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This paper analyzes the modeling of a computer-aided piano music automatic notation algorithm, combines the influence of music on psychological detachment, and designs the piano music automatic notation algorithm in psychological detachment model construction. This paper investigates the multiresolution time-frequency representation constant Q-transform (CQT), which is common in music signal analysis, and finds that although CQT has higher frequency resolution at low frequencies, it also leads to lower temporal resolution. The variable Q-transform is introduced as a tool for multibasic frequency estimation of the time-frequency representation of music signals, which has better temporal resolution than CQT at the exact frequency resolution and efficient coefficient calculation. The short-time Fourier transform and constant Q-transform time-frequency analysis methods are implemented, respectively, and note onset detection and multibasic tone detection are implemented based on CNN models. The network structure, training method, and postprocessing method of CNN are optimized. This paper proposes a temporal structure model for maintaining music coherence to avoid manual input and ensure interdependence between tracks in music generation. This paper also investigates and implements a method for generating discrete music events based on multiple channels, including a multitrack correlation model and a discretization process. In this paper, the automatic piano music notation algorithm can play an influential role in significantly enhancing the actual effect of psychological detoxification. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the direction of arrival (DOA) estimation problem with unfolded coprime linear array (UCLA) and propose a low computational complexity signal-subspace fitting (SF) algorithm. SF algorithm is able to achieve excellent DOA estimation performance while it requires global angular search (GAS). Especially in the several source signals situation, expensive complexity cost causes. To decrease computational complexity, we propose an initialized based SF (ISF) algorithm, which involves the several one dimensional (1D) partial angular search (PAS) instead of the multidimensional GAS. Consequently, the complexity is significantly decreased. Due to the full utilization of the array aperture, the proposed method in UCLA can attain better performance than general CLA (GCLA). In addition, as the SF is attractive in practical application, the proposed ISF algorithm lowers the computational cost, while achieving almost approximate estimation performance as traditional SF and noise subspace fitting (NF). Moreover, numerical simulations are provided and verify the effectiveness and the superiority of the proposed algorithm for the UCLA. [ABSTRACT FROM AUTHOR]

This paper deals with the design of a novel modified supertwisting fast nonlinear sliding mode controller (MSTFNSMC) to stabilize a quadrotor system under time-varying disturbances. The suggested control strategy is based on a modified supertwisting controller with a fast nonlinear sliding surface to improve the tracking performance. The paper suggests a simple optimization tool built-in MATLAB/Simulink to tune the proposed controller parameters. Fast convergence of state variables is established by using a nonlinear sliding surface for rotational and translational subsystems. The modified supertwisting controller is developed to suppress the effect of chattering, reject disturbances, and ensure robustness against external disturbance effect. The stability of the proposed controller (MSTFNSMC) is proved using the Lyapunov theory. The performance of the proposed MSTFNSMC approach is compared with the supertwisting sliding mode controller (STSMC) by numerical simulations to verify its effectiveness. [ABSTRACT FROM AUTHOR]

Finite element method is an important method to solve mathematical problems in engineering. Many mathematical equations are difficult to solve, but it becomes very simple after using the finite element method. In this paper, the finite element method is applied to the calculation of gravity anomaly. First, the variational equation of gravity anomaly calculation is established, and then the gravity anomaly value ten times the distance away from the anomaly body is used as the boundary condition. By comparing the gravity anomaly obtained by solving the stiffness matrix with the analytical solution, it can be found that the method in this paper has high accuracy. Finally, the model of Jinchuan copper nickel deposit is used for calculation, and the calculated gravity anomaly field is inverted with Growth3D. It can be found that the inversion result is very close to the model, which verifies the effectiveness of the algorithm in this paper. [ABSTRACT FROM AUTHOR]

This work delivers a novel technique to detect brain tumor with the help of enhanced watershed modeling integrated with a modified ResNet50 architecture. It also involves stochastic approaches to help in developing enhanced watershed modeling. Cancer diseases, primarily the brain tumor, have been exponentially raised which has alarmed researchers from academia and industry. Nowadays, researchers need to attain a more effective, accurate, and trustworthy brain tumor tissue detection and classification approach. Different from traditional machine learning methods that are just targeting to enhance classification efficiency, this work highlights the process to extract several deep features to diagnose brain tumor effectively. This paper explains the modeling of a novel technique by integrating the modified ResNet50 with the Enhanced Watershed Segmentation (EWS) algorithm for brain tumor classification and deep feature extraction. The proposed model uses the ResNet50 model with a modified layer architecture including five convolutional layers and three fully connected layers. The proposed method can retain the optimal computational efficiency with high-dimensional deep features. This work obtains a comprised feature set by retrieving the diverse deep features from the ResNet50 deep learning model and feeds them as input to the classifier. The good performing capability of the proposed model is achieved by using hybrid features of ResNet50. The brain tumor tissue images were extracted by the suggested hybrid deep feature-based modified ResNet50 model and the EWS-based modified ResNet50 model with a high classification accuracy of 92% and 90%, respectively. [ABSTRACT FROM AUTHOR]

With the evolution of smart cities, images are used in a wide range of services such as smart healthcare and surveillance. How to ensure that images are transmitted and shared securely is of paramount importance for smart cities. To this end, a secure and efficient scheme for image transmission is proposed in this paper, which uses sparse signal transformation (SST) and parallel compressive sensing (CS). The primary employed techniques are sparse signal transformation (SST), parallel CS, and diffusion-permutation operation. The compression performance is achieved by parallel CS, whereas the encryption performance is derived from SST, parallel CS, and diffusion-permutation procedure. SST is exploited to change energy information before CS sampling and incorporated into diffusion-permutation framework, which not only balances the security and the efficiency of the algorithm, but also improves the transmission efficiency of the cipher image. We introduce chaotic system to generate the measurement matrix, SST matrix, and diffusion matrix to improve security. Furthermore, numerical simulation results and theoretical analyses confirm the security performances and effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper constructs a dynamic multiobjective location model; three objectives are considered: the first objective maximizes the total utility of relief supplies, the second objective minimizes the number of temporary facilities needed to operate, and the third objective maximizes the satisfaction for all demand points. We propose an improved NSGA-II to solve the optimization problem. The computational experiments are divided into two sections: In the first procedure, the numerical experiment is constructed by the classical functions ZDT1, ZDT2, and DTLZ2; the results show that the proposed algorithm generates the exact Pareto front, and the convergence and uniformity of the proposed algorithm are better than the NSGA-II and MOEA/D. In the second procedure, the simulation experiment is constructed by a case in emergency management; the results show that the proposed algorithm is more reasonable than the traditional algorithms NSGA-II and MOEA/D in terms of the three objectives. It is proved that the improved NSGA-II algorithm, which is proposed in this paper, has high precision application for the sudden disaster crisis and emergency management. [ABSTRACT FROM AUTHOR]

This paper proposes that fire parameter data of smoke, temperature, and CO is fused by sparse representation algorithm. It designs a kind of overcomplete dictionary and obtains the sparse solution of fire recognition through L1 norm, L3/4 norm, L1/2 norm, and L1/4 norm, respectively, in order to select more suitable norm type. A comprehensive classification method is proposed for fire identification. The simulation results show that L1 norm and L3/4 norm are used to obtain the solution with remarkable sparsity and high accuracy. The comprehensive classification method is more effective than minimum residual method and sum of weight coefficients method. This paper uses DSP TMS320F28022 as the core chip, TC72 as the temperature sensor, MQ-7 as the CO gas sensor, and MQ-9 as the smoke sensor to design the hardware of fire detection system. Code Composer Studio (CCS) software is used to compile and debug the program. Proteus software is used to load the program into the hardware circuit for joint simulation. The simulation results show that system design is feasible. [ABSTRACT FROM AUTHOR]

In this paper, boundary element and augmented Lagrangian methods for Coulomb friction contact problems are presented. Based on the projection technique, both unilateral contact and Coulomb friction conditions are reformulated as fixed point problems. The original problem is deduced to a variational formulation with boundary integral operators. Then, we propose a new augmented Lagrangian method which can be dealt with the semismooth Newton method. Short theoretical results and the algorithm description are given. Numerical simulations show the performance of the method proposed. [ABSTRACT FROM AUTHOR]

It is well-known that the stability of a first-order autonomous system can be determined by testing the symmetric positive definite solutions of associated Lyapunov matrix equations. However, the research on the constrained solutions of the Lyapunov matrix equations is quite few. In this paper, we present three iterative algorithms for symmetric positive semidefinite solutions of the Lyapunov matrix equations. The first and second iterative algorithms are based on the relaxed proximal point algorithm (RPPA) and the Peaceman–Rachford splitting method (PRSM), respectively, and their global convergence can be ensured by corresponding results in the literature. The third iterative algorithm is based on the famous alternating direction method of multipliers (ADMM), and its convergence is subsequently discussed in detail. Finally, numerical simulation results illustrate the effectiveness of the proposed iterative algorithms. [ABSTRACT FROM AUTHOR]

This paper studies the stabilization problem for a class of unified chaotic systems subject to uncertainties and input nonlinearity. Based on the sliding mode control theory, we present a new method for the sliding mode controller design and the control law algorithm for such systems. In order to achieve the goal of stabilization unified chaotic systems, the presented controller can make the movement starting from any point in the state space reach the sliding mode in limited time and asymptotically reach the origin along the switching surface. Compared with the existing literature, the controller designed in this paper has many advantages, such as small chattering, good stability, and less conservative. The analysis of the motion equation and the simulation results all demonstrate that the method is effective. [ABSTRACT FROM AUTHOR]

Optimization is the process of achieving the best solution for a problem. LabVIEW based on an SVM model is proposed in this paper to get the best SVM parameters using the hybrid CS and PSO method. PCA is used as a preprocessor of SVM for reducing the dimension of data and extracting features of training samples. Also, SVM parameters are optimized for Parkinson's disease data by combining CS and PSO. The designed system is used to determine the best SVM parameters, and it is compared to PSO and CS optimization methods and found that the used CS-PSO hybrid optimization method is better. The hybrid model shows that the accuracy of the performance achieved is 97.4359%. Also, the data classification results obtained by using SVM parameters determined by optimization are measured by precision, recall, F1 score, false positive rate (FPR), false discovery rate (FDR), false negative rate (FNR), negative predictive value (NPV), and Matthews' correlation coefficient (MCC) parameters. [ABSTRACT FROM AUTHOR]

According to the application of range-free localization technology for wireless sensor networks (WSNs), an improved localization algorithm based on iterative centroid estimation is proposed in this paper. With this methodology, the centroid coordinate of the space enclosed by connected anchor nodes and the received signal strength indication (RSSI) between the unknown node and the centroid are calculated. Then, the centroid is used as a virtual anchor node. It is proven that there is at least one connected anchor node whose distance from the unknown node must be farther than the virtual anchor node. Hence, in order to reduce the space enclosed by connected anchor nodes and improve the location precision, the anchor node with the weakest RSSI is replaced by this virtual anchor node. By applying this procedure repeatedly, the localization algorithm can achieve a good accuracy. Observing from the simulation results, the proposed algorithm has strong robustness and can achieve an ideal performance of localization precision and coverage. [ABSTRACT FROM AUTHOR]

The paper presents application of dynamic games methods, multistage positional and multistep matrix games, to automate the process control of moving objects, on the example of safe control of own ship in collision situations when passing many ships encountered. Taking into consideration two types of ships cooperation, for each of the two types of games, positional and matrix, four control algorithms for determining a safe ship trajectory supporting the navigator’s maneuvering decision in a collision situation are presented. The considerations are illustrated by examples of computer simulation in Matlab/Simulink software of safe trajectories of a ship in a real situation at sea. Taking into account the smallest final deviation game trajectory from the reference trajectory of movement, in good visibility at sea, the best is trajectory for cooperative matrix game, but in restricted visibility at sea, the best is trajectory for cooperative positional game. [ABSTRACT FROM AUTHOR]

Interharmonics and flickers have an interrelationship between each other. Based on International Electrotechnical Commission (IEC) flicker standard, the critical flicker frequency for a human eye is located at 8.8 Hz. Additionally, eye strains, headaches, and in the worst case seizures may happen due to the critical flicker. Therefore, this paper introduces a worthwhile research gap on the investigation of interrelationship between the amplitudes of the interharmonics and the critical flicker for 50 Hz power system. Consequently, the significant findings obtained in this paper are the amplitudes of two particular interharmonics are able to detect the critical flicker. In this paper, the aforementioned amplitudes are detected by adaptive linear neuron (ADALINE). After that, the critical flicker is detected by substituting the aforesaid amplitudes to the formulas that have been generated in this paper accordingly. Simulation and experimental works are conducted and the accuracy of the proposed algorithm which utilizes ADALINE is similar, as compared to typical Fluke power analyzer. In a nutshell, this simple predictive method for critical flicker detection has strong potential to be applied in any human crowded places (such as offices, shopping complexes, and stadiums) for human healthy precaution purpose due to its simplicity. [ABSTRACT FROM AUTHOR]

An accurate localization algorithm tailored for anisotropic wireless sensors networks (WSNs) is proposed in this paper. Using the proposed algorithm, each regular or position-unaware node estimates its distances only to reliable anchors or position-aware nodes. The latter are properly chosen following a new reliable anchor selection strategy that ensures an accurate distance estimation making thereby our localization algorithm more precise. It is shown that the proposed algorithm is implementable in both 2-dimensional (2D) and 3-dimensional (3D) scenarios. A power saving mechanism aiming to enhance the WSN lifetime is also envisaged in this paper. It is proven that the proposed algorithm could easily incorporate such a mechanism. Simulations show that our algorithm, whether combined or not with the power saving mechanism, consistently outperforms the best representative localization algorithms currently available in the literature in terms of accuracy, even with the presence of nonuniform node distribution or radiation irregularities. [ABSTRACT FROM AUTHOR]

Spectrum sensing is one of the key tasks in cognitive radio. This paper proposes a fast two-step energy detection (FED) algorithm for spectrum sensing via improving the sampling process of conventional energy detection (CED). The algorithm adaptively selects N-point or 2N-point sampling by comparing its observed energy with prefixed double thresholds, and thereby is superior in sampling time and detection speed. Moreover, under the constraint of constant false alarm, this paper optimizes the thresholds from maximizing detection probability point of view. Theoretical analyses and simulation results show that, compared with CED, the proposed FED can achieve significant gain in detection speed at the expense of slight accuracy loss. Specifically, within high signal-to-noise ratio regions, as much as 25% of samples can be reduced. [ABSTRACT FROM AUTHOR]

The iterative approach is important for image reconstruction with ill-posed problem, especially for limited angle reconstruction. Most of iterative algorithms can be written in the general Landweber scheme. In this context, appropriate relaxation strategies and appropriately chosen weights are critical to yield reconstructed images of high quality. In this paper, based on reducing the condition number of matrix ATA, we find one method of weighting matrices for the general Landweber method to improve the reconstructed results. For high resolution images, the approximate iterative matrix is derived. And the new weighting matrices and corresponding relaxation strategies are proposed for the general Landweber method with large dimensional number. Numerical simulations show that the proposed weighting methods are effective in improving the quality of reconstructed image for both complete projection data and limited angle projection data. [ABSTRACT FROM AUTHOR]

Design of conventional correlation filters requires explicit knowledge of the appearance and shape of a target object, so the performance of correlation filters is significantly affected by changes in the appearance of the object in the input scene. In particular, the performance of correlation filters worsens when objects to be recognized are partially occluded by other objects, and the input scene contains a cluttered background and noise. In this paper, we propose a new algorithm for the design of a system consisting of a set of adaptive correlation filters for recognition of partially occluded objects in noisy scenes. Since the input scene may contain different fragments of the target, false objects, and background to be rejected, the system is designed in such a manner to guarantee equally high correlation peaks corresponding to parts of the target in the scenes. The key points of the system are as follows: (i) it consists of a bank of composite optimum filters, which yield the best performance for different parts of the target; (ii) it includes a fragmentation of the target into a given number of parts in the training stage to provide equal intensity responses of the system for each part of the target. With the help of computer simulation, the performance of the proposed algorithm for recognition partially occluded objects is compared with that of common algorithms in terms of objective metrics. [ABSTRACT FROM AUTHOR]

For a class of single-input single-output (SISO) dual-rate sampling processes with disturbances and output delay, this paper presents a robust fault-tolerant iterative learning control algorithm based on output information. Firstly, the dual-rate sampling process with output delay is transformed into discrete system in state-space model form with slow sampling rate without time delay by using lifting technology; then output information based fault-tolerant iterative learning control scheme is designed and the control process is turned into an equivalent two-dimensional (2D) repetitive process. Moreover, based on the repetitive process stability theory, the sufficient conditions for the stability of system and the design method of robust controller are given in terms of linear matrix inequalities (LMIs) technique. Finally, the flow control simulations of two flow tanks in series demonstrate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes an offset-free proportional-type output voltage-tracking algorithm embedding the disturbance observers (DOBs) for the N-phase interleaved DC/DC boost converter through a systematical multivariable approach. The contributions of this article fall into two parts. The first one is to design the first-order nonlinear DOBs for exponentially estimating the disturbances caused by the model-plant mismatches. The second one is to prove that the proposed proportional-type controller equipped with the DOBs guarantees the performance recovery property as well as the offset-free property. The performance of the proposed method is evaluated through simulations and experiments using a 3-kW four-phase interleaved DC/DC boost converter, comparing the proposed and feedback linearizing (FL) methods. [ABSTRACT FROM AUTHOR]

This paper investigates the path following control problem for an underactuated unmanned surface vehicle (USV) in the presence of dynamical uncertainties and time-varying external disturbances. Based on fuzzy optimization algorithm, an improved adaptive line-of-sight (ALOS) guidance law is proposed, which is suitable for straight-line and curve paths. On the basis of guidance information provided by LOS, a three-degree-of-freedom (DOF) dynamic model of an underactuated USV has been used to design a practical path following controller. The controller is designed by combining backstepping method, neural shunting model, neural network minimum parameter learning method, and Nussbaum function. Neural shunting model is used to solve the problem of “explosion of complexity,” which is an inherent illness of backstepping algorithm. Meanwhile, a simpler neural network minimum parameter learning method than multilayer neural network is employed to identify the uncertainties and time-varying external disturbances. In particular, Nussbaum function is introduced into the controller design to solve the problem of unknown control gain coefficient. And much effort is made to obtain the stability for the closed-loop control system, using the Lyapunov stability theory. Simulation experiments demonstrate the effectiveness and reliability of the improved LOS guidance algorithm and the path following controller. [ABSTRACT FROM AUTHOR]

Ultrasound elastography is an imaging modality to evaluate elastic properties of soft tissue. Recently, 1D quasi-static elastography method has been commercialized by some companies. However, its performance is still limited on high strain level. In order to improve the precision of estimation during high compression, some algorithms have been proposed to expand the 1D window to a 2D window for avoiding the side-slipping. But they are usually more computationally expensive. In this paper, we proposed a modified 2D multiresolution hybrid method for displacement estimation, which can offer an efficient strain imaging with stable and accurate results. A FEM phantom with a stiffer circular inclusion is simulated for testing the algorithm. The elastographic contrast-to-noise rate (CNRe) is calculated for quantitatively comparing the performance of the proposed algorithm with conventional 1D elastography using phase zero estimation and the 1D elastography using downsampled (d-s) baseband signals. Results show that the proposed method is robust and performs similarly as other algorithms in low strain but is superior when high level strain is applied. Particularly, the CNRe of our algorithm is 15 times higher than original method under 4% strain level. Furthermore, the execution time of our algorithm is five times faster than other algorithms. [ABSTRACT FROM AUTHOR]

Maritime piracy is posing a genuine threat to maritime transport. The main purpose of simulation is to predict the behaviors of many actual systems, and it has been successfully applied in many fields. But the application of simulation in the maritime domain is still scarce. The rapid development of network and measurement technologies brings about higher accuracy and better availability of online measurements. This makes the simulation paradigm named as dynamic data driven simulation increasingly popular. It can assimilate the online measurements into the running simulation models and ensure much more accurate prediction of the complex systems under study. In this paper, we study how to utilize the online measurements in the agent based simulation of the maritime pirate activity. A new random finite set based data assimilation algorithm is proposed to overcome the limitations of the conventional vectors based data assimilation algorithms. The random finite set based general data model, measurement model, and simulation model are introduced to support the proposed algorithm. The details of the proposed algorithm are presented in the context of agent based simulation of maritime pirate activity. Two groups of experiments are used to practically prove the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a state estimation method for a sampled-data descriptor system by the Kalman filtering method. The sampled-data descriptor system is firstly discretized to obtain a discrete-time nonsingular model. Based on the discretized nonsingular system, a strong tracking unscented Kalman filter (STUKF) algorithm is designed for the state estimation. Then, a defined suboptimal fading factor is proposed and added to the prediction covariance for decreasing the weight of the prior knowledge on the conventional UKF filtering solution. Finally, a simulation example is given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of group behavior recognition. Our approach is to merge all the possible features of group behavior (individuals, groups, relationships between individuals, relationships between groups, etc.) with static context information relating to particular domains. All this information is represented as a set of features by classification algorithms. This is a very high-dimensional problem, with which classification algorithms are unable cope. For this reason, this paper also presents four feature selection alternatives: two wrappers and two filters. We present and compare the results of each method in the basketball domain. [ABSTRACT FROM AUTHOR]

This paper addresses the localization of a timing signal source based on the time of arrival (TOA) measurements that are collected from nearby sensors that are position known and synchronized to each other. Generally speaking, for such TOA-based source localization, the corresponding observation equations contain nonlinear relationship between measurements and unknown parameters, which normally results in the nonexistence of any efficient unbiased estimator that attains the Cramer-Rao lower bound (CRLB). In this paper, we devise a new approach that utilizes linearization and adopts suitable coordinate system translation to eliminate nonlinearity from the converted observation equations. The performance analysis and simulation study conducted show that our proposed algorithm can achieve the CRLB when the zero-mean Gaussian and independent measurement errors are sufficiently small. [ABSTRACT FROM AUTHOR]

This paper considers the m-machine flow shop scheduling problem with the no-wait constraint to minimize total completion time which is the typical model in steel production. First, the asymptotic optimality of the Shortest Processing Time (SPT) first rule is proven for this problem. To further evaluate the performance of the algorithm, a new lower bound with performance guarantee is designed. At the end of the paper, numerical simulations show the effectiveness of the proposed algorithm and lower bound. [ABSTRACT FROM AUTHOR]

Noncoding RNAs (ncRNAs) play important roles in various cellular activities and diseases. In this paper, we presented a comprehensive review on computational methods for ncRNA prediction, which are generally grouped into four categories: (1) homology-based methods, that is, comparative methods involving evolutionarily conserved RNA sequences and structures, (2) de novo methods using RNA sequence and structure features, (3) transcriptional sequencing and assembling based methods, that is, methods designed for single and pair-ended reads generated from next-generation RNA sequencing, and (4) RNA family specific methods, for example, methods specific for microRNAs and long noncoding RNAs. In the end, we summarized the advantages and limitations of these methods and pointed out a few possible future directions for ncRNA prediction. In conclusion, many computational methods have been demonstrated to be effective in predicting ncRNAs for further experimental validation. They are critical in reducing the huge number of potential ncRNAs and pointing the community to high confidence candidates. In the future, high efficient mapping technology and more intrinsic sequence features (e.g., motif and k-mer frequencies) and structure features (e.g., minimum free energy, conserved stem-loop, or graph structures) are suggested to be combined with the next- and third-generation sequencing platforms to improve ncRNA prediction. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel method for solving the single-trial evoked potential (EP) estimation problem. In this method, the single-trial EP is considered as a complex containing many components, which may originate from different functional brain sites; these components can be distinguished according to their respective latencies and amplitudes and are extracted simultaneously by multiple-input single-output autoregressive modeling with exogenous input (MISO-ARX). The extraction process is performed in three stages: first, we use a reference EP as a template and decompose it into a set of components, which serve as subtemplates for the remaining steps. Then, a dictionary is constructed with these subtemplates, and EPs are preliminarily extracted by sparse coding in order to roughly estimate the latency of each component. Finally, the single-trial measurement is parametrically modeled by MISO-ARX while characterizing spontaneous electroencephalographic activity as an autoregression model driven by white noise and with each component of the EP modeled by autoregressive-moving-average filtering of the subtemplates. Once optimized, all components of the EP can be extracted. Compared with ARX, our method has greater tracking capabilities of specific components of the EP complex as each component is modeled individually in MISO-ARX. We provide exhaustive experimental results to show the effectiveness and feasibility of our method. [ABSTRACT FROM AUTHOR]

Identification of B-cell epitopes is a fundamental step for development of epitope-based vaccines, therapeutic antibodies, and diagnostic tools. Epitope-based antibodies are currently the most promising class of biopharmaceuticals. In the last decade, in-depth in silico analysis and categorization of the experimentally identified epitopes stimulated development of algorithms for epitope prediction. Recently, various in silico tools are employed in attempts to predict B-cell epitopes based on sequence and/or structural data. The main objective of epitope identification is to replace an antigen in the immunization, antibody production, and serodiagnosis. The accurate identification of B-cell epitopes still presents major challenges for immunologists. Advances in B-cell epitope mapping and computational prediction have yielded molecular insights into the process of biorecognition and formation of antigen-antibody complex, which may help to localize B-cell epitopes more precisely. In this paper, we have comprehensively reviewed state-of-the-art experimental methods for B-cell epitope identification, existing databases for epitopes, and novel in silico resources and prediction tools available online. We have also elaborated new trends in the antibody-based epitope prediction. The aim of this review is to assist researchers in identification of B-cell epitopes. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed shortest-distance problem of multiagent systems where agents satisfy the same continuous-time dynamics. The objective of multiagent systems is to find a common point for all agents to minimize the sum of the distances from each agent to its corresponding convex region. A distributed consensus algorithm is proposed based on local information. A sufficient condition also is given to guarantee the consensus. The simulation example shows that the distributed shortest-distance consensus algorithm is effective for our theoretical results. [ABSTRACT FROM AUTHOR]

This paper considers the localization problem in mobile sensor networks. Such a problem is a challenging task, especially when measurements exchanged between sensors may contain outliers, that is, data not matching the observation model. This paper proposes two algorithms robust to outliers. These algorithms perform a set-membership estimation, where only the maximal number of outliers is required to be known. Using these algorithms, estimates consist of sets of boxes whose union surely contains the correct location of the sensor, provided that the considered hypotheses are satisfied. This paper proposes as well a technique for evaluating the number of outliers to be robust to. In order to corroborate the efficiency of both algorithms, a comparison of their performances is performed in simulations using Matlab. [ABSTRACT FROM AUTHOR]

Power assignment in wireless ad hoc networks is an important issue of topology control which assigns power for each wireless node so that the induced communication graph satisfies some desired properties such as the connectivity and the energy spanner. In this paper, we study the problem of power assignment in order that its induced communication graph meets the following properties: (1) it is an energy-t-spanner which is energy efficient; (2) it is k-fault resistant which can withstand up to k - 1 node failures where k⩾1; (3) the interference is minimal. We propose algorithms to address this problem. Both the theoretic analysis and the simulations in the paper prove that our algorithms can induce a k-fault resistant energy spanner and furthermore the interference is minimized. To the best of our knowledge, this is the first paper to study the power assignment problem simultaneously considering spanner properties, the fault tolerance, and the interference reduction. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are tightly linked with the practical environment in which the sensors are deployed. Sensor positioning is a pivotal part of main location-dependent applications that utilize sensornets. The global topology of the network is important to both sensor network applications and the implementation of networking functionalities. This paper studies the topology discovery with an emphasis on boundary recognition in a sensor network. A large mass of sensor nodes are supposed to scatter in a geometric region, with nearby nodes communicating with each other directly. This paper is thus designed to detect the holes in the topological architecture of sensornets only by connectivity information. Existent edges determination methods hold the high costs as assumptions. Without the help of a large amount of uniformly deployed seed nodes, those schemes fail in anisotropic WSNs with possible holes. To address this issue, we propose a solution, named PPA based on Poincare-Perelman Theorem, to judge whether there are holes in WSNs-monitored areas. Our solution can properly detect holes on the topological surfaces and connect them into meaningful boundary cycles. The judging method has also been rigorously proved to be appropriate for continuous geometric domains as well as discrete domains. Extensive simulations have been shown that the algorithm even enables networks with low density to produce good results. [ABSTRACT FROM AUTHOR]

In order to suppress the interference of the strong fractional noise signal in discrete-time ultra-wideband (UWB) systems, this paper presents a new UWB multi-scale Kalman filter (KF) algorithm for the interference suppression. This approach solves the problem of the narrowband interference (NBI) as nonstationary fractional signal in UWB communication, which does not need to estimate any channel parameter. In this paper, the received sampled signal is transformed through multiscale wavelet to obtain a state transition equation and an observation equation based on the stationarity theory of wavelet coefficients in time domain. Then through the Kalman filter method, fractional signal of arbitrary scale is easily figured out. Finally, fractional noise interference is subtracted from the received signal. Performance analysis and computer simulations reveal that this algorithm is effective to reduce the strong fractional noise when the sampling rate is low. [ABSTRACT FROM AUTHOR]

Under complicated situations, such as the low slippery road surface and split-μ road surface, traction control system is the key issue to improve the performance of vehicle acceleration and stability. In this paper, a novel control strategy with engine controller and active pressure controller is presented. First and foremost, an ideal vehicle model is proposed for simulation; then a method for the calculation of optimal slip ratio is also brought. Finally, the scheme of control method with engine controller and active brake controller is presented. From the results of simulation and road tests, it can be concluded that the acceleration performance and stability of a vehicle equipped with traction control system (TCS) can be improved. [ABSTRACT FROM AUTHOR]

This paper develops an extended ellipsoidal outer-bounding set-membership estimation (EEOB-SME) algorithm with high accuracy and efficiency for nonlinear discrete-time systems under unknown-but-bounded (UBB) disturbances. The EEOB-SME linearizes the first-order terms about the current state estimations and bounds the linearization errors by ellipsoids using interval analysis for nonlinear equations of process and measurement equations, respectively. It has been demonstrated that the EEOB-SME algorithm is stable and the estimation errors of the EEOB-SME are bounded when the nonlinear system is observable. The EEOB-SME decreases the computation load and the feasible sets of EEOB-SME contain more true states. The efficiency of the EEOB-SME algorithm has been shown by a numerical simulation under UBB disturbances. [ABSTRACT FROM AUTHOR]

The shuffled frog leaping algorithm (SFLA) easily falls into local optimum when it solves multioptimum function optimization problem, which impacts the accuracy and convergence speed. Therefore this paper presents grouped SFLA for solving continuous optimization problems combined with the excellent characteristics of cloud model transformation between qualitative and quantitative research. The algorithm divides the definition domain into several groups and gives each group a set of frogs. Frogs of each region search in their memeplex, and in the search process the algorithm uses the “elite strategy” to update the location information of existing elite frogs through cloud model algorithm. This method narrows the searching space and it can effectively improve the situation of a local optimum; thus convergence speed and accuracy can be significantly improved. The results of computer simulation confirm this conclusion. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) model by analyzing the limitations of existing localization algorithm is proposed in this paper. Specifically, neither the anchor nor the ID is required in the proposed model, and the node function is limited. The hardware function of the node in our model is independent of the MAC protocol. Moreover, based on the proposed WSN model, we design an energy efficient anchor-free localization algorithm for no-identity WSN. Instead of multiple measurements, mathematical calculation is adopted to acquire the position information. Subsequently, the effectiveness of the algorithm is confirmed by rigid theory. The simulation results show that the proposed algorithm can improve the performance of localization and energy efficiency in WSN significantly. [ABSTRACT FROM AUTHOR]

Backpressure based scheduling has revealed remarkable performance in wireless multihop networks as reported in a lot of previous work. However, its lack of consideration on energy use efficiency is still an obstacle for backpressure based algorithms to be deployed in resource-constrained wireless sensor networks (WSNs). In this paper, we focus on studying the design of energy efficient backpressure based algorithm. For this purpose, we propose a gradient-assisted energy-efficient backpressure scheduling algorithm (GRAPE) for WSNs. GRAPE introduces a new link-weight calculation method, based on which gradient information and nodal residual energy are taken into account when making decisions on backpressure based transmission scheduling. According to the decisions made by this new method, packets are encouraged to be forwarded to nodes with more residual energy. We theoretically prove the throughput-optimality of GRAPE. Simulation results demonstrate that GRAPE can achieve significant performance improvements in terms of energy use efficiency, network throughput, and packet delivery ratio as compared with existing work. [ABSTRACT FROM AUTHOR]

Gas source localization (GSL) is a critical component in wireless sensor network (WSN) which can be used to estimate the position of gas leaks through the gas concentration measured by the monitoring nodes. In this paper, we first derive a gas diffusion model under windy conditions based on the gas diffusion theory. Because of the nonlinearity of the gas diffusion model under windy conditions, the traditional weighted centroid algorithm for gas source localization produces very inaccurate estimation of the source location. To address this problem, we propose a novel gas source localization algorithm which combines the weighted centroid method and particle filter. Simulation results validate the effectiveness of our algorithm under unknown gas source strength. Compared to weighted centroid method alone, our algorithm shows a higher accuracy in location. Moreover, its convergence rate is improved by the preestimation of initial value. [ABSTRACT FROM AUTHOR]

Ethernet passive optical network is a point-to-multipoint structure, and an effective media access protocol should be designed for collision avoidance and bandwidth allocation. Most previous researches have aimed to solve the problem using dynamic bandwidth allocation (DBA) algorithms with polling-based schemes. Unfortunately, idle channel time among the polling cycles cannot be overcome efficiently. In this paper, a dual-polling DBA (DP-DBA) algorithm which consists of an interpolling mechanism and an intrapolling mechanism is proposed. The interpolling mechanism helps eliminate the idle time problem while the intrapolling part aggregates the unused bandwidth of the light-loaded users and those due to nondefragmentation of the Ethernet frames of the general users. We also evaluate the performance of the DP-DBA algorithm by a comparison with the interleaved polling with adaptive cycle time (IPACT), the dynamic bandwidth allocation with a modified grant table generation algorithm (DBA2), the Double-Phase Polling (DPP), and the adaptive DBA algorithm with sorting report messages (Sort-DBA) schemes with simulation results. For greater realism with regard to general applications, we also simulate the asymmetric traffic loads at the optical network units (ONUs).The results show that DP-DBA outperforms the other DBA schemes under asymmetric load conditions. [ABSTRACT FROM AUTHOR]

One category of the superresolution algorithms widely used in practical applications is dictionary-based superresolution algorithms, which constructs a single high-resolution (HR) and high-clarity image from multiple low-resolution (LR) images. Despite the fact that general dictionary-based superresolution algorithms obtain redundant dictionaries from numerous HR-LR images, HR image distortion is unavoidable. To solve this problem, this paper proposes a multiframe superresolution reconstruction based on self-learning methods. First, multiple images from the same scene are selected to be both input and training images, and larger-scale images, which are also involved in the training set, are constructed from the learning dictionary. Then, different larger-scale images are constructed via repetition of the first step and the initial HR sets whose scale closely approximates that of the target HR image are finally obtained. Lastly, initial HR images are fused into one target HR image under the NLM idea, while the IBP idea is adopted to meet the global constraint. The simulation results demonstrate that the proposed algorithm produces more accurate reconstructions than those produced by other general superresolution algorithms, while, in real scene experiments, the proposed algorithm can run well and create clearer HR images from input images captured by cameras. [ABSTRACT FROM AUTHOR]

This paper presents an opportunistic reception (OR) algorithm for energy-efficient transmission in cooperative wireless sensor networks (WSNs), where the characteristics of random linear network coding and the energy consumption property of WSNs are jointly considered. In OR, the sensor nodes in intermediate cluster generate the independent coding vector through simple forwarding or decoding-recoding manners opportunistically, so that the number of received packets can be reduced. To evaluate the algorithm, we derive the average number of received packets, the transmitting nodes, and decoding failure probability under specific assumption. The obtained theoretical and simulation results verify the effectiveness offered by the proposed approach. [ABSTRACT FROM AUTHOR]

This paper reports the performance of particle swarm optimization (PSO) for the assignment of blood to meet patients’ blood transfusion requests for blood transfusion. While the drive for blood donation lingers, there is need for effective and efficient management of available blood in blood banking systems. Moreover, inherent danger of transfusing wrong blood types to patients, unnecessary importation of blood units from external sources, and wastage of blood products due to nonusage necessitate the development of mathematical models and techniques for effective handling of blood distribution among available blood types in order to minimize wastages and importation from external sources. This gives rise to the blood assignment problem (BAP) introduced recently in literature. We propose a queue and multiple knapsack models with PSO-based solution to address this challenge. Simulation is based on sets of randomly generated data that mimic real-world population distribution of blood types. Results obtained show the efficiency of the proposed algorithm for BAP with no blood units wasted and very low importation, where necessary, from outside the blood bank. The result therefore can serve as a benchmark and basis for decision support tools for real-life deployment. [ABSTRACT FROM AUTHOR]

Node localization is essential in wireless sensor networks (WSNs). However, due to limited node energy and overhead network, node localization faces many challenges in WSNs. In addition, it is difficult to meet the requirement of localization accuracy and energy consumption at the same time in existing algorithms. In this paper, we proposed a novel eliminating algorithm called NPLA which achieves high localization accuracy and reduces energy consumption effectively. It makes full use of multiple-hop neighbor nodes of unknown node to eliminate the possible location area (PLA) of the unknown node. Simulations show that the localization error of algorithm NPLA can be controlled within 20%. [ABSTRACT FROM AUTHOR]

Most applications of industrial wireless sensor networks (IWSNs) should converge process data generated by each node to the central manager. The data collection operation results in an important communication primitive referred to as convergecast. Convergecast is a many-to-one communication paradigm as a critical functionality deployed for industrial monitoring and control. Delaying of process data may degrade the overall control performance and even lead to the malfunction of industrial applications. Therefore, timeslot and channel resources should be scheduled efficiently for real-time communication. This paper is interested in determining a TDMA schedule that minimizes the number of timeslots and completes convergecast with a limited number of channels. In order to achieve the lower bound derived by theoretical analysis, we proposed a source aware scheduling algorithm for general network. For IWSNs with a fixed number of available channels, we present a source aware scheduling algorithm with constrained channel. According to simulation results, we demonstrate that the performance of our algorithm is close to the lower bound on latency with a limited number of channels. Our algorithm is also scalable for schedules with multiple packets and specific transmission latency of a single packet. [ABSTRACT FROM AUTHOR]

Due to the nonexistence of end-to-end path between the sender and the receiver in delay tolerant networks and mobile social networks, consequently successful message transmission faces great challenges. In this paper, an adaptive routing algorithm taking full use of gregariousness characteristics of moving nodes is proposed. We first abstract all social relationships and uniformly represent them using friendship. Then by dynamically dividing nodes into different social groups, we finish flooding message among the target social group where destination node resides. In addition, we propose a social group based flooding model and a message redundancy control model to select fewer but better relay nodes and further reduce message redundancy. Extensive simulations have been conducted based on the synthetic traces generated by working day movement model and the results show that the proposed routing algorithm can get a higher message delivery ratio and a lower overhead ratio compared to Bubble Rap, Epidemic, and ProPHET, thus proving a better routing performance. [ABSTRACT FROM AUTHOR]

Localization is a key issue for wireless sensor network. The traditional localization methods achieve high location accuracy using received signal strength (RSS) in line of sight (LOS) environment. But the localization accuracy degrades significantly in nonline of sight (NLOS) environment. So the localization in NLOS environment is one of the most challenging for wireless sensor network. In this paper, we propose an RSS based localization algorithm in NLOS environment. A NLOS identification algorithm is firstly proposed. This algorithm does not need the parameters of the NLOS in prior. Then we correct the NLOS measurements by subtracting the mean of NLOS errors. Finally, the Kalman filter is employed to mitigate the process noise. In simulations, we demonstrate that the proposed algorithm achieves higher localization accuracy compared with other methods. [ABSTRACT FROM AUTHOR]