Blended learning incorporates online learning experiences and helps students for meaningful learning through flexible online information and communication technologies, reduced overcrowded classroom presence, and planned teaching and learning experience. This study has conducted surveys of various tools, techniques, frameworks, and models useful for blended learning. This article has prepared a comprehensive

The current deep learning models detecting relevant web pages show low accuracy because of the poor quality of the training data. In this paper, we propose a novel algorithm to automatically generate high-quality training data based on the frequency of the document including the entity of interest. Our experimental results with movies and cellphones data sets show that the average $F_{1}$ -score of

Facial recognition is a biometric recognition technology that verifies identity using information about human facial features so it is used for access control systems. Current access control systems are implemented using traditional Radio Frequency Identification (RFID) technology or keys. Users must carry an access card or key and the access card or a key can be forgotten, lost or copied by others

Vision-based manipulation has been largely used in various robot applications. Normally, in order to obtain the spatial information of the operated target, a carefully calibrated stereo vision system is required. However, it limits the application of robots in the unstructured environment which limits both the number and the pose of the camera. In this study, a calibration-free monocular vision-based

Wireless charging is a convenient method of charging Electric Vehicles (EVs). Its application has been widely studied for electric cars but so far there have been limited experiments for electric bicycles (e-bikes). This paper addresses the design process and implementation details related to a wireless charger for a specific e-bike. Some issues such as the position of the secondary coil or the control

Dual active bridge (DAB) converters have experienced a boom thanks to the demands of new applications where bidirectional energy transfer, galvanic isolation and high efficiency are essential. In order to improve the efficiency of the stage without losing sight of the converter advantages, previous works have proposed complex forms of control that are not always applicable. This paper proposes the

Currently, research work is primarily dependent on the collection of large sets of data from systems and making predictions based on the knowledge obtained from the data, which is generally termed as ‘data mining’. These data mining algorithms are of great importance in improving the performance of different applications. In this regard, Machine Learning (ML) algorithms have been demonstrated to be

In a DC microgrid, while integrating the low voltage sources such as Fuel cells and photovoltaics, an efficient DC-DC converter is crucially needed. Contributing to a high voltage whilst being operated on a low duty ratio is among the demanded features of the converter. For high voltage applications, the conventional converters are to be operated beyond the prescribed range of the duty ratio which

With the increased use of high-performance thin steels, detecting internal or external micro defects in a manufacturing process has become critical for the steel manufacturing process’s cost and quality management. For detecting defects, magnetic-based nondestructive testing (NDT) methods have been researched widely due to their simplicity, ease of use, and contactless nature. However, magnetic-based

Identifying human activities using short-range and low-power radars has attracted much attention among the researchers and consumer electronics industry. This paper considers human activity recognition in the context of a single Frequency Modulated Continuous Wave (FMCW) radar as the measurement tool. A classification pipeline is proposed to handle the data pre-processing and feature extraction and

Computation-intensive mobile applications are explosively increasing and cause computation overload for smart mobile devices (SMDs). With the assistance of mobile edge computing and mobile cloud computing, SMDs can rent computation resources and offload the computation-intensive applications to edge clouds and remote clouds, which reduces the application completion delay and energy consumption of SMDs

This paper proposes a method to solve the joint optimization of test node selection and fiber thread connection. The problem is modeled as an integer linear programming problem with two classes of decision variables. The first class of decision variables are composed of the number of parallel fiber threads throughout each potential testing route, the other class of decision variables are imposed just

Suspicious region segmentation is one of the most important parts of CAD systems that are used for breast cancer detection in mammograms. In a CAD system, there can be so many suspicious regions determined for a mammogram because of the complex structure of the breast. This study proposes a hybrid thresholding method to use in the CAD systems for efficient segmentation of the mammograms and reducing

Virtual bidding provides a mechanism for financial players to participate in wholesale day-ahead (DA) electricity markets. The price difference between DA and real-time (RT) markets creates financial arbitrage opportunities for financial players. Physical market participants (MP), referred to as participants with physical assets in this paper, can also take advantage of virtual bidding but in a different

On-device DNN processing has been common interests in the field of autonomous driving research. For better accuracy, both the number of DNN models and the model-complexity have been increased. To properly respond to this, hardware platforms structured with multicore-based CPUs and DNN accelerators have been released, and the GPU is generally used as an accelerator. When multiple DNN workloads are sporadically

The railway-rolling stock wheel flange lubricator protects the wheels and railhead by lubricating their contacts. Failed or missing flange lubricators can lead to excessive wheel wear, wheel flats, wheel cracks, rolling contact fatigue, rail damage, and derailment accidents. In extreme cases, missing or worn flange lubricators due to nonlinear rail conditions may lead to fire hazards, particularly

State of Health (SOH) Diagnosis and Remaining Useful Life (RUL) Prediction of lithium-ion batteries (LIBs) are subject to low accuracy due to the complicated aging mechanism of LIBs. This paper investigates a SOH diagnosis and RUL prediction method to improve prediction accuracy by combining multi-feature data with mechanism fusion. With the approach of the normal particle swarm optimization, a support

In this work we implement a COVID-19 infection detection system based on chest X-ray images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer aided diagnosis tools in medical applications. Model estimations with high uncertainty should be carefully analyzed by a trained radiologist. We aim to improve uncertainty estimations using unlabelled data through the MixMatch

A graph-based particle filter bathymetric simultaneous localization and mapping (BSLAM) method is proposed to solve the oscillation problem of the trajectories estimated by particles when using a low precise vehicle motion model and obtain accurate navigation results for autonomous underwater vehicles (AUVs). A graph-based trajectory update method is proposed to update the trajectories stored in particles

This paper presents a new constrained optimization-based smoothing algorithm for walking step length estimation using waist-mounted inertial sensors, where the total walking distance is known. The walking trajectory is estimated by double integrating acceleration. Due to sensor noises, the walking step length estimation accuracy degrades as the walking distance becomes longer. To tackle this problem

Latent fingerprint identification is one of the leading forensic activities to clarify criminal acts. However, its computational cost hinders the rapid decision making in the identification of an individual when large databases are involved. To reduce the search time used to generate the fingerprint candidates’ order to be compared, fingerprint indexing algorithms that reduce the search space while

The challenges associated with acquiring the clear images of objects in underwater environment are difficult to overcome due to the absorptive and scattering nature of seawater. Recently, the research community has focused on mitigating these effects. The recent developments in image enhancement algorithms and strategies of signal light enhancement have brought improvement in some application areas

The Field Programmable Gate Array (FPGA) structure poses several constraints that make the implementation of complex asynchronous circuits such as Time–Mode (TM) circuits almost unfeasible. In particular, in Programmable Logic (PL) devices, such as FPGAs, the operation of the logic is usually synchronous with the system clock. However, it can happen that a very high–performance specifications demands

This paper investigates the performance of a dual-hop mixed free-space optical (FSO)/radio frequency (RF) system. To this end, the FSO link is subject to the Gamma-Gamma distribution effects that account for pointing error impairments and under both heterodyne detection and intensity modulation with direct detection (IM/DD) methods, while the RF link undergoes the $\alpha $ - $\mathcal {F}$ distribution

This paper proposes a non-weighted maximal approach of multi-attribute risk assessment for production accidents, which comes from the Chinese practice of risk management rather than the theoretical weighted multi-attribute approach. The existing literature for risk assessment of pipeline accidents, there is an absence of or lack of explicit consideration of some special dimensions, i.e., environmental

We propose an innovative electricity demand forecasting framework based on three model-selection techniques to maximize the forecasting accuracy. In the framework, we forecast the day-ahead electricity demand every 15 minutes based on temperature and cloud data, which are sampled from 35 weather stations. We develop three progressive techniques for selecting the model and data. First, using a stagewise

This study analyzes the relationship between the housing and stock markets, focusing on housing market bubbles. Stock market dynamics generally have a more significant impact on housing price movements than housing market dynamics have on stock dynamics. However, if housing market information is provided as a signal, housing price movements can predict stock market volatility. Accordingly, we build

Over the last few years, interference has been a major hurdle for successfully implementing various end-user applications in the fifth-generation (5G) of wireless networks. During this era, several communication protocols and standards have been developed and used by the community. However, interference persists, keeping given quality of service (QoS) provision to end-users for different 5G applications

Spectrum occupancy prediction is a key enabling technology to facilitate a proactive resource allocation for dynamic spectrum management systems. This work focuses on the prediction of duty cycle (DC) metric that reflects spectrum usage (in the time domain). The spectrum usage is typically measured on a shorter time scale than needed for prediction. Hence, data thinning is required and we apply block

In this work, we demonstrate the high-power and high-responsivity performance of the dual multiplication (M-) layers in In 0.52 Al 0.48 As based avalanche photodiode (APD). The dual M-layer design in our APD structure effectively constrains the multiplication process to a thin high-field region rather than the whole thick M-layer. It thus minimizes the space charge effect (SCE) within and avoids increasing

The problem of gender and age identification has been addressed by many researchers, however, the attention given to it compared to the other related problems of face recognition in particular is far less. The success achieved in this domain has not seen much improvement compared to the other face recognition problems. Any language in the world has a separate set of words and grammatical rules when

We are currently witnessing an immense proliferation of natural language processing (NLP) applications. Natural language generation (NLG) has emerged from NLP and is now commonly utilized in various applications, including chatting applications. The objective of this paper is to propose a deep learning-based language generation model that simplifies the process of writing medical recommendations for

Cracks and bugholes (surface air voids) are common factors that affect the quality of concrete surfaces, so it is necessary to detect them on concrete surfaces. To improve the accuracy and efficiency of the detection, this research implements a novel deep learning technique based on DeepLabv3+ to detect cracks and bugholes on concrete surfaces. Firstly, in the decoder, the $3\times 3$ convolution of

Sentiment analysis is the extraction and categorization of sentiments that have been expressed in text data using text analysis techniques. Manifested by earlier studies, sentiment analysis of drug reviews has a large potential for providing valuable insights to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed. Such insights help safeguard

Permanent Magnet Linear Synchronous Generators (PMLSG) has high power density and high efficiency, which is always employed in industrial application field. However, as temperature increases, it is easy to cause demagnetization of the permanent magnet, and the applications of PMLSG is limited. In this paper, a PMLSG is optimized which is used in a low speed power generation system. Firstly, a multi-physical

This paper studies the suitability of neuromorphic event-based vision cameras for spaceflight and the effects of neutron radiation on their performance. Neuromorphic event-based vision cameras are novel sensors that implement asynchronous, clockless data acquisition, providing information about the change in illuminance $\ge 120dB$ with sub-millisecond temporal precision. These sensors have huge potential

This paper presents the synthesis of multiband frequency selective surfaces (FSSs) using supervised machine learning (ML) with the decision tree (DT) algorithm. The proposed FSS structure is composed of an array of metallic patches printed on a dielectric substrate for stopband spatial filtering microwave applications. The shapes of the metallic patches are based on the sunflower ( helianthus annus

Using the real wireless spectrum occupancy status in 2.4 and 5 GHz bands collected at a railway station as representative of a heavy wireless LAN (WLAN) traffic environment, this paper studies the modeling of durations of busy/idle (B/I) status and its predictability based on predictability theory. We first measure and model the channel status in the heavy traffic environment over almost all of the

Although game-based interactive technology has long enhanced flow experiences crucial for learning, its effects have been unclear. Thus, this study gathered students’ electroencephalogram (EEG) information during their work in game-based learning environments with different levels of technological interactivity (LTIs; low, mid, and high LTIs). Multiple measurements were used in a relatively small sample

Network traffic monitoring uses empirical entropy to detect anomalous events such as various types of attacks. However, the exact computation of the entropy in high-speed networks is a difficult process due to the limited memory resources available in the data plane hardware. In this paper, we present a method and hardware accelerator to approximate the empirical entropy of a large data set with high

Image super-resolution reconstruction has been widely used in remote sensing, medicine and other fields. In recent years, due to the rise of deep learning research and the successful application of convolutional neural networks in the image field, the super-resolution reconstruction technology based on deep learning has also achieved great development. However, there are still some problems that need

In this work, the general structure of a hybrid NPP/NPC seven-level inverter is derived from a dual-T-type inverter. Compared with the original structure, the general structure is more flexible as there are four specific circuit configurations to meet different applications. In addition to self-balanced capacitor voltages and boost capability, various commercial NPP and NPC modules can be used to speed

Cerebellum measurements of routinely acquired ultrasound (US) images are commonly used to estimate gestational age and to assess structural abnormalities of the developing central nervous system. Investigating associations between the developing cerebellum and neurodevelopmental outcomes post partum requires standardized cerebellum measurements from large clinical datasets. Such investigations have

Recent efforts in semantic segmentation using deep learning framework have made notable advances. While achieving high performance, however, they often require heavy computation, making them impractical to be used in real world applications. There are two reasons that produce prohibitive computational cost: 1) heavy backbone CNN to create high resolution of contextual information and 2) complex modules

The magnetic field of end windings affects the leakage reactance of end windings and the eddy current losses of end structures in the large electrical machine. With the rising of single-machine capacity, such effect becomes more obvious. However, due to the complexity of end structures of large electrical machine, it will cause difficulty in meshing and long calculation time to calculate the magnetic

Since 2003, when the European Union (E.U.) announced the restriction of hazardous substances (RoHS), multiple efforts have been made to replace lead zirconate titanate (PZT) based piezoelectric materials. However, despite these efforts, very few PZT alternatives have been found. The Lithium niobate (LN) is one such lead-free piezoelectric material often used in acoustic applications due to its high

The monitoring of cutting tool wear has a great significance for the processing quality and stability. To overcome the difficulty to reflect all the wear mechanisms for the monitoring method based on finite element model or the excessive dependence on data extraction quality for the monitoring method based on sensor data, this paper proposes a model and data fusion method based on particle filter algorithm

Accurate system marginal price and load forecasts play a pivotal role in economic power dispatch, system reliability and planning. Price forecasting helps electricity buyers and sellers in an energy market to make effective decisions when preparing their bids and making bilateral contracts. Despite considerable research work in this domain, load and price forecasting still remains to be a complicated

Nowadays traffic congestion has become significantly worse. Not only has it led to economic losses, but also to environmental damages, wastage of time and energy, human stress and pollution. Generally, traffic congestion is a ripple effect of a road congestion on neighboring roads. When congestion occurs, it will propagate through the road network due to increasing traffic flow. One of the complexities

Authentication is the process of confirming the legal identity of communicating entities, and it is the first line of defense for security communication. Most of the existing tag-based physical layer security authentication (PLSA) requires distributing the shared keys in advance. In the large scale internet of things scenario, nodes frequently join and leave the wireless networks that cause the distribution

Amputees with lower limb loss need special care during daily life activities to make the movement natural as before amputation. No such work exists covering the main aspects from causes of amputation to the psycho-social impact of the amputees after using the prosthetic device. This review presents for lower limb prosthesis; the study of lower limb amputation, design & development, control strategies

An integrated method comprising DEA and machine learning for risk management is proposed in this paper. Initially, in the process of risk assessment, the DEA cross-efficiency method is used to evaluate a set of risk factors obtained from the FMEA. This FMEA-DEA cross-efficiency method not only overcomes some drawbacks of FMEA, but also eliminates several limitations of DEA to offer a high discrimination

In this paper, the effects of rotor-rotor interaction on the wake structure and thrust generation of a quadrotor unmanned aerial vehicle (UAV) are experimentally investigated in the rotor tip Reynolds number range of 34000 – 54000. The interaction strength is manipulated by varying the number of rotating rotors and the normalized rotor separation distance. A stronger rotor-rotor interaction places

This paper proposes a new model-free sliding mode tracking differentiator with adaptive gains for reliable filtering and derivative estimations from noisy signals by improving a Levant and Yu’s sliding mode tracking differentiator. Particularly, the proposed tracking differentiator employs a nested generalized signum function for reducing overshoot during convergence. Moreover, a model-free adaptive

Unsupervised cross-domain image-to-image translation is a very active topic in computer vision and graphics. This task has two challenges: 1) lack of paired training data and 2) numerous possible outputs from a single image. The existing methods rely on either paired data or perform one-to-one translation. A novel Multi-Style Unsupervised image synthesis model using Generative Adversarial Nets (MSU-GAN)

Vehicular networks support intelligent transportation system (ITS) to improve drivers’ safety and traffic efficiency on the road by exchanging traffic-related information between vehicles and also between vehicles and infrastructure. Routing protocols that are designed for vehicular networks should be flexible and able to adapt to the inherent dynamic network characteristics of these kind of networks

Pyroelectric infrared (PIR) sensors are much promising for device-free localization (DFL) due to their advantages of lower cost, low power consumption, and privacy protection. Most PIR-based localization methods usually assume some geometric models according to the detection principle of PIR sensors, which are however not accurate or robust due to the various cases of infrared radiation from human

The development of multi-industry compatibility and the coexistence of multiple services and multiple functional communication networks will cause rapid growth in mobile communication system traffic. Users will have increasingly strict requirements for quality of service (QoS), e.g., a high rate, low latency, and low energy consumption. To address these problems, it is helpful to combine network slicing

Cooperation evolution emerges in diversities of social networks by way of individuals’ interactions and influence propagations. The influence on cooperation evolution owing to the multiplex nature of social networks and interactions is still an open question. In this work, we focus on the impacts of the individuals’ heterogeneous properties and the networks’ multilayer nature on cooperation evolution

Deep neural networks have enhanced the performance of decision making systems in many applications, including image understanding, and further gains can be achieved by constructing ensembles. However, designing an ensemble of deep networks is often not very beneficial since the time needed to train the networks is generally very high or the performance gain obtained is not very significant. In this