Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Pavement Crack Detection Based on YOLOv5-AH

Object detection on pavement cracks plays a significant role in the overall condition assessment of the pavement. However, the automatic crack detection remains a tough work due to the complex background and the scale change. In order to solve the related issues, a deep learning model named YOLOv5-AH is proposed on the basis of the YOLOv5. We integrate convolutional block attention module (CBAM) to extract attention area, which can emphasize the crack features and suppress the background information. Moreover, we optimize the network architecture by adding one more prediction head to detect different-scale cracks. To further improved our proposed YOLOv5-AH, a series of advanced strategies are integrated into the model training including data augmentation. A pavement crack dataset in different enviroments is built by a mobile robot. The precision and recall of our crack detection method reach 0.924 and 0.988 respectively. The prediction speed of the model can achieve real-time effect (72FPS).
Phase Reassignment with Efficient Estimation of Phase Difference

The recently developed Matched Phase Reassign-ment (MPR) gives a time-frequency local measure of phase difference between short oscillatory transient signals. However, the resulting phase estimate is not satisfactory as it has poor resolution for high oscillatory frequencies. The MPR is also sensitive to high noise levels and is computationally cumbersome. In this paper, a novel reassignment method for phase difference estimation is proposed and evaluated. In comparison to the MPR the accuracy is increased and the computational time is reduced. Simulations show that the proposed technique also outperforms state-of-the-art methods in terms of efficiency. An illustrative example of phase difference estimates of the electrical signals measured from the brain is included.
UNet with Two-Fold Training for Effective Segmentation of Lung Section in Chest X-Ray

Segmentation and evaluation of the Region of Interest (ROI) in medical imaging is a prime task for disease screening and decision-making. Due to accuracy, Convolutional-Neural-Network (CNN) based ROI segmentation has been widely employed in recent years to evaluate a class of medical images recorded using chosen modality. The proposed work aims to demonstrate the segmentation performance of the UNet scheme with a one-fold and two-fold training process. To experimentally verify the merit of the proposed scheme, segmentation of the lung section from the chest X-ray is studied. This research includes the following parts; (i) Resizing the test image and image mask to pixels, (ii) Training the UNet with one-fold and two-fold approaches, (iii) Extracting the ROI, (iv) Comparing the ROI with the mask to compute the image metrics and (v) Validating and confirming the segmentation performance of UNet. The performance of UNet is then verified with UNet+ and UNet++. The investigational ending substantiates that the proposed approach helps to get better Jaccard (>95%), Dice ((>97%), and Accuracy (>98%) in two-fold training compared to other methods considered in this study.
Early Detection of Heart Disease Using Advances of Machine Learning for Large-Scale Patient Datasets

Heart disease is one of the significant causes of death all over the world. The Healthcare industry produces a large amount of data; thus, heart disease prediction is becoming a challenging task in IoT-based healthcare systems. Machine learning plays a vital role in predicting the disease accurately. Many studies have been conducted in this area; however, they do not use large-size datasets to explore the real power of machine learning techniques in predicting heart disease. In this paper, we used four large-scale multi-dimensionality heart disease datasets collected from different sources. We applied various traditional machine learning techniques, namely Decision Tree (DT), Naïve Bayes (NB), K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) to predict heart disease. The results are compared to an ensemble model for the diagnosis of the heart disease.
Design and Evaluation of On-chip DCT accelerators based on Novel Approximate Reverse Carry Propagate Adders

Reverse carry propagate full adders have shown significant performance benefits over other approximate full adder architectures, which is attributed mainly to the engineered reverse path of carry output, thereby the approximate sum output bit does not anymore depend on the the combinatorial function of preceding carry generated bits. The paper proposes two new performance-efficient approximate full adders configured to propagate carry in reverse order. In addition, a novel Tri-Split Hybrid Adder (TSHA) is introduced by adding another split inside the Bi-Split Hybrid Adder (BSHA) to increase the parallelism and leverage the performance and accuracy benefits of two different reverse carry adders. TSHA exhibited the average percentage improvement (percentage improvement for the best Figure of Cost design) of 51%(62%), 10%(11%), 3%(4%), and 55%(66%) towards performance, energy, area, and energy-delay-product (EDP) respectively, when applied across 8, 12, 16, 20, 21, and 24 bits of approximation in a 32-bit adder subsystem, with respect to 32-bit exact full adder (FA) based on Ripple carry adder design. The efficiency of proposed approximate adders including TSHA was investigated for designing On-Chip Discrete Cosine Transform (DCT) accelerator unit adopted for the JPEG image compression module. The DCT designed compression block offered a maximum of 40% performance the benefit with acceptable accuracy and no significant degradation in the image quality.
Real-time Vehicle Localization and Pose Tracking in High-Resolution 3D Maps

In this paper we introduce a novel approach for accurate self-localization and pose tracking for Lidar and GPS-equipped autonomous vehicles (AVs) in high-density (more than 5000 points/m2) 3D localization maps obtained through Mobile Laser Scanning (MLS). Our solution consist of two main steps: First, starting from a poor GPS-based initial position, we estimate the 3DoF pose (planar position and yaw orientation) of the ego vehicle by aligning its sparse (50-500 points/m2) Lidar point cloud measurements to the MLS prior map, using a novel approach of matching static landmark objects of the scene. Second, to effectively deal with the lack of pairable objects in certain time frames (e.g. due to scene segments occluded by a large moving tram), we track the estimated 3DoF pose of the AVs by a Kalman filter. Comperative test are provided on roads with heavy traffic in downtown city areas with large (5-10 meters) GPS positioning errors. The proposed approach is able to reduce the location error of the vehicle by one order of magnitude and keep the yaw angle error around 1° during its whole trajectory without considerable drift, while running in real-time (20-25 Hz).
Total Blooming Effect in Active Elements of Gigawatt Terahertz Compressors

Prospective three-mirror quasi-optical compressors with laser-driven switching open the way to reach gigawatt pulsed power in terahertz band. The compressor uses a Gaussian beam that can pass through the active element without significant reflection and loss at the stage of pumping. Such blooming of the active element can be provided with an optical interference minimum and/or the Brewster effect. Reaching gigawatt power level faces a challenge of minimizing power losses inside the compressor, when even the difference between a Gaussian beam and a plane wave really matters. A novel approach of using the wave equation instead of the parabolic equation to solve the problem of a Gaussian beam propagation through an arbitrary stratified media with a complex permittivity helps us to avoid artifacts of nonzero electric field divergence. Using an advanced Fourier transformation based numerical model of integration in a wave vector space with an algorithm of lattice truncation, the study estimates and reveals a significant difference between a plane wave and a corresponding Gaussian beam that might be visible and confirmed in the near future experiments.
Frequency Reconfigurable Antenna Based on Multimode Resonance

In this design, a method called dual-mode com-pression is applied for dipole antennas. In this method, the varactor diodes are loaded on the dipole antenna. By changing the bias voltage across the varactor diode, the resonant frequency of the third mode remains almost unchanged, while the resonant frequency of the first mode changes accordingly, enabling the antenna's frequency to be reconfigured. For the designed antenna model, the simulation results indicate that the resonant frequency of the first mode can vary in the range of 0.858GHz-1.2GHz, while the resonant frequency of the third mode remains almost unchanged at 2.43GHz. At the resonant frequency of the third mode, the antenna gain ranges from 2.92dBi to 3.17dBi.
Energy Trading for Multi-type Prosumer in Context of Electricity-Carbon Coupled Market

The design of the transaction mechanism is one of the key points in the construction of the power market. This paper presents an energy trading method based on VCG auction mechanism for multi-type prosumer market. Firstly, the basic theory of VCG auction mechanism is introduced. Then, the mechanism of VCG market framework is designed. For the multi-type markets, an auxiliary parameter is used to establish the augmented Lagrangian model. The objective function of the model is transformed into several sub-problems, and the alternating direction multiplier method (ADMM) is used to solve the multi-block problem. In the real-time market phase, the stochastic nature of renewable power generation and fixed load is considered for real-time transactions with the power grid. Finally, the simulation results show that the proposed market transaction mechanism is economically beneficial to prosumers and the power grid, has high computational efficiency, and is beneficial to the sustainable development of the prosumer market.
Flexible Formation Control Using Hausdorff Distance: A Multi-agent Reinforcement Learning Approach

While fixed topology formation control with a cen-tralized controller has been studied for multi-agent systems, it remains challenging to develop robust distributed control policies that can achieve a flexible formation without a global coordinate system. In this paper, we design a fully decentralized displacement-based formation control policy for multi-agent sys-tems, which can achieve any formation after one-time training. In particular, we use a model-free multi-agent reinforcement learning (MARL) approach to obtain such a policy in the centralized training process. The Hausdorff distance is adopted in the reward function for measuring the distance between the current and target topology. The feasibility of our method is verified by both simulation and implementation on omni-directional vehicles.
Linearized Zverev Transform and its application for modeling radio occultations

The multiple phase screens technique is often used for modeling wave propagation and radio occultation sounding of the atmosphere. The last step of this procedure is the propagation from the last phase screen to the observation orbit of the spaceborne receiver. This step was formerly performed by the computation of multiple diffractive integrals, which impairs the numerical efficiency of the algorithm. We introduce an asymptotic method of wave propagation in vacuum from the last phase screen to a generic observation path. The exact solution is written in the form of the Zverev Transform, which belongs to the class of Fourier Integral Operators (FIO). The phase function of such an operator can be derived from the geometric optical equations. We construct an approximation for the Zverev Transform, utilizing the linearization of the equation of the geometric optical ray propagation in the vicinity of a smooth model of the ray structure. This permits the design of a fast numerical algorithm based on an FFT. Numerical simulations are performed for microwave propagation through a model atmosphere based on realistic gridded global fields of meteorological parameters. The new method based on the Linearized Zverev Transform is compared with the standard combination of multiple phase screens and diffractive integrals and with the asymptotic forward modeling for the propagation of centimeter waves in the atmosphere on limb paths. This method is especially useful for modeling LEO–LEO microwave occultations at frequencies of 9–30 GHz. The simulations demonstrate a high accuracy and numerical efficiency of the proposed method.
Two-dimensional Eletrostatically Actuated MEMS Micromirror

Recently, the development of LiDAR in the field of vehicle has made the core micromirror with essential characteristics of large tilted angle and small size. We proposed a two-dimensional electrostatically actuated MEMS micromirror with a slender torsion bar. With the modified theoretical formula, two different torsional modes and corresponding torsional frequencies of the micromirror are obtained, which are then compared to the results by finite element simulation, and revealed a good agreement. The rotation angle of the micromirror is improved through optimizing the micromirror structure. Meanwhile, it is found that the driving voltage and the moment of inertia introduce the softening behavior of the spring, which will then affect the torsion frequency. To prevent the torsion bar stress is too large, with the sinusoidal driving voltage of 75V and 100V, the corresponding mechanical rotation angles are 14.8° and 15.3° at a driving frequency of 340.5Hz and 1500Hz, respectively.
A Voting-Based Machine Learning Strategy to Detect False Data Injection Attack in Cyber-Physical Power Systems

Integrating the information and communications technology (ICT) infrastructures with the physical layer of the electrical grid puts the power system at the risk of a wide range of cyber intrusions. Cyber-physical power systems (CPPS) require effective and applicable prevention, detection, and mitigation strategies to defend the system against malicious attacks. In this process, precise attack detection is one of the vital phases. This paper designs a novel voting-based detection tool to identify cyber intrusions in the system. In the considered cyber-attack, the attacker attempts to inject a wide range of false data into the phasor measurement units (PMU) to simulate false short circuit conditions in the system. The proposed detection strategy utilizes several machine learning (ML) algorithms like ensemble learning, discriminant analysis, naive bayes, feedforward neural network (FNN), recurrent neural network (RNN), k-nearest neighbors (KNN) classification, support vector machine (SVM), and decision tree. The voting-based technique calculates the average output based on the performance of the detectors and can recognize FDI attacks from real short circuit faults. Training features are selected optimally among several mechanical and electrical features of the system with maximum relevancy and minimum redundancy (MRMR) method. The simulations on the IEEE 39 bus test system illustrate the performance of the proposed detection tool for each type of short circuit fault.
GEONEAR: Stata module to find nearest neighbors using geodetic distances

geonear finds the nearest neighbors using geodetic distances, i.e. the length of the shortest curve between two points along the surface of a mathematical model of the earth. Both great-circle and ellipsoid distances are available. geonear returns a specific count of nearest neighbors and/or all neighbors within a specified distance. geonear uses a divide and conquer strategy to significantly reduce the number of distances that must be computed. The approach involves splitting the initial set of locations into progressively smaller geographic regions while at the same time safely reducing the set of potential neighbors for each region. Generally, geonear will predictably find the nearest neighbors in a linear time even though the size of the overall problem increases exponentially. In other words doubling the size of both location sets simply doubles the number of regions and thus doubles the run time.
Parallel Implementation Of Clutter Suppression Of Passive Radar Based On LDLT

With the increasing signal bandwidth of oppor-tunistic radiators, real-time clutter suppression has become a serious challenge for passive radars. To solve the problem, this paper designs an inter-segment parallel algorithm based on the extended cancellation algorithm batches (ECA-B) of LDLT decomposition. Firstly, based on the GPU multi-thread parallel processing technology, the effectiveness of the ECA-B algorithm is improved by segmented parallel processing, which is combined with the same sub-module characteristics of each segment of the ECA-B algorithm. Then, the cost of the time of data transmission in the inversion process of traditional ECA-B algorithm is economized by proposing a parallel iterative inversion method based on LDLT leveraging the conjugate symmetry property of covariance matrix, which is processed by two CUDA kernel function and further improves the execution efficiency of the parallel algorithm between segments. Experimental results show that the algorithm performs higher effectiveness compared with the traditional algorithms.
Impact of Cyber Attacks on Cost Oriented Power Routing Schemes in Microgrids

The distributed economic dispatch (ED) algorithm carried out in an AC microgrid (MG) is a promising solution which guarantees flexibility, scalability and reliability over single point failure as compared to the centralized approach. Not to mention, the integration of communication infrastructure for information exchanges on one hand adds feasibility for the distributed operation but at the same time, is a threat to the smart grid. The attackers can penetrate in the communication links and inject malicious data in order to gain economical benefits, disrupt the proper functioning of the system etc. Hence, the investigation of the effect of cyber attacks on cooperative energy management (CEM) is an important concern both theoretically and practically. This paper analyses the impact of cyber attacks on a CEM, optimizing ED to a sub-optimal value in an islanded AC MG. The response of the system over false data injection (FDI) and hijacking attacks is further demonstrated on a real-time (RT) co-simulation platform.
Malicious Domain Name Detection based on Knowledge Graph

In recent years, there have been many examples of attackers exploiting vulnerabilities in the domain name system to infiltrate and steal information and assets. There have been breakthroughs in the research on malicious domain name detection using artificial intelligence. However, there is no specific plan for the research on knowledge graph and malicious domain name detection. Therefore, we innovatively combine the two and propose a malicious domain name detection model based on knowledge graph. We first combined the DNS flow graph with the DNS domain name hierarchy graph by means of rule alignment, and established a new DNS information knowledge graph. Second, we simultaneously complete the vectorization of entities and attributes in a joint learning manner as the input of the detection module. Finally, we combine DNS information, knowledge graph and neural network, and convert the DNS information into entities and attributes in the knowledge graph based on the knowledge graph. Experiments show that the malicious domain name detection model proposed in this paper has excellent performance and good generalization ability.
RISC-V Based Processor Architecture for an Embedded Visible Light Spectrophotometer

The miniaturization of sensing systems often requires embedding an electronic subsystem for local or edge computing, or to interface with the sensor for pre-processing operations. The sensing part of the work presented in this paper is an optoelectronic system that measures neurotransmitters concentration based on visible spectroscopy and that is currently implemented with an external processor in a computer. This paper presents a System on a Chip (SoC) design based on the RISC-V processor and the required peripheral interfaces to replace the current system. The new design is first simulated and then implemented on Cyclone IV and Xilinx ZCU102 FPGAs to explore the usability and advantages of the approach. Both architectures were similar in terms of memory and register use, but the ZCU102-based system used 18016 logic elements, while the Cyclone IV-based one used much less, 13468 logic elements. We also observed a significant difference in frequency of operation, with Cyclone IV was running at 27.84 MHz and ZCU102 at 125 MHz clock speeds.
Review of Power Converter Topologies for Electrochemical Impedance Spectroscopy of Lithium-Ion Batteries

Frequency domain impedance of Li-ion batteries contains valuable information about the state of charge (SOC) and state of health (SOH). Normally, electrochemical impedance spectroscopy (EIS) is performed during the relaxation of battery cells. However, performing EIS during the batteries operation has been achieved through switching power converters. This paper reviews the power converter topologies for both online and offline Electrochemical Impedance Spectroscopy (EIS) characterization of batteries. The information that can be extracted from EIS Nyquist plots are discussed. Comparative analysis between converter topologies is presented. Finally, challenges are identified and new converter topologies are proposed for further consideration in online/offline EIS characterization.
Multinomial Logistic Regression Classification Model for Arrhythmia Detection

In the present medical era, Electrocardiography has been used to record heart activities for the basic visualization of various cardiac diseases. ECG signal properties analysis for effective arrhythmia classification has been considered a challenging issue due to the interruption of various sorts of noises that includes baseline wander, power line interference, and motion artifact noise. This challenge has been addressed in this research through the Multinomial logistic Regression (MLR) classification model. This mathematical model has been structured for the effective removal of various noises and to improve the classification ratio for effective arrhythmia detection from ECG signals. In MLR, Fractional Wavelet Transform is used for preprocessing of ECG signal for removing the noise and to determine the QRS interval from the ECG signal. From the Pre-processed signal Stacked Autoencoder (SAE) is used to validate the dimensionality of the retrieved features for effective prediction and classification of multiple forms of arrhythmias. Based on the extracted features of the ECG data MLR classification model obtains a maximum classification ratio for accurate arrhythmia identification. The experimental findings show an improved classification ratio of 98.95% with reduced noise factors, Signal to Noise Ratio (SNR) of 35.7dB when compared to conventional algorithms.