Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Frequency Electromagnetic Field in Microenvironments and Their Possible Health Impacts

The electromagnetic field is an inseparable part of the contemporary world. Ubiquity of low and extremely low-frequency electromagnetic field produced by the distribution of household electricity has attracted special attention. A number of dosimetric analyzes of the electromagnetic field realized by various researchers have detected high exposure values in the different microenvironment of human life. Other studies report potential associations between exposure to low and extremely low-frequency electromagnetic field and various health outcomes. This paper shows an overview of the electromagnetic situation in our life microenvironments and her possible health impacts.
Multi-mode Time-Cost-Robustness Trade-Off Project Scheduling Problem under Uncertainty

Time/cost trade-off problems is a well-known project scheduling problem. These years many researchers begin to focus on the project scheduling under uncertainty, such as resource idleness, high inventory and missing deadlines. This paper addresses a time-cost-robustness trade-off project scheduling problem with multi-mode under uncertainty. A multi-objective model of three objectives is constructed and three propositions are proposed. A ε-constraint method nesting genetic algorithm is designed to solve this multi-objective problem along with four improvement measures, and the computational experiment is conducted to validate the effectiveness of the proposed algorithm.
Wavelet-Based Respiratory Rate Estimation Using Electrocardiogram

Respiratory rate (RR) is expressed as breath per minute (BrPM). RR can function as early detection of chronic heart disease (COPD) and congestive heart failure (CHF). Besides, it is physiologically stated that there is a connection between the heart system and the pulmonary system. From the facts described above, it can be concluded that RR monitoring is needed effectively and periodically. ECG signal is used to find out RR characteristics. This study designed an ECG instrument using HPF 0.05 Hz and LPF 100 Hz analog filter. The DWT method is proposed to obtain an estimate of the respiratory rate signal through the ECG signal. DWT decomposition uses level 8 with a sampling frequency of 250 Hz. Testing is done in three ways and is obtained by each error, including the subject is in the supine position (2.35 ± 1.75), upstairs (2.8 ± 2.18) and treadmill (3.65 ± 3.11). Real-time processing of DWT experiences a delay time of 3 seconds because the computation on DWT to the 8th level takes 3 seconds. Real-time DWT testing was a delay of 3 seconds from breathing signal readings.
Discrete Gravitational Search Algorithm (DGSA) applied for the Close-Enough Travelling Salesman Problem (TSP / CETSP)

TSP (travelling salesman problem) is a NPhard problem, and several exact and heuristics solutions exist. Exact solutions consume too many resources (computation and time) and heuristic solutions do not provide global optimum path. We propose another heuristic variation of the TSP and CETSP solver (DGSA-TSP and DGSA-CETSP), based on discrete gravitational search algorithm and two novel rubber-band algorithm (RBA) implementations; we benchmark it against the GSOA (growing self-organizing array) variant and obtain similaraccuracy results.
Deep Learning for Plant Classification in Precision Agriculture

Deep learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multidisciplinary agriculture technologies domain. In this research, we present a deep learning classification system of diverse plants, in order to enable precision agriculture applications. This classification problem was achieved thanks to the public dataset “Plant Seedlings Dataset”, which contains images of approximately 960 unique plants belonging to 12 species at several growth stages. The database has been from Aarhus University Flakkebjerg Research Station in collaboration between the University of Southern Denmark and Aarhus University. A classification comparison was used to determinate which of three pre-trained models; InceptionV3, VGG16 and Xception; reach the best accuracy performance for the database used in this work. Results determined that (1) Xception was the best model for plant classification obtaining 86.21%, overcoming other networks in 7.37% with a time processing around 741 seconds. (2) GPU hardware changes the classification model results impacting strongly in their accuracy score.
Position Control of Flexible Joint Robots with PD Controller Based on Disturbance Observer

With the trend of “human-robots interaction”, robots will inevitably come into contact with people in task execution. In order to ensure human security, flexible reduction elements are actively introduced into robot joints. Compared to traditional rigid robots, Flexible Joint Robots (FJRs) have advantages on these aspects, such as low output impedance, energy saving and shock tolerance. However, their position control problem still faces great difficulties, mainly because of uncertainties in the actuation and external environment. In this paper we propose an feasible method, which integrates a disturbance observer (DOB) and a proportional-derivative (PD) controller. The lumped disturbance, including modeling inaccuracy and external uncertainty, is estimated by the DOB, while PD controller is utilized to stabilize the system. It is mathematically proven that the global asymptotic convergence of closed-loop system is guaranteed. The effectiveness of the proposed method will be verified on a self-built 2 degrees of freedom (DOF) planar FJR platform and experimental results demonstrate the good performance of this approach.
Intrusion Detection System Based on Support Vector Machine Optimised with Cat Swarm Optimization Algorithm

intrusion detection system (IDS) like firewall, access control and encryption mechanisms no longer provide the much-needed security for systems and computer networks. Current IDS are developed on anomaly detection which helps to detect known and unknown attacks. Though, these anomaly-based IDS feature a high false rate. To reduce this false alarm rate, in this paper, we proposed an intrusion detection model based on support vector machine (SVM) optimized with Cat swarm optimization (CSO) algorithm. We use the information gain (IG) for attribute reduction and perform classification using the optimized Support vector. The result obtained shows that our model performs well with the least false alarm rate and good accuracy value compare with other classification algorithms evaluated using the same datasets.
Regulation Document Search Based on Themes using Cosine Similarity and Naive Bayes

Ministry of Administrative and Bureucratic Reform main task is to make policies. The policies that are produced are Regulations that can be in the form of Undang-Undang (Act), Peraturan Pemerintah (Government Regulation), Peraturan Presiden (Presidential Regulation), and Peraturan Menteri (Ministerial Regulation). In making these regulations, one of the main concern is to look for regulation that has the same theme as the regulation that is still being drafted, so that the regulation that will be produced later, did not conflict or overlap with existing regulation. So far the search process take a long time because the search process is only based on the title, type and year in which the regulation created. Furthermore, the search process will not produce results if you don’t use the right keywords. And after we have succeeded in finding the regulations that we are looking for, in order to facilitate the consultation on these regulations, each regulation document must be classified based on the unit that made it.This paper discusses the search for regulation based on themes using the Cosine Similarity and then classify the regulation document using the Naïve Bayes. The result is that the searching process using Cosine Similarity only requires less than 1 second and the classification of the regulations documents using Naïve Bayes has an accuracy of 82%. The conclusion is that Cosine Similarity method can be used in the Regulation Document searching process. And Naïve Bayes method can also be used in Regulation document classifying process.
A Frequency-Selective EMI Reduction Method for Tightly Coupled Wireless Power Transfer Systems Using Resonant Frequency Control of a Shielding Coil in Smartphone Application

Current wireless power transfer (WPT) technology is widely used for various applications because of convenience and safety. However, WPT systems have problems with electromagnetic field (EMF) leakage, which can cause electromagnetic interference (EMI) issues for nearby electrical devices at various frequency ranges. In the case of the Qi standard that operates from 105 to 205 kHz frequency range, the third or fifth harmonic can cause EMI problems for the amplitude modulation bands of a radio corresponding from 530 to 1700 kHz. For the first time, in this article, we propose a frequency-selective EMI reduction method with an additional shielding coil for the effective suppression of EMI radiation of a WPT system. The proposed method reduces the EMI in a specific frequency range and has insignificant effect on the power transfer efficiency (PTE). We experimentally verified that the proposed method with the shielding coil reduced the EMF leakage by 8.96 dB of the third harmonics without reduction of the PTE. Furthermore, we mathematically designed the shielding coil using the proposed method. The mathematical calculation based on the equivalent circuit models is strongly correlated with the experimental measurement results.
High-Frequency Sensitive Generative Adversarial Network for Low-Dose CT Image Denoising

Low-dose computed tomography (LDCT) imaging has attracted tremendous attention because it reduces the potential cancer risk for patients by decreasing the radiation dose. However, reducing the radiation dose may cause image quality degradation due to the introduction of noise and artifacts. The details of pathological information mainly exist in the high-frequency domain of LDCT image. Therefore, some useful details may be lost or destroyed while removing the noise and artifacts. To address this problem, we propose a high-frequency sensitive generative adversarial network (HFSGAN). The new generator includes two sub-networks. One is the high-frequency domain U-Net, which is specially designed to deal with the high-frequency components decomposed from LDCT image. The other is image space U-Net, which is used to process information from the whole image of LDCT. In addition, the discriminator in HFSGAN adopts an inception module to increase the receptive field and width of network, and to extract the multi-scale features of the true and false images. The experiments show that the proposed network preserves more texture details of denoised image while removing noise and artifacts. Compared with the state-of-the-art networks, the proposed denoising method achieves better performance both quantitatively and visually.
Design, Fabrication and Experiment of 3-DOF Pneumatic Flexible Actuator

In the field of robotics, more and more scholars have a strong interest in the research of software robots, which has led to the development of pneumatic flexible actuator (PFA), and the PFA has been widely used in the research of bionic robots. But the current research of PFA generally have the problem of large driving pressure or small deformation. Based on elastic silicone Ecoflex0030 and bellows braided sleeve, a simple structure of 3-DOF PFA is proposed and the characteristics of PFA are tested experimentally. The experimental results show that the actuator can reach the bending angle of more than 270° under the gas pressure of 0.2 Mpa in the inner cavity. Therefore, the structure of PFA can effectively solves the above problem.
Characterization of Low Cost Wideband Direct Modulation Radio over Fiber Link

A low cost Radio-over-Fiber link is characterized by directly modulating the VCSEL with a single frequency sweep and comparing the received information to the transmitted using the S parameters from a Vector Network Analyzer. Frequencies were swept from 100MHz to 10GHz to plot the response of the whole link. The performance of the RoF link is analyzed by varying the DC Bias and the RF input power signal modulating the VCSEL. The optimized link was achieved by using a specific bias point of 1.65V and a specific RF input power of −20dBm for the link. With this biasing, the link is usable up to 2.9GHz that can be useful for multiple RF telecommunications and mobile network technologies like in LTE applications.
End-Users and Service Providers: Trust and Distributed Responsibility for Account Security

In a between-subjects study design, we compared responses from 170 online account users of two major service providers to explore issues relating to responsibility and trust for account security. Participants attributed clear roles between themselves and their service providers for preventing attacks, monitoring their accounts, and recovering their accounts. The emerging mental model of distributed responsibility does not match service providers stated terms of use. Users identified a variety of reasons for trusting different service providers, but reputation was viewed as especially important.
Research of Dynamic Operation Modes of Transformer Voltage Regulator on Mathematical Model

Transformer voltage regulator can be used to regulate the voltage in the electrical network. The authors developed a voltage regulator prototype. Transformer regulator introduces a boost or buck voltage in the line and allows one to adjust the network voltage in amplitude (longitudinal voltage control) and phase (transversal voltage control). Mathematical models of the network with transformer voltage regulator developed in SamSim. The simulation results allowed determining the parameters of the longitudinal and transverse perturbations that can be compensated by voltage regulator. The influence of the transformer regulator model parameters on its dynamic characteristics has been investigated.
A Full WR-3 Band and Low-Loss 90° Waveguide Twist Based on CNC

Modern terahertz (THz) receivers used in radio astronomy are aimed at developing multipixel instruments, in which the polarization twists are often required. In this article, an all-in-one and low-loss 90° waveguide twist is developed for working over the full WR-3 band. A square waveguide junction with a doubler-corner-cut is integrated between the H-plane and E-plane waveguides to provide 90° polarization rotating. All the vertical corners are fillet to comply with the fabrication constraints of the current computer numerical control (CNC) machining. The prototype integrated with a twist section of one-fourth wavelength thickness and two straight waveguides with total 12 mm length is processed through means of split block. Measured results exhibit a low insertion loss of 0.4 dB and better than 25 dB return loss across the entire WR-3 band, which are all in good agreement with the simulations. Such excellent performance is highlighted comparing with the reported THz waveguide twists, which indicates that this compact design is well suited for the delicate CNC-machining restrictions in the THz band.
Foot trajectory planning of bipedal walking robot based on a uniform acceleration method

This paper proposes a uniform acceleration method to plan the trajectory of the biped robot walking process. This method decreases the peak acceleration of the foot during the walking process of the robot, optimizes the hip joint torque, and reduces the risk of motor overrun at the hip joint. Meanwhile, the Center of Mass (CoM) and foot trajectory formulas are derived in this paper, and the effectiveness and feasibility of this method are also verified by simulation experiment.
Tactile Ring: Multi-Mode Finger-Worn Soft Actuator for Rich Haptic Feedback

This paper presents a novel finger-worn actuator capable of generating three distinctive haptic effects: static pressure, high-frequency vibration, and an impact. Our new design makes this multi-mode feedback with a single actuator possible in a small form factor. A ring-shaped air bladder made with soft silicone is inflated or deflated to generate feedback on the finger. The airflow is controlled by a pair of air valves connected to a compressed air tank. Besides static pressure, the actuator can generate high-frequency vibration with high acceleration and crisp impact feedback due to fast controllability of the valves with a strong air source and a lightweight bladder membrane. In addition, our special design and fabrication of the bladder – a combination of stretchable and non-stretchable membrane layers in the ring – allows for stronger feedback. Rendering algorithms for three kinds of feedback are also presented. The performance of the system and the characteristics of the feedback are thoroughly examined using a series of measurement experiments, revealing that the system can generate a static force up to 6.3 N, the vibration up to 2.2 g magnitude and 250 Hz frequency, and an impact with less than 5 ms of latency. The whole system weighs only 255 grams (4.5 grams for the actuator and 250 grams for the controller). Finally, we demonstrate the wearability of the system by integrating the modules into a self-contained haptic device in the form of a wristlet.
Forward Secure and Fine-grained Data Sharing for Mobile Crowdsensing

Secure task-driven data sharing can improve the sensing data usage and protect data confidentiality in mobile crowdsensing (MCS). However, the existing data sharing schemes lack efficient support of forward secrecy, i.e., if the secret key of a data requester is compromised, all the historically shared data will be leaked. In this paper, we propose a forward secure and fine-grained data sharing scheme in mobile crowdsensing to provide a strong security guarantee and flexible access control over the sensing data. Specifically, by incorporating puncturable encryption and attribute based encryption, a shared symmetric key for data sharing can be encrypted by an access structure over the attributes of data requesters and the introduced Bloom filter attributes. Moreover, the shared key establishment between the MCS server and data requesters can be done jointly with the both sides authentication. By utilizing the structure of the Bloom filter, the update of a private key which is used to achieve forward secrecy only needs several deletion operations and no communication with the key distributor is involved. The security proof shows our scheme is provably secure under the security model. Experiment results demonstrate the practicability of the scheme.
Robust License Plate Recognition With Shared Adversarial Training Network

Recently, deep learning has greatly promoted the performance of license plate recognition (LPR) by learning robust features from numerous labeled data. However, the large variation of wild license plates across complicated environments and perspectives is still a huge challenge to the robust LPR. To solve the problem, we propose an effective and efficient shared adversarial training network (SATN) in this paper, which can learn the environment-independent and perspective-free semantic features from wild license plates with the prior knowledge of standard stencil-rendered license plates, as standard stencil-rendered license plates are independent of complicated environments and various perspectives. Besides, to correct the features of heavily perspective distorted license plates perfectly, we further propose a novel dual attention transformation (DAT) module in the shared adversarial training network. Comprehensive experiments on AOLP-RP and CCPD benchmarks show that the proposed method outperforms state-of-the-art methods by a large margin on the LPR task.
Automatic Diagnosis of Familial Exudative Vitreoretinopathy Using a Fusion Neural Network for Wide-Angle Retinal Images

Familial exudative vitreoretinopathy (FEVR) is a hereditary disorder that can damage the retina. This retinal damage can lead to vision loss and even blindness in the late stages. Thus, early diagnosis and prevention of the disease’s progression are critical. The purpose of this study was to develop an automated diagnosis system for FEVR based on combining deep learning and domain knowledge. A transfer learning scheme was designed to train a deep convolutional neural network (DCNN) to provide segmentation of the retinal vessels. Based on this vessel segmentation and prior clinical knowledge, the vascular characteristics, including the retinal avascular area, vessel angle, fractal dimension, branching and density of blood vessels, were automatically evaluated. Finally, the diagnosis of FEVR was achieved by a feature fusion neural network. Our method was evaluated on 300 images with 168 healthy and 132 FEVR images. By combining deep features and handcrafted features (extracted vascular characteristics), the proposed method achieved an average F1-score of 0.95, with excellent accuracy (94.34%) and sensitivity (91.43%); the quadratic weighted $\kappa $ was 0.88 for the diagnosis of FEVR. We demonstrated the effectiveness and robustness of the proposed method using five-fold cross-validation. The proposed automatic diagnosis system can assist doctors for better judgment and make sense of early diagnosis and prevention of the disease’s progression.
One-dimensional attitude control for BIT flying Robot

A flying space spherical robot (SSR) with a maximum diameter of 90mm is designed to assist astronauts in detecting and troubleshooting in small spaces of space stations. Flywheels on three faces of frame are used to generate torque through accelerated rotating motion, so that SSR rotates reversely. In this paper, one-dimensional dynamic model of SSR is presented as referring to a small size design for space applications. Ground experiments simulating zero gravity were carried out to verify the effectiveness of the dynamic model and system operation. Results of lab tests show the feasibility of the BIT flying robot by using the proposed dynamic model for its controlled operation.
Time-Frequency Representations for Single-Channel Music Source Separation

Inspired by the success of image classification and speech recognition, deep learning algorithms have been explored to solve music source separation. Solving this problem would open to a wide range of applications like automatic transcription, audio post-production, and many more. Most algorithms usually use the Short Time Fourier Transform (STFT) as the Time-Frequency (T-F) input representation. Each deep learning model has a different configuration for STFT. There is no constant STFT parameters that is used in solving music source separation. This paper explores the different parameters for STFT and investigates another representation, the Constant-Q Transform, in separating three individual sound sources. Results of experiments show that dilated convolutional layers are great for STFT while normal convolutional layers are great for CQT. The best T-F representation for music source separation is STFT with dilated CNNs and a soft masking method. Furthermore, researchers should still consider the parameters of the T-F representations to have better performance for their deep learning models.