysis and the superiority of antenna weighting technique.
Laser-induced break down spectroscopy for quantitative analysis of electrolytes (Na, K, Ca, Mg) in human blood serum

The objective of this research was to develop a quantitative assay for analysis of electrolytes (Na, K, Ca, Mg) in blood serum by use of simple procedure and single pulse Laser-Induced Breakdown Spectroscopy (LIBS). Serum samples were homogenized and encapsulated in a polyacrylamide gel drop during gel formation and fixed on a silicon (polysiloxan) glue coated glass planch, with two internal standards for signal normalization, better reproducibility and accuracy: Rb was chosen for alkali metals and Ba for alkali earth metals signal correction. Water was gently evaporated on a hot plate at 80oC together with gel formation and fixation, and the resulting gel film, containing the sample, analysed by repeatedly shooting the laser on its surface and accumulating spectra. No additional sample preparation was necessary with 5 to 20 μl of Serum consumption. Limits of detection of 6, 0.6, 1.9 and 1.7 μg/mL for Na, K, Ca and Mg respectively were obtained with the proposed setup. The resulting linearity for the four elements ranged from 3 times below to 2 times above the expected blood serum concentration levels variation, at less than 10% RSD. Finally, the methodology was assessed in the determination of the metals in real samples of human blood serum and the performance compared to a standard, automated clinical laboratory assay where Ca and Mg are determined by complexometry/colorimetry, and Na and K – electrochemically by use of ion selective electrodes, in a biochemical analyzer assembly.
Motion Planning Algorithm of a Multi-Joint Snake-Like Robot Based on Improved Serpenoid Curve

In order to study the winding motion of a multi-joint snake-like robot with multi-degree of redundancy in plane, a motion planning algorithm of a multi-joint snake-like robot based on improved Serpenoid curve equation is proposed in this paper. Firstly, the kinematics and dynamics models of a multi-joint snake-like robot are established, and the joint angle curve equation and the thrust expression of each joint of the robot relative to time are obtained. Next, the existing Serpenoid curve equation is improved to calculate the axial bending moment function with joint angle amplitude adjustment factor and turn angle adjustment factor. By analyzing the relationship between the forward thrust of the robot and the improved Serpenoid curve equation, a simple, efficient and reliable closed-loop control system was designed. Then, MATLAB and SimWise4D were used for simulation to obtain the motion trajectory of the robot based on the improved Serpenoid curve motion planning algorithm, and the influence of different parameters on the forward velocity of the multi-joint snake robot was analyzed. Finally, the validity of the motion planning algorithm of a multi-joint snake-like robot based on improved Serpenoid curve equation is verified by the actual experiment.
LeapDetect: An Agile Platform for Inspecting Power Transmission Lines from Drones

Given a large set of unlabeled images taken by drones for power transmission line inspection, how can we efficiently and cost-friendly develop and deploy defect detection models with high accuracy? To date, power transmission line inspection is increasingly based on drones and there is an expectation of over 100K images taken daily in 2020. These images, in current practice, are generally inspected by inspection engineers. In this paper, we present LeapDetect, an agile platform for rapidly developing, deploying and iteratively upgrading detection models for drone-based power transmission line inspection. The platform consists of a labeling system, an online detection service system, and a training system to cover a complete life cycle of a new coming detection task. It supports building models from a cold start or by an upgrade. LeapDetect has helped us to develop a couple of object detection models for power transmission line inspection. We have demonstrated how LeapDetect helps develop and iteratively upgrade models for power transmission line inspection with two case studies (i.e., bird nest and split pin).
Sensitivity range optimization of surface acoustic wave humidity ultrasonic sensors incorporating a polyvinyl alcohol (PVA) layer

In this paper, Surface Acoustic Wave (SAW) delay line (DL) based humidity sensors are optimized. These sensors comprise two ultrasonic interdigital transducers (IDT) for generating and detecting SAW. The SAWs propagate along a piezoelectric substrate covered with a polyvinyl alcohol (PVA) layer that is sensitive to humidity. PVA layers are conventionally used in capacitive, resistive or acoustic resonance humidity sensors because of their sensitivity to humidity and low fabrication cost. The PVA layers are generally prepared with 10 wt% and their sensitivity range is from 10% to 90% relative humidity (RH) at room temperature. However, some applications require sensors capable of measuring RH greater than 90%. Therefore, in this study, several PVA layers with different mass percentages were considered to optimize and increase the sensitivity range of these ultrasonic sensors. It is shown that a 5 wt% PVA layer has a sensitivity range from 10% to 98% RH with good repeatability, as well as hysteresis of less than 0.5%. Finally, the mechanisms underlying the response of SAW humidity sensors are also discussed and analyzed.
Magnetic Functional Integrated DC Motor Drive

This paper presents the concept of magnetic functional integrated DC motor drive. It uses the field winding of a wound-field DC motor as the inductor of its drive circuit. The field winding in a wound-field DC motor not only builds the required magnetic field for the electric-mechanic energy conversion, but also acts as the inductor to buffer the energy for the drive circuit. One magnetic component is shared to realize different functions for motor and its drive circuit, simultaneously. The weight, loss and cost of the drive system can be reduced due to the saving of extra bulky inductors for the drive circuit. This concept can be applied to all DC-DC drive circuits using inductor as its energy buffer. An example of magnetic functional integrated DC motor drive using the buck-boost converter is used to explain the concept in detail. The feasibility and the new features are verified experimentally by comparing with the conventional motor drive.
Adaptive Nonlinear Control Algorithm for a Self-Balancing Robot

We previously developed a novel composite wheel-leg-track explosive ordnance disposal (EOD) robot with high mobility, able to switch between a track or a self-balancing motion mode according to environmental conditions, named Scorpio. In this paper, we propose an adaptive nonlinear control algorithm for improving the stability of the robot in the self-balancing mode. First, a model of the dynamics of the robot was established, with which we designed the nonlinear cascade controller for combined balance and motion control. With our system, the attitude of the robot is estimated using a Kalman filtering algorithm. Based on this, an adaptive adjustment algorithm amends the parameters of the controller in real time according to the state of the robot, for improved stability. In addition, we formulated an adaptive zero-offset angle identification algorithm to compensate for deviations caused by changes to the robot’s center of gravity (due to changes to its mechanical structure), ensuring that this stability could be maintained. Results of experiments conducted to verify their operation show that self-balancing control of Scorpio can be achieved with the proposed algorithms.
Exercise Difficulty Prediction in Online Education Systems

In Online Education System (OES), it is popular to assess examinee by providing exercises with different difficulty. So Exercise Difficulty Prediction (EDP) is a fundamental task of OES, such as medicine examination and school assignment. In view of the rich semantic information embedded in exercise which contains texts and images, it is hard to represent exercise and predict its difficulty. In this paper, we propose a novel Bayesian inference-based Exercise Difficulty Prediction (BEDP) framework to predict difficulty of visual-textual exercise. In BEDP, given exercises with texts, images and difficulty label, we first apply a pretrained Deep Multimodal Embedding model to obtain the unified multimodal representations of each exercise. Then we propose a Bayesian inference-based Softmax Regression classifier to predict the difficulty of exercise. Variational Inference (VI) is applied for approximately solve probabilistic model with a joint distribution of observations and latent variables. By experimenting on two self-collected dataset, we demonstrate the effectiveness of our proposed framework on predicting the difficulty of exercise, and conduct convergence diagnostics for Bayesian inference.
Speech Enhancement Integrating the MVDR Beamforming and T-F Masking

In this paper, a multi-channel speech enhancement method with the minimum variance distortionless response (MVDR) beamforming method based on the time-frequency (T-F) masking is proposed. In this study, First, the logarithmic power spectrum (LPS) features of multichannel signals are used as input features to estimate a T-F mask of the reference microphone by the deep neural network (DNN) model. Then, the estimated mask is utilized to calculate speech covariance matrix that is used to estimate a steering vector for constructing the MVDR beamformer. The steering vector is estimated by the generalized eigenvalue decomposition (GEVD) method. Finally, the output speech of the beamformer is processed by the DNN-based IRM model. In order to prove the effectiveness of the proposed method, the perceptual evaluation of speech quality (PESQ) and the segment signal-to-noise ratio (SSNR) are employed. The experimental results show that the proposed method effectively increased the PESQ and SSNR.
Know Your Enemies and Know Yourself in the Real-Time Bidding Function Optimisation

Real-time bidding (RTB) is a popular method to sell online ad space inventory using real-time auctions to determine which advertiser gets to make the ad impression. Advertisers can take user information into account when making their bids and get more control over the process. The goal of an optimal bidding function is to maximise the overall effectiveness of the ad campaigns defined by the advertisers under a certain budget constraint. A straightforward solution would be to model the bidding function in an explicit form. However, such functional solutions lack generality in practice and are insensitive to the stochastic behaviour of other bidders in the environment. In this paper, we propose to formulate the online auctions into a general mean field multi-agent framework, in which the agents compete with each other and each agent's best response strategy depends on its opponents' actions. We firstly introduce a novel Deep Attentive Survival Analysis (DASA) model to estimate the opponent's action distribution on the ad impression level which outperforms state-of-the-art survival analysis. Furthermore, we introduce the DASA model as the opponent model into the Mean Field Deep Deterministic Policy Gradients (DDPG) algorithm for each agent to learn the optimal bidding strategy and converge to the mean field equilibrium. The experiments have shown that with the inference of the market, the market converges to the equilibrium faster while playing against both fixed strategy agents and dynamic learning agents.
Enhanced Mobile Robot Localization with Lidar and IMU Sensor

Lidar technology is widely used in performing robot localization. However, when only Lidar is being used, the accuracy of localization is not high. In this paper, we present a technique to integrate IMU sensor into a mobile robot equipped with Lidar module. With the heading and direction information (odometer) generated from IMU, the localization error introduced by Lidar can be corrected from time to time. Experimental results show that the robot is able to perform better localization with the introduction of IMU sensor.
Robust Real-Time Energy Management Control Strategy based on the Prediction of Hybrid Vehicle’s Futures States

The energy management of a hybrid vehicle consists in developing a strategy, which determines at each moment the distribution of thermal and electrical energy flows, minimizing the overall consumption of the vehicle. Hybrid vehicle consumption modeling makes it possible to write this problem in the form of a dynamic optimization problem under evolution constraints. This problem is solved optimally when all the rolling conditions are known a priori. Optimal control obtained serves as a reference for evaluating the performance of the strategies embedded in the vehicle. Based on the theory of optimal optimization, the proposed robust energy management control strategy based on the Prediction of hybrid vehicle’s futures states were created. For plug-in hybrids, their energy capacity and the ability to recharge on the electricity grid release constraints in the problematic of energy optimization. That is why a new specific strategy has been developed with the aim of making the most of on-board electrical energy to minimize vehicle emissions. For all hybrid vehicles, the battery is a key component of aging changes its economic and energy profitability. This is why an Adaptive Extended Kalman Filter (AEKF) is used to estimate the battery states of each individual cell, and the estimated output voltage is compared with the measured voltage to generate a residual. This information is used by a specific strategy optimizing consumption while preserving the battery of extreme temperatures, harmful to its longevity.
Integrated Cuckoo and Monkey Search Algorithm for Energy Efficient Clustering in Wireless Sensor Networks

In large scale wireless sensor networks, energy consumption and optimization are a major challenge. Most of the protocols designed for cluster based routing problems suffer from unbalanced energy consumption. In this research work, an energy efficient clustering and routing protocol has been devised. An improved cuckoo search algorithm which uses a novel multiobjective fitness function have been proposed for optimal cluster head selection. Further, Monkey search algorithm has been used to find the optimal path from the cluster head to the sink nodes. The algorithm has been compared with some existing metaheuristics and significant improvements in number of alive nodes, energy efficiency has been observed.
Investigation of Automatic Video Summarization using Viewer’s Physiological, Facial and Attentional Features

Video summarization aims at the selection of a concise and representative set of keyframes or video segments that allows the identification of the video content. In either cases, traditional summarization techniques usually work by segmenting the video into shots, representing video frames as feature vectors of color, texture, audio, among other features, clustering frames with similar features and selecting most representative keyframes or segments, sometimes guided by a video-to-summary ratio target. The resulting summaries are typically subject independent and do not take into account specific viewer’s behavior. Instead of using intrinsic features extracted from the video for summarization, in this article we study whether personalized (subject dependent) video summaries can be obtained from physiological, facial, and attentional data captured from the viewers. More specifically, we study the relationship between personalized video summaries reported by viewers and their data captured during the display of different video genres. A dataset of fifteen videos was used in the experiments. During the exhibition of the videos, the viewer’s physiological, facial, and attentional data were recorded, analyzed and synchronized. Several machine learning models were trained to test our hypothesis. We obtained k-fold cross validation accuracies that were above the chance for the best learned models. As a result of this study, we conclude that it is possible to train a learning machine that can produce customized summaries that are closer to user preferences compared to randomly produced summaries.
Multiple modules speech enhancement in mixed noise and low SNR environments

Achieving stationary speech enhancement in low signal-to-noise ratio (SNR) environments is a challenging problem. Because noise energy is dominant in noisy speech at low SNR level, the existence of numerous obvious random noises may lead neural network to forget some useful information obtained by early training. Moreover, it is difficult for a single neural network to obtain effective speech features and noise features. Therefore, this paper designs to utilize multiple neural networks in two stages to discriminately learn a certain type of noise features and reduce the introduction of interference. Experiment results demonstrate that proposed method leads to consistently better source-to-distortion ratio (SDR) and perceptual evaluation of speech quality (PESQ) than baseline models in low SNR condition. And the results indicate that the method can suppress the forgetting of early information of neural network.
H∞ Filtering for 2D Discrete Switched Stochastic Systems: An Extended Average Dwell Time Approach, Part I

This paper deals with the problem of H∞ filtering for 2D discrete switched stochastic systems described by the Fornasini-Marchesini model. Our purpose is to design a filter such that the filtering error system is guaranteed to be mean square asymptotically with a prescribed H∞ disturbance attenuation level by using the switched quadratic Lyapunov function and the piecewise Lyapunov function approach under the arbitrary switched signals. Moreover, the notion of time instant for 2D switched systems is introduced. To highlight the effectiveness of the proposed filter design, a numerical example is given.
Photometric Stereo with Only Two Images: A Generative Approach

Traditional photometric stereo can recover pixel-wise surface normal from at least three images captured under same viewpoint but different illumination directions. Under some special and extreme conditions, more than three images are not available. Given less than three images, the system becomes incompletely restrained and cannot be resolved in a close-form. In this paper, we propose to use a Generative Adversarial Network, named iDCGAN, to generate the third image under a novel illumination direction given only two images of the same surface. With the generated new image, photometric stereo can then be conducted to recover surface normal. We tested our proposed method on a public dataset, and the experiment results exhibit its effectiveness. Deep learning can be used to solve the restrained problem in traditional three-dimensional reconstruction fields.
Novel Method for Rotor Bar Fault Diagnosis of Induction Machines Containing Space Harmonics

This paper proposes a novel method for the rotor bar fault diagnosis of induction machines containing space harmonics. This method is based on the analyses of the magnitude of the stator current and the asynchronous actions due to the space harmonics on the torque-speed characteristic of the induction machine with and without broken rotor bars. To achieve this objective, three squirrel cage induction machine models were simulated in the dynamic and steady-state conditions in the case of healthy machine, one broken rotor bar, and two broken rotor bars machine. These models take into account the 5th, 7th, 11th, and 13th space harmonic. The results shown that, the proposed method gives more information on the state of the machine to carry out a more precise diagnosis study.