Migration Algorithm Research Articles

Application of Air-Coupled Ground Penetrating Radar Based on F-K Filtering and BP Migration in High-Speed Railway Tunnel Detection.

As the number and length of high-speed railway tunnels increase in China, implicit defects such as insufficient lining thicknesses, voids, and poor compaction have become increasingly common, posing a serious threat to train operation safety. It is, therefore, imperative to conduct a comprehensive census of the defects within the tunnel linings. In response to this problem, this study proposes a high-speed railway tunnel detection method based on vehicle-mounted air-coupled GPR. Building on a forward simulation of air-coupled GPR, the study proposes the F-K filtering and BP migration algorithms based on the practical considerations of random noise and imaging interference from the inherent equipment. Through multi-dimensional quantitative comparisons, these algorithms are shown to improve the spectrum entropy values and instantaneous amplitude ratios by 4.6% and 11.6%; and 120% and 180%, respectively, over the mean and bandpass filtering algorithms, demonstrating their ability to suppress clutter and enhance the internal signal prominence of the lining. The experimental results are consistent with the forward simulation trends, and the verification using the ground-coupled GPR detection confirms that air-coupled GPR can meet the requirements of high-speed railway tunnel lining inspections. A comprehensive GPR detection model is proposed to lay the foundation for a subsequent defect census of high-speed railway tunnels.

Efficient online migration mechanism for memory write-intensive virtual machines

Online migration of virtual machines (VMs) is indispensable for system maintenance as it helps to achieve several resource management objectives such as load balancing, proactive fault tolerance, green operation, and resource management of data centers. The migration efficiency and reliability are two major challenges in the online migration of memory write-intensive VMs. For example, pre-copy migration transfers a large amount of data and takes a long time to migrate. This study proposes an efficient and reliable adaptive hybrid migration mechanism for memory write-intensive VMs. The mechanism optimizes the data transfer mode of the common migration method and improves the performance of conventional hybrid migration. First, the virtual machine (VM) memory data to be migrated are divided into dynamic and static data based on the bitmap marking method, and the migration efficiency is improved through parallel transmission based on different networks. Second, to accelerate the migration reliability, an iterative convergence factor is proposed to evaluate the current system load state and adaptively calculate the switching time of the migration mode for adaptive hybrid migration based on the convergence factor. Through adaptive hybrid migration can achieve migration completed successfully, shorten the post-copy migration duration, and minimize the impact on the performance of VMs. Finally, this paper implements the system prototype based on a kernel-based virtual machine (KVM), and experiments are performed using multiple memory write-intensive load VMs. The results show that the proposed migration algorithm can significantly improve migration performance and complete migration quickly to solve the pre-copy migration failure problem with a memory write-intensive load. Compared with the traditional hybrid migration with only one round of pre-copy, the proposed migration algorithm reduces the total migration time and transmits data by 23.2% and 26.7%, respectively.

Building adjoint operators for least-squares migration using the acoustic wave equation

Seismic imaging can be solved as an inversion problem, which can be implemented as a least-squares migration (LSM). Compared with conventional migration algorithms, an LSM can produce imaging results with enhanced illumination and resolution. However, solving an inversion problem faces difficulties in convergence, stability, and computational efficiency. To address these issues, efforts have been spent on examining the key elements in an LSM, including the modeling operators, the migration operators, the inversion solvers, etc. Advanced modeling operators are developed to accurately simulate the seismic data. Innovative migration algorithms are implemented to precisely compute the subsurface image. Fast inversion solvers are used to improve computation efficiency. Although all these techniques are robust to improve the LSMs, they often are studied independently. It is not often considered whether these elements are properly combined when used in an LSM. We have constructed LSMs with adjoint modeling and migration operators, and we develop algorithms to prepare input shot data for these LSMs.

Tutorial: Least Squares Migration and Full Waveform Imaging

A major underlying assumption of migration is that the input data are adequately sampled in terms of surface coverage. In addition, we hope that the subsurface is adequately illuminated, and that the migration algorithm itself is based on an acceptable numerical approximation of the wave equation. However, in general these assumptions and aspirations are never fully met, leading to amplitude imbalance and blurring of the output image. To some extent, this blurring and amplitude imbalance can be removed from the migrated data via application of some form of localised deconvolution, generally referred to as least-squares migration. This image modification can be performed in either the data or the image domain and can be achieved via an iterative or a single pass process, under the assumption that the velocity model is acceptable and that no coherent noise such as multiples, contaminates the input data. In this tutorial, we outline some of the various possible approaches to least-squares migration, and comment on the emerging ideas of adapting full waveform inversion to supplant some of the more expensive forms of least-squares image modification.

Research on Winter Wheat Growth Stages Recognition Based on Mobile Edge Computing

The application of deep learning (DL) technology to the identification of crop growth processes will become the trend of smart agriculture. However, using DL to identify wheat growth stages on mobile devices requires high battery energy consumption, significantly reducing the device’s operating time. However, implementing a DL framework on a remote server may result in low-quality service and delays in the wireless network. Thus, the DL method should be suitable for detecting wheat growth stages and implementable on mobile devices. A lightweight DL-based wheat growth stage detection model with low computational complexity and a computing time delay is proposed; aiming at the shortcomings of high energy consumption and a long computing time, a wheat growth period recognition model and dynamic migration algorithm based on deep reinforcement learning is proposed. The experimental results show that the proposed dynamic migration algorithm has 128.4% lower energy consumption and 121.2% higher efficiency than the local implementation at a wireless network data transmission rate of 0–8 MB/s.

Preserving Resource Handiness and Exigency-Based Migration Algorithm (PRH-EM) for Energy Efficient Federated Cloud Management Systems

On-demand computing ability and efficient service delivery are the major benefits of cloud systems. The limitation in resource availability in single data centers causes the extraction of additional resources from the cloud providers group. The federation scheme dynamically increases resource availability in response to service requests. The dynamic increase in resource count leads to excessive energy consumption, maximum cost, and carbon footprints emission. Hence, the reduction of resources is the major requirement to construct the optimized cloud source models for profit maximization without considering energy mix and CO2. This paper proposes the novel migration method to reduce carbon emissions and energy consumption. The initial stage in the proposed work is the categorization of data centers based on the MIPS and cost prior to job allocation offers scalable and efficient services and resources to the cloud user. Then, the job with the maximum size is allotted to the VM only if its capacity is less than the cumulative capacity of data centers. A novel migration based on overutilized and underutilized levels provides the services to the user even if the particular VM fails. The proposed work offers efficient maintenance of resource availability and maximizes the profit of the cloud providers associated with the federated cloud environment. The comparative analysis of the proposed algorithm with the existing methods regarding the response time, accuracy, profit, carbon emission, and energy consumption assures the effectiveness in a confederated cloud environment.

An explicit stable Q-compensated reverse time migration scheme for complex heterogeneous attenuation media

Prestack reverse-time migration (RTM) is a popular imaging technique for complex geological conditions, since the amplitude attenuation and velocity dispersion are common in seismic recordings. To image attenuated seismic recordings accurately, a robust migration algorithm with a stable attenuation compensation approach should be considered. In the context of the Q-compensated RTM approach based on the decoupled fractional Laplacians (DFLs) viscoacoustic wave equation, amplitude compensation can be implemented by flipping the sign of the dissipation term. However, the non-physical magnification of image amplitude could lead to a well-known numerical instability problem. The explicit stabilization operator can rectify the amplitude attenuation and suppress the numerical instability. However, limited by the inconvenient mixed-domain operator, the average Q value rather than the real Q value is often used in the compensation operator, lowering the compensated accuracy of the migration image. To overcome this problem, we propose a novel explicit Q-compensation scheme. The main advantage of the proposed compensation operator is that its order is space-invariant, making it more suitable for handling complex heterogeneous attenuation media. Several two-dimensional (2D) and three-dimensional (3D) synthetic models are used to verify the superiority of the proposed approach in terms of amplitude fidelity and image resolution. Field data further demonstrates that our approach has potential applications and can greatly enhance the resolution of seismic images.

A Vehicle Service Migration Strategy Algorithm in 5G NR-V2X

In recent years, a new technology called mobile edge computing (MEC) is proposed to alleviate mobile devices’ increasing demands on computing resources and service latency. However, users may stay away from the fixed MEC server or even leave the service scope of the MEC server during the mobile process, which will lead to service performance degradation or service interruption. Such an interruption is intolerable for vehicle in V2X. Therefore, it is necessary to consider the issue of service migration. In this paper, we model the service migration problem in V2X as a multidimensional Markov decision process problem and consider a variety of metrics including computing cost, migration cost and energy consumption. A novel user mobility model is also considered to accurately reflect the motion state of users in reality. Then, a novel service migration algorithm is proposed to solve this multidimensional MDP problem. Our proposed algorithm applies Actor-Critic and entropy to ensure both convergence and exportability. Experimental results show that our proposed algorithm outperforms the baselines and has relatively strong robustness.

Scheduling optimization of AGVs in workshop based on cost-efficiency-emission competition mechanism

The untimely material transportation induced by the backward logistics scheduling method in the ship panel block construction workshop is a complex engineering issue with multi-parameter coupling. To overcome the problem, the cost-efficiency-carbon emission competition model is proposed to solve the scheduling optimization for the AGVs in ship panel block construction workshop. Firstly, the logistics path plan of the panel block construction workshop is obtained by establishing the path-planning node map of panel block construction workplace. Secondly, a scheduling model for AGVs is constructed based on the competition mechanism of the lowest transportation cost, maximum transportation efficiency, and lowest carbon emission. Then, the optimized schedule is achieved by a novel hybrid method of the genetic and population migration algorithm considering the competition mechanism. Finally, the case validation indicates that the solving ability of the proposed hybrid method is improved by 52.4% compared with Genetic Algorithm under competitive mechanism. Besides, the results demonstrate material transportation time can be reduced by 17.5%.

A Hybrid GPU and CPU Parallel Computing Method to Accelerate Millimeter-Wave Imaging

The range migration algorithm (RMA) based on Fourier transformation is widely applied in millimeter-wave (MMW) close-range imaging because of its few operations and small approximation. However, its interpolation stage is not effective due to the involved intensive logic controls, which limits the speed performance in a graphics processing unit (GPU) platform. Therefore, in this paper, we present an acceleration optimization method based on the hybrid GPU and central processing unit (CPU) parallel computation for implementing the RMA. The proposed method exploits the strong logic-control capability of the CPU to assist the GPU in processing the logic controls of the interpolation stage. The common positions of wavenumber-domain components to be interpolated are calculated by the CPU and stored in the constant memory for broadcast at any time. This avoids the repetitive computation consumed in a GPU-only scheme. Then the GPU is responsible for the remaining matrix-related steps and outputs the needed wavenumber-domain values. The imaging experiments verify the acceleration efficiency of the proposed method and demonstrate that the speedup ratio of our proposed method is more than 15 times of that by the CPU-only method, and more than 2 times of that by the GPU-only method.

A Fast 3-D Chirp Scaling Imaging Technique for Millimeter-Wave Near-Field Imaging

In this article, a novel 3-D imaging method based on the chirp scaling algorithm (CSA) is proposed for millimeter-wave near-field imaging. We deduce the theoretical formula of CSA applied to multiple-input–multiple-output (MIMO)/single-input–single-output (SISO) array imaging and then analyze the computational complexity and phase error. Compared to the classical Fourier-based algorithm, such as range migration algorithm (RMA), they can replace the redundant interpolation with the phase multiplication. Meanwhile, it can handle the large relative bandwidth system compared with frequency scaling algorithm (FSA). It greatly improves imaging speed and broadens the application scenarios. The rearrangement operation of the MIMO wavenumber spectrum is also accelerated by the sparse matrix multiplication. To further reduce the computation time, we perform the direct rearrangement of the MIMO wavenumber spectrum before phase compensation. It works well but also sacrifices part of the signal-to-noise ratio (SNR), so it may not be suitable for the weakly scattering targets. The simulation data of a 2-D uniform array with the large number of antennas and a published dataset are used to verify the effect of the proposed algorithm. The simulation and experiment results show that the proposed method can effectively improve the computational efficiency.

Research on Wickerwork Patterns Creative Design and Development Based on Style Transfer Technology

Traditional craftsmanship and culture are facing a transformation in modern science and technology development, and the cultural industry is gradually stepping into the digital era, which can realize the sustainable development of intangible cultural heritage with the help of digital technology. To innovatively generate wickerwork pattern design schemes that meets the user’s preferences, this study proposes a design method of wickerwork patterns based on a style migration algorithm. First, an image recognition experiment using residual network (ResNet) based on the convolutional neural network is applied to the Funan wickerwork patterns to establish an image recognition model. The experimental results illustrate that the optimal recognition rate is 93.37% for the entire dataset of ResNet50 of the pattern design images, where the recognition rate of modern patterns is 89.47%, while the recognition rate of traditional patterns is 97.14%, the recognition rate of wickerwork patterns is 95.95%, and the recognition rate of personality is 90.91%. Second, based on Cycle-Consistent Adversarial Networks (CycleGAN) to build design scheme generation models of the Funan wickerwork patterns, CycleGAN can automatically and innovatively generate the pattern design scheme that meets certain style characteristics. Finally, the designer uses the creative images as the inspiration source and participates in the detailed adjustment of the generated images to design the wickerwork patterns with various stylistic features. This proposed method could explore the application of AI technology in wickerwork pattern development, and providing more comprehensive and rich new material for the creation of wickerwork patterns, thus contributing to the sustainable development and innovation of traditional Funan wickerwork culture. In fact, this digital technology can empower the inheritance and development of more intangible cultural heritages.

Imaging of thrust structures under Colombian foothills landscape through Marchenko approach

The Marchenko imaging approach successfully deals with multiple scattering during seismic migration, obtaining clear images in structurally complex geological zones below at a specific depth. Through the Marchenko equation, the method retrieves Green's function of a virtual source in profundity using reflections recorded on the surface and a smoothed velocity model. According to the theorem of reciprocity, Green's function represents the medium's response to a source on the surface recorded by a virtual receiver in-depth and decomposes it into a down-going wavefield and an up-going wavefield. Both wave fields' deconvolution provides the reflection response of virtual sources and virtual receivers repositioned in the subsurface, mimicking redatumming. The result is an image free of multiple internal reflections from the area below virtual both sources and receivers. After successfully testing with synthetic seismograms generated in a plane-horizontal layers model, we applied the Marchenko algorithm to seismic data of the Upper Magdalena Valley project using an available velocity model. Compared to an image supplied by a Kirchhoff pre-stack depth migration algorithm, the new image correctly positions the seismic events avoiding misleading interpretations in locating complex structures.

Least-squares reverse-time migration in VTI media using a T-distribution-based misfit function

Abstract Anisotropy of subsurface media can affect the seismic wave travel time estimates, and then affect the imaging of target regions. To calibrate the effect of anisotropy, a VTI (transverse isotropy media with vertical symmetry axis) least-squares reverse-time migration (LSRTM) algorithm is used. However, if seismic data contains noise especially the outliers, the high-quality images cannot be produced by LSRTM based on an L2 norm. Considering that Student's t-distribution has better robustness than the L2 norm, this study derives a VTI-LSRTM algorithm based on Student's t-distribution (T-VTI-LSRTM) misfit function. The plane-wave encoding is introduced into the T-VTI-LSRTM to improve the computational efficiency. We conduct numerical tests using the complex models to illustrate the effectiveness and adaptability of the method. The sensitivity analysis of velocity and anisotropy parameters demonstrate that anisotropy and velocity parameters have a distinct effect on imaging results, in which velocity sensitivity is the strongest, then the epsilon and parameter delta sensitivity is the weakest.

A bandwidth-aware service migration method in LEO satellite edge computing network

With the development of modern sensing technology, large amounts of data are generated in areas that lack terrestrial networks, such as the field and space. This data is transmitted by satellite networks, where the interesting data is only a small part. By introducing edge computing technology, identifying and transmitting interesting data will improve the efficiency of satellite network and the quality of service for users. A satellite edge computing (SatEC) system is proposed in this paper that provides computing services through LEO satellite network. For satellite characteristics with limited resources and mobility, a bandwidth-aware service migration problem is proposed to minimize the long-term total delay cost of the system while all tasks do not exceed its deadline. Converting long-term optimization problem into a series of one-time MINLP problems, an online lazy migration algorithm is firstly proposed to solve optimal service migration decision scheme with ϵ(1+1β)-competitive ratio. Then a hierarchical iterative algorithm is proposed to decouple communication and computing resources. Finally, a bandwidth-aware virtual CPU scheduling for post-copy migration algorithm is proposed to improve the migration speed. Simulation results show that the proposed scheme effectively deal with the topology change of satellite network and reduce the long-term total delay cost of the SatEC system.

Generation of Multiple Frames for High Resolution Video SAR Based on Time Frequency Sub-Aperture Technique

Video Synthetic Aperture Radar (ViSAR) operating in spotlight mode has received widespread attention in recent years because of its ability to form a sequence of SAR images for a region of interest (ROI). However, due to the heavy computational burden of data processing, the application of ViSAR is limited in practice. Although back projection (BP) can avoid unnecessary repetitive processing of overlapping parts between consecutive video frames, it is still time-consuming for high-resolution video-SAR data processing. In this article, in order to achieve the same or a similar effect to BP and reduce the computational burden as much as possible, a novel time-frequency sub-aperture technology (TFST) is proposed. Firstly, based on azimuth resampling and full aperture azimuth scaling, a time domain sub-aperture (TDS) processing algorithm is proposed to process ViSAR data with large coherent integration angles to ensure the continuity of ViSAR monitoring. Furthermore, through frequency domain sub-aperture (FDS) processing, multiple high-resolution video frames can be generated efficiently without sub-aperture reconstruction. In addition, TFST is based on the range migration algorithm (RMA), which can take into account the accuracy while ensuring efficiency. The results of simulation and X-band airborne SAR experimental data verify the effectiveness of the proposed method.

Migration Algorithm: A New Human-Based Metaheuristic Approach for Solving Optimization Problems

Migration Algorithm: A New Human-Based Metaheuristic Approach for Solving Optimization Problems

Black Widow Optimization Algorithm for Virtual Machines Migration in the Cloud Environments

Black Widow Optimization Algorithm for Virtual Machines Migration in the Cloud Environments

Load Balancing and Server Consolidation for Energy Management in Cloud Data Center

Users worldwide can access utility-oriented computing services through cloud computing. It enables the pay-as-you-go model to host well-known consumer, educational, and business applications. However, cloud data centers encounter significant issues in effectively managing resources, ensuring optimal performance, and reducing energy usage as the demand for cloud computing services is increasing rapidly. A possible strategy to reduce energy consumption and operating expenses is the energy-efficient management of virtual machines (VMs) and server consolidation. In this paper, a VM migration and server consolidation algorithm is proposed, which is energy efficient and SLA aware. The proposed approach is evaluated using the CloudSim simulation kit and PlanetLab workload. The experiment result shows that the proposed approach has reduced energy consumption and SLA violation.

Analysis and Compensation for Systematical Errors in Airborne Microwave Photonic SAR Imaging by 2-D Autofocus

In the area of synthetic aperture radar (SAR), the ultra-high resolution is always a continuous pursuit for researchers. To realize ultra-high resolution, microwave photonics (MWP) technology is an excellent solution since it has advantages of low transmission loss, ultra-wide bandwidth (UWB), etc. However, with the improvement of resolution, new problems arise as the impact of more systematical errors becomes non-negligible. For example, the synchronization error between the transmit channel and the mixing channel, unknown system delay during signal reception, and fluctuation of frequency for UWB signals. These factors will bring degradation of the focusing quality of SAR images together with the motion errors and the trajectory measurement errors, not only cause residual range cell migration (RCM) and azimuth phase error (APE), but also higher-order phase error along the range frequency dimension. In this paper, we discuss the adverse influence of the above factors and give a detailed analysis of the 2-D phase error of the coarse focused image processed by range migration algorithm (RMA). Then, to compensate for the above unknown systematical errors, a 2-D autofocus method is proposed and the effectiveness is validated by experiments on both simulated and real data.