Computational Complexity Of Problem Research Articles

Time-Domain Sound Field Reproduction with Pressure and Particle Velocity Jointly Controlled

Particle velocity has been introduced to improve the performance of spatial sound field reproduction systems with an irregular loudspeaker array setup. However, existing systems have only been developed in the frequency domain. In this work, we propose a time-domain sound field reproduction method with both sound pressure and particle velocity components jointly controlled. To solve the computational complexity problem associated with the multi-channel setup and the long-length filter design, we adopt the general eigenvalue decomposition-based approach and the conjugate gradient method. The performance of the proposed method is evaluated through numerical simulations with both a regular loudspeaker array layout and an irregular loudspeaker array layout in a room environment.

A proactive failure tolerance method for LEO mega-constellation based on traffic engineering

The LEO mega-constellation has attracted increasing attention due to its potential for wide coverage,high throughput and low latency communication. However, given the complex space environment and its own characteristics,LEO mega-constellation needs to study the fault tolerance problem. Therefore, this paper focuses onhow to ensure that the network traffic is still forwarding without congestion after the failure of satellites.Specifically, a proactive fault-tolerant method called SN-FFC based on traffic engineering is proposed.The main idea is to make the network uncongested if the concurrent failures is up to $k$ ($k$ is a parameter) through reasonable link redundancy.SN-FFC first designs a fault-tolerant model based on traffic engineering, then proposes an algorithm based on dual theoryto solve the high computational complexity problem of the model, which reduces the complexity from $O(C_n^k)$ to $O(n)$.The simulation experiments based on the Starlink constellation model (first stage) show that SN-FFC can realize uncongested forwarding under failurescompared with the existing method, and can achieve low computing time and throughput cost.

Direction-of-Arrival Estimation of Electromagnetic Wave Impinging on Spherical Antenna Array in the Presence of Mutual Coupling Using a Multiple Signal Classification Method

A spherical antenna array (SAA) is the configuration of choice in obtaining an antenna array with isotropic characteristics. An SAA has the capacity to receive an electromagnetic wave (EM) with equal intensity irrespective of the direction-of-arrival (DoA) and polarization. Therefore, the DoA estimation of electromagnetic (EM) waves impinging on an SAA with unknown mutual coupling needs to be considered. In the spherical domain, the traditional multiple signal classification algorithm (SH-MUSIC) is faced with a computational complexity problem. This paper presents a one-dimensional MUSIC method (1D-MUSIC) for the estimation of the azimuth and elevation angles. An intermediate mapping matrix that exists between Fourier series and the spherical harmonic function is designed, and the Fourier series Vandermonde structure is used for the realization of the polynomial rooting technique. This mapping matrix can be computed prior to the DoA estimation, and it is only a function of the array configuration. Based on the mapping matrix, the 2-D angle search is transformed into two 1-D angle findings. Employing the features of the Fourier series, two root polynomials are designed for the estimation of the elevation and azimuth angles, spontaneously. The developed method avoids the 2-D spectral search, and angles are paired in automation. Both numerical simulation results, and results from experimental measured data (i.e., with mutual coupling effect incorporated), show the validity, potency, and potential practical application of the developed algorithm.

Reconfigurable Intelligent Surface Empowered Symbiotic Radio Over Broadcasting Signals

Symbiotic radio (SR) is a promising technology for energy- and spectrum-efficient wireless communication, which exploits passive communication for Internet-of-Things (IoT) transmission and achieves a mutualistic spectrum sharing between the passive and active transmissions. In this paper, we study an reconfigurable intelligent surface (RIS) empowered symbiotic radio over a broadcasting system, i.e., a base station (BS) broadcasts signals to multiple primary receivers (PRs) under the assistance of an RIS, while the RIS also transmits information to an IoT receiver (IR) by riding over the broadcasting signals. We formulate a problem to minimize the BS’s transmit power by jointly optimizing the BS’s active precoding and the RIS’s passive beamforming, under the signal-to-noise-ratio constraints of the primary and IoT transmissions. However, the problem is challenging to be solved optimally, since the variables are coupled and the constraints are non-convex. An iterative algorithm based on block coordinated descent (BCD) and semidefinite relaxation (SDR) techniques is first proposed, and its convergence together with complexity are analyzed. Then, to tackle the problem of high computational complexity caused by SDR technique, we further propose an alternative algorithm based on generalized power method (GPM) technique. Simulation results validate that the proposed system outperforms the traditional broadcasting system without RIS. The GPM-based algorithm achieves nearly the same transmit power performance as SDR-based algorithm, with a significantly reduced computational complexity.

Contrast threshold adaptive adjustment algorithm for remote sensing image matching

Remote sensing image processing has been widely used in environmental monitoring, terrain survey, military investigation, disaster early warning, and other fields. The remote sensing images matching is a key step, and the accuracy and real-time of the alignment have a great impact on these applications. Due to the high resolution and complexity of remote sensing images, scale-invariant feature transformation (SIFT) algorithm has the problems of high computational complexity and poor matching effect. Adaptive threshold adjustment algorithm has proposed in the remote sensing image matching, but minor changes in the contrast threshold can bring about drastic changes in the image matching quality, which will affect applications such as monitoring, measurement, and early warning. To improve the matching quality of a SIFT detector, an adaptive contrast threshold SIFT method based on image complexity calculation (CACT-SIFT) is proposed. The CACT-SIFT finds two contrast thresholds for the target image and the reference image, respectively, and achieves the target by minimizing the proposed criteria Cik. Experiments show that the method can be applied to the matching of remote sensing images. The information of reference images and target images can be detected at the same time, and the key points can be extracted in a robust way, with better accuracy and real-time accuracy of the alignment.

An innovative hybrid technique for road extraction from noisy satellite images

Remote sensing takes an important role in the detection and monitoring of natural resources. It is also used in abundant fields, including geography, topographical survey, and geoscience disciplines such as land monitoring, forest management, crop identification, soil mapping, identification of ocean resources, road extraction, etc. This road extraction has a vital application in which it plays a key role in the development of GIS. Automatic updating of GIS information has become very important in our daily life. Semi-automated road extraction had a problem of computational complexity, high rate; consume a large amount of manpower and time. A fully Automated process is better than this but due to the quality of RSI, pre-processing techniques, segmentation, etc., the extraction differs. This paper proposes a new hybrid technique for the extraction of roads with better accuracy. Gaussian filter and CLAHE are used to improve the quality of the image, and to reinforce the features with the neighboring pixels. This approach of extraction from RSI images is based on Histogram equalizer, fuzzy – c mean algorithm. This is achieved by mat-lab and applies to high-resolution satellite images.

Advances in spam detection for email spam, web spam, social network spam, and review spam: ML-based and nature-inspired-based techniques

Despite the great advances in spam detection, spam remains a major problem that has affected the global economy enormously. Spam attacks are popularly perpetrated through different digital platforms with a large electronic audience, such as emails, microblogging websites (e.g. Twitter), social networks (e.g. Facebook), and review sites (e.g. Amazon). Different spam detection solutions have been proposed in the literature, however, Machine Learning (ML) based solutions are one of the most effective. Nevertheless, most ML algorithms have computational complexity problem, thus some studies introduced Nature Inspired (NI) algorithms to further improve the speed and generalization performance of ML algorithms. This study presents a survey of recent ML-based and NI-based spam detection techniques to empower the research community with information that is suitable for designing effective spam filtering systems for emails, social networks, microblogging, and review websites. The recent success and prevalence of deep learning show that it can be used to solve spam detection problems. Moreover, the availability of large-scale spam datasets makes deep learning and big data solutions (such as Mahout) very suitable for spam detection. Few studies explored deep learning algorithms and big data solutions for spam detection. Besides, most of the datasets used in the literature are either small or synthetically created. Therefore, future studies can consider exploring big data solutions, big datasets, and deep learning algorithms for building efficient spam detection techniques.

ECLP: Friend Recommendation Using Ensemble Approach for Detecting Communities Performing Link Prediction

Social networks provide a variety of online services that play an important role in new connections among members to share their favorite media, document, and opinions. For each member, these networks should precisely recommend (predict) the link of members with the highest common interests. Because of the huge volume of users with different types of information, these networks encounter challenges such as dispersion and accuracy of link prediction. Moreover, networks with numerous users have the problem of computational and time complexity. These problems are caused because all the network nodes contribute to calculations of link prediction and friend suggestions. In order to overcome these drawbacks, this paper presents a new link prediction scheme containing three phases to combine local and global network information. In the proposed manner, dense communities with overlap are first detected based on the ensemble node perception method which leads to more relevant nodes and contributes to the link prediction and speeds up the algorithm. Then, these communities are optimized by applying the binary particle swarm optimization method for merging the close clusters. It maximizes the average clustering coefficient (ACC) of the whole network which results in an accurate and precise prediction. In the last phase, relative links are predicted by Adamic/Adar similarity index for each node. The proposed method is applied to Astro-ph, Blogs, CiteSeer, Cora, and WebKB datasets, and its performance is compared to state-of-the-art schemes in terms of several criteria. The results imply that the proposed scheme has a significant accuracy improvement on these datasets.

Adaptive Differentiator-Based Predefined-Time Control for Nonlinear Systems Subject to Pure-Feedback Form and Unknown Disturbance

In this article, by utilizing the predefined-time stability theory, the predefined-time output tracking control problem for perturbed uncertain nonlinear systems with pure-feedback structure is addressed. The nonaffine structure of the original system is simplified as an affine form via the property of the mean value theorem. Furthermore, the design difficulty from the uncertain nonlinear function is overcome by the excellent approximation performance of RBF neural networks (NNs). An adaptive predefined-time controller is designed by introducing the finite-time differentiator which is used to decrease the computational complexity problem appeared in the traditional backstepping control. It is proved that the proposed control method guarantees all signals in the closed-loop system remain bound and the tracking error converges to zero within the predefined time. Based on the controller designed in this paper, the expected results can be obtained in predefined time, which can be illustrated by the simulation results.

Finite-time observer-based trajectory tracking control of underactuated USVs using hierarchical non-singular terminal sliding mode

ABSTRACT In this paper, a finite-time control method has been proposed for underactuated unmanned surface vehicles (USVs) with external disturbances in order to implement trajectory tracking control. Considering the complexity of the marine environment and the high-accuracy and rapidity required by USVs to complete complex marine missions, such as water quality detection, underwater pipe-laying, rescue operations and so on, a novel hierarchical sliding mode base on non-singular terminal sliding mode control (NTSMC) method is designed for underactuated USVs to ensure that all tracking error can faster converge to a neighbourhood around zero within finite time and address effectively the singularity problem which always exist in terminal sliding mode control (TSMC). The underactuated problem is addressed by hierarchical sliding mode technique. Meanwhile, the dynamic surface control (DSC) is employed to address the explosion problem of computational complexity in traditional method. Further, a novel finite-time disturbance observer (FDO) is devised to estimate accurately the unknown environmental disturbances and for practicality, a saturation constraint function is used to limit the input of the controller. Finally, the effectiveness and stability of the proposed method are validated by simulations and comparisons.

Currency Exchange Forecasting Using Sample Mean Estimator and Multiple Linear Regression Machine Learning Models

In recent time, there is an increasing growth in the amount of trading taking place in the currency exchange market. However, effective analysis and simulation tools for performing accurate prediction of these exchange rates are lacking. To alleviate this challenge, this work presents an hybrid machine learning and prediction model by suitably combining the Sample Mean Estimator (SME) simulation architecture with the multiple linear regression technique based training of feed-forward parameters. The developed model has the capability to overcome prediction inaccuracy, inconsistent forecasting, slow response due to computational complexity and scalability problems. The SME method is used to overcome the problems of uncertainty and non-linearity nature of the predictive variable as it’s always affected by economic and political factors. The implementation of the proposed currency exchange rate forecasting system is achieved through the use of a developed in-house Java program with Net Beans as the editor and compiler. Performance comparison between the present system and two baseline methods which are the Autoregressive Moving Average and the Deep Belief network techniques demonstrates that the present forecasting model out-performed the baseline methods studied. The experimental result shows that the precision rate of the present system are equal to or greater than 70%. Therefore, the present foreign exchange predictive system is capable of providing usable, consistent, efficient, faster and accurate prediction to the users consistently at any-time.Keywords- currency exchange,, feed-forward. Forecasting, Sample Mean Estimator, multiple linear regressions, prediction

Quantum blockchain system

Blockchain technology represented by Bitcoin and Ethereum has been deeply developed and widely used due to its broad application prospects such as digital currency and IoT. However, the security of the existing blockchain technologies built on the classical cryptography depends on the computational complexity problem. With the enhancement of the attackers’ computing power, especially the upcoming quantum computers, this kind of security is seriously threatened. Based on quantum hash, quantum SWAP test and quantum teleportation, a quantum blockchain system is proposed with quantum secure communication. In classical cryptographic theory sense, the security of this system is unconditional since it has nothing to do with the attackers’ computing power and computing resources.

A new PHD-SLAM method based on memory attenuation filter

Aiming at the problem that the low signal-to-noise ratio in the complex indoor environment will lead to the complex data association and low accuracy of the simultaneous localization and mapping (SLAM) method, a PHD-SLAM method based on the memory attenuation (MA) filter and mixed newborn maps information (MBMA-PHD-SLAM) is proposed in this paper. First of all, this method based on a probability hypothesis density (PHD) filter. Therefore, this method avoids data association and solves the problem of high computational complexity. Besides, the general PHD-SLAM method tends to cause the filter to diverge when the indoor signal-to-noise ratio is low. Therefore, MA filter is combined into PHD-SLAM. This method can reduce the influence of old data on SLAM, which improves the filtering divergence problem and improves the accuracy of SLAM. What’s more, since conventional SLAM algorithms usually have a problem of lack of prior information and in order to further improve the accuracy of SLAM on the basis of the above method, a method of mixed newborn maps information is proposed to solve this problem. Finally, the experiment mainly compares the method in this paper with the classic Rao-Blackwellised (RB) implementation of the PHD-SLAM (RB-PHD-SLAM). The results show that this method outperforms the PHD-SLAM method in terms of estimating map features and localization accuracy. The method in this paper effectively improves the ability of the mobile robot to explore unknown indoor environments.

Event-Triggered Robust Fuzzy Adaptive Finite-Time Control of Nonlinear Systems With Prescribed Performance

In this article, an event-triggered robust fuzzy adaptive prescribed performance finite-time control strategy is presented for a class of strict-feedback nonlinear systems with external disturbances. The relative-threshold-based event-triggered signal is introduced to reduce communication burden, and the dynamic surface control technique is applied to address the computational complexity problem. A disturbance observer is designed to estimate the compounded disturbances, which are composed of external disturbances and fuzzy approximation errors. The proposed control strategy can guarantee that the closed-loop system is semiglobally practically finite-time stable, and the tracking error converges to a small residual set by incorporating the prescribed performance bound in finite-time. Finally, simulation results are provided to verify the effectiveness of the proposed robust fuzzy control strategy.

Accurate fraud detection in credit card transactions using hybrid heuristic and meta-heuristic algorithms

Credit card based online transactions, payment methods developed rapidly, Credit card fraud also additionally expanded simultaneously. The particular crime in banking system is credit card fraud. The main objective of proposed AFD-HHM technique is, to detect the fraudulent transaction with high accurate rate and solve the user side problems. Some most dangerous credit card crimes are credit card application fraud, phishing scams, credit card skimming, online sales fraud, Credit Card Imprints, which leads to following user problems such as class imbalance problem, verification latency and concept drift. This paper focus to detect the fraud transactions with higher efficeiency rate solves the user side problems, high computational complexity problems. Initially, we propose Improved Egyptian Vulture Optimization (IEVO) algorithm (meta-heuristic) to grouping all cardholders into different groups based on the transaction behaviours. Second, we can aggregate each group transactions and compute the optimal attributes for each cardholder by using of trust rule strategy. Third, we propose Differential Evolution based Neural Network (DENN) classifier for differentiate transaction as normal or fraud. The result of proposed approach gives better accuracy with higher rate which is comparing with existing methods.

A Similarity Algorithm Based on Hash Feature and Random Mapping

Similar text algorithm is the main method for news topic extraction and online public opinion hot spot tracking. The traditional text similarity method has the problems of high data dimension and high computational complexity. This paper proposes a method of news text clustering based on hash feature and random mapping. The method based on hash feature reduces the dimensionality of the text. At the same time, the article clustering preprocessing based on random mapping can speed up retrieval. Experiments show that the algorithm proposed in this paper performs well on related data sets.

Path planning for mobile robots using Morphological Dilation Voronoi Diagram Roadmap algorithm

This paper presents Morphological Dilation Voronoi Diagram Roadmap (MVDRM) algorithm to address unsafe path computation accompanied by high time and space computation complexity problems of roadmap path planning methods in complex environments for mobile robots. Morphological dilation was employed to inflate the obstacles in the environment of the robot before computing the path to ensure safe path computation for the robots. To reduce time and space complexities in computing the path, a scale factor is introduced to provide sparse and uniform distribution of sample nodes in the environment of the robot for the computation of the roadmap. The purpose of this technique is to reduce the sample nodes and computation time to provide fast-path computation to aid robots to make quick decisions to avoid collision with obstacles. Simulation results using maps of different complexities demonstrate better performance of the proposed method compared to Probabilistic Roadmap (PRM) and conventional VD methods in terms of safe path computation as well as time and space computation complexities. Results indicate that the proposed method is 1.69 and 7.03 times faster than conventional VD and PRM methods, respectively. The proposed method is 88.32 times faster than VD and 1.08 times faster than PRM. The path computation success rate is 96.3% better than the PRM method. MVDRM is a promising roadmap path planning method for computing a safe and quick path for autonomous vehicles.

OQPSK Synchronization Parameter Estimation Based on Burst Signal Detection

The fast estimation of synchronization parameters plays an extremely important role in the demodulation of burst signals. In order to solve the problem of high computational complexity in the implementation of traditional algorithms, a synchronization parameter (frequency offset, phase offset, and timing error) estimation algorithm based on Offset Quadrature Phase Shift Keying (OQPSK) burst signal detection is proposed in this article. We first use the Data-Aided (DA) method to detect where the burst signal begins by taking the segment correlation between the receiving signals and the known local Unique Word (UW). In the sequel, the above results are adopted directly to estimate the synchronization parameters, which is obviously different from the conventional algorithms. In this way, the complexity of the proposed algorithm is greatly reduced, and it is more convenient for hardware implementation. The simulation results show that the proposed algorithm has high accuracy and can track the Modified Cramer–Rao Bound (MCRB) closely.

The 2D-LC-ESPRIT algorithm based on Massive MIMO

This paper proposes a 2D-LC-ESPRIT localization algorithm based on Massive MIMO, aiming at the problem of high computational complexity caused by positioning accuracy caused by abundant antenna array resources. Firstly, the antenna matrix is partitioned, then the received signals are superposition and dimensioned. And then the eigenvalues and corresponding eigenvectors are obtained by solving the cross-covariance matrix. Finally, simulation results show that the performance of the proposed algorithm is improved compared with the traditional two-dimension ESPRIT algorithm.

An Improved Rapid Power and Energy Prediction Method of Drilling Process for Sustainable Manufacturing

Energy modeling and energy-saving have attracted extensive attention in the manufacturing industry. Energy monitoring, modeling, and management issues of grinding, milling, and turning processes have been widely studied. However, special research on drilling power and energy consumption needs to be strengthened. The existing drilling power and energy models have the problems of high computational complexity and low practicability. To address this issue, an improved rapid power and energy prediction method of drilling process is proposed in this study. The motivation of the proposed method is to reduce the computational complexity and improve the practicability without losing the predictive accuracy for drilling power and energy. To verify the effectiveness of the proposed method, experimental and case studies were carried out. The results show that the number of formulas, variables, coefficients of the proposed method are all decreased significantly, therefore, the computational complexity is greatly reduced. Meanwhile, power predictive accuracy is improved by 1.91% instead of decreasing compared with the traditional method. Consequently, the simpler model, lower computational complexity, and higher power accuracy make the proposed method more practical in manufacturing industry.