In this paper, we present the concept and the prototype implementation of our novel “Smart Observatory of Involuntary Medical Seizures (SOIMS)”. SOIMS merges Wireless Body Area Networks (WBAN), Internet of Things (IoT) and Machine Learning (ML) as an intelligent platform for the prediction and modelling of involuntary seizures. The prediction process is elaborated with our proposed algorithms, namely

High mobility is the essential feature of vehicular ad-hoc networks (VANETs), which however challenges the low cost and real-time security of the network. The concept of Trust Communication (TC) is proposed to address the issue. Following this concept, each vehicle gains a trust value depending on its performance of communication. Then, other vehicles choose to communicate with the vehicle owning a

With the combined advantages of satellite communications, aerial networks and terrestrial systems, a space-air-ground integrated network has gradually become a promising architecture for the next generation wireless communication. Due to heterogeneous characteristics of different layers, it is necessary to perform efficient resource allocation. Motivated by this fact, we propose a novel architecture

The energy consumption of an enterprise energy management system (EMS) is a complex process with nonlinearity, time-variance, larger delay, greater inertia and other dynamic characteristics, resulting in the failure of a single-item prediction model to achieve satisfactory prediction results. In this paper, a combination prediction method, based on BP neural network, was proposed to predict the energy

The exchanged folded hyerpcube is a brand-new interconnection network proposed by Qi et al. This paper considers the case such that n = 1 + s + t, and the exchanged folded hypercube is denoted by EFH(s, t). The bus topology is one of the most well-known topologies in WDM optical networks. A bus-topology WDM optical network with N terminals can be described by a linear array graph Ln, where N = 2n.

Machine learning has been increasingly used for making informed public policy decisions, however, its application in the area of social protection in developing societies has been largely overlooked. We have employed unsupervised machine learning K-means clustering technique for exploring a big data that comprised of 88 attributes and 570 instances for better targeting of households that are in urgent

This paper takes education data mining as the research theme, mine the existing massive education big data, compares the analysis methods of existing data models, and proposes an improved random forest reference model. The information gain of various features is calculated by introducing the feature weighting system, and the evaluation index is used to improve the existing data analysis. The simulation

This paper proposes a delay-balanced adaptive channel allocation (DB-ACA) mechanism for improving the channel access performance of an LTE-U and WiFi coexistence system. To support the DB-ACA mechanism, an integrated network architecture is introduced for coordinating the channel access in the coexistence system. Based on the integrated network architecture, the coexistence system is able to adaptively

Currently most of the existing indoor fingerprint positioning algorithms are based on fingerprint database. The accuracy of the fingerprint database will directly affect the final positioning accuracy. Therefore, through the research of fingerprint data, a method based on skewness-kurtosis normality test and Kalman filter fusion is proposed. In the training phase, the RSSI (Received Signal Strength

In recent years, aged care living has drawn attention because of population aging and extension of average lifespan. Moreover, the rapid development of information communication technology and the internet of things lay the foundation for the application of sensor networks and cloud computing on medical care. Most elderly often suffer from chronic diseases due to weaker immunity causing a higher infection

Capturing all-in-focus images with 3D scenes is typically a challenging task due to depth of field limitations, and various multi-focus image fusion methods have been employed to generate all-in-focus images. However, existing methods have difficulty simultaneously achieving real-time and superior fusion performance. In this paper, we propose a region- and pixel-based method that can recognize the

The outbreak of chronic diseases such as COVID-19 has made a renewed call for providing urgent healthcare facilities to the citizens across the globe. The recent pandemic exposes the shortcomings of traditional healthcare system, i.e., hospitals and clinics alone are not capable to cope with this situation. One of the major technology that aids contemporary healthcare solutions is the smart and connected

Nowadays various distributed stream processing systems (DSPSs) are employed to process the ever-expanding real-time data. The DSPSs are highly susceptible to system failure, and the fault-tolerance issue is a major problem, which is getting lot of attention nowadays. Flink is a popular streaming computing framework that implements a lightweight, asynchronous checkpoint technique based on the barrier

The teaching effectiveness of China’s university design theory course has not undergone any major reforms in recent years to keep pace with new technology, modern teaching methodologies or student expectations. This is mainly due to the traditional approach of emphasizing practice rather than design theory and is inherent in design education in China, a view supported by design major students. The

This paper considers the power allocation problem in device-to-device (D2D) communication underlaying a cellular network and investigates the impact of different transmitting and interference power constraints on the energy efficiency and spectral efficiency of the network. We formulate the power allocation problem in D2D communication as a nonlinear fractional programming problem with an objective

This paper investigates the multi-objective problem of deploying wireless sensor networks with cooperative distance-based sensing coverage. This problem considers deploying a number of sensor nodes to cove multiple target points on a deployment area. Based on the locations of target points and the sensor nodes with their own inner and outer coverage radii, the distance-based sensing coverages of target

Existing network security situational awareness assessment and prediction are not fully considered network defense utility, time factor and other indicators, and the randomness of attacks and the accuracy of the prediction of attack intentions and methods may appear both in active and passive defense this situation leads to uncertainty in attack prediction, which makes it impossible to discover defects

The huge computational overhead limits the inference of convolutional neural networks on mobile devices for object detection, which plays a critical role in many real-world scenes, such as face identification, autonomous driving, and video surveillance. To solve this problem, this paper introduces a lightweight convolutional neural network, called RSANet: Towards Real-time Object Detection with Residual

Question answering is an indispensable link in high school teaching. Through question answering, on the one hand, it can solve students’ learning doubts, on the other hand, it can provide teachers with teaching feedback. However, through the investigation and research, it is found that with the expansion of student size, the effect of question answering in high school is not satisfactory. This paper

In recent years, word embedding models receive tremendous research attentions due to their capability of capturing textual semantics. This study investigates the issue of employing word embedding models into storage-constrained edge devices for personalized item-of-interest recommendation in a smart store. The challenge lies in that the existing embedding models are often too large to fit into a storage-constrained

When focusing on the general area of data mining, high-utility itemset mining (HUIM) can be defined as an offset of frequent itemset mining (FIM). It is known to emphasize more factors critically, which gives HUIM its intrinsic edge. Due to the flourishing development of the IoT technique, the uncertainty patterns mining is also attractive. Potential high-utility itemset mining (PHUIM) is introduced

Single modal medical images provide limited information and cannot reflect all the details of the relevant tissues, which may lead to misdiagnosis in clinical medicine. Therefore, a medical image fusion algorithm based on non-down-sampling shear wave transform (NSST) is proposed. This algorithm fuses multi-modal medical images, enriches the information of fused images, improves the image quality, and

Chinese word segmentation is an important research direction in related research on elementary mathematics knowledge extraction. The speed of segmentation directly affects subsequent applications, and the accuracy of segmentation directly affects corresponding research in the next step. In the machine learning methods for extracting basic mathematical knowledge points, the Conditional Random Field

Nowadays, people pay more and more emotional to the emotional analysis of specific goals. Due to the long training time of many networks, this paper proposes a neural network with specific Objective sentiment analysis. Compared with the current neural network, the algorithm proposed in this paper has a shorter training time, which can effectively make up for the lack of emotional mechanism. Finally

The information dissemination in social networks is affected by many factors. However, node attitude which is an important influence factor of information dissemination in social network have not been fully considered in the previous works. Aiming at the problem of the influence of node attitude on information dissemination, this paper proposes an information propagation model based on evolutionary

Reviews of product or stores exist extensively in online e-commerce platform which is important for customers to make decisions. For economic reasons some dishonest people are employed to write fake reviews which is also called “opinion spamming” to promote or demote target products and services. Previous researches have made use of text similarity, linguistics, rating patterns, graph relationship

Sensor-based activity recognition (AR) depends on effective feature representation and classification. However, many recent studies focus on recognition methods, but largely ignore feature representation. Benefitting from the success of Convolutional Neural Networks (CNN) in feature extraction, we propose to improve the feature representation of activities. Specifically, we use a reversed CNN to generate

Network Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service, called Service Function Chains (SFCs). Regarding to Quality of Service (QoS) requirements and network features and states, SFCs are

This paper describes a new comparison-based model for fault diagnosis in wireless ad hoc networks. Fault diagnosis is crucial for ensuring the dependability of systems. Wireless ad hoc networks are highly prone to faults as consequence of their dynamical conditions. The comparison approach is a practical diagnosis model that has been used to develop self-diagnosis systems in wired and wireless networks

Acoustic signals are used for communication in underwater sensor networks (UWSNs) because radio signals attenuate heavily when propagating in water, while optical signals have large scattering in water. Data transmission in UWSNs faces great challenges due to the characteristics of underwater acoustic channels. Moreover, high energy consumption and long latency bring about increased challenges for

The application of the traditional single frame character image super-resolution reconstruction method has some problems, such as noise can not be removed completely and anti-interference performance is poor. A new method for the super-resolution reconstruction of single frame character image based on wavelet neural network is proposed. The structure and interface of image acquisition unit of solid

Incentives are very important to be employed in any defensive mechanism against DDoS attack. Incentive is a major concept abandoned by most of the defensive mechanisms that have been proposed so far. It is a tool that can motivate users to send data wisely into the network. Therefore, in this paper, we have proposed a two layered multi-attribute auction mechanism for incentivising users by imposing

The Internet of Everything (IoE) provides a platform that allows devices to be remotely connected, sensed, and controlled across the network infrastructure. The smart home in the era of the IoE is born on the basis of the high integration of emerging communication technologies such as big data, sensors, and machine learning. In this paper, we focus on wireless detection technologies using smartphones

In this paper, the chaotic characteristics of air traffic flow are studied, ADS-B data easily available to ground aviation users are selected as the basic data of traffic flow, and a high-dimensional prediction model of air traffic flow time series based on the non-iterative PSR-ELM algorithm is established. The prediction results of the proposed algorithm are then compared with those of the SVR algorithm

Utilizing wireless signals for gesture recognition and human identification is an emerging type of technology for touchless user interface, which allows the computer to automatically identify the user and interpret his/her gestures as commands. Such techniques extract features to profile the fluctuation of time series wireless signals to infer human gestures/identities. Among which, device-free approach

The existing building surface data fusion algorithms do not extract the segmented data features, resulting in inaccurate fusion results. In heterogeneous environment, a Clustering Fusion Algorithm Based on mutual information and fractal dimension is proposed. The regression coefficient is used to express the sequence, and the data feature representation and data dimension reduction are realized. The

Compared with common deep learning methods (e.g., convolutional neural networks), transfer learning is characterized by simplicity, efficiency and its low training cost, breaking the curse of small datasets. Medical image analysis plays an indispensable role in both scientific research and clinical diagnosis. Common medical image acquisition methods include Computer Tomography (CT), Magnetic Resonance

In order to overcome the uncertainty of the data sampling period of the sensor due to equipment reasons, a mobile robot localization system is developed under the uncertain sampling period for the tightly-fused light detection and ranging (LiDAR), compass, and encoder data. The errors of position and velocity, the robot’s yaw, and the sampling period are chosen as state variables. The ranges between

During the process of fine segmentation of medical images, although a single threshold can improve the efficiency of processing, there will be the problem of fuzzy features and non-convergence of threshold in denoising of details such as contour extraction. To extract contour information of medical images, a method based on multi-threshold optimization is proposed. This paper analyzes the influence

In order to improve the search performance of rich text content, a cloud search engine system based on rich text content is designed. On the basis of traditional search engine hardware system, several hardware devices such as Solr index server, collector, Chinese word segmentation device and searcher are installed, and the data interface is adjusted. On the basis of hardware equipment and database

Analysis of medical CT images directly affects the accuracy of clinical case diagnosis. Therefore, feature extraction problem of medical CT images is extremely important. A feature extraction algorithm for medical CT images of sports tear injury is proposed. First, CT images are decomposed into a low frequency component and a series of high frequency components in different directions by wavelet fast

As two different modalities of medical images, Magnetic Resonance (MR) and Computer Tomography (CT), provide mutually-complementary information to doctors in clinical applications. However, to obtain both images sometimes is cost-consuming and unavailable, particularly for special populations. For example, patients with metal implants are not suitable for MR scanning. Also, it is probably infeasible

This paper proposes a personalized learning resource recommendation method based on dynamic collaborative filtering algorithm. Pearson correlation coefficient is used to calculate the data similarity between learning users or project resources in the network, and the unscored value is obtained. In order to solve the problems of sparse data and poor scalability in collaborative filtering algorithm,

A better understanding of Driving Patterns and their relationship with geographical driving areas could bring great benefits for smart cities, including the identification of good driving practices for saving fuel and reducing carbon emissions and accidents. The process of extracting driving patterns can be challenging due to issues such as the collection of valid data, clustering of population groups

Cloud computing is an important technology to offer consumer appliances a wide pool of elastic resources. The heterogeneous network faces collision while making communication, which reduces the entire network performance. The future cloud-edge networks will deal with a vast amount of clients and servers, such as the Internet of Things (IoT) and the 6G networks, which require flexible solutions. From

Continuous activity recognition (CAR) plays an important role in human daily indoor activity monitoring and can be widely used in smart home, human-computer interaction and user authentication. Due to the privacy issue and limited coverage of video signals, RF-based CAR has attracted more and more attention in recent years. This paper focuses on three key problems in RF-based CAR: denoising, segmentation

Smart parking techniques are widely used in smart city and intelligent transportation systems. However, how to build a friendly and effective smart parking system in a large city is still a challenge. This paper proposes a P2P network based smart parking system using Edge Computing. It utilizes Cloud Computing, Edge Computing, and P2P network techniques to provide plenty of services including parking

Blockchain applications have continuously improved ever since its first debut on cryptocurrency. From then on, its uses have branched out from the financial realm, finding their way into numerous industries such as health, environmental, and governmental. Businesses are starting to take advantage of the intrinsic traits that made blockchain so notorious into their operations, such as security, integrity

Organizations’ own personnel now have a greater ability than ever before to misuse their access to critical organizational assets. Insider threat detection is a key component in identifying rare anomalies in context, which is a growing concern for many organizations. Existing perimeter security mechanisms are proving to be ineffective against insider threats. As a prospective filter for the human analysts

Recently, evolving technologies such as 5G and cloud computing have offered new prospects in mobile ad hoc networks (MANETs). However, achieving a high quality of service (QoS) in multimedia routing over MANET–cloud using 5G networks remains challenging owing to the dynamic nature of mobile devices. The present study addresses this problem by proposing a three-tier architecture in cloud-assisted MANETs

In recent years, Wireless Sensor Network (WSN) became a key technology for monitoring and tracking applications in a wide application range. Still, an energy-efficient data gathering protocol has become the most challenging issue. This is because each sensor node in the network is equipped with limited energy resources. To achieve better energy efficiency, better network communication, and minimized

Train-to-Train (T2T) communication is expected to increase efficiency of train operation and reduce life cycle cost in construction for urban rail transit system. In this paper, we introduce the millimeter wave (mmWave) and multiple input multiple output (MIMO) technologies to improve the reliability and capacity of T2T communication, and a novel mmWave MIMO based transmission scheme is proposed. By

Vehicle detection is a fundamental challenge in urban traffic surveillance video. Due to the powerful representation ability of convolution neural network (CNN), CNN-based detection approaches have achieve incredible success on generic object detection. However, they can’t deal well with vehicle occlusion in complex urban traffic scene. In this paper, we present a new occlusion-aware vehicle detection

In healthcare organizations such as hospitals, it is important to have an efficient healthcare monitoring system in which the patients’ vital signs are collected from multiple sensors and transformed into the decisions makers to be analyzed and take the appropriate actions. The IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) was developed to act as an appropriate routing protocol in new

The rapid growth in nursing demand in P.R.China and the slow increase in the number of qualified nurses has led to a serious increased in workload of existing nurses. Most of their works include heavyweight handling which causes low back pains. Thus a mobile, flexible, comfortable and wearable waist assist device, the Mobile Wearable Waist Assist Robot using pneumatic artificial muscles as power actuators

Fiber Bragg Grating (FBG) is achieved by refractive index modulation. Temperature and strain can be measured by FBG. The echo spectral distribution is determined by the refractive index of the fiber and the grating pitch. Therefore, there are few types of conventional refractive index modulation methods, and there are few types of echo spectrum. In this paper, an index code modulation based on IoT

With the progress of medical science and technology and the healthy changes in eating habits, the proportion of aged population is gradually increasing. Smart-home elderly care has thus attracted a lot of research attention in the recent past, and remains an active issue. Internet of Things (IoT) has been recognized as a key enabler for smart-home elderly care realization. In the literature, a large

Predicting the stock market is a widely studied field, either due to the curiosity in finding an explanation for the behavior of financial assets or for financial purposes. Among these studies the best techniques use neural networks as a prediction technique. More specifically, the best networks for this purpose are called recurrent neural networks (RNN) and provide an extra option when dealing with

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications

Advanced metering infrastructure (AMI) is the core component of the smart grid. As the wireless connection between smart meters in AMI is featured with high packet loss and low transmission rate, AMI is considered as a representative of the low power and lossy networks (LLNs). In such communication environment, the routing protocol in AMI network is essential to ensure the reliability and real-time