Next generation of Internet of Things (IoT) services imposes stringent requirements to the future networks that current ones cannot fulfill. 5G is a technology born to give response to those requirements. However, the deployment of 5G is also accompanied by profound architectural changes in the network, including the introduction of technologies like multi-access edge computing (MEC), software defined

Coexisting radar and communication (CRC) systems on the same spectrum band have been recently recognized as a promising approach to solve the spectrum scarcity problem. Additionally, with the new requirements of achieving higher energy efficiency and restoring the consumed power, an initiative has been exploited to embed both information and energy within the same signal. Motivated by that, this paper

In pattern recognition, the elimination of unnecessary and/or redundant attributes is known as feature selection. Irreducible testors have been used to perform this task. An objective of the Minimum Description Length Principle (MDL) applied to feature selection in pattern recognition and data mining is to select the minimum number of attributes in a data set. Consequently, the MDL principle leads

The increasing demand of routing in the field of communication is the most important subject in ad hoc networks now a days. Flying Ad Hoc Network (FANET) is one of the emerging areas that evolved from Mobile Ad Hoc Networks. Selecting the best optimal path in any network is a real challenge for a routing protocol. Because the network performance like throughput, Quality of Service (QoS), user experience

In general, grid map based path planning algorithms are employed in the robotics arena. The algorithm uses a grid map to represent environmental information, standardized. Compared with feature maps and topological maps, the algorithm realizes the construction of environmental maps in a more direct way, and has the characteristics of fast, simple and efficient.The integration and prediction of terrain

Object tracking is one of the most fundamental and important fields in computer vision with a wide range of applications. Although great progress has been made in object tracking combined with detection, there is still enormous challenges in real-time applications and for the computer cannot effectively capture the temporal correlations of targets and background clutter. In order to improve the performance

Nowadays, the usage of the fog cloud-based Internet of Things (IoT) applications among users has been growing progressively. These applications may be E-Transport, E-Healthcare, Augmented Reality, and 3D-Game. Generally, contemporary cloud frameworks offer services based on virtual machines. These frameworks incurred with following issues such as long boot-time, overhead and unnecessary cost to run

In this paper, we propose a verifiable quantum cloud computation scheme based on blind quantum computation which effectively solves the privacy and verifiability problem for clients when interfacing with quantum computer in cloud computation. Different from most of present works (e.g. using brickwork state as resource state), we use cluster state with grid distributed structure to realize the universal

In multi-objective evolutionary algorithms (MOEAs), convergence and diversity are two basic issues and keeping a balance between them plays a vital role. There are several studies that have attempted to address this problem, but this is still an open challenge. It is thus the purpose of this research to develop a dual-population competitive co-evolutionary approach to improving the balance between

In this work, device parameters for GaN vertical trench MOSFETs have been investigated systematically to further improve the device characteristics. The n − GaN drift layer, the p + GaN layer and the trench gate are designed and optimized systematically using Silvaco ATLAS 2-D simulation, in order to get the best trade-off between $V_{\mathrm {BR}}$ and specific on-resistance $R_{\mathrm {on}}$ . Three-terminal

Diffusion curves image (DCI) is a kind of vector graphics with its geometric primitives defined by a set of Bézier curves. As one of the vector graphics, editability is an important property for diffusion curves. However, most of the current DCI shape manipulation methods are typically limited to single curve. It is tedious for users to edit the shape of DCI by deforming each curve individually, especially

With the formulation of United Nations Convention on the Law of the Sea, marine pollution has received widespread attention from various countries. Green navigation is an important requirement for route planning, in which energy consumption is its primary focus. Ships are affected by complex marine meteorological environments, so it is difficult to plan a reasonable route. Some methods have been proposed

In the clinical vascular interventional surgery, experienced surgeons usually rely on visual feedback to reason tool-tissue interaction, because haptic forces are easily contaminated by frictions between the catheter and the introducer sheath, which brings difficulty in collision force perceptions. Thus, cognitive skills are highly demanded for novice surgeons to thoroughly interpret the relative positions

Large scale deployment of Internet of Things (IoT) devices poses challenges in resource allocation. In this paper, alternating direction method of multipliers (ADMM) is adopted to solve such large scale resource allocation problems. Based on this, three optimization problems are investigated in a hierarchical IoT network. Considering ADMM could not solve a non-convex optimization problem directly,

This paper focuses on the kinematic design issues for a three degrees-of-freedom (DOFs), i.e., two-rotational and one-translational (2R1T) end-effector to perform continuous contact operations, such as deburring, grinding, and polishing. The proposed end-effector design employs a 3-legged prismatic-prismatic-spherical (3-PPS) parallel mechanism due to its desired kinematic characteristics and dynamic

This paper presents a novel method to determine the discriminative image representation via visual dictionary learning framework for image classification task. Visual dictionary learning has the capacity to represent input image using an over-complete element set. Sparsity restrains distractors and prevents over-fitting. The two main characteristics benefit the classification solution. However, one

Aiming at the difficulty of deploying wireless sensor networks (WSNs) on three-dimensional (3D) surfaces, based on the grey wolf optimizer (GWO), an enhanced version of the grey wolf optimizer is proposed for deploying WSNs on 3D surfaces, namely the enhanced grey wolf optimizer (EGWO), which is characterized by enhanced exploitation and exploration ability of the algorithm. The novelty of EGWO is

In numerous applications the rapid increase of data size with time makes classical clustering algorithms too slow because of the high computational cost. In the present contribution, a novel method for online spectral clustering algorithm is introduced, which can be applied for simulation of structure and properties of titanate in chemical engineering. The proposed algorithm uses the recent results

In the analysis of coordinated network attacks on electric power cyber-physical system (CPS), it is difficult to restore the complete attack path, and the intent of the attack cannot be identified automatically. A method is therefore proposed for the extracting patterns of coordinated network attacks on electric power CPS based on temporal-topological correlation. First, the attack events are aggregated

Improving the efficiency and end-use of energy distribution can significantly reduce global energy consumption. As the magnetic contactor (MC) is the most common switchgear used for control in most grids, minimizing its energy consumption has motivated the development of a permanent magnet (PM) MC that exhibits several advantages over conventional solenoid MCs. However, analyzing the transient magnetic

Sleep stage classification, including wakefulness (W), rapid eye movement (REM), and non- rapid eye movement (NREM) which includes three sleep stages that describe the depth of sleep, is one of the most critical steps in effective diagnosis and treatment of sleep-related disorders. Clinically, sleep staging is performed by domain experts through visual inspection of polysomnography (PSG) recordings

The main limitation to obstacle avoidance nowadays has been negative road anomalies which is the term we used to refer to potholes and cracks due to their negative drop from the surface of the road. This has for long been a limitation because of the fact that they exist in different, random and stochastic shapes. Today’s technology lacks the presence of sensors capable of detecting negative road anomalies

Recently, social media platforms have been widely used as a communication tool on social networks. Many users have utilized these platforms to reflect their personal lives. These users differ in terms of background, language, age, and educational level. The close relationship between these platforms and their users has created rich information that is related to these users and can be exploited by

There is a close logical correlation between structural reform of the financial sector and the enhancement of financial institutions’ economic performance. In the existing research, however, there are disagreements and gaps relating to the efficiency measurement of the financial system’s input and output, as well as the impact of financial efficiency on the credit constraints facing the industrial

Image inhomogeneity often occurs in real-world images and may present considerable difficulties during image segmentation. Therefore, this paper presents a new approach for the segmentation of inhomogeneous images. The proposed hybrid active contour model is formulated by combining the statistical information of both the local and global region-based energy fitting models. The inclusion of the local

The $\alpha $ - $\eta $ - $\kappa $ - $\mu $ fading model is a very useful instrument to accurately describe various radio wave propagation scenarios. In this paper, we study the effective throughput performance of communication systems over the $\alpha $ - $\eta $ - $\kappa $ - $\mu $ fading channels. Novel and exact expressions for the effective throughput over $\alpha $ - $\eta $ - $\kappa $ - $\mu

Since the earliest times, humans have sought the ability to produce rhythms and tones using devices external to the human body. As technology developed, so too did the desire for this sound production to become automated. Initially peaking around the Industrial Revolution, traditional automated musical production devices went into a steep decline with the arrival of the phonograph. However, factors

Presently, Coronary Acute Syndrome (CAS) is a widespread and extreme heart disease which is considered as one of the major concerns for death in the world with long-term disability. For early intervention and care, the prediction of CAS clinical risk is more critical during analysis. A minimum number of manually selected dimensions of risk are used for current CAS risk assessment models and statistical

The sparse view problem of image reconstruction encountered in computed tomography (CT) is an important research issue due to its considerable potential in lowering radiation dose. Among the researches, the total variation (TV) method is especially effective in sparse view CT reconstruction for its good ability to preserve sharp edges and suppress noise. However, TV-based methods often produce undesired

Support vector machine (SVM) is one of the most popular machine learning algorithms. It predicts a pre-defined output variable in real-world applications. Machine learning on encrypted data is becoming more and more important to protect both model information and data against various adversaries. While some studies have been proposed on inference or prediction phases, few have been reported on the

Analyzing radar signals is a critical task in modern Electronic Warfare (EW) environments. However, the pulse streams emitted by radars have flexible features and complex patterns which are difficult to be identified from a statistical perspective. To solve this problem, pulse repetition interval (PRI) is used as a distinguishing parameter of emitters to be identified. However, traditional PRI modulation

The problem of portfolio management relates to the selection of optimal stocks, which results in a maximum return to the investor while minimizing the loss. Traditional approaches usually model the portfolio selection as a convex optimization problem and require the calculation of gradient. Note that gradient-based methods can stuck at local optimum for complex problems and the simplification of portfolio

In this paper, we present a hybrid denoising algorithm dedicated to division-of-focal plane (DoFP) polarization images. The proposed algorithm, centered around the Block-Matching and 3D Filtering (BM3D) and K-times Singular Value Decomposition (KSVD) denoising algorithms, is capable of significantly enhancing the grouping step in the second round of collaborative filtering by purifying the “Semi-Filtered”

Math anxiety (MA), i.e. a trait factor describing the feelings of tension, apprehension, and fear during mathematics-related situations, has attracted increasing interest in recent years, due to its importance in people’s daily life and career development, especially in our modern digital world. Although the measurement of individuals’ trait MA has mostly relied on self-reported psychological scales

In this paper, a parallel transmission-line transformer with complex terminal loads is introduced for dual-frequency design. Through analyzing the different cases of complex terminal loads conditions, there are four different mapping patterns of designable regions on the Electrical Length-Electrical Length ( EL-EL ) plane. By adding an extra vertical Frequency Ratio ( FR ) axis, a novel 3D cube can

Due to the complexity of the anatomical structure for human organs, medical image segmentation is always a challenging computer vision task. The Convolutional Neural Network (CNN) requires a rich feature representation, which not only needs the convolutional layers from shallow to deep,but also requires the resolution from small to large. Although CNN can be used to fuse mid-level features that are

Diabetic Retinopathy (DR) is considered as the complication of Diabetes Mellitus that damages the blood vessels in the retina. This is characterized as a serious vision-threatening problem in most of the diabetic subjects. Effective automatic classification of diabetic retinopathy is a challenging task in the medical field. The feature extraction plays an eminent role in the effective classification

Uncertainty measurement of the basic probability assignment function has always been a hot issue in Dempster-Shafer evidence. Many existing studies mainly consider the influence of the mass function itself and the size of the frame of discernment, so that the correlation between the subsets is ignored in the power set of the frame of discernment. Without making full use of the information contained

To address problems that the loss function does not correlate well with perceptual vision in super-resolution methods based on the convolutional neural network(CNN), a novel model called the ADTV-SRGAN is designed based on the adaptive diagonal total-variation generative adversarial network. Combined with global perception and the local structure adaptive method, spatial loss based on the diagonal

Recently, a new trend has emerged in the field of parallel and high performance computing, the hybrid implementation using CPU-GPU modules. In such implementations, the computational load is shared between the CPU and GPU, in order to improve the computational efficiency. However, the task of sharing the computational load between the two modules is a rather difficult one, with a number of limitations

Accurate and effective object detection in remote sensing images plays an extremely important role in marine transport, environmental monitoring and military operations. Due to the powerful ability of feature representation, region-based convolutional neural networks (RCNNs) have been widely used in this field, which firstly generate candidate regions through extracted feature maps and then classify

Deep learning approach has been used extensively in image analysis tasks. However, implementing the methods in 3D data is a bit complex because most of the previously designed deep learning architectures used 1D or 2D as input. In this work, the performance of deep learning methods on different 3D data representations has been reviewed. Based on the categorization of the different 3D data representations

Person re-identification (REID) is an important task in video surveillance and forensics applications. Many previous works often build models on the assumption that they have same resolution cross different camera views, while it is divorced from reality. To increase the adaptability of person REID models, this paper focuses on the low-resolution person REID task to relax the impractical assumption

Human facial expression is the core carrier of feedback. Facial expression recognition(FER) has been introduced into mickle fields, such as auxiliary medical care, safe driving, marketing assistance, distance education. However, in the real production process, facial expression image samples collected in different scenarios have problems such as complex backgrounds, which causes the FER model to train

Low-terahertz (Low-THz, 100 GHz-1.0 THz) technology is expected to provide unprecedented data rates in future generations of wireless system such as the 6 th generation (6G) mobile communication system. Increasing the carrier frequencies from millimeter wave to THz is a potential solution to guarantee the transmission rate and channel capacity. Due to the large transmission loss of Low-THz wave in

This paper develops a recent methodology based on social spider optimizer (SSO) to determine the optimal sizing of a hybrid renewable energy sources (RESs) integrated microgrid (MG). It comprises photovoltaic (PV), wind turbine (WT), battery, diesel generator (DG), and inverter. The cost of energy (COE) is proposed as fitness function. The objective of the proposed SSO is to determine three design

As a part of the energy transmission chain, gearboxes are considered as important components in rotating machines, and the gearbox failure results in costly economic losses. Therefore, it is necessary to detect the appearance of incipient gearbox faults by implementing an appropriate detected model. The incipient failure characteristics of the gearbox are weak and hidden in a set of time-varying series

Neurofeedback targets self-regularized brain activity to normalized brain function based on brain-computer interface (BCI) technology. Although BCI software or platforms have continued to mature in other fields, little effort has been expended on neurofeedback applications. Hence, we present BrainKilter, a real-time electroencephalogram (EEG) analysis platform based on a “4-tier layered model”. The

Fungal pathogens have evolved the ability to cause serious plant diseases and threaten the world food security. Fungal effectors are proteins that exploit the host cellular functions to facilitate infection. Effector identification is crucial for disease control in crops and to understand plant-pathogen interactions. However, fungal effector identification has been challenging as most fungal effectors

Biodiversity monitoring has become a critical task for governments and ecological research agencies for reducing significant loss of animal species. Existing monitoring methods are time-intensive and techniques such as tagging are also invasive and may adversely affect animals. Bioacoustics based monitoring is becoming an increasingly prominent non-invasive method, involving the passive recording of

In this paper, an adaptive dynamic surface control (DSC) method based on neural network for the flight path angle of an aircraft is investigated in view of the parameters uncertainty, multi-disturbance and nonlinearity of the aircraft. First, a traditional backstepping controller is derived as a base. To enhance the adaptability and robustness, radial basis function (RBF) neural networks are introduced

This paper presents a design of 6.8 mW all digital delay locked loop (ADDLL) with digitally controlled dither cancellation (DCDC) for time to digital converter (TDC) in ranging sensors. ADDLL uses the accumulator (ACC) to control the delay of digitally controlled delay line (DCDL) during phase locking which utilizes less power and area as compared to analog delay locked loop (DLL). In the lock state

Securing transmission of information between legitimate transmitter and receiver sides is a great challenge for mathematicians, computer scientists and engineers in recent years. This paper aims at achieving three goals. The first of them is to introduce a novel fractional order two dimensional (2D) map having very complex chaotic behavior and distinct large positive values of Lyapunov exponents over

A great number of computer vision publications have focused on distinguishing between human action recognition and classification rather than the intensity of actions performed. Indexing the intensity which determines the performance of human actions is a challenging task due to the uncertainty and information deficiency that exists in the video inputs. To remedy this uncertainty, in this paper we

This paper proposes a communication protocol for Unmanned Aerial Vehicles (UAVs) using ZigBee technology. A review of the state-of-the-art of Flying Ad-hoc Networks (FANETs) and its main respective technologies is presented in detail. A comparison among Long Term Evaluation (LTE), WiFi, and ZigBee is performed, thus showing that ZigBee stands out as a good alternative to scenarios without proper infrastructure

Recently, there are several studies focusing on the bottleneck optimization objective in Spatial Crowdsourcing (SC). However, these studies usually do not consider the deadline constraint. Different from these studies, we take deadlines into consideration and identify the Fully Online Bottleneck Matching with Deadlines (FOBMD) problem in SC. Because of the deadlines, consideration must be given to

This paper presents an efficient gridless sparse reconstruction algorithm for the coprime planar array in two-dimensional (2-D) direction-of-arrival (DOA) estimation problem. According to the equivalent second-order statistic signals derived from the covariance matrix of the coprime planar array, we construct a virtual 2-D difference co-array extended from the coprime line arrays along two directions

In the modern era of Internet, mobile and digital information technology, image retrieval for object identification, just as wine label retrieval from a wine bottle image, has become an important and urgent problem in artificial intelligence. In comparison with the general image retrieval, it is rather challenging because there are a huge number of object identification or brand images which are very

Objectively assessing the perceptual quality of an ocular fundus image is essential for the reliable diagnosis of various ocular diseases. A fair amount of work has been done in this field to date. However, the generalizability of the current work is limited, as the existing quality models were developed and evaluated with data-sets built with limited subjective inputs. This paper aims at addressing

It is a common sense that people harbors the belief that stragglers exert huge influence upon the performance conducted by the analysis systems of big data for the reason of poor performance made by some computing nodes, data skew and so on. Accordingly, stragglers have been billed as an indispensable bottleneck in Map-Reduce framework processing. However, existing studies on stragglers identification