Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Structural Health Monitoring Method Utilizing Sparse Representation for Acoustic Emission Signals in Rails

The structural health of rails significantly impacts the safety of railway transport, which requires long-term and accurate monitoring. An improved structural health monitoring (SHM) method is proposed to enhance the damage information and construct an adaptive weighted index for evaluating the structural health states of rails. In this method, modified singular value decomposition (SVD) with a double-layer Hankel matrix compensation algorithm enhances the damage information and ensures real-time detection. Based on the inflection point of the inner product gradient, the step size of the sparsity adaptive matching pursuit (SAMP) algorithm is adaptively adjusted to reconstruct damage signals. Subsequently, an innovative index is constructed with adaptive weighting for SHM of rails. Tensile tests are performed to verify the effectiveness of the proposed method. The results show that the proposed method avoids the defects of traditional nondestructive testing (NDT) techniques that rely on a priori information about the damage signal (such as damage frequency acoustic emission (AE) hits rate and waveform characteristics). Furthermore, using the variation in signal energy, the proposed method has the capability to accurately delineate the three structural health stages under complex waveform conditions, which is not possible with conventional detection methods. This method provides guidance for the damage analysis and monitoring techniques of rails.
Distributed ADMM for Time-Varying Communication Networks

The distributed alternating direction method of multipliers (ADMM) is an efficient distributed optimization algorithm, which however shows poor convergence in time-varying network topologies. To solve the challenge, we propose TV-ADMM, a novel distributed ADMM algorithm for time-varying communication networks. More specifically, importance weight parameters are introduced in message fusion, with the purpose of mitigating the potential error brought by the network topology dynamics. Based on that, the updating rules are designed with the first-order approximation and a Bregman divergence term, which can reduce the variance caused by the randomness and enhance the robustness. Moreover, we consider two different practical scenarios with time-varying communication network. In Scenario One, the communication between two nodes succeeds with certain probabilities, based on which the importance weight parameters are designed. Scenario Two considers mobile agents, where the communication link is determined by the distance between two agents. We derive the connectivity probability in this scenario and get the corresponding importance weight. Numerical simulations validate the effectiveness of the proposed algorithm in both scenarios, in comparison with the subgradient-based method.
Exploring the Potential of Error-permissive Communication in Multi-FPGA-based Edge Computing

In recent years, Edge Computing has been attracting a lot of attention as it has shown its ability to mitigate some issues found in Cloud Computing (e.g., high latency, centralization issues, programmability, etc.), Moving computation from the Cloud to the Edge has resulted in an exponential increase in the number of devices to be connected. With such an increase, interconnection has become a major design and performance factor, especially in terms of power efficiency. Thus, the question that a lot of research has been trying to answer is how to connect such devices in a power-efficient way while making sure to maintain high-performance and accuracy at the lowest cost. In this paper, we investigate the Error-permissive communication paradigm and its potential to answer the aforementioned question. In particular, we focus on the importance of using this paradigm on Multi-FPGA systems which have gained significant attention in Edge Computing and can be a good candidate for Error-permissive communication. We also present a preliminary evaluation of a novel concept to reduce the power consumption by undervolting the supply voltage of the serial transceivers, and we show its potential to reduce the total FPGA power consumption by up to 26%.
Hyperparameter-Tuned CNN for the Classification of Ten Tomato Plant Diseases

Tomato plant diseases caused by microorganisms such as fungus, bacterial etc. have been threatening the quality and quantity of tomato production for a few centuries. Therefore, precaution is necessary during the early stage to detect and cure the diseases that occurred on the leaves before the diseases spread to result in plant death. However, the detection method employed currently is inconsistent and inefficient. Most farmers employ manual visualisations to identify the types of plant diseases as the automatic monitoring and classification of diseases cannot be achieved. Such obstacles drive the need for a plant disease detection system, aided by artificial intelligence (AI). Therefore, in this work, four different Convolutional Neural Network (CNN) models for tomato plant diseases detection, namely VGG-16, ResNet-50, DenseNet-121 and ResNet-101 were implemented and the hyperparameters were manipulated for parametric studies. The performance of the different architectures with the selected hyperparameters was evaluated and compared according to the well-established evaluation metrics for deep learning; Precision, Recall, F1 score, Matthews correlation coefficient (MCC) and Accuracy. Combining all its merits, the ResNet-50 model which had achieved an accuracy of 100% with a shorter training time was proposed as the final model to be implemented in the tomato plant diseases classification system. In short, a CNN model that is capable of effective classification of tomato leaf diseases has been developed and its benefits for tomato cultivation are much anticipated.
Particle Trapping and Transportation with MEMS Zeroth-order Bessel Beam Acoustic Transducer (BBAT)

As a promising non-contact and non-invasive manipulation technology, acoustic particle manipulation (APM) has wide application potential in fields like material science, chemical analysis, biomedical engineering, etc., particularly given its mechanical vibration nature and impressive biocompatibility. One of the key research areas in APM is designing and implementing different acoustic beams for different application scenarios. Bessel beams, in both zeroth-order or high order form, have thus gained great attention in academia for its excellent propagation characteristics, like non-diffraction, self-healing, etc., which are quite favored in applications like targeted drug delivery. In this paper, we propose a MEMS zeroth-order Bessel beam acoustic transducer (BBAT) based on soft-lithography, in terms of its design, simulation, fabrication, and experiments over its capacity for negative impedance contrast particle trapping and transportation in water medium. The proposed BBAT has advantages over existing technology from the perspectives of monolithic compactness, portability, and bio-compatible. Experimental results well demonstrated that the BBAT can firmly trap and transport particle in long distance, which can benefit cell manipulation and drug delivery.
Sim-to-real: Quadruped Robot Control with Deep Reinforcement Learning and Parallel Training

In recent years, deep reinforcement learning methods provide a new realization idea for the motion control of the quadruped robot, which makes the neural network controller have the characteristics of strong adaptability and high stability. In this paper, we propose an end-to-end neural network framework that, contains an estimator network for estimating the robot's ontology states in addition to the critic and actor networks. These states serve as an important observation data for the critic and actor networks. In addition, in order to solve the disadvantage of high cost of neural network training time, we exploit the mechanism of parallel training and deploy the entire training process in GPU, which improves the speed of network convergence. Finally, we reconstruct the forward network on the CPU through the Eigen library, and transfer the model to the real robot, successfully implementing sim-to-real. We demonstrate our model on SDUQuad-48, and experiments show that the learned policy can achieve various dynamic motions with strong robustness.
Information Retrieval from Internet Data Using Textual Mining and Statistical Models for Stock Market Price Movement Prediction

In this research, it is strictly based on the procedures of normal data analysis. They start from data (text) acquisition that is collected from various and objective sources, to data processing by utilizing unstructured data processing, TF-IDF, and Bag-of-words model methods, and finally to the data (text) analysis by setting up mathematical models like linear regression and neural network models, in the following, picking out the keywords with higher frequencies in the datasets. Eventually, after accurate calculations and impartial analysis in New Energy, Real Estate, and Steel industries, it concludes that the industries related to the selecting keywords have strong correlations to the stock prices.
NOMA-Based Full-Duplex UAV Network with K-Means Clustering for Disaster Scenarios

In this paper, we propose and assess the performance of a downlink non-orthogonal multiple access (NOMA)based full-duplex (FD) unmanned aerial vehicle (UAV) network for disaster scenarios. The K-means algorithm is used to partition the user equipments (UEs) residing in the disaster region into a number of clusters. The UAVs are located at the cluster centers and act as decode-and-forward relays to secure coverage from an operational base station (BS) into the disaster region. The power-domain NOMA used at the BS and the UAVs operating in FD mode improve the system performance in terms of outage probability and sum rate compared to orthogonal multiple access and half-duplex mode. In particular, analytical expressions for the outage probability and sum rate are derived. Numerical results are provided to reveal the impact of system parameters on the performance of the NOMA-based FD UAV network over Nakagami-m fading which in turn illustrate design options for applications in disaster scenarios.
A Microfluidic Sensor for Single Cell Detection and Counting Bull Sperms Cell

A microfluidic sensor chip for bull sperm detection with ability of detection and counting is presented. The chip was fabricated by UV and soft lithography process in order to create electrodes and microchannel for cell alignment and delivery. Moreover, the chip was integrated with a pair of gold microelectrodes acted as a cell counter based on LCR-impedance circuit. Main objective of this specially designed circuit is reducing the large interface impedance of the microelectrodes, make them easy to use, for higher accuracy and efficiency in counting process. However other cells than those spermatozoa in semen also cause change of impedance when passing the pair of electrodes and interfere with the spermatozoa count. This work demonstrated that the change in electrical impedance is related to the size of cells passing through the electrodes, allowing distinguishable between spermatozoa and other cells, i.e. beads suspended in washing media. Work has been designed to measure average impedance of three samples; media, beads and spermatozoa investigated at signal frequency of 2 MHz, 2.5V. The averaged output impedances were 143.14 kΩ, 151.26 k$\Omega$, and 152.67 k$\Omega$, respectively. Then, these data were normalized as aspect to compare the differences between signal of beads and bull sperms. All analyzed values show consistencies of data with p-value lower than 0.05. This work provided important information as the device can be developed utilizing the impedance as a distinguishable cell-counter when spermatozoa and beads flow pass the area of detecting electrodes.
A comprehensive analysis using neural network-based model for thyroid disease prediction

Nowadays data analysis has an important role in building a machine-learning model, especially in the case of medical data analysis. Statistical analysis tools help us to analyze large amounts of data and are also used to identify common trends and patterns in the dataset. This statistical analysis can be used to convert big data into meaningful information. In the case of medical datasets, the major issue is inconsistent data representation, for example, after diagnosis, some medical experts will represent the gender as F and M for male and female some others will represent it as 1 and 0, and sometimes the same expert will use a different format for the same gender representation, so the data pre-processing has an important role here. For the statistical analysis of the medical dataset, some python tools are used. Here thyroid medical datasets are used for the statistical analysis. After statistical analysis, this dataset is passed over to a deep learning neural network and got an accuracy of 99.07%, F1-score of 93.69%, Recall of 89.66%, and Precision of 98.11%.
Material Fracture Life Prediction Under High Creep Conditions Using Decision Trees and Rule-based Techniques

Several elements in power plants suffer from high creep conditions in its normal service life. This fact makes that high efficiency materials are used to manufacture critical components of the system. Among them, high chrome content alloy steels are widely applied in these cases to optimize the final mechanical properties of critical elements. Although knowing the mechanical properties at creep conditions is not an easy task, since creep tests are a high time consuming process. Due to these problems, the use of regression models to get a better understanding and a prediction of mechanical properties of the material is a really helpful technique. In this work, several regression techniques, based on decision trees and decision rules, are applied to predict the time when the material fracture happens. In order to build these models, a representative dataset of the problem was studied to get a better knowledge of the problem using several techniques of multivariate analysis. Then, a validation methodology based on cross validation training and simple validation testing was applied to verify the generalization of the models. The algorithms applied in this methodology show how decision trees and decision rules techniques can achieve accurate results in their prediction, obtaining low RMSE close to a 7%. And finally, among the studied algorithms, the one based on rule-based cubist technique performed the most accurate results.
The Impact of Co-Located Clusters of Inverter-Based Resources on a Performance-Based Regulation Market Metric

This paper demonstrates that a faster Automatic Generation Control (AGC) response provided by Inverter-Based Resources (IBRs) can improve a performance-based regulation (PBR) metric. The improvement in performance has a direct effect on operational income. The PBR metric used in this work was obtained from a California ISO (CAISO) example and is fully described herein. A single generator in a modified three area IEEE 39 bus system was replaced with a group of co-located IBRs to present possible responses using different plant controls and variable resource conditions. We show how a group of IBRs that rely on variable resources may negatively affect the described PBR metric of all connected areas if adequate plant control is not employed. However, increasing the dispatch rate of internal plant controls may positively affect the PBR metric of all connected areas despite variable resource conditions.
Differential evolution based on hybrid crossover operators

Differential evolution (DE) is a newly proposed intelligent optimization algorithm, which has shown good search abilities in many optimization problems. However, DE as well as other population-based stochastic algorithms can be easily trapped into local optima when solving complex multimodal problems. In order to enhance the performance of DE, this paper presents a novel DE variant (HCDE) based on hybrid crossover operators. Simulation studies on ten wellknown benchmark functions show that the proposed approach HCDE achieves better results when compared with other two DE variants.
Beam Squint Effect in Multi-Beam mmWave Massive MIMO Systems

In multicarrier wideband millimeter-wave (mmWave) communications, as the size of antenna array increases, as in massive multiple-input multiple-output (MIMO), an additional propagation delay of the electromagnetic wave at each antenna element is introduced comparable to (or greater than) the symbol duration, resulting in producing a displacement in beam direction at each subcarrier frequency or as it calls beam squinting. With multi-beam transmission in massive MIMO systems, due to beam squinting effect, an additional inter-beam interference (IBI) can be introduced to the system which significantly decreases the overall system capacity. Therefore, in this paper, IBI under beam squint effect is modeled for different subcarrier frequencies and beam angles in a multi-beam mmWave massive MIMO system. In addition, the impact of this IBI model on the system capacity is evaluated. The analysis results show that beam squinting causes a significant increase in IBI level, even when transmitting with orthogonal beams. Moreover, the results provide the optimal number of beams for a given antenna array size to maximize the system’s capacity considering beam squinting phenomena.
Employing Various Data Cleaning Techniques to Achieve Better Data Quality using Python

In the world of information, data plays a key role in all fields of Science, Engineering and Technology. Many important industries and services constantly deal with large volume of data. Important decisions that determine the future of organizations are taken from the data such gathered. However, oftentimes, the data thus obtained is replete with inaccuracies. No data set is error free, therefore a host of tools are developed to deal with issues such as, missing data, miscoding, null sets, spelling mistakes and duplicate entries. Therefore, Data Cleaning plays a significant role in guaranteeing that the consequent processes for analysis of data are run efficiently and quickly. Inaccurate data may lead to unpredictable and unusable conclusion, thus cleaning data forms an important part of grooming data prior to analyze it. The existing techniques which include using various programs such as Excel are intensive and complex to use - which consume more time and demand a wide knowledge of the use of formulas. This paper explores with the different techniques & tools available for cleaning in the market were summarized with their application using Python. The paper intends to discuss the various issues that can arise during the application of these techniques and methods to overcome such hurdles. Data cleaning techniques using Python are more efficient and quicker compared to other classical methods, this leading to more accurate and cleaner data that can be processed.
Dysphonic Speech Reconstruction Using Machine Learning Approach

The speech disordered persons will be in the position to produce hoarse speech. In this paper we focused to convert and reconstruct the hoarse speech to a normal speech by using mel frequency cepstral coefficients (MFCC). The extracted features are used to train cascaded Gaussian Mixture Model (GMM). The necessary speech data for the work is collected from speech impaired subjects.
Improved Dead Reckoning Localization using IMU Sensor

In the upcoming years, autonomous vehicle technology will advance quickly and spread widely. The most crucial component of such systems is vehicle localization or position estimation. The global navigation satellite system (GNSS) is the one of the most advanced positioning system currently in use. But the GNSS signals could be interrupted or degraded due to interference. Additionally, while GNSS delivers precise information for outdoor systems but is unable to provide a solution for indoor. An indoors-and-outdoors-usable device called an inertial measurement unit (IMU) sensor is employed in this paper to suggest a location estimate technique. The study's proposed IMU dead reckoning method makes use of novel techniques to filter noise data and accurately determine the position of the vehicle. The GNSS-based vehicle localization system can be improved by using this technique in GNSS-denied scenarios. In this paper, the IMU sensor's accelerometer, magnetometer, and gyroscope data are utilized to calculate the vehicle's velocity, orientation, and position. The experimental results on a real autonomous vehicle demonstrate that the system is effective, with average errors in rotation and translation of 1.03 degrees and 1.04 meters, respectively.
Autonomous Decentralized User Association Method to Maximize Integrated System Throughput for Multi-service Coexistence

In this paper, we propose an autonomous decentralized user association method that maximizes the integrated system throughput, which is defined across multiple services. The quality of service (QoS) of each service is defined by the system throughput, which takes into account the trade-off between spectrum efficiency and fairness among users for each service. Then, the total quality of all services is defined by the integrated system throughput, which is calculated by the weighted generalized average of the system throughput levels of each service. The proposed method achieves the optimal user association that maximizes the integrated system throughput according to the distribution of base stations (BSs) and users in the system coverage based only on a small amount of control information broadcasted by each BS through autonomous decentralized control of each user terminal. Computer simulations assuming a heterogeneous network show that the proposed method increases the system throughput of each service and integrated system throughput compared to the cell range expansion (CRE) method, which is widely used in 4G and 5G mobile communication systems.
Design and Implementation of Two Element Miniaturized Multiband Semi-Circular Disk Slotted MIMO Antenna using Fr4 And Rubber as Substrate Material for Future Mobile Communications

In this paper, Semi-Circular Disk Slotted 2x1 MIMO Antenna with Fr4 and rubber as substrate material for future 5G Mobile communication is proposed. patch antenna in shape of rectangle is designed at 2.4GHz frequency and semi-circular disk slots were made on the patch at two corners in opposite direction using HFSS simulation software. The slot dimensions are selected such a way that 1/4th part area of rectangle is considered and with this radius 1/4th part of circle shape slots were made. The novelty in this approach is if such a dimension and such a shape of slot is made compensating opposite corners, 1/4th part of patch dimension in terms of length and width can be removed such that miniaturization encounters for patch This antenna is designed and simulated to enhance the future technologies upcoming in the market. Proposed antenna is operated at Hexa-band frequencies 2.4-2.6 GHz, 3.2G-3.6 GHz, 4.8-5.2 GHz, 7.2-7.8 GHz, 8.2-8.4GHz, 9.6-10GHz. Obtained Gain of the proposed Semi-Circular Disk Slotted Patch Antenna is 7. 5dBi. Proposed miniaturized multiband Antenna shell be used for future mobile communications including Bluetooth, WIMAX, Wi-Fi, VOLTE and 5G frequency bands.