Hybrid Block Diagonalization Precoding for Multi-User Weighted Sum-Rate Maximization

This paper studies large-scale multi-input multi-output(MIMO)orthogonal frequency division multiplexing(OFDM)communications in a broadband frequency-selective channel,where a massive MIMO base station(BS)communicates with multiple users equipped with multi-antenna.We develop a hybrid precoding design to maximize the weighted sum-rate(WSR)of the users by optimizing the digital and the analog precoders alternately.For the digital part,we employ block-diagonalization to eliminate inter-user interference and apply water-filling power allocation to maximize the WSR.For the analog part,the optimization of the PSN is formulated as an unconstrained problem,which can be efficiently solved by a gradient descent method.Numerical results show that the proposed block-diagonal hybrid precoding algorithm can outperform the existing works.

CRB:A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation of various rumors.In order to block the outbreak of rumor,one of the most effective containment measures is spreading positive information to counterbalance the diffusion of rumor.The spreading mechanism of rumors and effective suppression strategies are significant and challenging research issues.Firstly,in order to simulate the dissemination of multiple types of information,we propose a competitive linear threshold model with state transition(CLTST)to describe the spreading process of rumor and anti-rumor in the same network.Subsequently,we put forward a community-based rumor blocking(CRB)algorithm based on influence maximization theory in social networks.Its crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes,which includes community detection,selection of candidate anti-rumor seeds and generation of anti-rumor seed set.Under the CLTST model,the CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the performance.Experimental results show that the proposed model can better reflect the process of rumor propagation,and review the propagation mechanism of rumor and anti-rumor in online social networks.Moreover,the proposed CRB algorithm has better performance in weakening the rumor dissemination ability,which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread,sensitivity analysis,seeds distribution and running time.

A Bitcoin Address Multi-Classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly.Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way.In this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities.The innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships.Specifically,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization consensus.The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible.By testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful features.In addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.

Kinematic calibration under the expectation maximization framework for exoskeletal inertial motion capture system

This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift.In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79%and 7.16%respectively in comparison to the traditional calibration method.

Sum-Rate Maximization in Active RIS-Assisted Multi-Antenna WPCN

In this paper,we propose an active reconfigurable intelligent surface(RIS)enabled hybrid relaying scheme for a multi-antenna wireless powered communication network(WPCN),where the active RIS is employed to assist both wireless energy transfer(WET)from the power station(PS)to energyconstrained users and wireless information transmission(WIT)from users to the receiving station(RS).For further performance enhancement,we propose to employ both transmit beamforming at the PS and receive beamforming at the RS.We formulate a sumrate maximization problem by jointly optimizing the RIS phase shifts and amplitude reflection coefficients for both the WET and the WIT,transmit and receive beamforming vectors,and network resource allocation.To solve this non-convex problem,we propose an efficient alternating optimization algorithm with the linear minimum mean squared error criterion,semidefinite relaxation(SDR)and successive convex approximation techniques.Specifically,the tightness of applying the SDR is proved.Simulation results demonstrate that our proposed scheme with 10 reflecting elements(REs)and 4 antennas can achieve 17.78%and 415.48%performance gains compared to the single-antenna scheme with 10 REs and passive RIS scheme with 100 REs,respectively.

The study of a neutron spectrum unfolding method based on particle swarm optimization combined with maximum likelihood expectation maximization

The neutron spectrum unfolding by Bonner sphere spectrometer(BSS) is considered a complex multidimensional model,which requires complex mathematical methods to solve the first kind of Fredholm integral equation. In order to solve the problem of the maximum likelihood expectation maximization(MLEM) algorithm which is easy to suffer the pitfalls of local optima and the particle swarm optimization(PSO) algorithm which is easy to get unreasonable flight direction and step length of particles, which leads to the invalid iteration and affect efficiency and accuracy, an improved PSO-MLEM algorithm, combined of PSO and MLEM algorithm, is proposed for neutron spectrum unfolding. The dynamic acceleration factor is used to balance the ability of global and local search, and improves the convergence speed and accuracy of the algorithm. Firstly, the Monte Carlo method was used to simulated the BSS to obtain the response function and count rates of BSS. In the simulation of count rate, four reference spectra from the IAEA Technical Report Series No. 403 were used as input parameters of the Monte Carlo method. The PSO-MLEM algorithm was used to unfold the neutron spectrum of the simulated data and was verified by the difference of the unfolded spectrum to the reference spectrum. Finally, the 252Cf neutron source was measured by BSS, and the PSO-MLEM algorithm was used to unfold the experimental neutron spectrum.Compared with maximum entropy deconvolution(MAXED), PSO and MLEM algorithm, the PSO-MLEM algorithm has fewer parameters and automatically adjusts the dynamic acceleration factor to solve the problem of local optima. The convergence speed of the PSO-MLEM algorithm is 1.4 times and 3.1 times that of the MLEM and PSO algorithms. Compared with PSO, MLEM and MAXED, the correlation coefficients of PSO-MLEM algorithm are increased by 33.1%, 33.5% and 1.9%, and the relative mean errors are decreased by 98.2%, 97.8% and 67.4%.

An Influence Maximization Algorithm Based on Improved K-Shell in Temporal Social Networks

Influence maximization of temporal social networks(IMT)is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely spread.To solve the IMT problem,we propose an influence maximization algorithm based on an improved K-shell method,namely improved K-shell in temporal social networks(KT).The algorithm takes into account the global and local structures of temporal social networks.First,to obtain the kernel value Ks of each node,in the global scope,it layers the network according to the temporal characteristic of nodes by improving the K-shell method.Then,in the local scope,the calculation method of comprehensive degree is proposed to weigh the influence of nodes.Finally,the node with the highest comprehensive degree in each core layer is selected as the seed.However,the seed selection strategy of KT can easily lose some influential nodes.Thus,by optimizing the seed selection strategy,this paper proposes an efficient heuristic algorithm called improved K-shell in temporal social networks for influence maximization(KTIM).According to the hierarchical distribution of cores,the algorithm adds nodes near the central core to the candidate seed set.It then searches for seeds in the candidate seed set according to the comprehensive degree.Experiments showthatKTIMis close to the best performing improved method for influence maximization of temporal graph(IMIT)algorithm in terms of effectiveness,but runs at least an order of magnitude faster than it.Therefore,considering the effectiveness and efficiency simultaneously in temporal social networks,the KTIM algorithm works better than other baseline algorithms.

Topology Driven Cooperative Self Scheduling for Improved Lifetime Maximization in WSN

In Wireless Sensor Network(WSN),scheduling is one of the important issues that impacts the lifetime of entire WSN.Various scheduling schemes have been proposed earlier to increase the lifetime of the network.Still,the results from such methods are compromised in terms of achieving high lifetime.With this objective to increase the lifetime of network,an Efficient Topology driven Cooperative Self-Scheduling(TDCSS)model is recommended in this study.Instead of scheduling the network nodes in a centralized manner,a combined approach is proposed.Based on the situation,the proposed TDCSS approach performs scheduling in both the ways.By sharing the node statistics in a periodic manner,the overhead during the transmission of control packets gets reduced.This in turn impacts the lifetime of all the nodes.Further,this also reduces the number of idle conditions of each sensor node which is required for every cycle.The proposed method enables every sensor to schedule its own conditions according to duty cycle and topology constraints.Central scheduler monitors the network conditions whereas total transmissions occurs at every cycle.According to this,the source can infer the possible routes in a cycle and approximate the available routes.Further,based on the statistics of previous transmissions,the routes towards the sink are identified.Among the routes found,a single optimal route with energy efficiency is selected to perform data transmission.This cooperative approach improves the lifetime of entire network with high throughput performance.

DeepWalk Based Influence Maximization (DWIM): Influence Maximization Using Deep Learning

Big Data and artificial intelligence are used to transform businesses.Social networking sites have given a new dimension to online data.Social media platforms help gather massive amounts of data to reach a wide variety of customers using influence maximization technique for innovative ideas,products and services.This paper aims to develop a deep learning method that can identify the influential users in a network.This method combines the various aspects of a user into a single graph.In a social network,the most influential user is the most trusted user.These significant users are used for viral marketing as the seeds to influence other users in the network.The proposed method combines both topical and topological aspects of a user in the network using collaborativefiltering.The proposed method is DeepWalk based Influence Maximization(DWIM).The proposed method was able tofind k influential nodes with computable time using the algorithm.The experiments are performed to assess the proposed algorithm,and centrality measures are used to compare the results.The results reveal its performance that the proposed method canfind k influential nodes in computable time.DWIM can identify influential users,which helps viral marketing,outlier detection,and recommendations for different products and services.After applying the proposed methodology,the set of seed nodes gives maximum influence measured with respect to different centrality measures in an increased computable time.

Guinea Pig Maximization Test for Trichloroethylene and Its Metabolites

Objectives To study the contact allergenic activities of trichloroethylene (TCE) and its three metabolites trichloroacetic acid, trichloroethanol and chloral hydrate. Methods A modified guinea pig maximization test (GPMT) was adopted. The skin sensitization (edema and erythema) was observed in trichloroethylene, trichloroacetic acid, trichloroethanol, chloral hydrate and 2,4-dinitrochlorobenzene. Results The allergenic rate of TCE, trichloroacetic acid and 2,4-dinitrochlorobenzene was 71.4%, 58.3% and 100.0% respectively, and that of trichloroethanol and chloral hydrate was 0%. The mean response score of TCE, trichloroacetic acid and 2,4-dinitrochlorobenzene was 2.3, 1.1, 6.0 respectively. The histopathological analysis also showed an induction of allergenic transfomation in guinea pig skin by both TCE and trichloroacetic acid. Conclusion TCE appears to be a strong allergen while trichloroacetic acid a moderate one. On the other hand, both trichloroethanol and chloral hydrate are weak sensitization potentials. Immunologic reaction induced by TCE might be postulated as the pathological process of this illness. Consequently, it is suggested that in the mechanism of Occupational Dermatitis Medicamentose-Like (ODML) induced by TCE, the chemical itself might be the main cause of allergy. As one of its metabolic products, trichloroacetic acid might be a subordinate factor.

Studies on unfolding energy spectra of neutrons using maximumlikelihood expectation–maximization method

Energy spectra of neutrons are important for identification of unknown neutron sources and for determination of the equivalent dose. Although standard energy spectra of neutrons are available in some situations, e.g., for some radiotherapy treatment machines, they are unknown in other cases, e.g., for photoneutrons created in radiotherapy rooms and neutrons generated in nuclear reactors. In situations where neutron energy spectra need to be determined, unfolding the required neutron energy spectra using the Bonner sphere spectrometer (BSS) and nested neutron spectrometer (NNS) has been found promising. However, without any prior knowledge on the spectra, the unfolding process has remained a tedious task. In this work, a standalone numerical tool named ‘‘NRUunfold’’ was developed which could satisfactorily unfold neutron spectra for BSS or NNS, or any other systems using similar detection methodology. A generic and versatile algorithm based on maximum-likelihood expectation– maximization method was developed and benchmarked against the widely used STAY’SL algorithm which was based on the least squares method. The present method could output decent results in the absence of precisely calculated initial guess, although it was also remarked that employment of exceptionally bizarre initial spectra could lead to some unreasonable output spectra. The neutron count rates computed using the manufacturer’s response functions were used for sensitivity studies. The present NRUunfold code could be useful for neutron energy spectrum unfolding for BSS or NNS applications in the absence of a precisely calculated initial guess.

Analysis of Current Status of Steam Cracking Feed Production and Measures for Maximization of Steam Cracking Feed

In recent years China has seen speedy development of its ethylene industry. Compared to other advanced countries the per capita ethylene consumption in China is still low. With successive startup of grassroots ethylene projects in China after 2006 and debottlenecking and expansion of existing ethylene units China will be confronted with the major issues related with increase of feedstocks for steam cracking. Naphtha is the main feedstock for producing ethylene, and the hydrocracked tail oil is increasing its share in the steam cracker feedstock pool over recent years. This article has analyzed the possibility for maximization of steam cracking feedstock and estimated steam cracker feedstock output based on processing 5 Mt/a of different crudes including the mixed crude transferred through Lu-Ning pipeline and Arabian light crude using corresponding process technologies at the refinery.

Capacity Maximization Based Power Loading Analysis for Digital Channelized Satcom Systems

For digital channelized frequency division multiple access based satellite communication(SATCOM) systems,it is a challenging but critical issue to improve the transponder power and spectrum efficiency simultaneously under limited and non-linear high-power amplifier conditions.In this paper,different from the traditional link supportability designs aiming at minimizing the total transponder output power,a maximal sum Shannon capacity optimization objective is firstly raised subject to link supportability constraints.Furthermore,an efficient multilevel optimization(MO) algorithm is proposed to solve the considered optimization problem in the case of single link for each terminal.Moreover,in the case of multiple links for one terminal,an improved MO algorithm involving Golden section and discrete gradient searching procedures is proposed to optimize power allocation over all links.Finally,several numerical results are provided to demonstrate the effectiveness of our proposals.Comparison results show that,by the MO algorithm,not only all links' supportability can be guaranteed but also a larger sum capacity can be achieved with lower complexity.

Incorporating Network Coding into TCP Grounded on Network Utility Maximization in Multi-Radio Multi-Channel Wireless Mesh Networks

A new approach, named TCP-I2NC, is proposed to improve the interaction between network coding and TCP and to maximize the network utility in interference-free multi-radio multi-channel wireless mesh networks. It is grounded on a Network Utility Maxmization (NUM) formulation which can be decomposed into a rate control problem and a packet scheduling problem. The solutions to these two problems perform resource allocation among different flows. Simulations demonstrate that TCP-I2NC results in a significant throughput gain and a small delay jitter. Network resource is fairly allocated via the solution to the NUM problem and the whole system also runs stably. Moreover, TCP-I2NC is compatible with traditional TCP variants.

An Influence Maximization Algorithm Based on the Mixed Importance of Nodes

The influence maximization is the problem of finding k seed nodes that maximize the scope of influence in a social network.Therefore,the comprehensive influence of node needs to be considered,when we choose the most influential node set consisted of k seed nodes.On account of the traditional methods used to measure the influence of nodes,such as degree centrality,betweenness centrality and closeness centrality,consider only a single aspect of the influence of node,so the influence measured by traditional methods mentioned above of node is not accurate.In this paper,we obtain the following result through experimental analysis:the influence of a node is relevant not only to its degree and coreness,but also to the degree and coreness of the n-order neighbor nodes.Hence,we propose a algorithm based on the mixed importance of nodes to measure the comprehensive influence of node,and the algorithm we proposed is simple and efficient.In addition,the performance of the algorithm we proposed is better than that of traditional influence maximization algorithms.

Joint optimization of sampling interval and control for condition-based maintenance using availability maximization criterion

Most of the maintenance optimization models in condition-based maintenance(CBM) consider the cost-optimal criterion, but few papers have dealt with availability maximization for maintenance applications. A novel optimal Bayesian control approach is presented for maintenance decision making. The system deterioration evolves as a three-state continuous time hidden semi-Markov process. Considering the optimal maintenance policy, the multivariate Bayesian control scheme based on the hidden semi-Markov model(HSMM) is developed, the objective is to maximize the long-run expected average availability per unit time. The proposed approach can optimize the sampling interval and control limit jointly. A case study using Markov chain Monte Carlo(MCMC)simulation is provided and a comparison with the Bayesian control scheme based on hidden Markov model(HMM), the age-based replacement policy, Hotelling’s T2, multivariate exponentially weihted moving average(MEWMA) and multivariate cumulative sum(MCUSUM) control charts is given, which illustrates the effectiveness of the proposed method.

3D placement of unmanned aerial vehicles and partially overlapped channel assignment for throughput maximization

This paper investigates a wireless system with multi-Unmanned Aerial Vehicles(UAVs)for improving the overall throughput.In contrast to previous studies that optimize the locations of UAVs and channel assignment separately,this paper considers the two issues jointly by exploiting Partially Overlapped Channels(POCs).The optimization problem of maximizing network throughput is formulated as a non-convex and non-linear problem.In order to find a practical solution,the problem is decomposed into two subproblems,which are iteratively optimized.First,the optimal locations of UAVs are determined under a fixed channel assignment scheme by solving the mixed-integer second-order cone problem.Second,an efficient POC allocation scheme is determined via the proposed channel assignment algorithm.Simulation results show that the proposed approach not only significantly improves system throughput and service reliability compared with the cases in which only orthogonal channels and stationary UAVs are considered,but also achieves similar performance using the exhaustive search algorithm with lower time complexity.

SPEECH ENHANCEMENT BASED ON SECOND ORDER ARCHITECTURE AND INFORMATION MAXIMIZATION THEORY

Based on the idea of adaptive noise cancellation (ANC), a second order architecture is proposed for speech enhancement. According as the Information Maximization theory, the corresponding gradient descend algorithm is proposed. With real speech signals in the simulation, the new algorithm demonstrates its good performance in speech enhancement. The main advantage of the new architecture is that clean speech signals can be got with less distortion.

Maximization of Acoustic Levitating Force for a Single-Axis Acoustic Levitation System Using the Finite Element Method

We investigate single-axis acoustic levitation using standing waves to levitate particles freely in a medium bounded by a driver and a reflector. The acoustic pressure at the pressure antinode of the standing wave counteracts the downward gravitational force of the levitating object. The optimal relationship between the air gap and the driving frequency leads to resonance and hence maximization of the levitating force. Slight deviation from the exact resonance condition causes a reduction in acoustic pressure at the pressure antinodes. This results in a significant reduction of the levitating force. The driving frequency is kept constant while the air gap is varied for different conditions. The optimal air gap for maximizing the levitation force is studied for first three resonance modes. Furthermore, a levitating particle is introduced between the driver and the reflector. The dependence of the resonance condition on the size of the levitating particle as well as the position of the particle between the driver and the reflector has also been studied. As the size of the levitating particle increases, the resonance condition also gets modified. Finite element results show a good agreement with the validated results available in the literature. Furthermore, the finite element approach is also used to study the variation of acoustic pressure at the pressure antinode with respect to the size of the reflector. The optimum diameter of the reflector is calculated for maximizing the levitating force for three resonance modes.

Easy proft maximization method for open-pit mining

A methodology applicable at any phase of a surface mining project for evaluating its current technical and economic feasibility is presented.It requires the typically available quantitative data on the ore-body,with its three-dimensional block model developed upon accurate interpolations.Thus it allows estimations of exploitable reserves in function of various cut-off grades,such as the average grade of mineable ore,the tonnages of ore and waste rock,stripping ratios and proft estimates for different production levels.If cost evaluations of essential mine operations are available(such as ore mining,waste removal,ore concentration,transportation,indirect project costs and expected concentrate selling prices),the methodology will provide clear indications on the economic feasibility of mining,including the best available options at any moment.Simple expressions are developed on the basis of a proft mathematical function and an application example is presented with data available from an existing iron ore deposit.