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Currently, high dynamic range (HDR) videos with high resolution (HR) have become popular due to the display and the rendered technological advancements. However, making ultra-high definition (UHD) with HDR videos is expensive. The legacy low-resolution (LR) standard dynamic range (SDR) format is still largely used in practice. It is necessary to search for a solution to transform LR SDR videos into UHD HDR format. In this paper, we consider joint super resolution and learning inverse tone mapping an issue of high-frequency reconstruction and local contrast enhancement, and we propose an architecture based on a generative adversarial network to apply joint SR-ITM learning. Specifically, we include the residual ResNeXt block (RRXB) as a basic module to better capture high-frequency textures and adopt YUV interpolation to achieve local contrast enhancement. By adopting a generative adversarial network as a pivotal training mechanism, our designs show advantages in both integration and performance. Our code is now available on GitHub: SR-ITM-GAN.

This paper presents a robust two stage maximum power point tracking (MPPT) system of the photovoltaic (PV) module using an improved restricted control set model predictive control (iRCS-MPC) technique. The suggested work is improved in two aspects; a revision in conventional P&O algorithm is made by employing three distinct step sizes for different conditions, and an improvement in conventional MPC algorithm. The improved MPC algorithm is based on the single step prediction horizon that provides less computational load and swift tracking of maximum power point (MPP) by applying the control pulses directly to the converter switch. The computer aided experimental results for various environmental scenarios revealed that compared with the conventional method (conventional P&O + MPC), for the PV power and inductor current, the undershoot and overshoot is decreased to 68% and 35% respectively under stiff environmental conditions. In addition, the settling time needed to reach a stable state is significantly reduced in the proposed system. The viability of the solution suggested is verified in MATLAB/Simulink and by hardware experimentation.

Environmental concerns and depletion of traditional energy lead to the booming development of renewable distributed generation (RDG) in the past decade. However, due to intermittent nature of renewable energy sources, to what extent RDG could provide capacity to power systems becomes a critical issue to the utility company when implementing long-term system strategic (generation expansion) planning. On the other hand, in a smart-grid frame, the popularization of different varieties of demand-side resources enables the system to operate at more flexible modes ever before. The potential variability in load demand not only introduces additional dynamics and uncertainties to the system, but could also affect the reliability benefits of RDG. Therefore, in practice, to effectively estimate the reliability value of RDG in power systems, the potential interaction between generation- and demand-side must be properly captured. In this paper, a study to assess the capacity credit (CC) of RDG in a context of distributed generation system is performed with consideration of the impacts of demand response (DR). A compound reliability model for DR is presented, which considers the uncertainties involved in both instant response and follow-on load recovery processes. On this basis, an assessment framework for the CC of RDG based on sequential Monte Carlo simulation is developed by which the inter-temporal characteristics of DR resources can be fully captured. The numerical study is implemented based on the IEEE-38 bus test case. The calculation results demonstrate that the CC of RDG would depend on a variety of factors, including penetration level, responsiveness of load demand and the correlations between RDG and DR availability. Also, it is shown that accounting for the underlying effect of DR is of absolute importance, otherwise the CC of RDG might be estimated erroneously in real practices.

With the popularity of social media, including micro-blog, mining effective information in short texts has become an increasingly important issue. However, due to the sparseness, high dimensionality and large amount of data, mining this information is a very challenging task. In this paper, we propose a method to extend the Dirichlet multinomial mixture (DMM) topic model by combining the user-LDA topic model based on internal data expansion with the potential feature vector representation of words trained on a very large external corpus (we refer to it as ULW-DMM). The experimental results show that the ULW-DMM model produces a relatively large improvement in topic consistency and classification tasks for topic modeling of microblog short texts.

In this paper, a mixture semisupervised Bayesian principal component regression-based soft sensor modeling method for nonlinear industrial process with multiple operating modes is presented. In many chemistry processes, part of output data samples may be unavailable due to the difficulties in measurement or recording. The semisupervised method is introduced to efficiently deal with the unlabeled data set. Moreover, the Bayesian regularization method is proposed to determine the unknown dimensionality of latent variables space in each submode by introducing three different formulations of two hyperparameters to construct the Gaussian prior distributions over the loading and regression matrices. The formulation of this method is derived in expectation maximization algorithm scheme, and the formulas to update unknown parameters are derived. The effectiveness of the proposed method is verified through a numerical example, the Tennessee Eastman benchmark process, and the comparisons with the existing method.

This paper presents an application of power-line communication (PLC) as a viable solution for frequency regulation in a smart microgrid. The microgrid under consideration comprises a diesel generator with a frequency controller and another synchronous generator operating in the constant power mode. Frequency regulation is achieved in the microgrid by communicating the frequency deviation (information) from the control center to the diesel generator using PLC. The information received at the other end is used for frequency regulation. A simple yet effective M -ary amplitude-shift keying (ASK) modulation-based PLC system is proposed for information transmission. A comparative analysis of M -ary ASK and M -ary frequency-shift keying (FSK)-based PLC systems is also presented. Approximate closed-form expressions for the average symbol error rate and the average achievable rate are derived for both the PLC systems, assuming that the PLC channel coefficients and the additive impulsive noise samples are derived from a log-normal distribution and a Bernoulli-Gaussian process, respectively. The results show that M -ary ASK is a better choice for PLC than M -ary FSK. Furthermore, the delay introduced in the control loop while using the proposed PLC system is negligible, and therefore, the stability of the microgrid is also ensured.

In this paper, we proposed an integrated hybrid antenna based on spoof surface plasmon polaritons (SSPPs) which combines with omnidirectional antenna (ODA) and the frequency beam scanning antenna (FBSA). The ODA is fed by the SSPPs and the operating principle of FBSA is based on the higher-order modes of the SSPPs. The radiation mechanisms of the proposed integrated antenna are that the low-pass transmission modes of SSPPs are used to excite the ODA and the higher-order modes of SSPPs are utilized to generate the frequency beam scanning waves. That is to say, the SSPP design not only serves as the feeding structure for the ODA, but also acts as the radiating element for the FBSA. Simulated results show that the proposed integrated antenna achieves two impedance bandwidths. Within the operating bandwidth 2.32 GHz to 4.42 GHz, the proposed integrated antenna achieves omnidirectional radiation patterns, which enables the simultaneous use for wireless local area network (WLAN) and 5 G applications. Meanwhile, from 11.45 GHz to 25.5 GHz, the proposed integrated antenna acts as a wide-angle frequency beam scanning characteristic and can be used for planar integrated communication systems. Additionally, a prototype of the proposed integrated hybrid antenna is fabricated and tested. The measured results have a good agreement with the simulated ones.

In traditional wireless sensor networks (WSN), data is transmitted to base stations through multiple-hop. However, multi-hop transmission will cause problems such as energy holes, uneven energy consumption, and unreliable data transmission. Aiming at the defects of traditional networks, this paper proposes a scheme for collaborative data collection using multiple mobile nodes (MN) as sink nodes. In this scenario, an important issue is how to reasonably plan the data collection path of each mobile node under a series of constraints such as energy. This paper studies routing strategy and path planning. Firstly, a dynamic clustering algorithm is used to cluster the randomly arranged sensor nodes, and then a sensor node with higher energy is manually arranged at the virtual cluster center generated by the clustering algorithm as a cluster head node to establish a data collection cluster. Then the monitoring area is divided into several parts according to the number of MN, so that the MN traverses the cluster head nodes in the respective monitoring areas for data collection. At the same time, in order to enable the MN to complete data collection within the energy limit and improve the path balance, a path-based path equalization algorithm (PEABR) is proposed to adjust the path of the MN, further reduce and equalize the path length of the MN to satisfy the constraint conditions, and optimize the path planning scheme. Finally, simulation was carried out by using Matlab simulator, and the simulation experiments which were performed in a laboratory environment verified the feasibility and effectiveness of the algorithm.

With the increase of network services, how to avoid link congestion and make full use of limited bandwidth resources in network virtualization environment have become the key challenges. In this paper, we introduce the cognitive method into virtual network topology reconfiguration and modify the connectivity to reach the target topology by sensing the traffic. Then, we formulate an optimization problem to maximize the ratio of cumulative saved resources to the square of changed link number (CSR/SCLNR) and determine which virtual link to be added or deleted. Finally, a heuristic algorithm called cognitive virtual network topology reconfiguration method based on traffic prediction and link importance (CVNTRM-TPLI) is proposed to solve this optimization problem. In the CVNTRM-TPLI, the link importance is presented in link deletion as the topological factor to avoid the ping pang effect. Also, a hybrid traffic prediction algorithm based on optimal parameter selection is put forward, where immune optimization is introduced to select the optimal parameters and the virtual network topology can react promptly to the traffic fluctuation without adding or deleting virtual link frequently. Simulation results show that the CVNTRM-TPLI not only has the highest CSR/SCLNR, but also solves the link congestion and makes full use of the limited bandwidth resources.

In the immediate aftermath of large-scale disasters, emergency logistics services play important roles in saving lives and reducing losses. Efficient relief logistics scheduling depends on the accurate transport time information for available routes. However, this information cannot be obtained precisely until a vehicle uses the road. Considering the correlation between information acquisition and logistics operations, this paper focuses on a multiperiod online decision-making problem to simulate the information acquiring process. This problem can be referenced for emergency resource scheduling scenarios in which previous decisions impact knowledge for future logistics plans. A multi-trip cumulative capacitated vehicle routing problem with uncertain transportation time is investigated as the basic model. The tradeoff between transportation efficiency and the unknown transport time discovery rate is considered in a multiobjective evolutionary algorithm (MOEA). A memetic algorithm (MA) and a robust optimization (RO) -MA for single-period post-disaster emergency logistics are also proposed to solve the problem for comparison. In these algorithms, evolutionary operators that benefit solution fixing and variation are proposed. In the experiments, a real-world instance is employed. A simulative experimental environment is established. Dynamic information gained within the process of logistics scheduling is highlighted via multi-period online optimization. Different scenarios corresponding to estimates in emergency situations are provided to validate the performance of the algorithms. The experimental results show that the hybrid strategy, MOEA+MA, can obtain the best result in more than half of the considered cases which demonstrates the necessary balance between obtaining information and transportation efficiency.

With the explosive growth of information and communication technology and its services, some popular Websites currently generate an enormous amount of Internet traffic. A content delivery network (CDN) would then become imperative for supporting such services efficiently. In this paper, we propose joint optimizing approaches for replica server placement, content caching in selected servers, and content request load assignment among the servers, aiming to minimize the ratio of unserved content request load when the network resources and server capacity are both limited. For this, we develop a mixed integer linear programming (MILP) optimization model. To mitigate the computational complexity of the MILP model, we further decompose the optimization problem into three sub-problems, including: 1) choosing the replica server nodes optimally; 2) deciding the content items to be cached in the replica servers; and 3) allocating the content request loads from users onto different servers. For these sub-problems, we develop corresponding heuristic algorithms and show that the proposed approach is not only efficient but also performs very close to the MILP model. We also find that a number of system limitations, such as different numbers of replica servers placed, link capacity, server processing capacity, and server storage capacity, jointly affect the performance of the CDN. Saturation trends are observed on the performance, which indicate that as long as sufficient resources have already been provided, augmenting the resources further may only lead to marginal additional performance improvement.

The presence of missing data is problematic in most quantitative research studies. Water distribution systems (WDSs) are not immune to this problem. In fact, missing data is an inherent feature of a WDS. There are various techniques and methods to address missing data ranging from simply deleting the data to using complex algorithms to impute missing data. This paper reviews the different imputation options available from traditional methods (such as deletion and single imputation) to more modern and advanced methods (such as multiple imputation, model-based procedures, and machine learning techniques). The concept, application, and qualitative advantages and disadvantages of these methods are discussed. In addition, a novel approach for selecting an applicable technique is presented. The approach is a “top-down bottom-up”two-prong approach for the selection of a data analysis and missing data technique. The bottom-up approach facilitates the top-down selection of a suitable technique by analyzing the data and narrowing down the selection options. As a use case, this paper also reviews techniques that are used to impute missing data in WDSs.

The use of Markov random fields (MRFs) is a common approach for performing image segmentation, where the problem is modeled using MRFs that incorporate priors on neighborhood nodes to allow for efficient Maximum a Posteriori inference. These local MRF models often result in smoothed segmentation boundaries, since they penalize the assignment of different labels to neighboring pixels and are limited in the use of long-range interactions. Although recent work on fully connected random fields and deep random fields has shown to be very promising in addressing these issues, both streams of approaches face certain limitations, which could affect inference performance and computational tractability. In this paper, we introduce the concept of deep randomly connected conditional random fields DRCRF, which fuse fully-connected random fields and deep random fields together to obtain benefits from long-range interactions while allowing for efficient inference using arbitrary potential functions. Leveraging random graph theory, the concept of stochastic cliques is incorporated into a deep CRF structure to take better advantage of long-range interactions while maintaining computational tractability. The experimental results demonstrate that the proposed DRCRF framework outperforms existing fully connected CRF frameworks and provides results comparable to the principled deep random field framework, which is among the state of the art in random field frameworks for image segmentation.

Mobile devices regularly broadcast WiFi probe requests in order to discover available proximal WiFi access points for connection. A probe request, sent automatically in the active scanning mode, consisting of the MAC address of the device expresses an advertisement of its presence. A real-time wireless sniffing system is able to sense WiFi packets and analyse wireless traffic. This provides an opportunity to obtain insights into the interaction between the humans carrying the mobile devices and the environment. Susceptibility to loss of the wireless data transmission is an important limitation on this idea, and this is complicated by the lack of a standard specification for real deployment of WiFi sniffers. In this paper, we present an experimental analysis of sniffing performance under different wireless environments using off-the-shelf products. Our objective is to identify the possible factors including channel settings and access point configurations that affect sniffing behaviours and performances, thereby enabling the design of a protocol for a WiFi sniffing system under the optimal monitoring strategy in a real deployment. Our preliminary results show that four main factors affect the sniffing performance: the number of access points and their corresponding operating channels, the signal strength of the access point and the number of devices in the vicinity. In terms of a real field deployment, we propose assignment of one sniffing device to each specific sub-region based on the local access point signal strength and coverage area and fixing the monitoring channel belongs to the local strongest access point.

Occupational health and safety hazards in the extreme work environment of underground mines remained a serious concern for both mine management and regulatory agency. Miners are often employed to perform different mining activities across the mine and are always exposed to health risks such as lung cancer. With the technology advancements, it is now possible to keep track of mine individuals and their health parameters. The current practice of health analysis is periodic in nature and is highly dependent on voluntary participation. Wearable health sensing system is an alternative solution to overcome these challenges and is able to provide insights on miners’ health conditions. Timely analysis of physiological parameters of the miners is immensely helpful to minimize the injuries and can also provide preventive measures for potential health hazards. In this paper, we propose a wireless health monitoring system, especially for underground mines. The contributions of this paper are twofold. First, it presents and discusses our proposed system architecture and solution followed by challenges of such system in the context of underground mines. Second, as a preliminary analysis, detailed discussion on the wireless link behavior for reliable data transmission and communication are presented. We performed real-world experimental measurements in an operational underground coal mine considering several deployment settings in straight, near face and curved mine galleries. The communication metrics (e.g., received signal strength and packet reception rate) are extensively evaluated.

The two-way relaying (TWR) technique has been confirmed to achieve higher spectral efficiency and average sum rate compared with the one-way relaying (OWR) technique in ultra-dense next generation networks with limited spectal resources. In this paper, an enhanced secrecy cooperative TWR scheme for cooperative non-orthogonal multiple access (NOMA) networks against untrusted relaying is highlighted. Specifically, with the application of NOMA principle, a base station (Bs) communicates with two trusted users, i.e., namely a near and a far users, where the communication with the far user takes place only via an untrusted relay (UR) employing both TWR and analog network coding (ANC) mechanisms. To minimize information leakage at the UR, the far user transmits its uplink signal simultaneously with Bs’s downlink signal to confuse the eavesdropping capability of the UR by increasing inter-user interference (IUI). To investigate the benefits of the proposed scheme, asymptotic lower bound expressions of the ergodic secrecy sum rate (ESSR) of uplink/downlink rates and their scaling law are derived to characterize the secrecy performance. The system parameters have been carefully studied to maitain the desired ESSR performance where we obtain an optimal value for the uplink power sharing coefficient with an arbitrary known value of the downlink one. Analytical and simulation results show that the proposed scheme can achieve scaling gain of $\boldsymbol {\frac {1}{8}\ln {\rho }}$ of positive ESSR over the untrusted OWR scheme with adaptive uplink/downlink jamming and a significant gain over the other conventional NOMA uplink/downlink schemes in two time slots of communication.

To ensure a long-time stable operation of the rolling bearing, it is important to accurately assess their working performance, especially the incipient degradation based on the massive service process data. As a new and effective tool, deep learning model is applied widely in the field of fault diagnosis but limited to rare labeled data. In this paper, a bearing performance assessment method based on signal component tracking is proposed to realize the bearing degradation detection. More general features are obtained by local convolution operation to represent the local characteristics in the spectrum or time–frequency distribution of vibration signal, which follows the forward features mapping process of the convolutional neural network (CNN). Then, a novel quantification criterion based on the comparison of those local features is used to provide the selection strategy of optimal fault components. The proposed method takes into account the abnormal information in degradation monitoring and utilizes it to achieve bearing incipient fault diagnosis. The experimental results prove that the features extracted by the proposed method possess high recognition efficiency when being used in incipient failure detection and diagnosis.

Vehicular ad hoc networks (VANETs) are a special type of wireless ad hoc network that requires highly scalable routing strategies to establishing reliable end-to-end communication. Because of the high dynamic of VANETs, the mobility of vehicle nodes increases the control traffic overhead. Accordingly, establishing reliable end-to-end communication paths depends entirely on the routing mechanism and the type of nodes mobility information. In this paper, we propose a new improvement to the mechanism of the Optimized Link State Routing Protocol(OLSR) protocol, named Cluster-based Adept Cooperative Algorithm (CACA), where each vehicle estimate a reliable low-overhead path using the cluster-based QoS algorithm. The CACA algorithm is introduced to improve the ability of the MPR scheme for maintaining long-lived routes. Moreover, the network scalability is enhanced by adaptively selecting most sustainable paths based on a signal strength beacon and the mobility degree of a node, which reduces significantly minimizes the size of control messages overhead as well the routing tables recalculation process. Simulation experiments using the network simulator are presented to demonstrate the effectiveness of our solution. The results show that the proposed algorithm can improve network performance effectively relative to other algorithms.

Device-free localization (DFL), requiring no extra devices equipped with a target, is an important field of research on the Internet of Thing (IoT). Energy efficiency issue is essential for the development of the IoT, but seldom of the existing papers are focus on it. So we investigate this issue with quantized data of only several bits under the compressive sensing (CS) framework, which can both reduce the required wireless link number and the bit number in the DFL scheme. First, through exploiting the discrete property of CS theory, we calculate the discrete measurement probability bypass computing the complex or uncalculated measurement probability density function (pdf), which can well represent the measurement distribution characteristic. Second, we design a unique quantization scheme for each wireless link according to their measurement probability and build a novel DFL model by analyzing the quantization error. Third, a new DF-QVBI algorithm is proposed to recover the target location, which can make great use of the quantization error. Finally, numerical simulations show the superiority and robustness of the proposed method.

In the past two decades, reversible data hiding (RDH), also referred to as lossless or invertible data hiding, has gradually become a very active research area in the field of data hiding. This has been verified by more and more papers on increasingly wide-spread subjects in the field of RDH research that have been published these days. In this paper, the various RDH algorithms and researches have been classified into the following six categories: 1) RDH into image spatial domain; 2) RDH into image compressed domain (e.g., JPEG); 3) RDH suitable for image semi-fragile authentication; 4) RDH with image contrast enhancement; 5) RDH into encrypted images, which is expected to have wide application in the cloud computation; and 6) RDH into video and into audio. For each of these six categories, the history of technical developments, the current state of the arts, and the possible future researches are presented and discussed. It is expected that the RDH technology and its applications in the real word will continue to move ahead.

Directional modulation (DM) as a physical layer security technique has been studied from many different aspects recently. Normally all existing designs based on antenna arrays lead to varying weight coefficients for different antennas and for different signal symbols, which poses a particular challenge for feed circuits design in analogue implementation. In this paper, to reduce the implementation complexity, a constant magnitude constraint is proposed for the first time, and the resultant non-convex constraint can be modified to a convex form so that the problem can be solved conveniently by existing convex optimisation toolboxes. Design examples are provided to show the effectiveness of the proposed design.

Multiple-object tracking (MOT) has received an increasing attention due to the rapid development of autonomous driving. However, the MOT problem is still challenging mainly due to the occlusion and scale variation. Motivated by the fact that the discriminative correlation filters-based (DCFB) tracking algorithms can tackle these problems and significantly improve the accuracy of single object tracking, how to exploit the DCFB tracking algorithms for MOT is worthy studying. Moreover, the corrupted training samples due to the occlusion make DCFB tracking methods to update the appearance model of target uncorrected and result in tracking drift. In this paper, we exploit Markov decision process to integrate the DCFB tracking method into our MOT framework and address the update problem of the appearance model in DCFB tracking method. Moreover, in order to overcome the challenges of occlusion and scale variation, to prevent target drift during tracking, we use two DCFB trackers with different update frequencies and a novel update strategy to predict the location of targets. The part-based method is used to extract robust features to tackling the challenges of occlusion and scale change. In order to verify the efficiency of our algorithm, experiments are performed based on KITTI tracking benchmark. The results demonstrate that our method achieves state-of-the-art performance and outperforms the state-of-the-art algorithms in road scenarios.

Hyperspectral transmission imaging may provide a means for rapid screening of breast tumors, but tissue has a strong nature of scattering, thus causing a great difficulty in identifying heterogeneity. In this paper, a combination of frame accumulation and deep learning was proposed to detect heterogeneity, and we designed the simulation experiment of collecting phantom images. On the basis of frame accumulation preprocessing, the heterogeneous detection is performed on multispectral images by using faster regions with convolutional neural networks (R-CNN) features and a single shot multibox detector (SSD), two typical detection frameworks of deep learning. The results show that the mean average precision (mAP) of faster R-CNN and SSD reach 90.8% and 95.1%, respectively, when three classes (including background) are detected with the help of the dataset provided in this paper, and the mAP of two frameworks both reach 99.9% when two classes (including background) are detected. The detection efficiency of the SSD is higher than faster, SSD’s detection speed can reach 50 fps, and the detection accuracy of the images after frame accumulation preprocessing is higher than that without frame accumulation processing. In summary, we validate the possibility of employing faster R-CNN and SSD to detect heterogeneity in multispectral images based on frame accumulation that improves image grayscale resolution, and it has a certain degree of reference significance for the application of deep learning in multispectral image detection.

Negative capacitance field-effect transistor (NCFET) addresses one of the key fundamental limits in technology scaling, akin to the non-scalable Boltzmann factor, by offering a sub-threshold swing below 60 mV/decade. In this paper, we investigate how the NCFET technology can open the doors not only for the continuation of Moore's law, which is approaching its end, but also for reviving Dennard's scaling, which stopped more than a decade ago. We study NCFET for the 7-nm FinFET technology node, from physics to processors and demonstrate that prior trends in processor design with respect to voltage and frequency can be revived with the NCFET technology. Our work focuses on answering the following three questions towards drawing the impact of NCFET technology on computing efficiency: In how far NCFET technology will enable processors: 1) to operate at higher frequencies without increasing voltage; 2) to operate at higher frequencies without increasing power density, which is substantial, because maintaining on-chip power densities under tight constraints due to limited cooling capabilities is inevitable; and 3) to operate at lower voltages, while still fulfilling performance requirements, which is substantial for the emerging Internet of Things, in which available power budgets for such devices are typically very restricted.

In this paper, a new image segmentation method is proposed by combining the FCM clustering algorithm with a rough set theory. First, the attribute value table is constructed based on the segmentation results of FCM under different clustering numbers, and the image is divided into several small regions based on the indistinguishable relationship of attributes. Then, the weight values of each attribute are obtained by value reduction and used as the basis to calculate the difference between regions and then the similarity evaluation of each region is realized through the equivalence relationship defined by the difference degree. Finally, the final equivalence relation defined by similarity is used to merge regions and complete image segmentation. This method is validated in the segmentation of artificially generated images, brain CT images, and MRI images. The experimental results show that compared with the FCM method, the proposed method can reduce the error rate and achieve better segmentation results for the fuzzy boundary region. And, the experimental results also prove that the algorithm has strong anti-noise ability.

Recently, many countries have spent great efforts on wind power generation. Although there have been many methods in the field of wind power forecasting, the persistence statistics model based on historical data is still being challenged due to the randomness and uncontrollability in wind power. Hence, a more accurate and effective wind power forecasting method is still required. In this paper, a new forecasting method is proposed by combining stacked auto-encoders (SAE) and the back propagation (BP) algorithm. First, an SAE with three hidden layers is designed to extract the characteristics from the reference data sequence, and the subsequent loss function is used in the pre-training process to obtain the optimal initial connection weights of the deep network. Second, after adding one output layer to the stacked auto encoders, the BP algorithm is used to fine tune the weights of the whole network. To achieve the best network architecture, the particle swarm optimization is adopted to decide the number of neurons of the hidden layer and the learning rate of each auto encoder. Experimental results show that, for short-term wind power forecasting, the proposed method achieves more stable and effective performance than the existing BP neural network and support vector machines. The improvement in accuracy is 12% on average under different time steps.

In this paper, the design of reduced-order and full-order interval observers for discrete-time system is investigated. First, the original system is transformed into a descriptor system with the ability to estimate the unknown noise of sensors. Next, a reduced-order interval observer design method for descriptor system is presented. The state matrix of the system is obtained by linear transformation, and a Sylvester equation is constructed to find the appropriate observer gain matrix. Then, an existing design method of full-order interval observer is introduced in this paper. Finally, a simulation example is given to demonstrate that the reduced-order interval observer has more accurate interval estimation results than the full-order interval observer.

The scope, scale, and intensity of real, as well as potential attacks, on the Smart Grid have been increasing and thus gaining more attention. An important component of Smart Grid cybersecurity efforts addresses the availability and access to the power and related information and communications infrastructures. We overload the term, Denial-of-Service (DoS), to refer to these attacks in the Smart Grid. In this paper, we provide a holistic and methodical presentation of the DoS attack taxonomies as well as a survey of potential solution techniques to help draw a more concerted and coordinated research into this area, lack of which may have profound consequences. To the best of our knowledge, the literature does not have such a comprehensive survey study of the DoS attacks and solutions for the Smart Grid.

Fingerprint-based recognition is widely deployed in different domains. However, the traditional fingerprint recognition systems are vulnerable to presentation attack, which utilizes an artificial replica of the fingerprint to deceive the sensors. In such scenarios, Fingerprint Liveness Detection (FLD) is required to ensure the actual presence of a live fingerprint. In this paper, a fingerprint matching method fused with liveness detection is proposed. Firstly, the similarity between two fingerprint images is calculated based on Octantal Neatest-Neighborhood Structure (ONNS), where the closest minutia to the central minutia is found from each sector of octant. Secondly, the FLD score of the fingerprint image is obtained by using the modified Residual Network (Slim-ResCNN). Finally, a score-level fusion is performed on the results of fingerprint matching and FLD by generating interaction features and polynomial features as the score feature vector. To classify whether a fingerprint image is a genuine live fingerprint or a spoof attack (including impostor live and fake fingerprints), the score feature vector is processed using logistic regression (LR) classifiers. The proposed method won the first place in the Fingerprint Liveness Detection Competition 2019 with an overall accuracy of 96.88%, which indicates it can effectively protect the fingerprint recognition systems from spoof attacks.

Nowadays, Wireless Sensor Networks (WSNs) are playing a vital and sustainable role in many verticals touching different aspects of our lives including civil, public, and military applications. WSNs majorly consist of a few to several sensor nodes, that are connected to each other via wireless communication links and require real-time or delayed data transfer. In this paper, we propose an autonomous Unmanned Aerial Vehicle (UAV)-enabled data gathering mechanism for delay-tolerant WSN applications. The objective is to employ a self-trained UAV as a flying mobile unit collecting data from ground sensor nodes spatially distributed in a given geographical area during a predefined period of time. In this approach, two Reinforcement Learning (RL) approaches, specifically Deep Deterministic Gradient Decent (DDPG) and Q-learning (QL) algorithms, are jointly employed to train the UAV to understand the environment and provide effective scheduling to accomplish its data collection mission. The DDPG is used to autonomously decide the best trajectory to adopt in an obstacle-constrained environment, while the QL is developed to determine the order of nodes to visit such that the data collection time is minimized. The schedule is obtained while considering the limited battery capacity of the flying unit, its need to return the charging station, the time windows of data acquisition, and the priority of certain sensor nodes. Customized reward functions are designed for each RL model and, through numerical simulations, we investigate their training performances. We also analyze the behavior of the autonomous UAV for different selected scenarios and corroborate the ability of the proposed approach in performing effective data collection. A comparison with the deterministic optimal solution is provided to validate the performance of the learning-based approach.

Frame arrival detection is the first crucial task for a digital communication receiver. In this paper, we propose a new frame arrival detection method based on the concept of matched filtering. The proposed training sequence consists of three repeated subsequences, each including a few repeated segments and exhibiting high sparsity in frequency domain. The proposed detection method includes the following two stages. The first matched filtering stage employs the subsequence as the filter coefficients, which matches the frequency sparsity of the received subsequence and could greatly improve the output signal to noise ratio. The second stage adopts delayed autocorrelation on the filtered signal to detect the presence of training sequence. It is demonstrated that the proposed method could outperform the conventional methods in terms of both missed detection and false alarm probability. We derive the theoretical analysis for both missed detection and false alarm performance. We further extend our performance analysis and numerical evaluation to the system with carrier frequency offset (CFO). The results illustrate accuracy of the analytical results and robustness of the proposed method against practical levels of CFO.

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Edge Computing has grown into a key solution for coping with the stringent latency requirements of 5G scenarios. Nevertheless, the edge node placement problem raises critical concerns regarding deployment and operational expenditures (i.e., mainly due to the number of nodes to be deployed),current backhaul network capabilities, non-technical placement limitations, etc. In this paper, a novel framework called EdgeON is presented aiming at reducing the overall expenses when deploying and operating an Edge Computing (EC) network, taking into account the usage and characteristics of the in-place backhaul network. The framework implements several placement and optimization strategies targeting the heavily constrained network-aware Edge Node Placement Problem (ENPP). The results obtained by our solution are promising, achieving an average of30%less Edge Nodes (ENs) deployed and 25% higher average usage ratio when compared to other widely used heuristics. Furthermore, our strategy achieved as core offset of less than2%in comparison to the implemented Mixed Integer Linear Programming (MILP).

This paper presents a novel algorithm for solving a Student-Project Allocation problem, a variation of a two-sided matching algorithm, where a large number of students are allocated to numerous projects with limited available allocation places. Several new or improved algorithms are proposed, inspired by recommender systems and combined into a genetic algorithm. The main two algorithms are (i) variating the master list of student average grade ranking inspired by a fuzzy approach and (ii) extending the incomplete project preference lists by exploring the similarities of student choices, thus minimizing the number of randomly allocated students. The algorithm was implemented for allocating more than 500 students to more than 200 projects, in a course at the University of Zagreb, Faculty of Electrical Engineering and Computing. We compared our algorithm with the Deferred Acceptance (DA) Algorithm to check its validity. Using the fuzzy approach, the number of unallocated students decreased by 10%, with almost no effect to the top-ranked students. The combined usage of all proposed algorithms increased the number of successfully allocated students by more than 25%.

Hajj, the annual pilgrimage to Makkah, is one of the most massive gathering events in the world. It is a mandatory religious activity, once in a lifetime for every sane well-off Muslim. Every year around 3 million Muslims across the globe visit Makkah to perform this spatiotemporally constrained set of religious activities, like circumambulation around the Kaaba and the encampment at Mina, Arafat and Muzdalifah. The key ritual at Rami-al-Jamarat (the stoning-of-the-pillars) is a place seen particularly crowded. The diversified nature of the crowd in terms of race, age, language and culture brings several administrative challenges for local organizers whose responsibility is to ensure the smooth organization of the event. The advent of digital technologies has helped researchers to explore and propose modern methods for better crowd management and devise control strategies to organize such a mass gathering. In this paper, we present a taxonomy that summarizes our survey on the utilization of technology areas for providing needful services and improving crowd management during the Hajj season. These technologies are Wireless, Computer Vision, Spatial Computing, Data Analytics, Mobile Applications, Immersive Technologies, and Crowd Modelling and Simulation. We have also included the current research work and its implementation plan for Hajj 2020. One dimension of the taxonomy is to understand different possible technologies for Crowd management. Another dimension is to understand the impact of technologies to handle such a massive crowd. The discussion section covers a comparison of technologies with multiple aspects that may help researchers to contribute more towards dealing with future mass gathering events.

High accuracy relative navigation (RELNAV) information is crucial for datalink network members in the situation of joint operations, especially the situations that some datalink network members may not be able to obtain their location with absolute navigation. In this paper, relative navigation algorithm based on JTIDS/INS/BA is proposed, in this system datalink joint units(JUs) are positioned in two-dimensional plane with multi-slots joint maximum likelihood estimation (MLE), and altitude is located by a height filter independently, which overcome the problem of poor positioning geometry of altitude. Multi-slots joint MLE convert TOA measurements in different slots to one moment with short-time inertial navigation system(INS) information, which lead to a higher localization accuracy in two-dimensional plane. We simulate a joint tactical information distribution system (JTIDS) network with multiple members, to verify the performance and analyze the influence factors of the proposed algorithm. The simulation results show that compared to EKF that process TOA measurements sequentially in each navigation slot and MLE without multi-slots joint processing, the multi-slots joint MLE relative navigation algorithm based on INS/JTIDS/BA performs better in two-dimensional plane location, and the height filter based on barometric altimeter (BA) improves the relative height positioning accuracy greatly.

Improving maximum power point tracking ability (MPPTA) and smoothing electric power fluctuation (EPF) are two important goals for optimizing wind power generation. Sufficient works have been done on both goals separately, but the multi-objective optimization of wind energy conversion system (WECS) is lack of theoretical analysis. In this paper, the small signal analysis method is applied to get a frequency-domain declaration for both MPPTA and EPF. The analysis results show when applying traditional optimal torque control (OTC), a larger moment of inertia of WECS is preferred for smoothing EPF, while a smaller moment of inertia is preferred for improving MPPTA, i.e., the two optimization goals contradict with each other. Furthermore, the existing control strategies for improving MPPTA are summarized as virtual-inertia embedded OTC, which turns out to have adverse impacts on EPF. To simultaneously optimize these two contradictory goals, a novel frequency-distinct optimal torque control approach is proposed, and a novel criterion for evaluating MPPTA is presented to facilitate controller parameter design. The analysis results and the proposed control strategy are fully verified by refined co-simulation platform based on GH Bladed and real time digital simulator.

Detecting the foreground region of interest (ROI) for video sequences is an important issue both for video codecs and monitoring systems. In this paper, we propose a flow-process-based method to detect foreground ROI using four steps: global motion compensation, motion block extraction, multi-layer segmentation, and model updating. The former two procedures extract the foreground motion blocks and form a motion mask, and the latter two procedures remove the pixels belonging to the background inside the motion mask and update the color distributions of the background model. In addition, a block-based to pixel-based detection scheme is proposed to allow detection flexibility. Another benefit of the proposed method is that it can be embedded in video codecs for real-time ROI detection and encoding. Experimental results demonstrate that our method achieves improved performance in terms of both detection accuracy and time consumption.

In this paper, the capacitated location-routing problem with fuzzy demands (CLRP-FD) is considered, which simultaneously solves two problems: locating the facilities and designing the vehicle routes among the established facilities and customers. In the CLRP-FD, the capacities of the employed vehicles and established facilities cannot exceed, and the demands of the customers are assumed to be triangular fuzzy variables. To model the CLRP-FD, a fuzzy chance constrained programming approach is designed using fuzzy credibility theory. To solve this problem, a hybrid particle swarm optimization (HPSO) algorithm, which includes a stochastic simulation and a local search strategy based on the variable neighborhood search algorithm, is proposed. Finally, the influence of the value of the dispatcher preference index ( DPI ) on the total distribution cost is analyzed by conducting numerical experiments. To evaluate the efficiency of the proposed HPSO and the performance of the CLRP-FD model, the results obtained using the HPSO were compared with their corresponding lower bound provided using the CPLEX solver. Moreover, we also evaluated the performance of HPSO through computational experiments on some well-known benchmark CLRP instances. The numerical results show that the proposed HPSO is competitive, and can give a satisfactory solution in a reasonable amount of time.

Recently, a new strong optimization algorithm called marine predators algorithm (MPA) has been proposed for tackling the single-objective optimization problems and could dramatically fulfill good outcomes in comparison to the other compared algorithms. Those dramatic outcomes, in addition to our recently-proposed strategies for helping meta-heuristic algorithms in fulfilling better outcomes for the multi-objective optimization problems, motivate us to make a comprehensive study to see the performance of MPA alone and with those strategies for those optimization problems. Specifically, This paper proposes four variants of the marine predators’ algorithm (MPA) for solving multi-objective optimization problems. The first version, called the multi-objective marine predators’ algorithm (MMPA) is based on the behavior of marine predators in finding their prey. In the second version, a novel strategy called dominance strategy-based exploration-exploitation (DSEE) recently-proposed is effectively incorporated with MMPA to relate the exploration and exploitation phase of MPA to the dominance of the solutions—this version is called M-MMPA. DSEE counts the number of dominated solutions for each solution—the solutions with high dominance undergo an exploitation phase; the others with small dominance undergo the exploration phase. The third version integrates M-MMPA with a novel strategy called Gaussian-based mutation, which uses the Gaussian distribution-based exploration and exploitation strategy to search for the optimal solution. The fourth version uses the Nelder-Mead simplex method with M-MMPA (M-MMPA-NMM) at the start of the optimization process to construct a front of the non-dominated solutions that will help M-MMPA to find more good solutions. The effectiveness of the four versions is validated on a large set of theoretical and practical problems. For all the cases, the proposed algorithm and its variants are shown to be superior to a number of well-known multi-objective optimization algorithms.

Background and objective: Blood pressure (BP) is one of the crucial indicators that contains valuable medical information about cardiovascular activities. Developing photoplethysmography (PPG)-based cuffless BP estimation algorithms with enough robustness and accuracy is clinically useful in practice, due to its simplicity and noninvasiveness. In this paper, we have developed and tested two frameworks for arterial blood pressure (ABP) estimation at the central arteries using photoplethysmography and electrocardiogram. Methods: Supervised learning, as adapted by most studies regarding this topic, is introduced by comparing three machine learning algorithms. Features are extracted using semi-classical signal analysis (SCSA) tools. To further increase the accuracy of estimation, another BP estimation algorithm is presented. A single feed-forward neural network (FFNN) is utilized for BP regression with PPG features, which are extracted by SCSA and later used by FFNN as the network input. Both BP estimation algorithms perform robustly against MIMIC II database to guarantee statistical reliability. Results: We evaluated the performance against the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS) standards, and we have compared the standard deviation (STD) of estimation error with current state of the arts. With the AAMI standard, the first method yields comparable performance against existing literature in the estimation of BP values. Regarding the BHS protocol, the second method achieves grade A in the estimation of BP values. Conclusion: We conclude that by using the PPG signal in combination with informative features from the Schrödinger’s spectrum, the BP can be non-invasively estimated in a reliable and accurate way. Furthermore, the proposed frameworks could potentially enable applications of cuffless estimation of the BP and development of mobile healthcare device.

This paper addresses the performance of downlink beamforming in fog radio access networks (F-RANs) with distributed antennas. The wireless channel is modeled as Rician fading. We analyze the asymptotic behavior of the signal-to-interference-plus-noise ratio (SINR) and the data rate of maximum ratio transmission (MRT) and regularized zero-forcing (RZF) beamformers in the large system limit, where the numbers of antennas and users go to infinity while keeping a fixed ratio. In some practical F-RAN environment, it is difficult to perform centralized processing and share channel state information (CSI) globally, so distributed beamforming schemes are of interest. Therefore, we propose the local zero-forcing (ZF) beamformer that relies only on local CSI. The simulation results show that the asymptotic results of MRT and RZF are accurate for finite system size, and the local ZF beamformer provides a tradeoff between MRT and RZF in terms of CSI sharing complexity and interference control ability.

This paper presents a compact, uni-planer wide band Multiple-Input-Multiple-Output (MIMO) antenna. The designed antenna array has a compact size of (26mm x 31mm) covering a wide frequency band from 3.1GHz - 11GHz. A partial ground plane shared by two radiating patches is used. A ground stub and a single column Electromagnetic Bandgap (EBG) structure in between the two radiating patches results in very low mutual coupling in the designed antenna. The proposed MIMO-antenna was evaluated through different performance metric indicators like channel capacity loss, far-field radiation pattern, S-parameters, envelope correlation coefficient, peak gain, diversity-gain and radiation efficiency. Our designed MIMO antenna has a very high isolation amongst the MIMO antennas ( $\text{S}_{21} < -25$ dB), a high Diversity-Gain (DG $>9.995$ dB), a very low Envelope Co-relation Coefficient of (ECC < 0.001) and low Channel-Capacity-Loss (CCL < 0.1bits/s/Hz). The said antenna has an average radiation efficiency of 85.5% and a peak gain of (5.67dB) within the ultra-wideband (UWB) spectrum. A 0.8mm thick FR-4 substrate is used to fabricate the MIMO-antenna and tested in an anechoic chamber. The measured results closely match with the simulated results.

With the popularity of Android smartphones, malicious applications targeted Android platform have explosively increased. Proposing effective Android malware detection method for preventing the spread of malware has become an emerging issue. Various features extracted through static and dynamic analysis in conjunction with machine learning algorithm have been the mainstream in large-scale malware identification. In general, static analysis becomes invalid in detecting applications which adopt sophisticated obfuscation techniques like encryption or dynamic code loading. However, dynamic analysis is suitable to deal with these evasion techniques. In this paper, we propose an effective dynamic analysis framework, called EnDroid, in the aim of implementing highly precise malware detection based on multiple types of dynamic behavior features. These features cover system-level behavior trace and common application-level malicious behaviors like personal information stealing, premium service subscription, and malicious service communication. In addition, EnDroid adopts feature selection algorithm to remove noisy or irrelevant features and extracts critical behavior features. Extracting behavior features through runtime monitor, EnDroid is able to distinguish malicious from benign applications with ensemble learning algorithm. Through experiments, we prove the effectiveness of EnDroid on two datasets. Furthermore, we find Stacking achieves the best classification performance and is promising in Android malware detection.

High-performance single-ended wideband and balanced bandpass filters loaded with stepped-impedance stubs are proposed in this paper. For the proposed wideband filter and the differential mode of the proposed balanced filter, two transmission zeros near the passband are realized by using simple stepped-impedance stubs, which enhances the passband selectivity of these new filters. The analytical and accurate design equations for transmission poles and zeros of these single-ended and balanced filters are provided based on the scattering-parameters theory. The single-ended bandpass filter, employing a T-shaped structure to produce a wide passband, is achieved with 3-dB fractional bandwidth of 79.33%, 19-dB passband return loss, and 35-dB harmonic suppression ( $1.56f_{\mathrm { {0}}}$ – $2.51f_{\mathrm { {0}}}$ , $f_{\mathrm { {0}}}$ is the center frequency of the wide passband). The 3-dB differential-mode fractional bandwidth of 33% with 22-dB passband return loss and the 30-dB harmonic suppression ( $1.29f_{\mathrm { {0}}}$ – $2.67f_{\mathrm { {0}}})$ are obtained in the proposed balanced bandpass filter. In addition, by inserting open-circuit stubs into the proposed balanced bandpass filter, over 25-dB common-mode suppression is obtained from 0 to $2.67f_{\mathrm { {0}}}$ . Good agreement is observed between the simulation and measurement results.

Emerging deep learning (DL) techniques have greatly improved pedestrian reidentification (PRI) performance. However, the existing DL-based PRI methods cannot learn robust feature representations owing to the single view of query images and the limited number of extractable features. Inspired by generative adversarial networks (GANs), this paper proposes a novel PRI method based on a pedestrian multiview GAN (PmGAN) and a classification recognition network (CRN). The PmGAN consists of three generators and one multiclass discriminator. The three generators produce pedestrian images from the front, side and back, while the multiclass discriminator determines whether the input image is a real image or a generated image. In addition to expanding the existing pedestrian datasets, the PmGAN can generate pedestrian images from front, side and back views based on a given query image and thereby increase the feature semantic space of the query image. To verify the performance of our method, the PmGAN was compared with mainstream pedestrian image generation models, and then the proposed method was contrasted with mainstream PRI methods. The results show that the proposed PmGAN greatly improved the performance of mainstream PRI methods. For example, the combination of the PmGAN and Pyramidal increased the mean average precision (mAP) on three common datasets by 1.2% on average. The research findings provide new insights into the application of multiview generation in PRI tasks.

Currently, to meet the requirements of modern power systems as fully and efficiently as possible, the electricity markets have diversified greatly. Under these conditions, it becomes difficult for a producer to determine the structure of transactions that is financially optimal. Starting from the operational rules of the power systems that have shaped the electricity markets structure, the objective of this paper is to develop an electricity market simulator model that includes the basics of a best practice guide for producers that compete on various electricity markets to carry out the trading activities and enhance their financial results. The market simulator model considers both the bilateral long- or mid-term agreements and short-term offers on day-ahead, ancillary services and balancing markets providing the entire trading scenario and associated cash-flow and risks. Its significance consists in assisting the producer to plan its resources and create projections by performing multiple trading scenarios and selecting the best one. Thus, this paper proposes to uncover constraints and business rules for a simulator model assisting the market players to access the electricity markets and select the best option using Multiple-Criterial Decision-Making (MCDM) methods (Electre, Topsis, Analytical Hierarchy Process) or the weighted Euclidean distance. The simulations comprise four trading scenarios for different types of producers (gas or fossil-powered generators) generating 100 MW, that are ordered by independent criteria. The results obtained with MCDM and the proposed method showed that they indicated the same scenario as the best trading option based on the type of the producer.

In this paper, a mode-switching sliding controller with the nonlinear gain generated by a fuzzy logic controller is put forward to enhance the tracking performance in an electro-pneumatic position control system which includes four solenoid ON-OFF valves and a double rod-end cylinder. The proposed method is designed to increase the response speed and reduce the overshoots by utilizing the nonlinear gain which is determined by a fuzzy logic controller. In addition, several improvements are made in the mode-switching sliding controller originally from previous works. The mode-switching strategy is totally redesigned and a hysteresis control scheme is introduced to the new mode-switching sliding controller. Moreover, the improved controller is properly combined with the fuzzy nonlinear gain. Simulation and experiments with various kinds of input are carried out to verify the effectiveness of the proposed method. All the experiments are conducted on a field programmable gate array platform, and the experimental results validate the reliability of the novel scheme.

This paper presents an effective constrained multiple model particle filtering (CMMPF) for bearings-only maneuvering target tracking. In the proposed algorithm, the process of target tracking is factorized into two sub-problems: 1) motion model estimation and model-conditioned state filtering according to the Rao–Blackwellised theorem and 2) the target dynamic system is modeled by multiple switching dynamic models in a jump Markov system framework. To estimate the model set, a modified sequential importance resampling method is used to draw the model particles, which can be restricted into the feasible area coincide with the constrained bound. To the model-conditioned state nonlinear filtering, a truncated prior probability density function is constructed by utilizing the latest observations and auxiliary variables (target spatio–temporal features), which can guarantee the diversity and accuracy of the sampled particles. The tracking performance is compared and analyzed with other conventional filters in two scenarios: 1) uniform and time-invariant sampling scenario and 2) non-uniform and sparse sampling scenario. A conservative Cramer–Rao lower bound is also introduced and compared with the root mean square error performance of the suboptimal filters. Simulation results confirm the superiority of CMMPF algorithm over the other existing ones in comparison with respect to accuracy, efficiency, and robustness for the bearings-only target tracking system, especially for the aperiodic and sparse sampling environment.

Neuromorphic computing systems are an emerging field that takes its inspiration from the biological neural architectures and computations inside the mammalian nervous system. The spiking neural networks (SNNs) mimic real biological neural networks by conveying information through the communication of short pulses between neurons. Since each neuron in these networks is connected to thousands of others, high bandwidth is required. Moreover, since the spike times are used to encode information in SNN, very low communication latency is also necessary. On the other hand, the combination of Two-dimensional Networks-on-Chip (2D-NoC) and Three-dimensional Integrated Circuits (3D-ICs) can provide a scalable interconnection fabric in large-scale parallel SNN systems. Although the SNNs have some intrinsic fault-tolerance properties, they are still susceptible to a significant amount of faults; especially, when we talk about integrating the large-scale SNN models in hardware. Consequently, the need for efficient solutions capable of avoiding any malfunctions or inaccuracies, as well as early fault-tolerance assessment, is becoming increasingly necessary for the design of future large-scale reliable neuromorphic systems. This paper first presents an analytical model to assess the effect of faulty connections on the performance of a 3D-NoC-based spiking neural network under different neural network topologies. Second, we present a fault-tolerant shortest-path k-means-based multicast routing algorithm (FTSP-KMCR) and architecture for spike routing in 3D-NoC of spiking neurons (3DFT-SNN). Evaluation results show that the proposed SP-KMCR algorithm reduces the average latency by 12.2% when compared to the previously proposed algorithm. In addition, the proposed fault-tolerant methodology enables the system to sustain correct traffic communication with a fault rate up to 20%, while only suffering 16.23% longer latency and 5.49% extra area cost when compared to the baseline architecture.

This paper addresses the problem of fault-tolerant containment control (FTCC) for linear multi-agent systems (MASs) with process faults. First, distributed observers for followers are designed by utilizing their relative output estimation errors. Then, a new distributed adaptive FTCC law based upon the output regulation theory is proposed, where a distributed adaptive observer is implemented to estimate the synthesized information for leaders. It is shown that no matter whether there exist faults or not, all the followers can asymptotically move into a convex hull formed by the leaders. Finally, an example is given to illustrate the effectiveness of the designed distributed control law.