Efficient Mapping Strategy Research Articles

Research on magnetic anomaly detection method assisted by geomagnetic map

Magnetic anomaly detection is a detection technology for underwater/underground ferromagnetic targets. In the geomagnetic anomaly area, the change of magnetic feld is very complicated, and the detection ability of traditional detection methods will decline sharply. To solve this problem, we construct a high-precision local geomagnetic map, which turns geomagnetic anomalies into known prior information to assist detection. Firstly, the accuracy of local geomagnetic map is improved by the optimized variation function of interpolation algorithm and an efficient mapping strategy. Secondly, the magnetic target detector assisted by geomagnetic map is designed on basis of BP neural network and its internal structure is optimized. Finally, simulation and experiment were carried out. The results show that the method improves the detection performance in the geomagnetic anomaly area by 75%–80%, and well-adapted to low SNR situations. This proposed method makes the detection of geomagnetic anomaly are effective.

An Efficient Real-Time Embedded Application Mapping for NoC Based Multiprocessor System on Chip

The Network on Chip architecture’s performance metrics and inter-core communication are significantly impacted by the acceleration of the evolution of the components integrated on a single chip. Therefore, it is crucial to offer an effective mapping between the cores so that communication between them improves in order to solve such problems. Throughput and latency both have a higher impact on outperforming the network’s performance in NoC. In this research paper, an efficient mapping strategy implemented on the real-time embedded applications named ERTEAM is presented. In this algorithm, based on the minimum core average distance the mapping region is finalized, ensuring the overall mapping area reduced. The PE’s mapped according to the minimum communication energy in the selected mapping region. This research is evaluated on a set of embedded applications, which reveals a reduction in latency at 12.3% and 8.4%, the simulation time reduces at an average of 19% and 9.6%, the throughput increases at 14.5% and 7.8% and reduces the communication energy by 15.6% and 5.2% against Branch and Bound Based Mapping (BBPCR) and segmented brute-force mapping respectively. The proposed ERTEAM is simulated and tested on Xilinxs Zynq UltraScale+ MPSoC ZCU104 Evaluation Kit using Xilinx Vivado 2020.2 software platform. The obtained hardware implementation results outperformed the delay and area metrics.

A Life-Long SLAM Approach Using Adaptable Local Maps Based on Rasterized LIDAR Images

Most real-time autonomous robot applications require a robot to traverse through a dynamic space for a long time. In some cases, a robot needs to work in the same environment. Such applications give rise to the problem of a life-long SLAM system. Life-long SLAM presents two main challenges i.e. the tracking should not fail in a dynamic environment and the need for a robust and efficient mapping strategy. The system should update maps with new information; while also keeping track of older observations. But, mapping for a long time can require higher computational requirements. In this paper, we propose a solution to the problem of life-long SLAM. We represent the global map as a set of rasterized images of local maps along with a map management system responsible for updating local maps and keeping track of older values. We also present an efficient approach of using the bag of visual words method for loop closure detection and relocalization. We evaluate the performance of our system on the KITTI dataset and an indoor dataset. Our loop closure system reported recall and precision of above 90 percent. The computational cost of our system is much lower as compared to state-of-the-art methods. Our method reports lower computational requirements even for long-term operation.

An Efficient Energy Cost and Mapping Revenue Strategy for Interdomain NFV-Enabled Networks

Future network based on software-defined networking (SDN) and network function virtualization (NFV) technologies is the main evolution tendency of current Internet, enabling telecommunication service providers (TSPs) to share their virtualized network resources with their contracted users in a flexible and economical manner. One key technical issue is virtualized resources allocation. In order to solve this issue, multiple mapping algorithms have been proposed. However, prior mapping algorithms focus on solving the allocation problem in one centralized underlying substrate network (SN), having the only goal of maximizing the TSPs' mapping revenue. As energy cost accounts for more than half of the total underlying network cost, it is crucial to minimize the total energy cost, while keeping high mapping revenue. In addition, in the real networking environment, multiple geographically distributed SNs, called interdomain networks, coexist. Hence, it is essential to embed each virtual network (VN) service among the interdomain SNs. Based on this, we first propose the formal problem model and the energy cost model. Then, we propose a novel and efficient mapping strategy, labeled EERID. Our EERID is able to map each VN service among interdomain SNs within polynomial time. The experimental results vividly reveal that EERID significantly reduces energy cost by approximately 18% over the existing energy-aware algorithms. At the same time, our EERID achieves higher embedding revenues than the existing energy-aware mapping algorithms, up to 23%.

Identifying homomorphic sex chromosomes from wild-caught adults with limited genomic resources.

We demonstrate a genotyping-by-sequencing approach to identify homomorphic sex chromosomes and their homolog in a distantly related reference genome, based on noninvasive sampling of wild-caught individuals, in the moor frog Rana arvalis. Double-digest RADseq libraries were generated using buccal swabs from 30 males and 21 females from the same population. Search for sex-limited markers from the unfiltered data set (411446 RAD tags) was more successful than searches from a filtered data set (33073 RAD tags) for markers showing sex differences in heterozygosity or in allele frequencies. Altogether, we obtained 292 putatively sex-linked RAD loci, 98% of which point to male heterogamety. We could map 15 of them to the Xenopus tropicalis genome, all but one on chromosome pair 1, which seems regularly co-opted for sex determination among amphibians. The most efficient mapping strategy was a three-step hierarchical approach, where R.arvalis reads were first mapped to a low-coverage genome of Rana temporaria (17 My divergence), then the R.temporaria scaffolds to the Nanorana parkeri genome (90 My divergence), and finally the N.parkeri scaffolds to the X.tropicalis genome (210 My). We validated our conclusions with PCR primers amplifying part of Dmrt1, a candidate sex determination gene mapping to chromosome 1: a sex-diagnostic allele was present in all 30 males but in none of the 21 females. Our approach is likely to be productive in many situations where biological samples and/or genomic resources are limited.

WeNA: Deterministic Run-time Task Mapping for Performance Improvement in Many-core Embedded Systems

Many-core embedded systems will feature an extremely dynamic workload distribution where massive applications arranged as an unpredictable sequence enter and leave the system at run-time. Efficient mapping strategy is required to allocate system resources to the incoming application. Noncontiguous mapping improves system throughput by utilizing disjoint nodes, however, the increasing communication distance and external congestion lead to high power consumption and network delay. This paper thus presents an enhanced noncontiguous dynamic mapping algorithm, aiming at decreasing interprocessor communication overhead and improving both network and application performance. Communication volumes are utilized to arrange the mapping order of tasks belong to the same application. Moreover, expanding parameter of each task is developed which directs the optimized mapping decision comparing to the current neighborhood and occupancy information. Experimental results show that our modified mapping algorithm Weighted-based Neighborhood Allocation (WeNA) makes considerable improvements on Average Weighted Manhattan Distance (8.06%) and network latency (9.8%) in comparison with the state-of-the-art algorithm.

Advanced backcross-QTL analysis in spring barley (H. vulgare ssp. spontaneum) comparing a REML versus a Bayesian model in multi-environmental field trials

A common difficulty in mapping quantitative trait loci (QTLs) is that QTL effects may show environment specificity and thus differ across environments. Furthermore, quantitative traits are likely to be influenced by multiple QTLs or genes having different effect sizes. There is currently a need for efficient mapping strategies to account for both multiple QTLs and marker-by-environment interactions. Thus, the objective of our study was to develop a Bayesian multi-locus multi-environmental method of QTL analysis. This strategy is compared to (1) Bayesian multi-locus mapping, where each environment is analysed separately, (2) Restricted Maximum Likelihood (REML) single-locus method using a mixed hierarchical model, and (3) REML forward selection applying a mixed hierarchical model. For this study, we used data on multi-environmental field trials of 301 BC2DH lines derived from a cross between the spring barley elite cultivar Scarlett and the wild donor ISR42-8 from Israel. The lines were genotyped by 98 SSR markers and measured for the agronomic traits “ears per m²,” “days until heading,” “plant height,” “thousand grain weight,” and “grain yield”. Additionally, a simulation study was performed to verify the QTL results obtained in the spring barley population. In general, the results of Bayesian QTL mapping are in accordance with REML methods. In this study, Bayesian multi-locus multi-environmental analysis is a valuable method that is particularly suitable if lines are cultivated in multi-environmental field trials.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-009-1021-6) contains supplementary material, which is available to authorized users.

Exploring networks with traceroute-like probes: Theory and simulations

Mapping the Internet generally consists in sampling the network from a limited set of sources by using traceroute-like probes. This methodology, akin to the merging of different spanning trees to a set of destination, has been argued to introduce uncontrolled sampling biases that might produce statistical properties of the sampled graph which sharply differ from the original ones. In this paper, we explore these biases and provide a statistical analysis of their origin. We derive an analytical approximation for the probability of edge and vertex detection that exploits the role of the number of sources and targets and allows us to relate the global topological properties of the underlying network with the statistical accuracy of the sampled graph. In particular, we find that the edge and vertex detection probability depends on the betweenness centrality of each element. This allows us to show that shortest path routed sampling provides a better characterization of underlying graphs with broad distributions of connectivity. We complement the analytical discussion with a throughout numerical investigation of simulated mapping strategies in network models with different topologies. We show that sampled graphs provide a fair qualitative characterization of the statistical properties of the original networks in a fair range of different strategies and exploration parameters. Moreover, we characterize the level of redundancy and completeness of the exploration process as a function of the topological properties of the network. Finally, we study numerically how the fraction of vertices and edges discovered in the sampled graph depends on the particular deployements of probing sources. The results might hint the steps toward more efficient mapping strategies.