Miracle-dz/UniFAR-FTUs huggingface dataset:
Copy-Move Forgery Detection: A State-of-the-Art Technical Review and Analysis

This paper presents the state-of-the-art technical reviews and analysis of recent copy-move forgery detection (CMFD) techniques. A new CMFD process pipeline was introduced. In addition, the techniques used in each stage of the CMFD pipeline are summarized and classified into small categories. Furthermore, the tables of comparison are provided as a quick reference. This technical review paper is expected to help provide useful insights and updated information regarding recent advancements in CMFD to researchers in the field.
A Robust Text Dependent Speaker Identification Using Neural Responses from the Model of the Auditory System

Speaker recognition is considered as a behavioral biometric to identify speaker's identity based on their voice features. In this study, a new speaker identification system is proposed based on the neural responses of human auditory system. For this, a very well-developed physiological based computational model of auditory periphery is used to simulate the neural responses for a given speech. The output, in the form of synapse responses, is then analyzed for the feature extraction. Neurograms are constructed for a range of characteristic frequencies from the output responses. Features are then calculated from the neurogram to train the system. The same extracted features for a given speaker are then used to identify the speaker in the testing phase. To test the reliability of the proposed system, the model has been tested both in quiet and noisy conditions. The results show that, neural response-based speaker identification system can substitute the existing technology and thus improve the performance for application of remote authentication and security system.
Convex Relaxation Methods for Unified Near-Field and Far-Field TDOA-Based Localization

This paper develops two convex relaxation solutions for the unified localization of a signal source using time difference of arrival measurements, regardless of whether the source is in the near field for coordinate positioning or in the far field for the direction of arrival estimation. The previous study on unified estimation only derived an iterative solution, which is sensitive to initialization. Albeit a coarse initialization was supplied to start the iteration, it may not be sufficient to ensure convergence to the global solution especially when the source is close to the sensors. The proposed solutions come from two novel formulations for optimization, one using the weighted least squares with the modified polar representation of the source position as variable and the other applying fractional programming with the Cartesian coordinate representation instead. Both the optimization problems are solved by first performing semidefinite relaxation and then tightening the relaxed problem by including a set of second-order cone constraints. The two formulations are created from different approaches. Nevertheless, we are able to prove that both the formulations reduce to solving exactly the same mixed semidefinite/second-order cone program and thus establish their equivalence. Furthermore, the proposed solution method is extended to the more practical scenario when sensor position errors are present. The results from both the simulated and real experiments show that the proposed method achieves almost the same performance of the iterative maximum likelihood estimator under ideal initialization.
On the Security of Secret Sharing Over a Ring and the Fast Implementation

Secret sharing is the method to share secrets among a group of shares, and the secret can be reconstructed if one obtains a predefined number of shares. The polynomial secret sharing is usually constructed over a field. In this letter, a novel polynomial secret sharing over a ring is proposed. In particular, by choosing a certain ring, the fast Fourier transform can be applied on the encoding of secret sharing. The analysis shows that the proposed secret sharing scheme requires <inline-formula><tex-math notation="LaTeX">$O(N\log _2 N)$</tex-math></inline-formula> Boolean operations per secret bit, which improves the prior result <inline-formula><tex-math notation="LaTeX">$O(8^{\log ^* N}N\log _2 N)$</tex-math></inline-formula> Boolean operations per secret bit. The simulation shows that the proposed scheme is in average four times faster than the conventional approach.
Comparative Study of PID and Fuzzy Level Control using Delta V Distributed System

The main purpose of the paper is to study the features of the Delta V distributed system in order to implement a level control system which uses PID or fuzzy logic controller (FLC). The characteristics of the two controllers' implementation in Delta V are described. The level control system is analyzed for each case in three stages. First, the tuning parameters of the two controllers are obtained with different tuning methods. Using the previously determined tuning parameters the control system's response is studied for changes of the set-point. Finally, the system response is analyzed for disturbance changes. The responses obtained with the PID and FLC are compared in terms of overshoot or settling times.
Embracing the Smart-Home Revolution in Asia by the Elderly: An End-User Negative Perception Modeling

There has been a rapid growth in the Internet of Things (IoT) enabled smart-homes recently. Although these IoT powered smart-homes offer new opportunities, their household penetration is quite low. The main objective of this paper is to develop a better understanding of the reasons underlying the end-user resistance to the adoption of the IoT enabled smart-homes, with elderly people as the target population. Due to a rapid increase in the worldwide elderly population, various forms of information and communication technology (ICT) services are used for their well-being, and hence the target of this paper. A very few research has focused on the end-user experience of using smart-homes with all of them using an acceptance-based approach. Using different negative-perception modeling, we try to identify the main barriers to smart-home usage by the elderly people. Accordingly, an online survey was conducted with 254 elderly people across four Asian countries. Partial least squares structural equation modeling (PLS-SEM) was used to test our theoretical model having ten constructs. Two constructs (perceived-uselessness and self-efficacy) were found to be non-significant, while the remaining proved to be significant (innovativeness, perceived-reliability, perceived-interoperability, service-cost, privacy-concern, psychological-barrier, home administrative policy, and government-policy). To the best of our knowledge, this is the first research studying the IoT enabled smart-homes using a resistive approach and should provide the groundwork for further research in user-experience studies with IoT technology.
Three-Dimensional Frequency-Dependent Thermal Model for Planar Transformers in LLC Resonant Converters

In this paper, a thorough thermal analysis is carried out in order to find an analytical three-dimensional (3-D) frequency-dependent thermal model of planar transformers (PTs) used in high-frequency power converters. Because of the tendency of having higher power density as well as miniaturization of power converters, the components have to work near their thermal limit margins. This issue is more critical for planar magnetics in LLC converters due to their variable high-frequency operation, making thermal modeling and heat transfer analysis tools necessary. The proposed 3-D frequency thermal model is obtained based on the lumped parameters network (LPN) according to different thermal resistances of the PT including convection, conduction, and radiation heat exchanges. It should be noted that not only the 3-D geometry effect of the core is applied to the proposed model, but thermal surface resistances are also considered to model the high-frequency operation effect. Moreover, the lumped capacitance thermal model is used to analyze the transient thermal variation of the transformer. The LLC resonant converter with two PTs, including EE58/11/38 and ER51/10/38 planar cores, is considered to show the compatibility of the proposed 3-D-frequency LPN model. In addition, the temperature distribution of different parts of the transformers is evaluated using finite element method modeling and compared with the proposed 3-D frequency-dependent LPN model. Experimental results confirm the improved accuracy of the proposed LPN model and show the proposed model predicts the temperature distribution in PTs with an error of less than 3%. Considering the improved accuracy and low computational time of the method, the proposed 3-D frequency-dependent model is a powerful and fast design tool to evaluate the temperature distribution for different designs, and so can be used to effectively optimize the transformer from thermal point of view.
White Light-Emitting Diodes With High Color Quality Fabricated Using Phosphor-in-Glass Integrated With Liquid-Type Quantum Dot

In this letter, high-quality white light-emitting diodes (WLEDs) were fabricated by combining a YAG:Ce3+ phosphor-in-glass (PiG) with a red liquid-type quantum dot (LQD). The PiG_LQD-based WLEDs achieve a warm white light with an excellent color rendering index (CRI; Ra = 93.9, R9 = 97.7, and R13 = 98.1) at the current of 100 mA. The corresponding correlated color temperature and luminous efficacy (LE) are 3764 K and 62 lm/W, respectively. In comparison with the PiG integrated with solid-type QD, the PiG_LQD exhibits high LE, excellent CRI, and low surface temperature due to the prevention of self-aggregation and surface defects of QDs in the solid state and the efficient heat dissipation of PiG_LQD. The results indicate that the fabricated WLEDs are promising lighting devices for high-quality lighting applications.
Environmental Noise Robustness for Korean Fricatives Using Speech Enhancement Generative Adversarial Networks

Currently, speech recognition technology has a high recognition rate in a quiet environment condition. However, noise processing is needed in environmental noise conditions because they lower the recognition rate. In particular, in the case of Korean fricatives corresponding to [s], , [h], [ç], [x], and , noise processing is relatively difficult because the acoustic characteristics are similar to environmental noise. This paper proposes speech enhancement of Korean fricatives using the Speech Enhancement Generative Adversarial Network (SEGAN) for environmental noise robustness. SEGAN is a Deep Neural Network (DNN)-based generation model that is used to improve the clarity and quality of environmental noisy speech. Enhanced Korean fricative speech data are generated using SEGAN-trained Korean fricative speech data. The results showed that using DNN-Hidden Markov Model (HMM)-based acoustic model training by adding enhanced speech as training data resulted in a 0.26% Character Error Rate (CER) in environmental noise conditions compared to the case without enhanced speech addition.
Eye Tracking based Real- Time Non-Interfering Driver Fatigue Detection System

Driver fatigue, cause fatal road accidents, and is a major socio-economic concern. The human body exhibits signs of fatigue, which can provide us with the information needed for fatigue detection. This study presents to develop an eye tracking based real time noninterfering driver fatigue detection system. In the proposed method Haar-like features based cascade classifiers are utilized to detect the face and eyes of the driver, skin colour based segmentation is used to calculate eye openness and Support Vector Machine (SVM) to deduce the eyes state as fatigued or non-fatigued.
Circuit Implementation of Nano-Scale TiO2 Memristor Using Only Metal-Oxide-Semiconductor Transistors

A metal-oxide-semiconductor (MOS)-based circuit model that emulates a TiO2 memristor is proposed in this study. First, a TiO2 (10-nm active layer thickness)-based memristor device was fabricated. The structural properties of the device were confirmed via energy dispersive X-ray spectroscopy. The memristive characteristics of the device were resolved by applying a time-dependent voltage. The typical pinched hysteresis memristive loops of the current-voltage curves were obtained. After physical implementation and characterization of the device, a circuit model was implemented using only four MOS transistors to emulate the fabricated TiO2 memristor. In addition to emulating the fabricated memristor, the proposed circuit model is suitable for use in general memristor-based applications. No active element or circuit blocks were used in the circuit to achieve memristive characteristics. The simulation and experimental results are in good agreement; moreover, the circuit is very simple compared to those presented in previous studies in the literature and it was able to emulate the fabricated memristor. All simulations were completed using 180 nm TSMC CMOS process parameters.
A Narrative Analysis on Deep Learning in IoT based Medical Big Data Analysis with Future Perspectives

The analysis of health-specific parameters and IoT based health monitoring system become a very challenging research scope to merge them with big data handling capability. This paper proposes an idea describing the possible ways to monitor and analyze health conditions collaborating IoT based medical big data through deep learning algorithm. The recent research trend regarding the concerning field often utilizes the conventional machine learning based algorithms those are not suitable for IoT based big medical data because of their manual feature extraction and less accuracy. On this contrary, this paper widely reviews the different research works regarding the big data handling in deep machine learning approaches and their proposals for health monitoring, applicability on IoT based system, accuracy, and suitability regarding big data analysis. Eventually, this paper focuses on deep learning based IoT system for health monitoring tools and contributes to providing relevant results to the different remote doctors in the area of IoT architecture to ensure propzer knowledge about critical patients. It is an approach to synchronize them in a platform that could be a potential solution for the upcoming researchers to implement a sustainable online based health monitoring system with big data accessing capability. In addition, this research will be effective for medical experts to ensure appropriate healthcare facilities to the mass people in the future.
A Behavioral Facilitation Tweet Detection Method

Many tweets encourage people to behave in a certain way. For instance, a tweet might say “You can lose weight if you eat only apples. ” to encourage readers to eat only apples if readers want to lose weight. For this study, we designate such tweets, which compel users to adopt a behavior, as behavioral facilitation tweets. These tweets do not always provide reliable advice; users should be alerted to unreliable tweets. To accomplish this goal, we first extract tweets that are behavioral facilitation tweets. Our studied behavioral facilitation tweets explicitly include behavior-related advice or encouragement. Furthermore, we target not only behavioral facilitation by the author but also behavioral facilitation by others. As described herein, we propose methods of three types to extract behavioral facilitation tweets automatically from numerous tweets: rule-based, support vector machine (SVM), and long short-term memory (LSTM). We use topics of three types to measure the benefits of our proposed methods, comparing rule-based, SVM, and LSTM-based methods to extract behavioral facilitation tweets. Results demonstrated that the LSTM-based method works best.
A Sliding Window Based Dynamic Spatiotemporal Modeling for Distributed Parameter Systems With Time-Dependent Boundary Conditions

Time/space separation based spatiotemporal modeling methods have been proven to be effective and efficient for modeling a class of distributed parameter systems (DPSs). However, these conventional methods may not work satisfactorily for DPSs with time-dependent boundary conditions. A sliding window based dynamic spatiotemporal modeling method is proposed for this kind of DPSs. First, the sliding window is appropriately designed to capture the most recent spatiotemporal data. Then, the conventional Karhunen–Loève method can be used to construct the analytical model. Besides, a more general sliding window method can be achieved by using a forgetting factor to adjust different influence of the current and previous data. This analytical model can be utilized for online performance prediction. Simulation experiments on a benchmark and a battery with unknown boundary cooling have demonstrated the superior performance of the proposed method on the DPSs with time-dependent boundary conditions.
A novel gene-tree based approach to infer relations among disease-genes across different cancer types

Due to advanced sequencing techniques, we now have abundant collection of gene sequence data. Creating a correlation among these sequence data to infer disease-related information seems to be a tough ask even now, due to the lack of inter-disease association studies. In this paper we have proposed a gene-tree based approach to infer disease-genes across three different cancer types with a view to analyzing the genes and infer their relationships across different pathways and gene ontologies. We have found a total of three significant gene clusters based on hierarchical clustering and found the genes significantly associated with different molecular pathways, which have previously been associated with poor cancer prognosis.
Hybrid identity based cryptographic scheme optimization using machine learning in WSN

Wireless sensor networks (WSN) security has become a very important issue considering the wide range of applications and implementation challenges. In this article we are proposing a machine learning based technique in order to optimize all aspects of wireless sensor network based applications that are being secured using a previously proposed Hybrid Distributed-Hierarchical Identity Based Cryptographic Scheme (HYDISH).
Two-Way Mixed RF/FSO Relaying System in the Presence of Co-channel Interference

In this paper, the performance of a two-way mixed radio frequency/free space optical (RF/FSO) relaying system in the presence of co-channel interference (CCI) is investigated. The RF links are modeled as Nakagami-m distribution, and FSO links adopt Gamma-Gamma turbulence model. To improve spectral efficiency, relay exchanges the information between two terminals in three phases via RF and FSO links. Meanwhile, CCIs are considered at both relay and destination. Novel closed-form upper bounds expressions for the signal to interference plus noise ratios are derived, based on that lower bounds outage probability, average bit error rate, and outage capacity are further presented for both RF/FSO and FSO/RF links. Then, asymptotic expressions are provided to predict the diversity order. Additionally, the effect of various parameters on system performance, such as interference numbers and power, are investigated and compared including interference-free condition. Simulation results show that, in the interference-effected system, when the average signal-to-noise ratio increases, system performance enhances first and then remains unchanged. This phenomenon does not occur in the interference-free system, which implies the system performance is limited by the numbers and power of CCIs. Moreover, the effect of pointing errors on system performance is further investigated.
Current Harmonic Reduction Based on Space Vector PWM for DC-Link Capacitors in Three-Phase VSIs Operating Over a Wide Range of Power Factor

This paper proposes a novel space vector pulsewidth modulation (SVPWM) strategy to reduce the switching-frequency-order inverter input current harmonics for a two-level three-phase voltage-source inverter operating over a wide range of the load power factor. The fluctuation of the inverter input current around its average value is minimized by optimization of the utilized voltage space vectors to reduce the inverter input current harmonics, which extends the lifetime of the dc-link electrolytic capacitors. Furthermore, the proposed strategy can be adapted to a motor drive system, where a wide variation of the load power factor is desired, by changing the optimized combinations of the voltage space vectors according to the polarities of the output phase currents. Experiments with the application of the novel SVPWM confirm that the inverter input current harmonics is reduced by up to 29.5% compared to that with the conventional SVPWM strategy. Moreover, the analytical and experimental results confirm that the proposed SVPWM reduces the inverter input current harmonics in both driving mode and regenerative braking mode, as well as with any load power factor.
Analysis of Electromagnetic Induction due to Lightning on a Large-Scale Solar Power Generation

Lightning causes intensive induced voltage and can be extremely harmful to a solar power plant. Particularly, due to the exposure to the open sky, Photo-Voltaic (PV) panels are highly susceptible to lightning that can damage the panels, DC lines, inverters and other equipment in the plant. To reduce this effect using a properly designed lightning protection system, in this paper, we investigate the induced voltage phenomena and its characteristics and effects on a large-scale solar power plant. The electromagnetic field and induced voltage in different places of the plant are measured by Virtual Surge Test Lab (VSTL) tool using Finite Difference Time Domain (FDTD) method. Varying characteristics of electromagnetic interference are analyzed for the soil resistance, lightning time, and lightning locations. Our early results verify that the electromagnetic field and induced voltage largely depend on the location of the lightning strike. The induced voltages are also found to vary with the increase of soil resistivity and lightning time. Presented findings would be helpful to the development of a sustainable, safe and effective large-scale solar power plant.
Measuring and detecting blood glucose by methods non-invasive

This article aims to discuss the detection and measurement of blood glucose by non-invasive methods, current or older blood glucose measurement methods, current and future research. The article aims to illustrate a new prototype based on spectroscopic methods, UV sensors, to monitor blood glucose. The problems addressed center on the absorption of light in the matter and the measurement of blood glucose.
Li-Fi Based Health Monitoring System for Infants

Generally, in Hospital Nurseries, nurses are taking care of abnormalities and the health of the newborn babies but they may not be available for taking care of the Infants 24x7. So, sometimes in the absence of care taker it may happens that health of the Infant becomes critical. Wireless technologies used by different Infant Monitoring Systems contain radiations that are highly harmful for the Infants. So, under these critical conditions, we have proposed an Automatic Wireless Li-Fi based Advanced Infant Monitoring System which continuously measures the intensive parameter of the child’s health using wearable sensors, if any abnormal condition occurs, it will indicate a notification.