Search

Fifth-Generation (5G) mobile cellular networks provide a promising platform for new, innovative and diverse IoT applications, such as ultra-reliable and low latency communication, real-time and dynamic data processing, intensive computation, and massive device connectivity. End-to-End (E2E) network slicing candidates present a promising approach to resource allocation and distribution that permit operators to flexibly provide scalable virtualized and dedicated logical networks over common physical infrastructure. Though network slicing promises the provision of services on demand, many of its use cases, such as self-driving cars and Google's Stadia, would require the integration of a Multi-Access Edge Computing (MEC) platform in 5G networks. Edge Computing is envisioned as one of the key drivers for 5G and Sixth-Generation (6G) mobile cellular networks, but its role in network slicing remains to be fully explored. We investigate MEC and network slicing for the provision of 5G service focused use cases. Recently, changes to the cloud-native 5G core are a focus with MEC use cases providing network scalability, elasticity, flexibility, and automation. A cloud-native microservices architecture, along with its potential use cases for 5G network slicing, is envisioned. This paper also elaborates on the recent advances made in enabling E2E network slicing, its enabling technologies, solutions, and current standardization efforts. Finally, this paper identifies open research issues and challenges and provides possible solutions and recommendations.

Bike sharing is an important tool to reduce congestion and pollution in urban areas. Electrically Power Assisted Bicycles (EPAC’s) make cycling possible also for sedentary people. Standard EPAC’s are difficultly integrable into a free-floating sharing system because the battery pack requires frequent recharging. This paper studies the challenges, opportunities and solutions of implementing a free-floating bike sharing system based on electric bicycles. The analysis revolves around the charge sustaining paradigm. The idea of charge sustaining leverages the metabolic efficiency gaps to reduce the overall physical effort required without determining a net discharge of the battery. Already validated in private bicycles, the idea needs to be modified and adapted to the challenges of a shared fleet. The paper analyzes two approaches to the fleet level energy management and assistance control of a fleet of charge sustaining bicycles. Specifically, we compare a fixed price approach against a flexible pricing approach where the user can select the cost based on the pedaling effort they are willing to exercise. A simulation framework (calibrated on data collected during a large trial in Milan, Italy) assesses the operational costs and revenues of the two approaches quantifying how they depend on the design and environmental parameters. We provide and validate a lower bound in terms of usage rate that guarantees economic sustainability, additionally showing that a flexible pricing strategy can lower this bound and grant more degrees of freedom to the users.

Grain-128AEAD is a lightweight authenticated encryption stream cipher and one of the finalists in the National Institute of Standards and Technology (NIST) Lightweight Cryptography (LWC) project. This paper provides an independent third-party analysis of Grain-128AEAD against fault attacks. We investigate the application of three differential fault attack models on Grain-128AEAD. All these attacks can recover the initial state of Grain-128AEAD. First, we demonstrate an attack using a bit-flipping fault that requires access to 27.80 faulty outputs to recover the initial state. Then, we demonstrate an attack with a more relaxed assumption of a random fault with a probabilistic approach. Our probabilistic random fault attack requires access to 211.60 faulty outputs and 210.45 fault injections to recover the initial state with a success rate over 99%. Both of the above two attacks are based on precise control on the fault target. Finally, we apply a random fault attack with a deterministic approach (can conclusively determine the random fault value) and using different precision controls. For the precise control, we use existing approaches that have been applied to other ciphers, such as Tiaoxin-346. We also propose a technique for less stringent precision models, such as moderate control and no control, which are more practical than the precise control. Our result indicates that the deterministic random fault attack with a precise control requires an average of 27.64 fault injections and a data complexity of 28.80. The deterministic random fault attack with moderate control requires a weak assumption on the fault injection and hence, is the best attack presented in this paper; and is expected to require about 29.39 fault injections with a data complexity of about 212.98. All the attacks discussed in this paper are verified experimentally.

This paper aims to provide an in-depth study of the detection of historical alarm subsequences, which are frequently used as an initial step for alarm flood analysis methods. Therefore, state-of-the-art approaches are comprehensively examined, evaluated, and compared. To overcome the limitations of these methods, a novel approach is presented, which uses outlier detection in time distances between alarm events (activation and return to normal) and an alarm coactivation constraint. The effectiveness and performance of the examined methods are illustrated by means of an openly accessible dataset, which is introduced in this paper. It is based on the “Tennessee-Eastman-Process”, a benchmark in process automation. The intent is to provide a suitable dataset for the development and evaluation of alarm management methods in complex industrial processes using both quantitative and qualitative information from different sources. It is shown that the integration of supplementary information is beneficial for the overall performance and robustness of the detection method proposed here. This method allows for a more accurate detection of coherent historical abnormal situations, including phases with active root-cause disturbances and the normalization phases that follow their termination. Furthermore, the proposed method has the advantage that the detection results are less influenced by the alarm count, the propagation velocity, the duration of the situation, and the time distance between two causally independent situations in comparison to state-of-the-art approaches.

With the rapid development of remote-sensing imaging technology, remote-sensing images have become increasingly diverse, and people are paying more attention to ocean remote-sensing research. Because ocean remote-sensing data are complex, and the ocean environment is diverse, results will differ, even if the same target is detected at different times in the same scene. To obtain more semantic features and better pixel-level prediction capabilities, this paper proposes a pixel-level ocean remote-sensing image algorithm (GLPO-Net) that combines local and global features. First, texture features, color features, and spatial relationship features are extracted. Second, the algorithm constructs a multiscale local cross-attention mechanism strategy to obtain feature weight information in different directions to fully mine the local features of ocean remote-sensing images. Concurrently, an algorithm constructs a multiscale global cross-attention mechanism strategy to obtain global features. Then, the fusion of global features and local features is described in each submodule to obtain more representative deep features. Finally, small-sample ocean remote-sensing is described via image pixel-level prediction. The algorithm proposed in this paper has been tested with three public ocean remote-sensing datasets. The experimental results show that the proposed GLPO-Net algorithm can learn features from small samples of ocean remote-sensing images. Compared to the prediction results of other remote-sensing image algorithms, GLPO-Net exhibits better prediction capabilities.

For the on-orbit service spacecraft attitude tracking task with external disturbances, considering the limited capacity of its actuators and high control performance required, an improved performance constraint control algorithm based on command filters is proposed in this paper. For the proposed control algorithm, before the theoretical control signal is transmitted to the actuator, it needs to be filtered by the command filter. This filter can constrain the rate, magnitude, and bandwidth of the signals, smooth the command signals and avoid peak interference. In terms of performance constraints, inspired by the performance constraint curve, this paper uses the determined points to re-design a new performance constraint function, which shows the maximum settling time parameters, allowing designers to pre-defined this limited time of the closed-loop system according to needs. Therefore, the maximum settling time of the actual closed-loop system will be less than the pre-defined maximum settling time. The controller based on the command filter is designed, and on this basis, the corresponding performance-constrained control algorithm with pre-defined maximum settling time guaranteed is proposed. Through the proof and simulation, the rationality and effectiveness of the proposed controller are further demonstrated.

The selection of the system-architecture is critical step in software-engineering lifecycle. Unfortunately, the selecting a suitable architecture is a hard mission. The difficulty of the adopting architecture increases in inconsistence case as in SOA and MSA. The paper answers “when architecture transform”, “to which architecture migrate” and “how”. The paper addresses the lack of evaluation ERPs in post-implementation phase. The paper provides static and dynamic analysis for monolith, SOA and MSA by numerical-comparison and Benchmark. This paper proposes a roadmap for migration-architecture. The roadmap consists of flowchart of migration and migration-decision model that is supported with criteria of evaluation form for assessing system’s baseline and making a consistence decision by Neutrosophic-ANP. Because of the dependency between factors of information system evolution, ANP is used. Furthermore, Neutrosophic set is selected because its accuracy in inconsistence. Benchmark assesses OpenBravoERP, Odoo12 and Metasfresh5.144 to represent monolith, SOA and MSA respectively. Benchmark assesses performance, time to the market, stability, future proof, network issue and cost. They are tested based on scenario tests which grantee that transactions are executed within more than one module of ERPS, simulates real business’s procedures, huge number of records and concurrent load by virtual users. The proposed model and benchmark help “Hitac’ enterprise in two decisions for its expansion-plan in COVID-19 crisis. The numerical steps of this case-study are provided. Most published papers and literatures about Microservices are not empirical. The main paper’s result is filling this gap by a one of the empirical aspects with an academic study support.

To fully mine the relationship between temporal features in load data, improve the accuracy and efficiency of short-term load forecasting and overcome the difficulties caused by load nonlinearity and volatility in accurate load forecasting. In this paper, a hybrid neural network short-term load forecasting model based on temporal convolutional network (TCN) and gated recurrent unit (GRU) is proposed. Firstly, the correlation between meteorological features and load is measured with the distance correlation coefficient, and the fixed-length sliding time window method is used to reconstruct the features. Next, temporal convolutional network is adopted to extract the hidden historical information and time relationship including meteorological features, electricity price, etc., and a better-performing gated recurrent unit is utilized for perdition. Furthermore, the state-of-the-art AdaBelief optimizer and Attention mechanism are utilized to enhance the prediction accuracy and efficiency. The effectiveness and superiority of the proposed model are verified by load and weather data from Spain and PJM power system data. Short-term load forecasting results in different periods and comprehensive comparisons with the performance of different models show that the proposed model can provide accurate load forecasting results rather quickly. The highlights of this paper are that temporal convolutional network and gated recurrent unit are combined for load forecasting for the first time, and the forecasting performance is improved by the novel optimizer AdaBelief and feature selection based on distance correlation coefficient.

Autonomous driving (AD) provides a reliable solution for safe driving by replacing human drivers responsible for the majority of accidents. The emergence of Machine Learning, specifically Deep Reinforcement Learning (DRL), and its ability to solve complex games proved its potential to address AD challenges. However, model-free methods still suffer from safety-related issues that can be resolved using safe-DRL approaches. The addition of model-based safety filters to the learning-based algorithms provides safety bounds on their performance and constraint satisfaction. In this paper, we investigate the addition of a safety filter based on Model Predictive Control and show an increase in mean testing episode reward by 110% from −75 mean episode reward during testing for 50 episodes for Deep Deterministic Policy Gradient ( $DDPG$ ) to 7.758. We study the impacts of safety filters (7.758 mean reward), heuristic rules, bounded additive noises (0.49% performance increase comparing to noise-free case), and exploration (3.425 mean reward) on the learning algorithm. We compare the effects of filters in the context of simulated exploration and bounded exploration and prove that bounded exploration results in 9.86% increase in mean reward and 12.95% decrease in std comparing to the other method. Additionally, inspired by Deep Internal Learning and biological mechanisms like brain plasticity, we investigate the idea of using each sample for training only once instead of utilizing stochastic batches which increases the mean testing accumulated reward by 1.87% and leads to the best performance (7.942 mean reward and 0.048 std). Finally, the results demonstrate better automotive results for our proposed method than DDPG. Our proposed method, DDPG with safety filter in bounded exploration and adaptive learning under noisy input conditions, has a success rate of 100% under different traffic densities for the simulation environment used in this paper and our assumptions. The proposed method’s automotive results are shown for a braking scenario to avoid collision with other road users.

This paper initially proposes an optimization problem and after presents its optimal solution. Then, this result is applied to obtain relaxed conditions to design controllers for nonlinear plants described by Takagi-Sugeno (TS) models, based on fuzzy Lyapunov function (FLF) and Linear Matrix Inequalities (LMI). The FLF is given by $V(x(t)) = x(t)^{T}P(\alpha (x(t)))x(t)$ , where $x(t)$ is the plant state vector, $P(\alpha (x(t))) = \alpha _{1}(x(t))P_{1} + \alpha _{2}(x(t))P_{2} + \cdots + \alpha _{r}(x(t))P_{r}$ , $P_{i}=P_{i}^{T} > 0$ and $\alpha _{i}(x(t))$ is the weight related to the local model $i$ in the representation of the plant by TS fuzzy models, for $i=1,2,\cdots,r$ . When one calculates the time derivative of this $V(x(t))$ , it appears the term $x(t)^{T}\dot {P}(\alpha (x(t)))x(t)$ , that is usually handled using conservative upper bounds, supposing that the bounds of the time derivative of $\alpha _{i}(x(t))$ , $i=1,2,\cdots,r$ , are available. The main result of this paper is a procedure to obtain optimal upper bounds for the term $x(t)^{T}\dot {P}(\alpha (x(t)))x(t)$ , such that they contemplate the maximum value and are always smaller than or equal to the maximum value. It is a relevant result on this subject, because these optimal upper bounds do not add any constraint. With these optimal upper bounds, a relaxed design method for stabilization of TS fuzzy models is proposed. Two numerical examples illustrate the effectiveness of this procedure.

This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study. Web of Science 9 57791 57757

A railway running near power lines is subjected to electromagnetic interference from power lines, which can adversely affect normal operation of the railway’s communication and signaling system, resulting in safety hazard on the railway equipment and personnel. Therefore, it is very important to assess such electromagnetic interference issues to ensure the railway system’s reliability and the public safety. In this paper, a literature review is conducted on electromagnetic interference between railways and nearby power lines, which has not been widely researched in the past. Although IEEE Standard 2746–2020 has raised the importance of such electromagnetic interference, but it only offers very basic information without an in-depth coverage. This paper provides a review, where critical aspects of electromagnetic interference between railways and power lines are summarized and the future research direction in this area is recommended.

The design fabrication and development of a 67.5 kHz capacitive micromachined ultrasonic transducer (CMUT) suited for Martian anemometry is presented in this paper. To have low signal attenuation under Martian conditions, the device operating frequency is limited to 100 kHz. This is due to the low-density carbon dioxide (CO2) atmosphere and acoustic impedance mismatch transduction losses. CMUTs capable of generating frequencies less than 100 kHz need either large Silicon area or higher operating voltages. This is a problem for the battery operation and portability of devices. The devices presented in this paper are designed and fabricated using low cost commercially available surface micromachining technique. COMSOL Multiphysics and MATLAB simulations were used to analyze the device critical design parameters and investigate the operability of devices. Simulation results show that the designed single cell 170- $\mu \text{m}$ radius membrane has a resonant frequency ~65 kHz. The device exhibits a static displacement of 105nm under 20 V DC bias. Using the developed single cell model, a $3\times 10$ array CMUT anemometer was fabricated and evaluated that generates a ~65 kHz acoustic signal in lab environment. This proposed CMUT anemometer can operate for a supply < 38 V. The device performance was evaluated using a commercial air-coupled capacitive microphone named CAP1. Successful transmit-receive of ultrasound from the developed 2D array to CAP1 for separation in the range of 1–15 cm was performed. The experiment results performed in lab environment show the speed of sound and the atmospheric attenuation can be accurately measured using this developed technology with a ±5% accuracy.

Clustering algorithms have become one of the most critical research areas in multiple domains, especially data mining. However, with the massive growth of big data applications in the cloud world, these applications face many challenges and difficulties. Since Big Data refers to an enormous amount of data, most traditional clustering algorithms come with high computational costs. Hence, the research question is how to handle this volume of data and get accurate results at a critical time. Despite ongoing research work to develop different algorithms to facilitate complex clustering processes, there are still many difficulties that arise while dealing with a large volume of data. In this paper, we review the most relevant clustering algorithms in a categorized manner, provide a comparison of clustering methods for large-scale data and explain the overall challenges based on clustering type. The key idea of the paper is to highlight the main advantages and disadvantages of clustering algorithms for dealing with big data in a scalable approach behind the different other features.

© Copyright 2021, A. O. Nyanteh et al. Software-Defined Networking (SDN) is an innovative technology which provides a programmable network control which is decoupled from the physical infrastructure. Network Virtualization (NV) is the phenomenon where a given physical network infrastructure and its resources are abstracted to create multiple logical virtual network slices of the underlying substrate. NV enables independent virtual networks to co-exist on one or more shared physical network infrastructure. Edge computing makes use of the edge resources in close proximity to end-users to reduce service delay and the network traffic volume in the end-to-end networks. Similarly, network slicing which is a key enabling technology for 5G networks is designed to support different services from different platforms at different scales enables sharing of physical network infrastructure on many different virtual network layers. These innovative technologies and strategies have gained significant attention from both academia and industry as they have the potential to maximize network resource utilization and optimize end–to–end network service delivery in 5G solutions deployment. To enable continuous simulation and development of applicable 5G networking concepts using these technologies, there is a need for an accessible and easy-to-learn testbed which is able to efficiently measure the performance of physical and virtual network capacities, provisioning approaches and management of multiple architectural models using large-scale network slicing configurations in a repeatable and controllable manner. These tools and toolkits provide scalable, lightweight and controlled cloud simulation environments necessary to analyse network traffic flows, allocation capacities and policies and the behaviour of multiple heterogeneous networks at an extremely low cost as compared to the huge financial commitments involved in conducting similar experiments in a real-life event. Existing solutions do not support Network Slicing and end- to -end heterogenous network automation which are key enablers of 5G network implementation. Hence in this paper, the CloudSimHypervisor framework is developed in this based on CloudSimSDN-NFV. The complete architecture and features of the CloudSimHypervisor framework and some used cases are presented in this paper. We validate the CloudSimHypervisor framework with two use case experiments in the cloud computing environment: Joint compute and network resource utilization and network traffic prioritization. Results from these experiments display the efficiency of the CloudSimHypervisor in estimating and measuring processing speed, transmission speed, compute and network usage efficiency and energy consumption.

DNA microarray data analysis is infamous due to a massive number of features, imbalanced class distribution, and limited available samples. In this paper, we focus on high-dimensional multi-class imbalanced problems. The high dimensional and multi-class imbalanced problem has posed acute challenges for the conventional classifiers to effectively perform classification tasks on both the minority and majority classes. Numerous efforts have been devoted to addressing either high dimensionality dataset or class imbalance problems. Nonetheless, few methods have been proposed to address the intersection of multi-class imbalanced and high-dimensional problems concurrently due to their intricate interactions. This paper presents novel hybrid algorithms for feature selection with the high dimensional multi-class imbalanced problem using multiple filter-based rankers (MFR) and hybrid Grasshopper optimization algorithm (GOA). The Simulated Annealing (SA) algorithm is incorporated into GOA. SA is used to enhance the best solution found by the GOA algorithm. The aim of using the SA here is to tackle the slow convergence and improve the exploitation by searching the high-quality regions found by the GOA. The experimental results confirm the effectiveness of the proposed methods in improving the classification performance in terms of area under the curve (AUC) compared to other well-known methods, which guarantees the ability of the proposed methods in searching the feature space and identifying very robust and discriminative features that best predict the minority class.

In a smart urban environment, providing accurate information about human activities is an important task. It is a trend to implement human activity recognition (HAR) algorithms and applications on smart phones, including health monitoring, self-management system, health tracking and so on. However, human activity recognition(HAR) is very complex, and it is important to use the best technology and machine learning to understand human activities. One of the main problems of the existing HAR strategies is that the classification accuracy is relatively low, and in order to improve the accuracy, it needs high computational overhead. The purpose of this paper is to use an exploratory data analysis method to deal with HAR, after the implementation of different data mining techniques, the results of dimensionality reduction and visualization are obtained. The HAR method based on smart phone and EDA proposed in this paper is a high precision method. Compared with other classifiers, its accuracy is 96.56%. This article will discuss computational prediction activities and the computational limitations of using exploratory data to analyze (EDA) on 564 feature data frames. The experimental results show that HAR-based exploratory data analysis is a common sensory signal processing technology. The GridSearchCV and LinearSVC algorithm can provide accurate automatic human activity recognition (HAR), for elderly and disabled patients in need of continuous care, and it is a decision-making tool to support sports coaches to make plans.

This paper presents the novel work of digitally linking the crime theories being worked for many years by sociologists to the data record of criminals. The proposed methodology ensures that societal theories can be unequally characterized based on a set of features. These features can be extracted from the criminal record of convicts. The paper presents the methodology and demonstrates its applicability by digitally mapping several records of convicted criminals in Pakistan on societal theories. The accuracy of the results is verified by presenting them to a panel of experts in criminology and sociology. This novel work can lead societies to cluster crimes being committed in their respective communities to one or more of social theories and then take the help of the rich work that has already been done by sociologists in curbing crimes in communities by the right intervention of policies, incentives, and enforcement.

Nowadays, under the pressure of numerous research publications, researchers increasingly pay attention to writing survey papers to track and understand one research topic they are interested in, and then begin to conduct more in-depth research. Until this moment, there are two types of survey papers: traditional review analysis and bibliometric statistical analysis. Compared with traditional review analysis, due to the analysis of various bibliometric information that can be quickly summarized to assess and predict one research field’s development, the bibliometric statistical analysis is progressively proposed. However, no research relied on the bibliometric approach to explore fuzzy inference system (FIS) -based classifiers. More importantly, since the current open-ended bibliometric analysis approaches have different assessment focuses, choosing a suitable approach is problematic. Therefore, based on the extraction, integration, and expansion of previous bibliometric analysis theories, this research proposes a new systematic and time-saving bibliometric statistical analysis approach. It is worth noting that the proposed approach eliminates the need to read the internal content of all publications. Two core parts (Publication Information and TOP 20 SETs) are generated by the proposed analysis approach. Among them, analyzing Author Keywords and TOP 20 SETs are unprecedented guiding features to assist researchers in exploring research topic. Significantly, this research relies on the proposed approach to explore FIS-based classifiers. Various assessments cover the bibliometric information of the entire related publications. In addition, these results may need to be considered to increase the citation rate of future publications.

This paper presents an averaged real-time model of an arbitrary size MVDC railway system. In contrast to references already available in the literature, focusing mainly on specific parts of such a railway network (e.g. power converter installed in a locomotive), this paper offers a thorough insight into the modeling of every part belonging to it. Through a set of reasonable assumptions, the concept of a sector is presented allowing for a straightforward synthesis of any MVDC railway network. Thereafter, real-time models of catenaries, rails, trains, as well as power converters supplying the system are presented in a comprehensive manner. Without loss of generality, the system analyzed hereafter represents a 200 km section of a double-track EuroCity line powered by an actively fed electrification system with three transformer-rectifier groups and two modular multilevel converters. The proposed modeling approach was verified on a large-scale hardware-in-the-loop system comprising one RT Box 3, hosting the model of the exemplary railway network, and two modular multilevel converter digital twins relying on the use of industrial ABB PEC800 controllers.

In this paper, a novel reversible data hiding method for encrypted images (RDHEI) is proposed. An efficient coding scheme based on cyclic binary Golay (23, 12) code is designed to embed additional data into the least significant bits (LSBs) of the encrypted image. The most significant bits (MSBs) are used to ensure the reversibility of the embedding process. The proposed scheme is lossless, and based on the receiver’s privileges, allows recovery of marked data, original data and embedded data. Furthermore, the scheme can be used with any type of data, however it is best suited to 16-bit DICOM images of monochrome photometric interpretation. A modification to the standard DICOM network model was also introduced, to point out an example application of the proposed RDHEI method, i.e. an anonymized data storage outsourcing. A computer-based analysis has been carried out and simulation results are shown at the end of this paper.

Solid-state power controllers (SSPCs) have been received increasing attention as they can configure the electrical system and protect the system by fast tripping mechanism at the same time. Although the high-voltage direct current (HVDC) electrical system can bring sustainable savings on the cables’ weight and losses, the protection can be considerably challenging. SSPCs have been successfully utilized for 28V DC aircraft onboard network. However, high-voltage ones are impeded by the vast over-voltage and the excessive losses generated from SSPC switching. This paper tries to fill this gap and presents the development of high-power SSPCs for a ±270VDC network of a future turboprop aircraft. Comprehensive designs of proper over-voltage suppression along with SSPC thermal management are presented in this paper. Besides, a comparative study on the SSPC device is carried out. Two prototypes, including a single MOSFET module and paralleling several discrete MOSFET devices, are built and then tested in the experiment. It has been validated that the proposed voltage clamping design cannot only effectively suppress the over-voltage, but also limit the SSPC temperature increase during switching for both candidates. After comparing the conduction losses, maximum junction temperature, power density, weight and volume, the single device solution is recommended as a preferable SSPC option for future aircraft.

To solve the problem that the trajectory tracking control system of the unmanned helicopter is limited by the accuracy of the physical model (parameters and dynamic characteristics) and external disturbance of the unmanned helicopter, this paper designs a trajectory tracking control system of the unmanned helicopter based on the linear/nonlinear hybrid Active Disturbance Rejection Control (ADRC). At the same time, this paper proposes a method for tuning the controller parameters based on the Bacterial Foraging Optimization-Flower Pollination Algorithm (BFO-FPA). Finally, the simulation test of spiral climb and “8”-figure climb is applied to verify the controller’s performance. The results show that the controller proposed in this paper can effectively overcome the influence of the unmanned helicopter’s internal and external disturbance, and the controller has the advantages of strong anti- disturbance ability and strong robustness. Simultaneously, the optimization algorithm can obtain the optimal global solution, and can improve the controller’s performance.

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 to be solved. For example, the current mainstream image super-resolution algorithms based on single or multiple frames pursue high performance indicators such as PSNR and SSIM, while the reconstructed image is relatively smooth and lacks many high-frequency details. It is not conducive to application in a real environment. To address such problem, this paper proposes a super-resolution reconstruction model of sequential images based on Generative Adversarial Networks (GAN). The proposed approach combines the registration module to fuse adjacent frames, effectively use the detailed information in multiple consecutive frames, and enhances the spatio-temporality of low-resolution images in sequential images. While the GAN was used to improve the effect of image high-frequency texture detail reconstruction, WGAN was introduced to optimize model training. The reconstruction results not only improved the PSNR and SSIM indexes but also reconstructed more high-frequency detail textures. Finally, in order to further improve the perception effect, an additional registration loss item RLT is introduced in the GAN network perception loss. Through extensive experiments, it shows that the model proposed in this paper effectively obtains the information between the sequence images. When the PSNR and SSIM indicators are improve, it can reconstruct better high-frequency texture details than the current advanced multi-frame algorithms.

A novel method for detection of important scenes in baseball videos based on correlation maximization between heterogeneous modalities via bidirectional time lag aware deep multiset canonical correlation analysis (BiTl-dMCCA) is presented in this paper. The proposed method enables detection of important scenes by collaboratively using baseball videos and their corresponding tweets. The technical contributions of this paper are twofold. First, since there are time lags between not only “tweets and corresponding multiple previous events” but also “events and corresponding multiple following posted tweets”, the proposed method considers these bidirectional time lags. Specifically, the representation of such bidirectional time lags into the derivation of their covariance matrices is newly introduced. Second, the proposed method adopts textual, visual and audio features calculated from tweets and videos as multi-modal time series features. Important scenes are detected as abnormal scenes via anomaly detection based on a generative adversarial network using multi-modal features projected by BiTl-dMCCA. The proposed method does not need any training data with annotation. Experimental results obtained by applying the proposed method to actual baseball matches show the effectiveness of the proposed method.

Identifying k ey frames is the first and necessary step before solving the variety of other B haratanatyam problems. The paper aims to partition the momentarily stationary frames (key frame s) from this dance video’s motion frames. The proposed key frame s (KFs) localization is novel, simple, and effective compared to the existing dance video analysis methods. It is distinctive from standard KFs detection algorithms as used in other human motion videos. In the dance’s basic structure, the occurrence of KFs during performances is often not completely stationary and varies with the dance form and the performer. Hence, it is not easy to decide a global threshold (on the quantum of motion) to work across dancers and performances. The earlier approaches try to compute the threshold iteratively. However, the novelty of the paper is: (a) formulating an adaptive threshold, (b) adopting Machine Learning (ML) approach and, (c) generating the effective feature by combining three frame differencing and bit-plane technique for the KF detection. In ML, we use Support Vector Machine (SVM) and Convolutional Neural Network (CNN) as the classifiers. The proposed approaches are also compared and analyzed with the earlier approaches. Finally, the proposed ML techniques emerge as a winner with around 90% accuracy.

In this paper, a robust leader-following consensus protocol for multi-agent systems (MASs) subject to faults in the information exchange and disturbance is presented. The problem under consideration is to guarantee the convergence of the agent trajectories to a leader agent when all the agent followers are under faults in the information exchange as smooth time-varying delays and disturbances. The main contribution in this paper is the design of a robust leader-following control through the Lyapunov approach and an optimal $\mathscr {H}_{\infty }$ criterion such that all agents follow a virtual leader agent despite faults in the information exchange and disturbances. Linear matrix inequalities (LMIs)-based conditions are obtained whose solution allows computing the robust controller gain. In order to show the effectiveness of the proposed approach, numerical examples are carried out comparing a state-of-the-art approach and the proposed strategy in a fleet of unmanned aerial vehicles (UAVs) subject to wind turbulence which are shown to achieve the formation control.

Electrification in the transportation industry is becoming more important to face global warming and replace fossil fuels in the future. Among the available energy sources Li-ion battery and proton exchange membrane fuel cell (PEMFC) are the most promising energy sources. Therefore, employing them in fuel cell hybrid electric vehicles (FCHEVs) to combine their advantages is one of the favorable solutions. However, they still face a major challenge residing in their aging that cause the drop of system performance. On one hand, the degradation is the result of the interaction between several aging mechanisms that react differently with various operating conditions. On the other hand, a hybrid system requires an essential energy management strategy (EMS) for fuel economy and optimal power share. At the end, this EMS has an important impact on the lifetime of sources in term of reducing or favorizing the degradation. Therefore, it is important to consider the degradation in the objectives of the designed EMS. Since the degradation is usually neglected when designing an EMS, this paper tends to review the possible methods for designing a health-conscious EMS. Hence, this paper presents a summary of the main fuel cell (FC) and Li-ion battery aging mechanisms as well as the useful degradation models for state of health estimation. In addition, the existing works that consider the degradation of on-board energy sources in their approaches for increasing their durability are classified and analysed. Remaining challenges are detailed along with a discussion and outlooks about current and future trends of health-conscious EMS.

Blockchain technology is a secure distributed ledger for lists of transactions, which has immense potential to solve traditional agri-food supply chain issues. An increasing number of research on blockchain-based traceability applications aims to improve food quality and safety. Still, relatively few works considered user interfaces when developing and reporting their applications, which could lead to usability issues. This paper aims to address this gap by reviewing existing works from user interface perspectives. We gathered 25 review papers on blockchain or agri-food supply chain and 39 research papers that presented screenshots of user interfaces of related applications. We first reviewed 7 review papers that focused on the blockchain-based agri-food supply chain to understand the benefits and challenges in the blockchain applications. We then analyzed 14 blockchain-based agri-food traceability applications and 10 non-blockchain-based agri-food traceability applications. The analysis resulted in categorizations of 5 target user groups, 3 main approaches for collecting data, 5 main approaches for visualizing data, and a discussion of other aspects of user interfaces. However, we found insufficient details and discussions on the user interfaces and design decisions of the applications for further usability assessment. Additionally, user involvement for evaluation is lower in blockchain-based researches than in non-blockchain-based researches. This trend could lead to usability problems of blockchain applications, causing blockchain technology to be underutilized. Finally, we discussed research gaps and future research directions related to user interface design, which should be addressed to ease future blockchain adoption.

Integrated circuits (IC) are fabricated on a wafer through stacked layers of circuit patterns. To ensure proper functionality, the overlay of each pattern layer must be within the tolerance. Inspecting each wafer’s overlay is unrealistic and impractical. Hence, wafers are selectively inspected at metrology stations through sampling strategies. With virtual metrology (VM), the metrology quality of the uninspected wafers can be estimated. Motivated by a real-world production environment of a 200mm semiconductor manufacturing plant (fab), a VM to estimate the overlay of the photolithography process is envisioned. Past researches on overlay VM leveraged fault detection and classification (FDC) data to estimate the overlay errors. As such, for fabs in the progress of completing their FDC development for photolithography equipment, a different modeling approach is required to realize an overlay VM that sustains the production line until FDC data can be leveraged for VM. With practical gaps that must be addressed in real fabs, this paper focuses on realizing an overlay VM for the photolithography process without leveraging FDC data. Therefore, the objectives of this paper are two folds: First, to identify the research challenges towards realizing the overlay VM. Second, to propose the future research perspectives of the envisioned overlay VM. Based on the future research perspectives, a two-steps overlay VM modeling approach utilizing data mining techniques is proposed toward realizing the envisioned overlay VM system. The proposed approach first classifies the process stability at the wafer lot level, and subsequently, performs overlay error estimations for wafers in the wafer lots classified with stable process. Linear regression models are proposed to perform overlay error estimations in this work to augment the interpretability of the overlay VM.

A number of recent papers have shown experimental evidence that suggests it is possible to build highly accurate deep neural network models to detect COVID-19 from chest X-ray images. In this paper, we show that good generalization to unseen sources has not been achieved. Experiments with richer data sets than have previously been used show models have high accuracy on seen sources, but poor accuracy on unseen sources. The reason for the disparity is that the convolutional neural network model, which learns features, can focus on differences in X-ray machines or in positioning within the machines, for example. Any feature that a person would clearly rule out is called a confounding feature. Some of the models were trained on COVID-19 image data taken from publications, which may be different than raw images. Some data sets were of pediatric cases with pneumonia where COVID-19 chest X-rays are almost exclusively from adults, so lung size becomes a spurious feature that can be exploited. In this work, we have eliminated many confounding features by working with as close to raw data as possible. Still, deep learned models may leverage source specific confounders to differentiate COVID-19 from pneumonia preventing generalizing to new data sources (i.e. external sites). Our models have achieved an AUC of 1.00 on seen data sources but in the worst case only scored an AUC of 0.38 on unseen ones. This indicates that such models need further assessment/development before they can be broadly clinically deployed. An example of fine-tuning to improve performance at a new site is given.

Half-bridge GaN power converters are susceptible to false turn-on events, which can lead to shoot-through and potentially device-damaging currents. There are three main parameters that can be adjusted in PWM schemes to reduce the likelihood of false turn-on events: negative gate bias, gate resistance, and deadtime. However, these PWM parameters also affect converter efficiency in the inverse way, meaning less false turn-on events must be balanced with lowered efficiency. The novelty of this paper is to investigate the trade-off between reducing GaN false turn-on events (by reducing the transient peak of gate to source voltage) and maximizing the power converter efficiency, which has not been done in prior work. This paper investigates this trade-off using a synchronous buck converter over numerous operating points with variation of the three key PWM parameters. Six converter scenarios are considered with input voltage of 200/400V, switching frequency of 50/100kHz, and output power of 500W/1kW. For each scenario, negative gate bias is set to ™4.4V and −5V, gate on-resistance is set to $10\Omega $ and $12.5\Omega $ , and deadtime is varied at 60ns, 80ns, and 110ns. The results are organized into Pareto plots to find optimal points for efficiency and reduction of false turn-on events. The experimental results show that a further negative gate bias (−5V) most significantly reduces the false turn-on voltage peak and still achieves very high efficiency with appropriate selection of gate resistance and deadtime.

Internet Exchange Points (IXPs) interconnect heterogeneous networks and transfer substantial traffic volumes. In the past decade, the number of IXPs has seen tremendous growth, with more operators connecting to these IXPs even though these IXPs faced various inter-domain routing limitations. Routers based on Border Gateway Protocol (BGP) forwards packets only based on destination IP prefix and selects only routes learned from their neighbors. IXPs designed using Software-Defined Network (SDN), called SDX, offer solutions for existing inter-domain routing problems. This paper presents the existing scalability limitations of inter-domain routing at IXP and how traditional IXP structural design can be transformed into a highly scalable SDX design by exploiting the SDN platform’s functionalities in different use cases of SDX. The paper then reviewed how the SDX improved various IXP operators’ scalability by reviewing and analyzing the latest SDX models and approaches, which provide enhanced policies to enhance providers’ management operations and offer good quality of services (QoS) to the various participating members. Finally, we discussed the open issues and challenges in this area that need further study and a solution to tackle them.

In this paper, a new Multi-Objective Arithmetic Optimization Algorithm (MOAOA) is proposed for solving Real-World constrained Multi-objective Optimization Problems (RWMOPs). Such problems can be found in different fields, including mechanical engineering, chemical engineering, process and synthesis, and power electronics systems. MOAOA is inspired by the distribution behavior of the main arithmetic operators in mathematics. The proposed multi-objective version is formulated and developed from the recently introduced single-objective Arithmetic Optimization Algorithm (AOA) through an elitist non-dominance sorting and crowding distance-based mechanism. For the performance evaluation of MOAOA, a set of 35 constrained RWMOPs and five ZDT unconstrained problems are considered. For the fitness and efficiency evaluation of the proposed MOAOA, the results obtained from the MOAOA are compared with four other state-of-the-art multi-objective algorithms. In addition, five performance indicators, such as Hyper-Volume (HV), Spread (SD), Inverted Generational Distance (IGD), Runtime (RT), and Generational Distance (GD), are calculated for the rigorous evaluation of the performance and feasibility study of the MOAOA. The findings demonstrate the superiority of the MOAOA over other algorithms with high accuracy and coverage across all objectives. This paper also considers the Wilcoxon signed-rank test (WSRT) for the statistical investigation of the experimental study. The coverage, diversity, computational cost, and convergence behavior achieved by MOAOA show its high efficiency in solving ZDT and RWMOPs problems.

An image signal processor (ISP) is a dedicated processor that transforms raw data obtained from camera sensors into an image that satisfies the requirements of a specific application or use case. An ISP typically has many tuning parameters due to the complexity of the image transformation. Until now, these are generally tuned by human experts manually, and this work takes a great deal of time. This paper proposes an application-level automatic ISP parameter tuning system. In particular, this paper focuses on a rear view monitoring (RVM) camera that is mounted on the rear side of the vehicle to prevent backovers in advanced driving assistance systems (ADAS). The proposed system consists of four steps. The first step is the input image generation, which captures a virtual scene including the test site and vehicle body whose three-dimensional (3D) models are created according to the test requirements and vehicle design. The second step is ISP processing, which transforms the input image into an ISP output image (RVM image) according to the ISP specification in the RVM system. The third step is to evaluate the RVM image’s fitness using evaluation criteria (EC) functions. Finally, ISP parameters are tuned by the grid-to-random search method. In the experiment, the proposed system is evaluated by using the 3D modeling data of six different test vehicle types. Experimental results show that the proposed system can effectively obtain the usable ISP parameters’ values that satisfy all RVM requirements for a given situation, regardless of test vehicle type, within 2~3 hours.

Motivation: The localization of objects in images is a longstanding objective within the field of image processing. Most current techniques are based on machine learning approaches, which typically require careful annotation of training samples in the form of expensive bounding box labels. The need for such large-scale annotation has only been exacerbated by the widespread adoption of deep learning techniques within the image processing community: deep learning is notoriously data-hungry. Method: In this work, we attack this problem directly by providing a new method for learning to localize objects with limited annotation: most training images can simply be annotated with their whole image labels (and no bounding box), with only a small fraction marked with bounding boxes. The training is driven by a novel loss function, which is a continuous relaxation of a well-defined discrete formulation of weakly supervised learning. Care is taken to ensure that the loss is numerically well-posed. Additionally, we propose a neural network architecture which accounts for both patch dependence, through the use of Conditional Random Field layers, and shift-invariance, through the inclusion of anti-aliasing filters. Results: We demonstrate our method on the task of localizing thoracic diseases in chest X-ray images, achieving state-of-the-art performance on the ChestX-ray14 dataset. We further show that with a modicum of additional effort our technique can be extended from object localization to object detection, attaining high quality results on the Kaggle RSNA Pneumonia Detection Challenge. Conclusion: The technique presented in this paper has the potential to enable high accuracy localization in regimes in which annotated data is either scarce or expensive to acquire. Future work will focus on applying the ideas presented in this paper to the realm of semantic segmentation.

Design defects affect project quality and hinder development and maintenance. Consequently, experts need to minimize these defects in software systems. A promising approach is to apply the concepts of refactoring at higher level of abstraction based on UML diagrams instead of code level. Unfortunately, we find in literature many defects that are described textually and there is no consensus on how to decide if a particular design violates model quality. Defects could be quantified as metrics based rules that represent a combination of software metrics. However, it is difficult to find manually the best threshold values for these metrics. In this paper, we propose a new approach to identify design defects at the model level using the ID3 decision tree algorithm. We aim to create a decision tree for each defect. We experimented our approach on four design defects: The Blob, Data class, Lazy class and Feature Envy defect, using 15 Object-Oriented metrics. The rules generated using decision tree give a very promising detection results for the four open source projects tested in this paper. In Lucene 1.4 project, we found that the precision is 67% for a recall of 100%. In general, the accuracy varies from 49%, reaching for Lucene 1.4 project 80%.

In intelligent unmanned warehouse goods-to-man systems, the allocation of tasks has an important influence on the efficiency because of the dynamic performance of AGV robots and orders. The paper presents a hierarchical Soft Actor-Critic algorithm to solve the dynamic scheduling problem of orders picking. The method proposed is based on the classic Soft Actor-Critic and hierarchical reinforcement learning algorithm. In this paper, the model is trained at different time scales by introducing sub-goals, with the top-level learning a policy and the bottom level learning a policy to achieve the sub-goals. The actor of the controller aims to maximize expected intrinsic reward while also maximizing entropy. That is, to succeed at the sub-goals while moving as randomly as possible. Finally, experimental results for simulation experiments in different scenes show that the method can make multi-logistics AGV robots work together and improves the reward in sparse environments about 2.61 times compared to the SAC algorithm.

The new “status quo” created by the emerging integration of Multi-Access Edge Computing (MEC) and Radio Access Network (RAN) infrastructures, combined with the openness of the 5G service market, have set new standards on how the multitude of 5G service domains should be incentivized and coordinated to comply with the performance requirements set on a per session basis. In this paper, we revisit the predominant offline contract-driven mobile data access model enabling users to gain access to the infrastructure of only a few network operators and propose a new blockchain-backed mobile data access model where the key 5G stakeholders can trade, share and consume mobile edge network assets (access to the internet, spectrum, processing, storage, local content etc.) in a fully decentralized, anonymous and highly-robust fashion. Blockchain-backed mobile data access should address critical practical challenges of blockchain decentralization, scalability and security in the context of 5G and Beyond networks. Accordingly, we provide a meticulous survey of existing solutions in the aforementioned three areas and present a comprehensive holistic framework of protocols to address the key challenges identified, including a delegated Proof-of-Stake (DPoS) for distributed blockchain consensus over 5G and Beyond mobile data networks, a future-proof smart-contract enabled payment relay service enabling instant off-chain payments in a highly robust fashion as well as a hybrid mixing protocol that enables payment relays to act as anonymous payment hubs while addressing the unique challenges opposed by the joint blockchain and network level interaction of the 5G and Beyond service components. A wide range of practical implications and mitigation measures for dishonest operation of the blockchain nodes are investigated and sophisticated yet highly-robust incentive engineering mechanisms are derived. Detailed numerical results also accompany the paper, bringing to light the unique performance trade-offs and valuable design guidelines towards blockchain-backed mobile data access for 5G and Beyond mobile data networks.

During last two decades, Differential Evolution (DE) proved to be one of the most popular and successful evolutionary algorithms for solving global optimization problems over continuous space. Proposing new mutation strategies to improve the optimization performance of (DE) is considered a significant research study. In DE, mutation operation plays a vital role in the performance of the algorithm. Therefore, in this paper, comprehensive analysis of the contributions on basic and novel mutation strategies that were proposed between 1995 and 2020 is presented. A new taxonomy based on the structure of the novel mutations is proposed. Numerical experiments on a set of 30 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions, including a comparison with classical DE schemes and recent mutations schemes are executed. Furthermore, theoretical, and empirical convergence behavior analysis of all mutations is discussed. The paper also presents many recommendations, guidelines, insights, and suggestions for experienced practitioners and interested researchers in designing and developing effective and efficient DE algorithms to address various optimization problems in different fields.

This paper presents an in-depth study and exploration of the health IoT architecture and related implementation technologies from both theoretical and practical aspects, with important theoretical significance and practical application value. The research includes cloud fusion health IoT architecture, multimodal information acquisition in health IoT perception layer, multi-level service quality assurance of health IoT based on human LAN, and emotional perception and emotional interaction in health IoT. In terms of health IoT architecture, the cloud convergence health IoT architecture is proposed to deeply integrate the health cloud platform and perception layer by integrating multiple communication technologies to optimize the user experience and make health IoT applications more closely connected with people. This paper describes the basic concepts and main components of multimodal sensing information collection, the design and implementation of a health monitoring cloud robotics platform, robotics-based multimodal data sensing and aggregation, and high comfort sustainable physiological signal collection based on smart clothes. The feasibility and performance of the QoS framework proposed in this paper are verified by computer simulations. In this paper, migration learning is used to implement emotion data labeling, continuous conditional random fields to identify emotions based on data collected from smartphones and smart clothes, respectively, and finally decision layer fusion for emotion classification prediction.

As a decentralized distributed ledger, blockchain has developed rapidly since its birth and has been highly valued by governments, academia, and industry. Start in the financial field, blockchain technology has been rapidly applied to various fields such as the Internet of Things, supply chain, and healthcare. Blockchain technology has made sufficient development and innovation, but it also faces many challenges, such as security, scalability, and waste of resources. This paper starts with the development history of blockchain and combines the characteristics and technical principles of it to analyze the current academic research status and application scenarios. Meanwhile, this paper analyzes the existing outstanding blockchain projects and elaborates on their key advantages and current challenges. With discussing the ongoing development trend of blockchain, the development direction, and the research trend of blockchain in the future, this paper provides a useful reference for related research.

Texture in synthetic aperture radar (SAR) images is a combination of the intrinsic texture of scene backscattering and the texture due to noncoherent high-frequency multiplicative noise (HMN) interactions that reflect erroneous information and lead to observation misinterpretation. The focus of this paper is the fractal analysis of KOMPSAT-5 SAR images of noncoherent sea-ice textures while being decomposed by discrete wavelet transform (DWT) processing. As a novel approach, fractal analysis relies on SAR sea-ice spatial backscattering data generation and time-frequency domain (TFD) formulations from the perspective of uncorrelated HMN. To the best of our knowledge, this is the first time that the extraction of the resolution profile and raw data for the reference KOMPSAT-5 SAR sea-ice image have been derived, evaluated and compared both qualitatively and quantitatively at each scale of DWT decomposition on the basis of the presence of HMN. This paper also presents a novel detailed modeling of the multiresolution probability distribution function of the HMN and its power spectral density function modeling at each scale of the decomposition. Other quality assessment techniques, such as two K-means clustering algorithms and several visualized verification methods, such as gradient vector field, advection mapping and tensor field mapping, have been implemented in this regard to investigate embedded HMN suppression and its adverse effects on the presence of pixel anomalies. As a result, as the decomposition continues, the HMN at each scale of decomposition is constantly altering from high-frequency uncorrelated anomalies to low-frequency joint spatial information within the observed 2-D data. In other words, excessive multiscale HMN suppression will result in spatial information loss that makes the DWT scale selection quite important for texture classification. The results also show that the importance of HMN suppression in SAR sea-ice images in the form of pixel anomaly decomposition for the purpose of further texture investigation should be in accordance with the spectral behavior of the HMN. The results are helpful for SAR remote sensing image restoration and data preservation when dealing with high-resolution SAR images, such as in time series analysis, sea-ice texture change detection, and polar structural mapping. The proposed approach is implemented on real KOMPSAT-5 SAR satellite sea-ice images, while fractal spatial resolution profile simulations are carried out based on the inversed equalized hybrid domain image formation algorithm.

Person re-identification can be seen as a process of open set recognition. Usually, the deep learning models consider the person re-identification model as a classification model with a softmax layer. However, the softmax layer cannot be extended to unknown classes because of its closed nature, so the classification model is just regarded as the feature extractor. To overcome the problem mentioned above and make the person re-identification process end-to-end, this paper cast the person re-identification into a regression process and calculates the probability that persons in the images belong to the same identity. First, this paper proposes a deep regression model, named deep regression neural network integrating adaptive multi-attribute fusion method (DRNN-AMAF), which can make the person re-identification as regression analysis. Second, attributes are taken as the basis of this model for calculating the probability of persons belonging to the same identity, and each attribute corresponds to each branch of the deep regression neural network. Finally, hard labels of multiple attributes are adaptively fused into a soft label by the proposed multi-label fusion method based on the idea of Bayesian inference, which makes the attribute labels suitable for regression tasks. The comprehensive experiments on available public databases are conducted, and the experimental results show that our model produces competitive performance compared with the state-of-the-art approaches.

Position-estimation systems for indoor localization play an important role in everyday life. The global positioning system (GPS) is a popular positioning system, which is mainly efficient for outdoor environments. In indoor scenarios, GPS signal reception is weak. Therefore, achieving good position estimation accuracy is a challenge. To overcome this challenge, it is necessary to utilize other position-estimation systems for indoor localization. However, other existing indoor localization systems, especially based on inertial measurement unit (IMU) sensor data, still face challenges such as accumulated errors from sensors and external magnetic field effects. This paper proposes a position-estimation algorithm that uses the combined features of the accelerometer, magnetometer, and gyroscope data from an IMU sensor for position estimation. In this paper, we first estimate the pitch and roll values based on a fusion of accelerometer and gyroscope sensor values. The estimated pitch values are used for step detection. The step lengths are estimated by using the pitching amplitude. The heading of the pedestrian is estimated by the fusion of magnetometer and gyroscope sensor values. Finally, the position is estimated based on the step length and heading information. The proposed pitch-based step detection algorithm achieves 2.5% error as compared with acceleration-based step detection approaches. The heading estimation proposed in this paper achieves a mean heading error of 4.72° as compared with the azimuth- and magnetometer-based approaches. The experimental results show that the proposed position-estimation algorithm achieves a high position accuracy that significantly outperforms that of conventional estimation methods used for validation in this paper.

The decreasing of accumulative error is a key issue for various multi-sensor fusion-based indoor localization systems that employ pedestrian dead reckoning (PDR) to improve their localization performance. Current studies mainly use activity-based map matching (AMM) to prevent the accumulative error. However, it is vulnerable to mismatch problems, which are usually caused by the randomness of human activities. This paper proposes a structure landmark map matching-based indoor localization approach. Structure landmarks refer to special spatial structures (e.g., intersections, corridors, or corners), which are visually salient in a local environment. These landmarks are visually recognizable in indoor spaces because of their distinct shapes. This paper integrates visual and inertial information to recognize the structure landmarks by using a Bayesian classifier. An algorithm is also proposed to realize indoor localization without prior knowledge of the initial location or the turning angles of people. This approach decreases the accumulative localization error of PDR by matching the detected structure landmarks to the ground-truth values. The experimental results showed that the identification accuracy of the structure landmark was about 90% and the matching accuracy was 92%. The mean off-line localization error was about 1.2 m. Compared with the AMM-based method, this approach is robust to the random turning activities of people and can realize indoor localization with a faster convergence speed.

Commodity object retrieval is a key issue in the application of self-service shopping and so on. In this paper, we propose a saliency object detection-based unsupervised commodity object retrieval scheme. Since most commodity objects are conspicuous and not complicated in commodity images, saliency detection could predict a saliency box that indicates approximate position information of objects. The proposed scheme utilizes the saliency box to filter the proposals extracted by selective search. The reserved proposals have a big overlapping ratio with saliency box to a large extent. This paper composes both the saliency box and the reserved proposals as saliency proposals. Furthermore, we propose a channel weighting generalized mean pooling feature to represent saliency proposals. On one hand, the reduction of proposals’ number after filtering significantly improves the computational efficiency; on the other hand, the new feature more accurately represents the objects to be retrieved, which results in higher retrieval precision. In addition, we built and manually annotated a commodity data set named PRODUCT to evaluate the proposed method. Extensive experiments are also conducted on the databases INSTRE and Flick32. The results demonstrate the superior performance of our scheme compared with the other state-of-the-art methods.

With the exponential rise in the number of devices, the Internet of Things (IoT) is geared toward edge-centric computing to offer high bandwidth, low latency, and improved connectivity. In contrast, legacy cloud-centric platforms offer deteriorated bandwidth and connectivity that affect the quality of service. Edge-centric Internet of Things-based technologies, such as fog and mist computing, offer distributed and decentralized solutions to resolve the drawbacks of cloud-centric models. However, to foster distributed edge-centric models, a decentralized consensus system is necessary to incentivize all participants to share their edge resources. This paper is motivated by the shortage of comprehensive reviews on decentralized consensus systems for edge-centric Internet of Things that elucidates myriad of consensus facets, such as data structure, scalable consensus ledgers, and transaction models. Decentralized consensus systems adopt either blockchain or blockchainless directed acyclic graph technologies, which serve as immutable public ledgers for transactions. This paper scrutinizes the pros and cons of state-of-the-art decentralized consensus systems. With an extensive literature review and categorization based on existing decentralized consensus systems, we propose a thematic taxonomy. The pivotal features and characteristics associated with existing decentralized consensus systems are analyzed via a comprehensive qualitative investigation. The commonalities and variances among these systems are analyzed using key criteria derived from the presented literature. Finally, several open research issues on decentralized consensus for edge-centric IoT are presented, which should be highlighted regarding centralization risk and deficiencies in blockchain/blockchainless solutions.

As an important cryptographic primitive in cloud computing and outsourced computing, fully homomorphic encryption (FHE) is an animated area in modern cryptography. However, the efficiency of FHE has been a bottleneck that impeding its application. According to Gentry’s blueprint, bootstrapping, which is used to control the noise propagation in ciphertexts, is the most important process in FHE. However, bootstrapping is also the most expensive process that affects the scheme’s efficiency. This paper has made three improvements to accelerate the bootstrapping. Firstly, as hundreds of serial homomorphic additions take most of the time of bootstrapping, we constructed the logical expression using truth table to reduce the amount of serial homomorphic additions by two-thirds and thus proposed an efficient FHE scheme with bootstrapping within 10 ms. Secondly, the most expensive parts in our bootstrapping, enhanced homomorphic constant multiplication and homomorphic addition, can be implemented in parallel, which may accelerate the bootstrapping. At last, we proposed a set of more efficient combinations of parameters. Analysis shows that our scheme’s security level is 128 bits and the correctness is improved compared with CGGI16 scheme in ASIACRYPT 2016. Experiments show that the running time of bootstrapping in this paper is within 10 ms, which is only 52% of CGGI16, and is less than CGGI17 in ASIACRYPT 2017.

In the process of constructing a domain semantic knowledge base based on ontologies, reusing existing domain knowledge bases not only facilitates sharing, integration, and reuse of the domain semantic knowledge base but also can accelerate the construction of the domain semantic knowledge base. The open and fast growing Freebase database is a good data source, which can be reused to construct the domain semantic knowledge base. However, extracting domain knowledge from the Freebase Resource Description Framework (RDF) dumps faces many challenges. For example, the dump package is too large to read or load; the dump package contains a lot of unnecessary and redundant facts; some ill-formed triples may cause the load to fail, and so on. In response to these obstacles and the deficiencies of existing research, this paper proposes a method to extract domain knowledge quickly, accurately, and completely from the Freebase RDF dumps and describes the domain knowledge using the semantic constructs in ontology standard description languages. Taking extracting the ontology schema and instance data of the medicine domain, including the facts pointing to semantically related domains, as an example, the principle and implementation process of the method are explained in detail and the algorithms of the key processes are described. Finally, the method of this paper is evaluated, including the comparison and analysis of related methods with work objectives, software tools used, processing results, processing performance, accuracy, completeness, and reusability.