Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning Approaches for Lung Cancer Prediction

Cancer is a long-term, exhausting disease that requires changes in all living conditions of the patient and his/her environment. Although there are regional variations in deaths from all causes in the world, it is in the 3rd rank. Lung cancer is among the most frequent cancer kinds worldwide, regardless of male or female. Cancer is a preventable disease. To prevent a disease, it is necessary to know its causes and avoid them. The use of tobacco and tobacco products is the main risk factor for all cancers, especially lung cancer. Early diagnosis of cancer is lifesaving. According to the Turkish Respiratory Research Association, 200,000 people are diagnosed with cancer every year in our country. With the accelerated developments in technologies and the digitalization of health services, a large amount of cancer data has been collected and this data has been used by many researchers, especially in low and middle-income countries, to reduce the cost of tests used to predict different cancer types and to predict different cancer types. This article is exploited various machine learning algorithms for predicting lung cancer. Experimental results show that random forest performed better by attaining 96.08% accuracy.
N258 Frequency Band Circularly Polarized Corner Perturbated Microstrip Patch Antenna

For millimeter-wave applications, a corner perturbated right hand circularly polarized patch antenna has been proposed. To obtain two degenerative modes required for circular polarization, the square patch antenna is perturbated at its two diagonal corners. A quarter wave transformer is used to match the impedance. The antenna is designed to operate at 28 GHz, which is the resonance frequency. The proposed antenna has an impedance bandwidth |S11| (<-10 dB)between 27.671 to 29.294 GHz(5.6982%) and axial ratio(< 3dB) bandwidth between 28.303 GHz to 29.146 GHz (2.934%). The gain is larger than 6 dBi, and the radiation efficiency is over 90%. The proposed antenna has a low profile, a high gain, and a high radiation efficiency, making it ideal for millimeter-wave applications.
Secured Data Integrity Scheme for Internet of Things

The provision of security to any information in an IoT network is a big challenge, which must be given the top priority for many current and future applications of IoT. Traditional data integrity verification approaches only use encryption algorithms to secure information, depending on Third-Party Auditors (TPAs). The blockchain’s basic principle is that information produced by users or nodes is tested for accuracy and cannot be altered once it is updated on the blockchain. Blockchain based data integrity schemes can effectively overcome TPA’s issues. In this paper, we propose a blockchain based data integrity technique with a bilinear design for IoT information. We achieve data integrity according to the characteristics of bilinear design in the form of blockchain communications. The proposed blockchain based framework for data integrity verification consists of different entities such as client, Key Generation Centre (KGC), cloud storage server, and blockchain. Proposed data integrity verification operates in three stages: Setup stage, Processing stage and Verification stage. We analysed the prformance of proposed scheme using different parameters like end to end delay, memory utilazation and accuracy rate.
Disturbance Rejection Based on Equivalent-Input-Disturbance Approach for Dynamic Positioning System of Offshore Drilling Platform

A dynamic positioning disturbance suppression method is presented for the offshore drilling platform based on the equivalent input disturbance (EID) approach in this paper. Firstly, the offshore drilling platform's low-frequency and wavefrequency motion components are separated by the Luenberger observer. Then a linear-quadratic regulator is designed to ensure the stability of the low-frequency motion closed-loop system. The EID approach is used to estimate and compensate for the unknown time-varying disturbances of the environment, thereby improving the disturbance suppression ability of the system. Simulation results of a supply vessel demonstrate the validity of the method.
Enhanced Detection and Mitigation on Sub Synchronous Resonance in Wind Farm with Series Compensated Line

Recently, wind energy generation grows quickly because of its economical features and it has less effect on mother earth. The long-distance between generation and customer reduces the maximum transmittable power. For addressing this issue series compensation is broadly used to raise the capacity of transmission. But the insertion of capacitors has the hazardous issue of Sub Synchronous Resonance (SSR). An Enhanced Detection Technique(EDT) is used in Double Fed Induction Generator (DFIG) based wind power system connected to the series compensated line(SCL) for fast detection of SSR. Comparison of enhanced detection technique with the traditional technique validate the superiority of EDT. SSR Damping Controller (SSRDC) in the static synchronous compensator (STATCOM) is applied for mitigation of hazardous effect of SSR. The voltage signal is the input for the detection circuit and line current signal is the input for SSRDC.
Data Leakage in Android and Anomaly Based Intrusion Detection and Prevention System

Digital era is the new emerging world. In this hyperconnected world, billions of people share platforms on some or the other operating systems. One of them is Android. Android smartphones are the most widely used digital devices, there are over 2.5 billion of android users worldwide. It is modern, fast and reliable. The amount of data that is being generated from these devices are stored in the data storage applications which basically involves third party applications or android system database. It comes with an inbuilt SQLite database application that is responsible for storage of the user data. This information being stored consists of sensitive security credentials such as passwords and confidential information. With the rise in technology, the threat to operating systems is also increasing. Attackers constantly look for vulnerabilities in these operating systems to exploit the loopholes. Data leakage is one of the major threats that is involved in applications. To improve the security of the device an Intrusion Detection and Prevention System (IDPS) is necessary. In this work, we have presented a Machine Learning (ML) based approach to combat the threats of the intrusions and to detect the anomalies in the network, thus protecting the system. We have virtually simulated the attack scenarios and detected the attacks using Snort.
Robust RGB-D Visual Odometry Using Point and Line Features

Robot localization with an RGB-D sensor has attracted substantial research attention lately. However, its robustness and accuracy are still poor in complex environments. To alleviate this issue, this paper presents a robust RGB-D visual odometry (VO) based on point and line features to allow accurate localization in complex scenarios including illumination change, low-texture and occlusion. Firstly, to enhance the robustness and accuracy of localization, we analyze the performance for different line feature extraction, and add the EDLine line feature to our VO system. Second, the EDLine lines are descripted using a sequence of point descriptors, and are matched by point correspondences and geometric constraints. Furthermore, a novel twin line reprojection errors optimization model is constructed by using the depth measurement information to correct the pose error. Finally, we integrate the line feature application into the RGB-D version VO part of ORB-SLAM2 to improve the estimation of the robot pose. The experimental results on public datasets show that our proposed VO approach outperforms other state-of-the-art VO solutions on accuracy and robustness under challenging environments.
Nonlinear Disturbance Observer Based Integral Terminal Sliding Mode Control for Hybrid Energy Storage System

This paper investigates a nonlinear disturbance observer (NDOB) based integral terminal sliding mode control (ITSMC) approach for hybrid energy storage system (HESS) subject to load disturbance and input voltage variation. The pro-posed control strategy can reasonably distribute the output power of the battery and supercapacitor (SC), provide the required power to the load and stabilize the direct current (DC) bus voltage. A strict stability analysis is also given. By comparing with the PI control and ITSMC methods, the proposed method obtains a better disturbance rejection ability when the load and input voltage are varying. Simulation results show that the proposed control strategy can stabilize the entire system and achieve better control goals.
Formation Control of Differential Mobile Robots with Diamond-shaped Input Constraints

This paper studies the formation control for differential wheeled mobile robots under diamond-shaped coupling input constraints. Firstly, the discrete-time and linearized kinematic model of the wheeled mobile robot is obtained. According to the trajectory tracking error equation, the model predictive control (MPC) algorithm is designed. Then, an optimal problem is solved in a specified diamond region at each optimization sampling time, so that the control inputs of the mobile robots always meet the diamond-shaped constraints. By means of the proposed model predictive control algorithm, the leader-follower formation control of three differential mobile robots is carried out. Numerical simulation results show the effectiveness of the proposed control algorithm.
New reaction rate for O-16(p, gamma)F-17 and its influence on the oxygen isotopic ratios in massive AGB stars

The O-16(p, gamma)F-17 reaction rate is revisited with special emphasis on the stellar temperature range of T=60-100 MK, important for hot bottom burning in asymptotic giant branch (AGB) stars. We evaluate existing cross-section data that were obtained since 1958 and, if appropriate, correct published data for systematic errors that were not noticed previously, including the effects of coincidence summing and updated effective stopping powers. The data are interpreted by using two different models of nuclear reactions, that is, a potential model and R-matrix theory. A new astrophysical S factor and recommended thermonuclear reaction rates are presented. As a result of our work, the O-16(p, gamma)F-17 reaction has now the most precisely known rate involving any target nucleus in the mass A >= 12 range, with reaction rate errors of about 7% over the entire temperature region of astrophysical interest (T=0.01-2.5 GK). The impact of the present improved reaction rate with its significantly reduced uncertainties on the hot bottom burning in AGB stars is discussed. In contrast to earlier results we find now that there is not clear evidence to date for any stellar grain origin from massive AGB stars.
An Automatic Method of Autoregression Part Selection for Gaussian Time Series Prediction

It is well known that the model's order has a great influence on the accuracy of the prediction and the complexity of calculation for the problem of time series autoregressive modelling. The higher the order, the more information may be contained, but the higher the computational complexity. Thus, it is practically important to find the right order to ensure an optimal trade-off. Traditional order-determination methods, such as linear autocorrelation function, and partial autocorrelation function, consider only the linear relationship among the parts in a time series and are computationally demanding to test the order one by one. To address this problem, this paper proposes a new method for auto regression (AR) part selection based on the maximal information coefficient, which can automatically determine the optimal AR part by measuring nonlinear functional relationships among various parts. In comparison with the wrapper method, much less computation time is required. Three case studies, in which Gaussian process is adopted as the regression tool, are carried out and the proposed method is compared with conventional 1-19th AR parts. Experimental results show that the proposed method improves effectively prediction accuracy while reducing the models structural complexity.
Rubidium, zirconium, and lithium production in intermediate-mass asymptotic giant branch stars

Context. A recent survey of a large sample of Galactic intermediate-mass (>3 M⊙) asymptotic giant branch (AGB) stars shows that they exhibit large overabundances of rubidium (Rb) up to 100‐1000 times solar. In contrast, zirconium (Zr) is not enriched compared to the solar abundances in these stars. These observations set constraints on our theoretical notion of the slow neutron capture process (s process) that occurs inside intermediate-mass AGB stars. Lithium (Li) abundances are also reported for these stars. In intermediate-mass AGB stars, Li can be produced by proton captures occuring at the base of the convective envelope. For this reason the observations of Rb, Zr, and Li set complementary constraints on different processes occurring in the same stars. Aims. We present predictions for the abundances of Rb, Zr, and Li as computed for the first time simultaneously in intermediate- mass AGB star models and compare them to the current observational constraints. Methods. We calculate the Rb, Zr, and Li surface abundances for stellar models with masses between 3 and 6.5 M⊙ and metallicities between 0.02 and 0.004. Results. We find that the Rb abundance increases with increasing stell ar mass, as is inferred from observations but we are unable to match the highest observed [Rb/Fe] abundances. Variations of the reaction rates of the neutron-capture cross sections involved with Rb production and the rate of the 22 Ne(�, n) 25 Mg reaction, responsible for neutron production inside these stars, yields only modest variations in the surface Rb content of≈ 0.3 dex. Inclusion of a partial mixing zone (PMZ) to activate the 13 C(�, n) 16 O reaction as an additional neutron source yields significant enhancemen ts in the Rb abundance. However this leads to Zr abundances that exceed the upper limits of the current observational constraints. If the third dredge-up (TDU) effi ciency remains as high during the final stages of AGB evolution as during the earlier stages, we can match the lowest values of the observed Rb abundance range. We predict large variations in the Li abundance, which are observed. Finally, the predicted Rb production increases with decreasing metallicity, in qualitative agreement with observations of Magellanic Cloud AGB stars. However stellar models of Z = 0.008 and Z = 0.004 intermediate-mass AGB stars do not produce enough Rb to match the observed abundances.
Counting Complexity of Reachability Problem for Boolean Control Networks with Free Control Sequence

In this paper, we investigate the counting complexity of reachability problem for Boolean control networks (BCNs) by Boolean counting constraint satisfaction problem (#CSP). We prove that the counting complexity of a class of Boolean #CSP by using Post's lattice in universal algebra is #P-complete, and further use it to classify the counting complexity of $M$-reachability problem (#M-RP) when $M=1$. Meanwhile, we prove that #M-RP is #P-complete.
Quantitative Evaluation of Fault Diagnosability Based on Maximum Mean Discrepancy

Quantitative evaluation of fault diagnosability is a crucial part of system design. Therefore, we propose a data-driven method for the quantitative evaluation of fault diagnosability. The evaluation results reflect the characteristics of the fault and are independent of any diagnosis algorithm. Assuming the measurement data follow the unknown multivariate distribution. Then, the multivariate distribution is mapped to the reproducing kernel Hilbert space (RKHS) through the characteristic kernel, and the distance of the multivariate distribution is represented by maximum mean discrepancy (MMD), so the quantitative evaluation of fault diagnosability is transformed into calculating the distance of the multivariate distribution in RKHS. Finally, an example is given to verify the effectiveness of the proposed method. Experimental results show that the proposed method can quantitatively evaluate the difficulty of fault diagnosis without relying on any diagnosis algorithm.
Formation of millisecond pulsars with CO white dwarf companions – I. PSR J1614−2230: evidence for a neutron star born massive

The recent discovery of a 2M binary millisecond pulsar not only has important consequences for the equation of state of nuclear matter at high densities but also raises the interesting question of whether the neutron star PSR J1614−2230 was born massive. The answer is vital for understanding neutron star formation in core collapse supernovae. Furthermore, this system raises interesting issues about the nature of the progenitor binary and how it evolved during its mass-exchanging X-ray phase. In this paper we discuss the progenitor evolution of PSR J1614−2230. We have performed detailed stellar evolution modelling of intermediatemass X-ray binaries undergoing Case A Roche lobe overflow (RLO) and applied an analytic parametrization for calculating the outcome of either a common envelope evolution or the highly super-Eddington isotropic re-emission mode. We find two viable possibilities for the formation of the PSR J1614−2230 system: either it contained a 2.2–2.6M giant donor star and evolved through a common envelope and spiral-in phase or, more likely, it descended from a close binary system with a 4.0–5.0M main-sequence donor star via Case A RLO.We conclude that the neutron star must have been born with a mass of either ∼1.95M or 1.7 ± 0.15M , which significantly exceeds neutron star birth masses in previously discovered radio pulsar systems. Based on the expected neutron star birth masses from considerations of stellar evolution and explosion models, we find it likely that the progenitor star of PSR J1614−2230 was more massive than 20M .
Robust Diffusion Adaptive Networks with Noisy Link and Input

In this paper, we study the problem of adaptive parameter estimation for multi-agent distributed networks, where the input regression vectors of network nodes contain Gaussian noises, while the output values and the communication link are polluted by impulse noises. In this case, the estimation performance of traditional diffusion LMS algorithms and most of the state-of-the-art robust distributed algorithms for output impulse noises will degrade significantly. Aiming at this problem, the Minimal Disturbance Bias-Compensated Diffusion Least Mean Square (MDBC-DLMS) algorithm proposed in this paper can effectively suppress noise interference and achieve an acceptable estimation result of the target parameter vector. MDBC-DLMS uses the principle of minimal disturbance to dynamically update the combination coefficients of the diffusion algorithm to effectively suppress the output and link impulse noise. At the same time, it performs dynamic real-time estimation of the input noise variance information to compensate for the estimation bias caused by the input noise. The simulation results show the excellent estimation performance and effectiveness of the method proposed in this paper, and it can accurately estimate the variance information of input noise at the same time.
Modeling of Correlation on the Stock Market Using Complex Network Methods

Volatility of stock indices can be manifested simultaneously in different stock markets. The need to study this phenomenon requires the application of nontrivial methods of modeling. Volatility correlation modeling is used to study the relationships between different geographical regions by means of complex networks in the crisis and non-crisis periods. It also allowed identifying clusters, the main nodes in correlation networks and the value of their impact.
Dual-Path GAN: A Method for Enhancing Small-scale Defect Detection on Metal Images

Automated surface inspection (ASI) is an important research content in computer vision. In recent years, with the application of deep learning models represented by convolutional neural networks (CNN) in computer vision, surface defect detection based on computer vision has made impressive progress. However, compared with a few or dozens of pictures in real industrial scenes, traditional deep learning methods require a large amount of annotation data for training, so it is quite difficult to adapt to the complex industrial scenarios with varying surface profiles, lighting conditions, imaging angles and environments. Despite these difficulties, there is still a wide gap in the performance between the detection of small-scale and large-scale objects. Traditional Generative Adversarial Nets (GANs) based augmentation method can only be used for classification networks or unsupervised learning, and if applied to detection networks, they need to be labeled, which requires much labor and time. In order to solve these problems, a metal surface small-scale defect images augmentation method is proposed. Our method contains two parts: generation part and augmentation part. In the generation part, two pairs of generators and discriminators are used to generate the defect areas and the background areas of the image, which can not only generate more realistic defect images than others but also save the training process for surface images. In the augmentation part, the real defect images and the generated defect images are concatenated before copy-pasting them on background images to generate annotated dataset for training. We conduct experiments on our metal surface dataset. The experimental results show that our method can generate high-quality defect samples and background samples, which greatly enriches the original dataset. We evaluate different augmentation strategies, and ultimately, we achieve 6.2% improvement on baseline and 4.4% on copy-pasting method.
Power-efficient and Multiplier-less FPGA Implementation of Self-adaptive Leaky Integrate-and-Fire Neuron

Modeling and implementation of neuron are vital for the engineering of biological neural networks and neuromorphic systems. Hodgkin-Huxley model, Izhikevich model, and leaky integrate-and-fire (LIF) model are the most commonly used models in biological neural networks. Considering the efficiency and cost of digital implementation, LIF model is a good choice to realize neural networks on chip. This paper presents a method to implement a self-adaptive LIF model on field-programmable gate array (FPGA). Based on the traditional LIF model, the calcium current of neurons is taken into consideration in our model, which enables the neuron to be self-adaptive and biologically plausible. Our efficient digital realization of LIF neuron has the highlight of (i) efficient hardware usage (654 logic elements per neuron) (ii) low power consumption (142.28 mW per neuron), and (iii) ability to realize different kinds of biological spiking behaviors. The behavior of coupled neurons model is also tested on multiple FPGA chips. The result shows that our self-adaptive LIF neuron model has a good performance of interaction between FPGA.
Command-Filter-Based Event-Triggered Adaptive Fuzzy Finite-Time Control for Nonlinear Systems

This paper investigates the adaptive fuzzy finite-time tracking control problem for nonlinear systems via an eventtriggered scheme. Fuzzy logic systems are used to handle the unknown functions in nonlinear systems. To overcome the “explosion of complexity” and “singularity” promblems caused by repeated differentiations on virtual control signals in existing recursive finite-time design methods, a finite-time command filter is designed in this paper. Meanwhile, a noval compensating mechanism is introduced to further alleviate the adverse effect from the command filter errors. By using finite-time control technique, a filter-based finite-time controller is developed, which can not only solve the complexity explosion problem that exists in traditional recursive design, but also guarantee the stability performance of finite-time convergence. Finally, an example is given to prove the validity of the proposed control strategy.