Miracle-dz/UniFAR-FTUs huggingface dataset:
Pavement Crack Detection Using Progressive Curvilinear Structure Anisotropy Filtering and Adaptive Graph-Cuts

Delineation of pavement cracks is essential for the damage assessment and maintenance of pavements. Existing methods are not sufficiently robust to interferences including varied illumination, non-uniform intensity, and complex texture noise. An integrated system for the automatic extraction of pavement cracks based on progressive curvilinear structure filtering and optimized segmentation techniques is proposed in this paper. Considering phase congruency and path morphological transformation, a phase congruency guided multi-scale path anisotropy filtering (PCmPA) method is first developed to generate a crack saliency map, significantly enhancing crack structures and eliminating isotropic texture noise. Phase congruency guided multi-scale free-form anisotropic filter (PCmFFA) is then presented as an extended curvilinear structure filter considering context information to enhance PCmPA. Finally, to accurately identify crack pixels and background, the two independent global filtering responses are incorporated with the phase congruency map and integrated into the graph-cuts based global optimization model with an adaptive regularization parameter. Experiments are conducted on two public pavement datasets and a self-captured laser-scanned pavement dataset, with results demonstrating that the proposed method can achieve superior performance compared to six existing algorithms.
Robust Nonconvex Nonnegative Low-rank Representation

Low-rank representation (LRR) has drawn increasing attention in many areas due to its pleasing efficiency in finding subspaces in high-dimensional data. However, the performance of LRR is effected by two problems. First, LRR may generate negative coding coefficients which lack physical meaning. Second, LRR usually obtains a suboptimal solution since the nuclear norm ||. ||* is a loose approximation of the rank function rank(.). To solve the limitations simultaneously, we propose a novel model named Robust Nonconvex Nonnegative Low-rank Representation, termed as RNNLRR. Besides, to rule out the trivial solution, diagonal elements of the coding coefficients are constrained to zero. Based on the alternating direction method of multipliers, an efficient optimization algorithm is derived to solve our model. Experiments on data clustering and noise removal demonstrate the superiority of the proposed RNNLRR.
Stage Actor Positioning Method Based on RealSense Point Cloud

A novel and convenient method for human positioning based on computer vision is proposed. The new camera Realsense D435 from Intel is used to provide richer and more effective visual information. The positioning problem can be simplified to the conversion problem between point cloud and space coordinate system. Experimental results indicated that only several test points were needed to establish the conversion relationship between the two coordinate systems, thereby obtaining the position of the person. The accuracy meets the demand of the stage performance.
A Dual-Purpose Refreshable Braille Display Based on Real Time Object Detection and Optical Character Recognition

This paper proposes a dual-purpose braille system for the visually impaired people. There are two main features of this system- object detection and optical character recognition. Real time object detection will help a visually impaired person to know about the things around him and optical character recognition will help him reading characters in both international (English) and local community (Bengali) language. In this paper, the detailed methodology of our proposed method is described. A pre-trained convolutional neural network (AlexNet) is used for classifying the objects and an OCR engine (Tesseract) along with basic image processing is used for optical character recognition. A refreshable braille display is also designed to show the braille characters.
Vector Decomposed Long Short-Term Memory Model for Behavioral Modeling and Digital Predistortion for Wideband RF Power Amplifiers

This paper proposes two novel vector decomposed neural network models for behavioral modeling and digital predistortion (DPD) of radio-frequency (RF) power amplifiers (PAs): vector decomposed long short-term memory (VDLSTM) model and simplified vector decomposed long short-term memory (SVDLSTM) model. The proposed VDLSTM model is a variant of the classic long short-term memory (LSTM) model that can model long-term memory effects. To comply with the physical mechanism of RF PAs, VDLSTM model only conducts nonlinear operations on the magnitudes of the input signals, while the phase information is recovered by linear weighting operations on the output of the LSTM cell. Furthermore, this study modifies the LSTM cell by adding phase recovery operations inside the cell and replacing the original hidden state with the output magnitudes that are recovered with phase information. With the modified LSTM cell, a low-complexity SVDLSTM model is proposed. The experiment results show that the proposed VDLSTM model can achieve better linearization performance compared with the state-of-the-art models when linearizing PAs with wideband inputs. Besides, in wideband scenarios, SVDLSTM model with much fewer parameters can present comparable linearzation performance compared to VDLSTM model.
Laser Scanner Position Measurement Method and Accuracy Verification Based on Monocular Vision

The method of position measurement based on monocular vision has a wide range of applications in various fields. In this paper, a method based on monocular vision for laser scanner position measurement is proposed and implemented. It can be applied to the three-dimensional reconstruction of point cloud by combining visual positioning technology with laser scanner. Firstly, this paper studies and designs a 3D target (the 3D target is mounted on the laser scanner) made by using the infrared LED light-emitting diode as the control points. Then use the EPnP solution to solve the position of the laser scanner, including spatial position coordinates (x, y, z) and three Euler rotation angles (α, β, γ). The measurement method proposed in this paper is simple and easy to operate, and the experimental results show high measurement accuracy.
Design and Fabrication of Wideband Dual-Polarized Dipole Array for 5G Wireless Systems

This paper presents simulation and measurement results of a wideband planar phased array for 5G. The desired wideband operation is achieved using a tightly coupled dipole array (TCDA). The proposed array consists of tightly coupled dipole units in dual-polarized configuration, and two thin parasitic layers separated apart by air gap for wide-angle impedance matching (WAIM). The top matching layer is loaded with a metasurface composed of sub-wavelength split-ring resonators (SRRs) to improve the scanning performance of array. The infinite array achieved a bandwidth from 23.5 to 29.5 GHz, with voltage standing wave ratio (VSWR) < 3 for scan up to ±60°in E, H and D-plane. The array has a height of only $0.144\lambda _{l}$ , and it exhibits high gains, high efficiency >71% (largest scan angle) and good cross-polarization. The simulations are validated by fabrication and measurement of an array prototype, which indicates agreement to simulated values. The proposed dual-polarized antenna array can be deployed in the future beam scanning applications for 5G.
Reconfigurable Dual-Band Bandpass Filter Using Stub-Loaded Stepped-Impedance Resonators

This paper proposes a reconfigurable dual-band microstrip bandpass filter based on dual-mode stub-loaded stepped-impedance resonators. The proposed structure has a high selectivity with the four transmission zeroes and two control voltages. A source-load coupling technique was employed to enhance the effective control of the passbands. Two identical varactors are loaded at the open ends of each resonator for the purpose of tuning. To verify, the proposed dual-band bandpass filter was designed and simulated. The results show two passbands, with center frequencies of 1.02 GHz and 2.3 GHz, a less than 0.5-dB insertion loss and a rejection of more than 35-dB between the two passbands. The first passband has a tuning range of 1.02 to 1.4Ghz, while the second passband’s frequency can be tuned from 2.0 GHz to 2.6 GHz.
Regularised Encoder-Decoder Architecture for Anomaly Detection in ECG Time Signals

Electrocardiography(ECG) is a procedure to record the electrical activity of the human heart. The recorded time series ECG signal are often used by medical professionals to detect any arrhythmia the subject may have. Work has been done to automate the task by modelling the problem as anomaly detection using encoder-decoder based techniques, training on just normal data and using distribution of loss to predict normal or abnormal data. We argue that normal encoder-decoder with just reconstruction loss suffers from two problem: 1. Latent vector is not smooth and continuous, which might lead to memorising signals 2. Network is prone to outliers as mean squared error is used for reconstruction loss. We propose a regularised encoder-decoder based architecture with KL divergence as regulariser for latent vector which solves the above two problem. The regulariser will enforce the network to minimise the distance between latent vector distribution and normal distribution, hence making latent vector smooth and continuous, at the same time as diverse as possible. We have experimented with various architectures such as Multilayer Perceptrons, Recurrent Neural Networks, Long Short Term Memory Networks, 1D Convolutional Neural Networks for encoder and decoder and found that regularised network outperforms normal network in all the cases. More specifically our regularised network with F1-score of 0.90 outperformed the current state of art for ECG anomaly detection which uses Long Short Term Memory networks for both encoder and decoder, resulting in F1-score of 0.88. As a result we present a regularise encoder-decoder network in this paper which outperforms current techniques for anomaly detection on ECG data.
Cancer-Related Gene Signature Selection Based on Boosted Regression for Multilayer Perceptron

Gene expression profiling is a useful technique for analyzing cellular function, and gene expression profiles are widely studied in human cancer research. Gene expression data usually consist of a very large number of features and a relatively small number of samples, and extracting a small number of important features from these data is a major challenge of gene expression-based analysis in cancer research. In this paper, we propose an embedded feature selection algorithm using boosted linear regression-based feature selection. The boosting technique is applied to derive the ensemble feature selector and improve the performance of linear regression-based feature selection. The proposed feature selection algorithm, called boosted regression-based feature selection for the multilayer perceptron (BREG-MLP), repeats the boosted feature selection process to extract the smallest feature subset while maintaining good classification performance. We apply the proposed BREG-MLP to some human cancer-related gene expression data sets for the purpose of extracting important features, and we confirm that BREG-MLP offers improved performance compared to single regression-based feature selection methods.
A Motif-based Classification Algorithm for Identifying Solar Panel Installations

With increasing energy requirements and limitation of non-renewable resources for traditional electricity generation and transmission, many households and premises across the world have installed solar systems. Power companies require information about solar panel installations to regulate the whole power system. In this paper, we propose a motif-based classification algorithm for identifying whether a customer has installed the solar panels. Firstly, we symbolize our time-series data with alphabets and classify those data. Then we evaluate our method by checking error rates of different settings. Later, we test our algorithm with different training and testing datasets. The motif-based classification algorithm analyzes electricity consumption data of households. Results show that our motif-based classification algorithm for identifying solar panel installations have a very good accuracy.
Transmission Lines Scenes Classification Based on Optimized VGG-16

Insulators are important part of transmission lines. Traditionally, insulator-detection methods mainly relied on manual operation, which suffered from low efficiency and poor safety. Due to the rapid development of deep learning, convolutional neural networks(CNNs) have been widely applied in the field of image classification. However, traditional CNNs have poor performance in transmission lines scenes classification. We propose an optimized deep new network based on traditional CNNs. The experimental results show that the proposed optimization method can improve the accuracy of transmission lines scenes classification.
Estimation of Cuffless Systolic and Diastolic Blood Pressure Using Pulse Transient Time

Blood Pressure is one of the most important parameters in clinical monitoring. For many years, cuff-based sphygmomanometer and arterial invasive line were the standard methods to access blood pressure. Recently, noninvasive indirect methods for achieving blood pressure have been studied. Using pulse transient time is one of the most important methods in order to estimate blood pressure. PTT is a feature that has a high correlation with systolic and diastolic blood pressure. To find and estimate PTT, we need to segment both electrocardiogram and photoplethysmogram signals. In this paper, we proposed a new method to segment the PPG signal to estimate PTT and heart rate. After estimating PTT and heart rate by segmenting and processing ECG signals and PPG signals, systolic pressure, diastolic pressure and mean arterial pressure were estimated using linear regression. The error of systolic pressure, diastolic pressure and mean arterial pressure were 11.62%, 11.21 %, and 10.02%, respectively.
An Exact Second Order Conic Programming Formulation with McCormick based Relaxation for OPF Solution

Efficient, reliable and economic operation of an electric network is one of the major challenges to the power system operators. Optimal Power Flow (OPF) is a frequently solved fundamental tool, to achieve this goal. OPF is also used as a sub-problem for other complex operational problems like Unit Commitment (UC). Hence, improving the solution quality of OPF with reduced computational burden is a prime motive. In order to attain the computational efficiency, usually the ACOPF models are approximated to linearized DCOPF models. However, these approximations sometimes do not even assure feasibility when implemented on AC network. Hence, computationally efficient strong relaxations of the ACOPF model are required, while achieving near global solution. In this paper, a novel Second Order Conic Programming (SOCP) formulation with McCormick relaxations (MCE-SOCP), has been developed with an aim to get an equivalent linear and convex model similar to the actual nonlinear, non-convex ACOPF. Further, tightening of the variables bound is implemented to improve the fidelity of the proposed model. The quality of the solutions corresponding to global optimum is evaluated on GAMS platform, using IEEE PES PGLib-OPF v19.05 benchmark library. The results thus achieved from MCE-SOCP, have been compared with those achieved from DCOPF, ACOPF, conventional SOCP models for radial networks (SOCP-I) and mesh networks (SOCP-II).
Development of Human-Machine Interactive System of Virtual Underwater Vehicle-Manipulator

Underwater vehicle-manipulator system (UVMS) can not only realize the observation of the environment, but also carry out operations such as grabbing and sampling. It has broad application prospects in marine scientific investigation and resource exploration. Due to the complex function of the UVMS structure and its position in the marine environment, it brings certain difficulties to its operation. Therefore, operating simulation of UVMS virtual interactive system has significant theoretical and practical significance. In this paper, UVMS simulation system with human-machine interaction interface is developed. It gives the constituent modules and implementation principles of the system, introduces the modeling method of the system kernel, and focuses on the design and implementation process of the display interface and controller of the system.
A Transcription Factor Binding Site Prediction Algorithm Based on Semi-Supervised Learning

Transcription factor binding site (TFBS) is a DNA sequence that binds to transcription factors and interacts with transcription factors to regulate the transcription process of genes. Most prediction methods for TFBS use artificial synthesis or interception of unanalyzed DNA fragments as negative samples in the model, so the prediction results with negative samples are rich in noise and will affect the performance of the classification model. Therefore, this paper proposes a model based on deeply convolutional autoencoders using only experimentally validated transcription factor binding site dataset. The prediction model uses a convolutional neural network with multiple hidden layers and classification layer, and the classification layer uses the sigmoid classifier; In addition, unsupervised learning and supervised learning are adopted as training strategies for predictive models. The model uses the ChIP-Seq data processed by the Gene Transcription Regulation Database (GTRD) as the experimental data set. The human and mouse datasets as independent testing, AUC and ACC were as high as 86% and 94%, respectively, the experiments have shown that a single model is superior to specific models in training 9 human transcription factors and 6 mouse transcription factors. The experimental results show that the proposed method is very feasible and effective in the prediction of TFBS.
Collaborative Robot Transport System Based on Edge Computing

Collaboration between heterogeneous robots promises increased robustness and efficiency of tasks with great potential in applications, such as manufacturing, search and rescue. In this paper, we present a collaboration system based on an edge computing framework, a mobile robot named “StellaX” and a manipulator. The robot StellaX is assembled by a three-wheel omnidirectional platform, a LIDAR scanner for indoor autonomous navigation, and a stereo camera which can capture objects' images. The information of objects, which can be used for grasping, is stored and shared with the manipulator through the edge computing framework with Docker and ROS. Finally, we test the collaboration system by conducting an object recognition experiment and a navigation experiment. The experimental results demonstrate the system effectiveness and prove that the system yields better real-time performance and reduces total task execution time than a cloud computing-based scenario.
Assistive Parking Systems Knowledge Transfer to End-to-End Deep Learning for Autonomous Parking

In numerous spots, parking a vehicle is challenging task and requires an experienced driver to maneuver and park the vehicle efficiently. With the advent of Automatic Parking Assist Systems (APAS), drivers can park their vehicles automatically and safely. These systems, however, still require driver intervention and constant attention while parking. The APAS system uses the onboard sensors to perceive the environment to identify the obstacles around and a proper parking space. The system then plans a collision-free trajectory and follows that trajectory to park the vehicle in the designated parking space. This paper presents an intelligent parking system for parking Unmanned Ground Vehicle (UGV) perpendicularly using Convolution Neural Networks (CNNs). To overcome the problem of dataset scarcity and quality APAS system is used to generate training data. The neural network model is trained to mimic the APAS system behavior captured in the generated dataset. The evaluation of the trained CNN model showed that the proposed intelligent parking system is able to park the vehicle perpendicularly with accurate orientation.
Finger Knuckle Surface Print Verification using Gabor Filter

The need for reliable user verification methods has increased due to severe security concerns. Hand-based biometrics plays an important role in providing security in real-time environments and are more successful in speed and accuracy. Finger knuckle images can also be used in forensic and criminal verification applications. This paper investigates an approach for personal verification using finger knuckle surface images. In this paper, after applying the pre-processing and noise reduction of finger knuckle images, by using Gabor filter extracting textural features from both proximal and distal phalanx knuckle regions. The textural features obtained from the Gabor filter are combined with the features of the gray-level co-occurrence matrix and finally classified by using K-nearest neighbor classifier and fuzzy K-nearest neighbor classifier. In the finger knuckle images database of 1435 Finger Knuckle print samples from 287 Fingers, we achieved an accuracy of 97.7% with fuzzy K-nearest neighbor classifier.
Deep Adversarial Imitation Learning of Locomotion Skills from One-shot Video Demonstration

Traditional imitation learning approaches usually collect demonstrations by teleoperation, kinesthetic teaching or precisely calibrated motion capture devices. These teaching interfaces are cumbersome and subject to the constraints of the environment and robot structures. Learning from observation adopts the idea that the robot can learn skills by observing human's behaviors, which is more convenient and preferable. However, learning from observation shows great challenges since it involves understanding of the environment and human actions, as well as solving the retarget problem. This paper presents a way to learn locomotion skills from a single video demonstration. We first leverage a weak supervised method to extract the pose feature from the experts, and then learn a joint position controller trying to match this feature by using the general adversarial network (GAN). This approach avoids cumbersome demonstrations, and more importantly, GAN can generalize learned skills to different subjects. We evaluated our method on a walking task executed by a 56-degree-of-freedom (DOF) humanoid robot. The experiment demonstrate that the vision-based imitation learning algorithm can be applied to high-dimensional robot task and achieve comparable performance to methods by using finely calibrated motion capture data, which are of great significance for the research on human-robot interaction and robot skill acquisition.
A High-Accuracy Capacitance-to-Voltage Converter for Capacitive Sensors

In this paper, a high-accuracy switched-capacitor (SC) capacitance-to-voltage (C/V) converter is presented. The proposed circuit can greatly reduce the influences of the channel charge injection and clock feedthrough from analog switches, and the finite gain of the op-amp using three kinds of voltages. Performances of the proposed circuit are simulated by HSPICE using 0.18 μm CMOS process parameters. Simulated results have demonstrated that the gain error is 0.00016% and the maximum nonlinear error is 0.00029% of the full scale. The conversion accuracy of 0.01% or better can be achieved even when low-gain amplifier such as single-stage differential amplifier is used. A prototype chip was fabricated in 0.18 μm CMOS process. The principles of operation of the prototype chip was confirmed by measurement results. The gain and maximum nonlinear errors are 2.34% and 0.41% of the full scale, respectively.