Migration Technique Research Articles

SENNA: Unified Hardware/Software Space Exploration for Parametrizable Neural Network Accelerators

Parametrizable neural network accelerators enable the deployment of targeted hardware for specialized environments. Finding the best architecture configuration for a given specification, however, is challenging. A large number of hardware configurations have to be considered, and for each hardware instance, an efficient software execution plan needs to be found, leading to a vast search space. Prior work has tackled this problem by dividing the search into subproblems for individual layers of a network. There is no guarantee, however, that the overall best hardware configuration that delivers the desired end-to-end performance across the entire network is among the best individual layer configurations. This work presents SENNA, a unified hardware/software space exploration framework for parametrizable neural network accelerators. To guide the exploration towards the overall best configuration, SENNA employs a multi-objective genetic algorithm with a novel design space representation that encodes the configuration of hardware and software parameters in a single chromosome. Using the Parallel Island Model (PIM), each layer is represented by one or more individual islands each containing a separate population to simultaneously search for the best configuration across the entire network. A tailored gene migration technique enables the exchange of genes between the populations of different islands. SENNA is evaluated with three parametrizable architectures and four neural networks. The evaluation result demonstrates that SENNA achieves upto 1.92x EDP improvement compared to the State-of-the-Art. With equivalent evaluation budgets, SENNA shows 2.5x-9.3x speedup compared to an Oracle scheme and the State-of-the-Art.

Influence of some Alu insertion sequences (insertion/deletion) variations in families discrimination

The current study focused on the use of Alu elements in determining identity among individuals and they were effective for determining identity, Sixty blood samples were collected, from 14 families in Babylon Governorate to study the identification of people based on the Alu elements, where 4 sites were identified, (2q21.1), (8q23.1), (13q34) and (15q23). A PCR device was used to amplify the required sites. Polyacrylamide migration technique used to separate bands and the results are represented by additions and deletions elements. In site A(2q21.1), the P-value was between (0.04-0.22), the PIC value ranged 0-0.444 and Chi-square (3-8). In site B(8q23.1), the P-value ranged (0.018-0.48) the PIC value (0-0.5) and the Chi-square (1.44-6). In the C(13q34) site, the P-value was (0.049-0.941) PIC value was (0.486-0.5) and Chi-square (1.44-6). In the site D(15q23), the P-value was (0.049-0.941). The PIC value was (0-0.5) and Chi-square (0.04-4) at the site. The observed, expected and allele frequencies percentages were calculated for site A(2q21.1). The addition was (0.75) and deletion was (0.25). Additions and deletions were: site B(8q23.1) (0.375), (0.625). C (13q34) (0.5), (0.5). D (15q23) (0.775), (0.225) respectively and the PIC value ranged 0.5 - 0.37, P-value 0- 0.953 and Chi-square 60-0.08 for all sites and for all samples. Finally, the similarity between individuals (60) ranged between 0-1.

Matrix imaging as a tool for high-resolution monitoring of deep volcanic plumbing systems with seismic noise

Volcanic eruptions necessitate precise monitoring of magma pressure and inflation for improved forecasting. Understanding deep magma storage is crucial for hazard assessment, yet imaging these systems is challenging due to complex heterogeneities that disrupt standard seismic migration techniques. Here we map the magmatic and hydrothermal system of the La Soufrière volcano in Guadeloupe by analyzing seismic noise data from a sparse geophone array under a matrix formalism. Seismic noise interferometry provides a reflection matrix containing the signature of echoes from deep heterogeneities. Using wave correlations resistant to disorder, matrix imaging successfully unscrambles wave distortions, revealing La Soufrière’s internal structure down to 10 km with 100 m resolution. This method surpasses the diffraction limit imposed by geophone array aperture, providing crucial data for modeling and high-resolution monitoring. We see matrix imaging as a revolutionary tool for understanding volcanic systems and enhancing observatories’ abilities to monitor dynamics and forecast eruptions.

Compensating Acquisition Footprint for Amplitude-Preserving Angle Domain Common Image Gathers Based on 3D Reverse Time Migration

Angle domain common image gathers (ADCIGs) play a crucial role in seismic exploration, offering prestack underground illumination information that aids in validating migration velocity and conducting prestack amplitude versus angle (AVA) analysis for reservoir characterization. This paper introduces an innovative approach for compensating amplitude errors caused by irregular seismic acquisition geometries in ADCIGs. By incorporating an angle domain illumination compensation factor, the proposed method effectively modifies these errors, preserving the amplitude of seismic reflectivity in the prestack angle domain. The effectiveness of the proposed approach is validated through comprehensive tests conducted on synthetic and field data examples. The results demonstrate the capability of the method to enhance the quality of ADCIGs derived from 3D reverse time migration (RTM), yielding accurate and reliable amplitude preservation. While the illumination compensation factor assumes a vertically linear velocity model, the method holds promise for extension to more complex media and diverse migration techniques. This suggests its applicability and adaptability beyond the specific assumptions considered in this study. In conclusion, this paper presents an innovative angle domain illumination compensation factor that significantly improves the quality of ADCIGs by addressing amplitude errors arising from irregular seismic acquisition geometries. The experimental validation using synthetic and field data confirms the effectiveness of the proposed method within the context of 3D RTM. Furthermore, the technique holds potential for broader application in more complex subsurface scenarios and various migration methodologies.

ApMove: A Service Migration Technique for Connected and Autonomous Vehicles

ApMove: A Service Migration Technique for Connected and Autonomous Vehicles

A secure VM live migration technique in a cloud computing environment using blowfish and blockchain technology

A secure VM live migration technique in a cloud computing environment using blowfish and blockchain technology

Efficient Method for Enhancing Reverse-Time Migration Images Using Vertical Seismic Profiling Data

Vertical seismic profiling has garnered widespread attention in the industry as a supplement to seismic exploration due to its higher data quality compared to surface seismic data. However, its unique observation system in which geophones are only distributed within observation wells results in uneven coverage of subsurface structures. This can lead to significant noise when directly applying conventional reverse-time migration techniques used in surface seismic imaging. This study addresses the issue of noise suppression in reverse-time migration imaging associated with walk-away vertical seismic profiling and presents two main innovations. First, a common-receiver reverse-time migration imaging method is proposed, which uses the observation signals as excitation signals for the corresponding shots after reverse-time processing. Second, an excitation-time-constrained cross-correlation imaging condition is introduced to eliminate non-contributing portions of the wavefield, thereby modifying the traditional cross-correlation imaging condition to include an excitation time constraint. The combination of these methods enhances imaging quality by effectively suppressing noise, as demonstrated through theoretical analysis and numerical simulations with synthetic models.

High-resolution cropland mapping in China’s Huang-Huai-Hai Plain: The coupling of machine learning methods and prior information

Numerous map products containing cropland data are currently available. However, due to their low temporal and spatial resolutions and limited accuracy, their practical applications are limited. Thus, research on the precise mapping of croplands with high temporal and spatial resolutions is still urgently needed. The Huang-Huai-Hai Plain, a critical grain production area in China, was used as the research area to produce high-resolution annual cropland maps from 2018 to 2022. Sentinel-2 imagery, digital elevation model (DEM) data, and China Agricultural Ecological Zoning (CAEZ) data were collected. Based on these data, the study area was stratified into different zones, and then the distinct characteristics and optimal timing for differentiating cropland from other land cover types within each zone were analyzed and used as prior information for guiding time-series image compositing. Furthermore, the spectral information divergence (SID) index was used to modify the existing sample migration technique to transfer training samples across years. Via the Google Earth Engine (GEE) platform, the random forest (RF) classification method and positive and unlabeled learning (PUL) method were combined to construct cropland maps from composited images and terrain information from DEMs. Compared with the seasonal or spectral-temporal metric (STM) image composition methods, the image composition method based on prior information significantly improved the distinction between cropland and other land cover types. Moreover, the newly proposed sample migration strategy substantially increased the accuracy of training samples transferred across years. Compared with existing cropland maps from products such as the ESA, ESRI, GlobeLand30, GLAD, and CLCD, the cropland maps generated in this study demonstrated superior accuracy, with average overall accuracy (OA) values from 88.81% to 91.66% for different zones, thus outperforming other products with OA values from 77.53% to 88.90%. This study supports the development of related agricultural and ecological research and provides a valuable reference for cropland mapping.

Design of Part Request Form Application and Data Migration

Abstract: The migration of the data form legacy systems to modern enterprise solutions is critical for optimizing business processes and enhancing performance. This paper focuses on comparison of the data migration techniques LSMW and S/4 HANA migration cockpit. The main objective is to optimize the business process through the creation of Part Request Form application in fiori launchpad and performing data migration using S/4 High performance analytic appliance(HANA) migration cock- pit. Traditional methods of data migration has several problems, including performance bottlenecks. Addressing this limitation is essential to ensure seamless data transfer and accurate system integration. This report explores these issues and presents a methodology to address them by using advanced capabilities of the S/4 HANA Migration Cockpit over the conventional Legacy system migration workbench(LSMW) tool. The primary objective of this work is to optimize business process using part request form application. The data comes from the application will be saved in the database table in the user system. That data is extracted into a file and send it to the IT firm where that data will be migrated to the HANA database. The tool used for the data migration is s/4 HANA migration cockpit. The software used for this project is SAP-ERP software. The Results from the simulation shows that migration cockpit process is 48% faster than LSMW technique. The part request form application uses 12 kilo bytes of memory which is very less and the processing time is 0.1 seconds which is very less. These results highlight advantages of using an application in data warehouse and the effectiveness of the S/4 HANA Migration Cockpit in optimizing business processes within warehouse environment.

Dielectrophoresis as an Adjunctive Technique for Fibroblast Cell Migration to Enhance Wound Closure

This study reports on DEP-based simulation and experimental validation of polystyrene (PS) beads and fibroblast cells for primary skin cell migration for enhancing wound closure. MyDEP software was used to calculate the numerical simulation of the Clausius-Mossotti factor (CMF). In order to examine particle trajectories based on input frequencies, the finite element technique (FEM) is used. The trajectories of PS beads and fibroblast cells were experimentally assessed to verify the impact of frequency applied on the polarisation of PS beads and fibroblast cells. The outcome illustrated the potential of employing FDEP to move particles and cells to regions of high and low electric field. Fibroblast cells exhibit negative dielectrophoresis (NDEP) at a broad range of frequencies. Thus, FDEP can be utilised for frequency optimisation to enhance wound closure.

Stable Q-compensated reverse time migration in TTI media based on a modified fractional Laplacian pure-viscoacoustic wave equation

Abstract The anisotropy and attenuation properties of real earth media can lead to amplitude reduction and phase dispersion as seismic waves propagate through it. Ignoring these effects will degrade the resolution of seismic imaging profiles, thereby affecting the accuracy of geological interpretation. To characterize the impacts of viscosity and anisotropy, we formulate a modified pure-viscoacoustic (PU-V) wave equation including the decoupled fractional Laplacian (DFL) for tilted transversely isotropic (TTI) media, which enables the generation of stable wavefields that are resilient to noise interference. Numerical tests show that the newly derived PU-V wave equation is capable of accurately simulating the viscoacoustic wavefields in anisotropic media with strong attenuation. Building on our TTI PU-V wave equation, we implement stable reverse time migration technique with attenuation compensation (Q-TTI RTM), effectively migrating the impacts of anisotropy and compensates for attenuation. In the Q-TTI RTM workflow, to remove the unstable high-frequency components in attenuation-compensated wavefields, we construct a stable attenuation-compensated wavefield modeling (ACWM) operator. The proposed stable ACWM operator consists of velocity anisotropic and attenuation anisotropic parameters, effectively suppressing the high-frequency artifacts in the attenuation-compensated wavefield. Synthetic examples demonstrate that our stable Q-TTI RTM technique can simultaneously and accurately correct for the influences of anisotropy and attenuation, resulting in the high-quality imaging results.

Enhancing the fill factor and power conversion efficiency of n-type TOPCon solar cells by using electron migration technique

Improving the efficiency and performance of the solar cells by enhancing the quality and conductivity of the contacts was studied using electron migration (EM). By using intense light that covers the entire cell area at high current densities, the process of EM creates a huge number of charge carriers and helps for enhancing overall cell performances. This study examined the effects of various parameters, including temperature, voltage, time, incident light, and current, on the enhanced performances of n-type tunnel oxide passivated contact (n-TOPCon) solar cells. According to experimental data, 60 V and 10 s at room temperature are the ideal circumstances for optimising both voltage and time. As compared to before the EM experiment in the n-TOPCon solar cells, the illuminated current–voltage (I-V) characteristics of the optimised condition are Jsc of 39.24 mA/cm2, Voc of 707.45 mV, FF of 69.954 %, and PCE of 19.42 %. After the EM condition, this study shows improvements in PCE (19.04 %) and FF (48.02 %). This work provides insight into optimizing the maximum performance in developing solar technologies.

High-precision imaging of small voids in tunnel lining based on reverse time migration

Abstract Diseases such as voids behind the initial support of the tunnel will lead to problems such as lining rupture and concrete damage in the tunnel structure, which seriously affects the driving safety in the tunnel. In tunnel construction, ground penetrating radar is frequently employed as a method for detecting hidden defects. However, when the cavity size is small, there will be a large error when the original radar data is directly interpreted, which cannot meet the needs of practical engineering. To enhance the precision in tunnel detection, the Finite-Difference Time-Domain method is used to simulate various small cavities of different shapes located behind the primary support lining of the tunnel, and the study involves examining the electromagnetic response characteristics of different kinds of holes. The migration techniques of KIRCHHOFF, F-K, and reverse time are employed to reconstruct the hole target. Furthermore, digital morphology is employed to enhance the clarity of the image. Finally, the edge detection technology is used to further extract the hole features. After that, small cavities of different shapes are buried in the established outdoor concrete model box and radar detection is carried out. The detection position of the reconstructed radar image and the actual position of the cavity in the model box are verified. The results show that compared with KIRCHHOFF migration and F-K migration, the image processed by reverse time migration is clearer and more intuitive, and can restore the contour of small holes well. The research findings can serve as a reference for the interpretation of radar data of the cavity behind the initial support of the tunnel.

Decapodes: A diagrammatic tool for representing, composing, and computing spatialized partial differential equations

We present Decapodes, a diagrammatic tool for representing, composing, and solving partial differential equations. Decapodes provides an intuitive diagrammatic representation of the relationships between variables in a system of equations, a method for composing systems of partial differential equations using an operad of wiring diagrams, and an algorithm for deriving solvers using hypergraphs and string diagrams. The string diagrams are in turn compiled into executable programs using the techniques of categorical data migration, graph traversal, and the discrete exterior calculus. The generated solvers produce numerical solutions consistent with state-of-the-art open source tools as demonstrated by benchmark comparisons with SU2. These numerical experiments demonstrate the feasibility of this approach to multiphysics simulation and identify areas requiring further development.

Tomographic imaging of South Rewa basin, Central India: Implications of Gondwana rifting and Late Cretaceous volcanism

We have imaged thick (3.0–5.0 km) sub-trappean Gondwana sediments deposited in a typical rift-environment of the south Rewa basin in Central India masked by the Deccan basalts. The basalts intrude through the basin forming several dykes, sills, and other intrusions along with large-scale undulations of the basement showing complex geology and tectonic settings due to Late Cretaceous volcanism (∼65 Ma). To delineate complex subsurface geological structures and understand the tectonic settings, we use long-offset seismic reflection data along the N-S trending 127 km Kuvari-Shahdol profile of the basin. The conventional stack and pre-stack time migration techniques fail to image the complex subsurface structures dominated by steeply dipping faults. The steeply dipping faults with small-scale structures are influenced by strong lateral and vertical velocity variations that cause problems in imaging the basin. Hence, a robust tomographic inversion with pre-stack depth migration technique is adopted to image fine-scale subtle subsurface geological structures with smooth velocity variations. The tomographic velocity model and pre-stack depth migration seismic image could decipher basaltic trap thickness, numerous dyke intrusions, alternate horsts and grabens with deposition of thick hydrocarbon-bearing sub-trappean Gondwana rocks along with the basement configuration. The deep basinal faults with highly distorted basement structures represent the intense tectonic activity of this Gondwana rift-basin. The deep-seated hydrocarbon reservoir is discovered with an excellent trapping mechanism forming both pre-rift and syn-rift settings of the rift-basin. The seismic image is further constrained and corroborated by the inversion of residual Bouguer gravity anomaly data to obtain corresponding density model derived from tomographic velocity model. The large basement faults, horsts and grabens, several intrusives like dyke, sill, laccolith, and thick sub-trappean Gondwana formations with Deccan trap cover indicate complex geology and tectonic settings of south Rewa basin forming the Late Cretaceous Volcanic Province of India.

CONCENTRATION OF NANO FLUID/BASE FLUID SUSPENSION ENHANCE SURFACE CHARGE WITH PH STABILITY FOR LOW TO MEDIUM TEMPERATURE PHASE CHANGE MATERIALS

Zeta potential and poly-dispersivity are used to characterize the samples that is obtained using absorption refrigeration system for low to medium temperature phase transition materials. Salicyclic acid, Benzanilide, Hydroquinone, Potassium thiocyanates, D-mannitol, Alunimium oxide, Iron oxide, and ZnO active concentration with base fluid, aspects including the influence of the PCMs property based on their phase transition mutual interaction are explored. In order to comprehend their behavior and improve their performance, functional materials synthesis and characterization depend heavily on the isoelectric point. Understanding the material surface charge role of the medium's pH stability to the many liquid-phase procedures involved in the synthesis of materials, since it conduct the processes like agglomeration, coagulation, peptization to form solid particles materials. Zeta potential measure, which commonly use concentration of volume fraction methods, electrophoretic migration techniques, are hence a valuable source of data.

Crash severity analysis: A data-enhanced double layer stacking model using semantic understanding

The crash severity analysis is of significant importance in traffic crash prevention and emergency resource allocation. A range of innovations offers potential traffic crash severity prediction models to improve road safety. However, the semantic information inherent in traffic crash data, which is crucial in enabling a deeper understanding of its underlying factors and impacts, has yet to be fully utilized. Moreover, traffic crash data are commonly characterized by a small sample size, which leads to sample imbalance problem resulting in prediction performance decline. To tackle these problems, we propose a semantic understanding-based data-enhanced double-layer stacking model, named EnLKtreeGBDT, for crash severity prediction. Specifically, to fully leverage the inherent semantic information within traffic crash data and analyze the factors influencing crashes, we design a semantic enhancement module for multi-dimensional feature extraction. This module aims to enhance the understanding of crash semantics and improve prediction accuracy. Then we introduce a data enhancement module that utilizes data denoising and migration techniques to address the challenge of data imbalance, reducing the prediction model's dependence on large sample crash data. Furthermore, we construct a two-layer stacking model that combines multiple linear and nonlinear classifiers. This model is designed to augment the capability of learning linear and nonlinear mixed relationships, thereby improving the accuracy of predicting the severity of crashes on complex urban roads. Experiments on historical datasets of UK road safety crashes validate the effectiveness of the proposed model, and superior performance of prediction precision is achieved compared with the state-of-the-arts. The ablation experiments on both semantic and data enhancement modules further confirm the indispensability of each module in the proposed model.

2D Marine Seismic data Analysis Using Comparison of Kirchhoff’s Migration Method and Finite Difference Method (Case Study: Nias Basin, North Sumatera)

Seismic migration is one of the important stages in seismic data processing which aims to map seismic events to their actual positions. The migration process used in this study is post-stack time migration in the time domain using the Kirchhoff migration technique and the finite difference method to determine the results of subsurface imaging from the two migration techniques and then compare them to determine the accuracy of selecting the appropriate migration for the L08 basin trajectory research area. Nias basin, North Sumatra. The processing steps are carried out according to the preprocessing to processing stages in the Promax 5000 software. Based on the results of the study, the optimum use of aperture migration in Kirchhoff migration will produce good subsurface cross-sectional imaging. The aperture value used is 3000 ms. In the finite difference migration, subsurface imaging is much more focused with a time step variation of 10 ms, whose function is to focus the hyperbolic diffraction energy on the migration data.

Outcome of parasitological examinations in dogs in Germany: a retrospective survey

Dog faecal samples examined from January 2019 to December 2019 were retrospectively analysed for frequency of endoparasites. The examinations were performed with several different methods: 29,219 samples were examined by flotation method and sodium acetate-acetic acid-formalin concentration (SAFC) technique, 1,330 samples by Baermann-Wetzel migration technique, 12,221 samples using a Giardia coproantigen enzyme-linked-immunosorbent assay (ELISA), 1,180 samples using a Cryptosporidium coproantigen ELISA, 1,671 samples by polymerase chain reaction (PCR) testing for Giardia duodenalis and 447 samples by PCR testing for Cryptosporidium spp.. A total of 7.1% of the samples were positive for parasites in the microscopical examination using the flotation method and SAFC technique. The parasites found included Cystoisospora spp. (2.8%), Giardia duodenalis (2.3%), Ancylostomatidae (1.8%), Toxocara canis (1.6%), Trichuris vulpis (0.7%), Toxascaris leonina (0.5%), Capillaria spp. (0.2%), Angiostrongylus vasorum (0.2%), Crenosoma vulpis (0.1%), Taeniidae (0.1%), Sarcocystis spp. (0.03%), Dipylidium caninum (0.01%), Diphyllobothrium latum (< 0.01%), Spirurida (< 0.01%) and Opisthorchiidae (< 0.01%). Using the Baermann-Wetzel migration technique, Angiostrongylus vasorum was found in 0.75% and Crenosoma vulpis in 0.3% of the samples. ELISAs for Giardia duodenalis and Cryptosporidium spp. revealed 13.9% and 1.0% positive faecal samples, and Giardia duodenalis and Cryptosporidium spp. PCRs 19.4% and 2.0%, respectively. Dogs in the first year of life were more frequently infected with parasites than older animals. In the microscopic examination using flotation method and SAFC technique, the significantly highest detection rates were found in dogs up to six months of age (p < 0.001).

Hybrid learning based service migration for cost minimization with deadlines in multi-user mobile edge computing systems

Mobile edge computing, a new computing model, aims to reduce network latency and improve user experience by placing computing resources closer to the users. However, when users move away from the current service areas, the quality of services will decline. To guarantee the quality of services, service migration technique is proposed and studied. Due to the wireless resource contention among users, there is service migration decision-making coupling among multiple users. Meanwhile, making real-time service migration decisions for multiple users to minimize service cost with application deadlines remains an open challenge because of the huge state space. In this paper, we formalize the service migration problem with deadlines as a Markov decision process (MDP) and propose a hybrid learning based service migration strategy. The proposed approach integrates Monte Carlo method and DQN learning, which adopts online training without Q-values and neural network approximation to generate service migration decisions for multiple users. Experimental results demonstrate that the proposed hybrid learning based service migration strategy outperforms the existing migration strategies in terms of service success ratio and service cost.