HgaNets:Fusion of Visual Data and Skeletal Heatmap for Human Gesture Action Recognition

Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual andskeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data,failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility toefficiently process both uniformand disparate input patterns.Thus, in this paper, an attention-enhanced pseudo-3Dresidual model is proposed to address the GAR problem, called HgaNets. This model comprises two independentcomponents designed formodeling visual RGB (red, green and blue) images and 3Dskeletal heatmaps, respectively.More specifically, each component consists of two main parts: 1) a multi-dimensional attention module forcapturing important spatial, temporal and feature information in human gestures;2) a spatiotemporal convolutionmodule that utilizes pseudo-3D residual convolution to characterize spatiotemporal features of gestures. Then,the output weights of the two components are fused to generate the recognition results. Finally, we conductedexperiments on four datasets to assess the efficiency of the proposed model. The results show that the accuracy onfour datasets reaches 85.40%, 91.91%, 94.70%, and 95.30%, respectively, as well as the inference time is 0.54 s andthe parameters is 2.74M. These findings highlight that the proposed model outperforms other existing approachesin terms of recognition accuracy.

Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.

Throughput Analysis of HARQ Scheme Based on Full-Duplex Two-Way AF SWIPT Relay

The simultaneous wireless information and power transfer(SWIPT)relay system is one of the emerging technologies.Xiaomi Corporation and Motorola Inc.recently launched indoor wireless power transfer equipment is one of the most promising applications.To tap the potential of the system,hybrid automatic repeat request(HARQ)is introduced into the SWIPT relay system.Firstly,the time slot structure of HARQ scheme based on full duplex two-way amplify and forward(AF)SWIPT relay is given,and its retransmission status is analyzed.Secondly,the equivalent signal-to-noise ratio and outage probability of various states are calculated by approximate simplification.Thirdly,the energy harvesting power in each state is calculated.Finally,the energy harvested-throughput sum function is constructed to characterize the performance of energy harvesting and data transmission.Simulation results show that the proposed HARQ scheme has better energy harvestedthroughput sum function than the traditional HARQ scheme.When P_(2)=22 dB,the maximum sum function is 54.86%(the proposed HARQ scheme)and 52.307%(the traditional HARQ scheme),respectively.

Two-Dimensional Images of Current and Active Power Signals for Elevator Condition Recognition

In this paper, an improved two-dimensional convolution neural network(2DCNN) is proposed to monitor and analyze elevator health, based on the distribution characteristics of elevator time series data in two-dimensional images. The current and effective power signals from an elevator traction machine are collected to generate gray-scale binary images. The improved two-dimensional convolution neural network is used to extract deep features from the images for classification, so as to recognize the elevator working conditions. Furthermore, the oscillation criterion is proposed to describe and analyze the active power oscillations. The current and active power are used to synchronously describe the working condition of the elevator, which can explain the co-occurrence state and potential relationship of elevator data. Based on the improved integration of local features of the time series, the recognition accuracy of the proposed 2DCNN is 97.78%, which is better than that of a one-dimensional convolution neural network. This research can improve the real-time monitoring and visual analysis performance of the elevator maintenance personnel, as well as improve their work efficiency.

Twin delayed deep deterministic policy gradient-based intelligent computation offloading for IoT

In view of the randomness distribution of multiple users in the dynamic large-scale Internet of Things(IoT)scenario,comprehensively formulating available resources for fog nodes in the area and achieving computation services at low cost have become great challenges.As a result,this paper studies an efficient and intelligent computation offloading mechanism with resource allocation.Specifically,an optimization problem is formulated to minimize the total energy consumption of all tasks under the joint optimization of computation offloading decisions,bandwidth resources and transmission power.Meanwhile,a Twin Delayed Deep Deterministic Policy Gradient-based Intelligent Computation Offloading(TD3PG-ICO)algorithm is proposed to solve this optimization problem.By combining the concept of the actor critic algorithm,the proposed algorithm designs two independent critic networks that can avoid the subjective prediction of a single critic network and better guide the policy network to generate the global optimal computation offloading policy.Additionally,this algorithm introduces a continuous variable discretization operation to select the target offloading node with random probability.The available resources of the target node are dynamically allocated to improve the model decision-making effect.Finally,the simulation results show that this proposed algorithm has faster convergence speed and good robustness.It can always approach the greedy algorithm with respect to the lowest total energy consumption.Furthermore,compared with full local and Deep Q-learning Network(DQN)-based computation offloading schemes,the total energy consumption can be reduced by an average of 15.53%and 6.41%,respectively.

Image defocus deblurring method based on gradient difference of boundary neighborhood

Background For static scenes with multiple depth layers,existing defocused image deblurring methods have the problems of edge-ringing artifacts or insufficient deblurring owing to inaccurate estimation of the blur amount,and prior knowledge in nonblind deconvolution is not strong,which leads to image detail recovery challenges.Methods To this end,this study proposes a blur map estimation method for defocused images based on the gradient difference of the boundary neighborhood,which uses the gradient difference of the boundary neighborhood to accurately obtain the amount of blurring,thereby preventing boundary ringing artifacts.The obtained blur map is then used for blur detection to determine whether the image needs to be deblurred,thereby improving the efficiency of deblurring without manual intervention and judgment.Finally,a nonblind deconvolution algorithm was designed to achieve image deblurring based on the blur amount selection strategy and sparse prior.Results Experimental results showed that our method improves PSNR(Peak Signal-to-Noise Ratio)and SSIM(Structural Similarity Index)by an average of 4.6%and 7.3%,respectively,compared to existing methods.Conclusions Experimental results showed that the proposed method outperforms existing methods.Compared to existing methods,our method can better solve the problems of boundary ringing artifacts and detail information preservation in defocused image deblurring.

Fundamental Limits of Doppler Shift-Based, ToA-Based,and TDoA-Based Underwater Localization

Dear Editor,This paper is concerned with the underwater localization based on acoustic signals. Specifically, we will focus on the search of an underwater target that can constantly broadcast a beacon signal, such as a black box. Common measurements for localization are Doppler shift [1], time of arrival(ToA) [2]–[4], time difference of arrival(TDoA) [5], [6], angle of arrival(AoA) [7], etc.

A Correlation-Based Stochastic Model for Massive MIMO Channel

In this paper,the channel impulse response matrix(CIRM)can be expressed as a sum of couplings between the steering vectors at the base station(BS)and the eigenbases at the mobile station(MS).Nakagami distribution was used to describe the fading of the coupling between the steering vectors and the eigenbases.Extensive measurements were carried out to evaluate the performance of this proposed model.Furthermore,the physical implications of this model were illustrated and the capacities are analyzed.In addition,the azimuthal power spectrum(APS)of several models was analyzed.Finally,the channel hardening effect was simulated and discussed.Results showed that the proposed model provides a better fit to the measured results than the other CBSM,i.e.,Weichselberger model.Moreover,the proposed model can provide better tradeoff between accuracy and complexity in channel synthesis.This CIRM model can be used for massive MIMO design in the future communication system design.

Physics-based battery SOC estimation methods:Recent advances and future perspectives

The reliable prediction of state of charge(SOC)is one of the vital functions of advanced battery management system(BMS),which has great significance towards safe operation of electric vehicles.By far,the empirical model-based and data-driven-based SOC estimation methods of lithium-ion batteries have been comprehensively discussed and reviewed in various literatures.However,few reviews involving SOC estimation focused on electrochemical mechanism,which gives physical explanations to SOC and becomes most attractive candidate for advanced BMS.For this reason,this paper comprehensively surveys on physics-based SOC algorithms applied in advanced BMS.First,the research progresses of physical SOC estimation methods for lithium-ion batteries are thoroughly discussed and corresponding evaluation criteria are carefully elaborated.Second,future perspectives of the current researches on physics-based battery SOC estimation are presented.The insights stated in this paper are expected to catalyze the development and application of the physics-based advanced BMS algorithms.

Optimization of rice wine fermentation process based on the simultaneous saccharification and fermentation kinetic model

Chinese rice wine making is a typical simultaneous saccharification and fermentation(SSF) process.During the fermentation process,temperature is one of the key parameters which decide the quality of Chinese rice wine.To optimize the SSF process for Chinese rice wine brewing,the effects of temperature on the kinetic parameters of yeast growth and ethanol production at various temperatures were determined in batch cultures using a mathematical model.The kinetic parameters as a function of temperature were evaluated using the software Origin8.0.Combing these functions with the mathematical model,an appropriate form of the model equations for the SSF considering the effects of temperature were developed.The kinetic parameters were found to fit the experimental data satisfactorily with the developed temperature-dependent model.The temperature profile for maximizing the ethanol production for rice wine fermentation was determined by genetic algorithm.The optimum temperature profile began at a low temperature of 26 °C up to 30 h.The operating temperature increased rapidly to 31.9 °C,and then decreased slowly to 18 °C at 65 h.Thereafter,the temperature was maintained at18 °C until the end of fermentation.A maximum ethanol production of 89.3 g·L^(-1)was attained.Conceivably,our model would facilitate the improvement of Chinese rice wine production at the industrial scale.

Synthesis and Microstructure Analysis of Autoclaved Aerated Concrete with Carbide Slag Addition

Synthesis of autoclaved aerated concrete(AAC) has been carried out with carbide slag addition, and the carbide slag could be used as a main material to produce the AAC with the compressive strength about 2 MPa and the density below 0.6 g?cm-3. In this study, quartz sand acted as frame structure phase in the matrix, and quartz addition also infl uenced the Si/Ca of starting material. Tobermorite and CSH gel were formed readily at 62%, which seemed to enhance the compressive strength of samples. Curing time seemed to affect the morphology of phase produced, and specimen with the plate-like tobermorite formed at 10 h appeared to have a better compressive strength development than the fiber-like one at 18 h. The higher curing temperature seemed to favor the tobermorite and CSH gel formation, which also exerted a significant effect on the strength development of the samples. On the micro-scale, the formed CSH gel was filled in the interface of the matrix, and the tobermorite appeared to grow in internal-surface of the pores and interstices. The tobermorite or/and CSH formation seemed to densify the matrix, and therefore enhanced the strength of the samples.

Distributed event-triggered consensus tracking of second-order multi-agent systems with a virtual leader

This paper investigates the consensus tracking problems of second-order multi-agent systems with a virtual leader via event-triggered control. A novel distributed event-triggered transmission scheme is proposed, which is intermittently examined at constant sampling instants. Only partial neighbor information and local measurements are required for event detection. Then the corresponding event-triggered consensus tracking protocol is presented to guarantee second-order multi-agent systems to achieve consensus tracking. Numerical simulations are given to illustrate the effectiveness of the proposed strategy.

Performance Analysis and Comparison of Two Fault-Tolerant Model Predictive Control Methods for Five-Phase PMSM Drives

Model predictive current control(MPCC)and model predictive torque control(MPTC)are two derivatives of model predictive control.These two control methods have demonstrated their strengths in the fault-tolerant control of multiphase motor drives.To explore the inherent link,the pros and cons of two strategies,the performance analysis and comparative investigation of MPCC and MPTC are conducted through a five-phase permanent magnet synchronous motor with open-phase fault.In MPCC,the currents of fundamental and harmonic subspaces are simultaneously employed and constrained for a combined regulation of the open-circuit fault drive.In MPTC,apart from the torque and the stator flux related to fundamental subspace,the x-y currents are also considered and predicted to achieve the control of harmonic subspace.The principles of two methods are demonstrated in detail and the link is explored in terms of the cost function.Besides,the performance by two methods is experimentally assessed in terms of steady-state,transition,and dynamic tests.Finally,the advantages and disadvantages of each method are concluded.

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with general sampling delay

In this paper we provide a unified framework for consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with a general sampling delay. First, a stochastic bounded consensus tracking protocol based on sampled data with a general sampling delay is presented by employing the delay decomposition technique. Then, necessary and sufficient conditions are derived for guaranteeing leader-follower multi-agent systems with measurement noises and a time-varying reference state to achieve mean square bounded consensus tracking. The obtained results cover no sampling delay, a small sampling delay and a large sampling delay as three special cases. Last, simulations are provided to demonstrate the effectiveness of the theoretical results.

Total Ionization Dose Effects on Charge Storage Capability of Al2O3/HfO2/Al2O3-Based Charge Trapping Memory Cell

Because of the discrete charge storage mechanism, charge trapping memory(CTM) technique is a good candidate for aerospace and military missions. The total ionization dose(TID) effects on CTM cells with Al_2O_3/HfO_2/Al_2O_3(AHA) high-k gate stack structure under in-situ 10 keV x-rays are studied. The C-V characteristics at different radiation doses demonstrate that charge stored in the device continues to be leaked away during the irradiation,thereby inducing the shift of flat band voltage(V_(fb)). The dc memory window shows insignificant changes, suggesting the existence of good P/E ability. Furthermore, the physical mechanisms of TID induced radiation damages in AHA-based CTM are analyzed.

Total Ionizing Dose Effects of 55-nm Silicon-Oxide-Nitride-Oxide-Silicon Charge Trapping Memory in Pulse and DC Modes

The ^(60)Co-γ ray total ionizing dose radiation responses of 55-nm silicon-oxide-nitride-oxide-silicon(SONOS) memory cells in pulse mode(programmed/erased with pulse voltage) and dc mode(programmed/erased with direct voltage sweeping) are investigated. The threshold voltage and off-state current of memory cells before and after radiation are measured. The experimental results show that the memory cells in pulse mode have a better radiation-hard capability. The normalized memory window still remains at 60% for cells in dc mode and 76% for cells in pulse mode after 300 krad(Si) radiation. The charge loss process physical mechanisms of programmed SONOS devices during radiation are analyzed.

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises and sampled-data

This paper is concerned with the stochastic bounded consensus tracking problems of leader-follower multi-agent systems,where the control input of an agent can only use the information measured at the sampling instants from its neighbours or the virtual leader with a time-varying reference state,and the measurements are corrupted by random noises.The probability limit theory and the algebra graph theory are employed to derive the necessary and sufficient conditions guaranteeing the mean square bounded consensus tracking.It is shown that the maximum allowable upper boundary of the sampling period simultaneously depends on the constant feedback gains and the network topology. Furthermore,the effects of the sampling period on the tracking performance are analysed.It turns out that from the view point of the sampling period,there is a trade-off between the tracking speed and the static tracking error. Simulations are provided to demonstrate the effectiveness of the theoretical results.

Efficient Deployment of Multiple UAVs for IoT Communication in Dynamic Environment

The application of unmanned aerial vehicle(UAV)-mounted base stations is emerging as an effective solution to provide wireless communication service for a target region containing some smart objects(SOs)in internet of things(IoT).This paper investigates the efficient deployment problem of multiple UAVs for IoT communication in dynamic environment.We first define a measurement of communication performance of UAVto-SO in the target region which is regarded as the optimization objective.The state of one SO is active when it needs to transmit or receive the data;otherwise,silent.The switch of two different states is implemented with a certain probability that results in a dynamic communication environment.In the dynamic environment,the active states of SOs cannot be known by UAVs in advance and only neighbouring UAVs can communicate with each other.To overcome these challenges in the deployment,we leverage a game-theoretic learning approach to solve the position-selected problem.This problem is modeled a stochastic game,which is proven that it is an exact potential game and exists the best Nash equilibria(NE).Furthermore,a distributed position optimization algorithm is proposed,which can converge to a pure-strategy NE.Numerical results demonstrate the excellent performance of our proposed algorithm.

Effects of Strong Turbulence on the Spiral Plane Mode of Whittaker-Gaussian Beam through Terrene-Atmosphere

The analytic formulae of probability distribution of spiral plane modes for the Whittaker-Gaussian(WG) beams with orbital angular momentum(OAM) in strong turbulence regime are modeled based on the modified Rytov approximation.Numerical results show that the crosstalk range of OAM modes in the vicinity of signal mode increases with the increasing refractive-index construction parameter.However,effects of change of the width of the Gaussian envelope and the parameter Wo of WG beams on normalization energy weight of signal mode can be ignored.We find theoretically that signal spiral plane mode of WG beams at each OAM level approximatively has the same normalization energy weight,inaplying that the channels with WG(pseudo non-diffraction) beam have higher channel capacity than the channels with the Laguerre-Gaussian beam.

Consensus of heterogeneous multi-agent systems based on sampled data with a small sampling delay

In this paper, consensus problems of heterogeneous multi-agent systems based on sampled data with a small sampling delay are considered. First, a consensus protocol based on sampled data with a small sampling delay for heterogeneous multi-agent systems is proposed. Then, the algebra graph theory, the matrix method, the stability theory of linear systems,and some other techniques are employed to derive the necessary and sufficient conditions guaranteeing heterogeneous multi-agent systems to asymptotically achieve the stationary consensus. Finally, simulations are performed to demonstrate the correctness of the theoretical results.