Cooperative distributed algorithm for AUV teams: A minimum entropy approach

The paper introduces an algorithm which allows the automatic segmentation of multi channel magnetic resonance images. We extended the Expectation Maximization-Mean Field Approximation Segmenter, to include Local Prior Probability Maps. Thereby our algorithm estimates the bias field in the image while simultaneously assigning voxels to different tissue classes under prior probability maps. The probability maps were aligned to the subject using nonrigid registration. This allowed the parcellation of cortical sub-structures including the superior temporal gyrus. To our knowledge this is the first description of an algorithm capable of automatic cortical parcellation incorporating strong noise reduction and image intensity correction.

We introduce Atropos, an ITK-based multivariate n-class open source segmentation algorithm distributed with ANTs ( http://www.picsl.upenn.edu/ANTs). The Bayesian formulation of the segmentation problem is solved using the Expectation Maximization (EM) algorithm with the modeling of the class intensities based on either parametric or non-parametric finite mixtures. Atropos is capable of incorporating spatial prior probability maps (sparse), prior label maps and/or Markov Random Field (MRF) modeling. Atropos has also been efficiently implemented to handle large quantities of possible labelings (in the experimental section, we use up to 69 classes) with a minimal memory footprint. This work describes the technical and implementation aspects of Atropos and evaluates its performance on two different ground-truth datasets. First, we use the BrainWeb dataset from Montreal Neurological Institute to evaluate three-tissue segmentation performance via (1) K-means segmentation without use of template data; (2) MRF segmentation with initialization by prior probability maps derived from a group template; (3) Prior-based segmentation with use of spatial prior probability maps derived from a group template. We also evaluate Atropos performance by using spatial priors to drive a 69-class EM segmentation problem derived from the Hammers atlas from University College London. These evaluation studies, combined with illustrative examples that exercise Atropos options, demonstrate both performance and wide applicability of this new platform-independent open source segmentation tool.

Neuromelanin magnetic resonance imaging (NM-MRI) has been widely used in the diagnosis of Parkinson’s disease (PD) for its significantly enhanced contrast between the PD-related structure, the substantia nigra (SN) and surrounding tissues. This paper proposes a novel network combining the priority gating attention and Bayesian learning for improving the accuracy of fully automatic SN segmentation from NM-MRI. Different from the conventional gated attention model, the proposed network uses the prior SN probability map for guiding the attention computation. Additionally, to lower the risks of over-fitting and estimate the confidence scores for the segmentation results, Bayesian learning with Monte Carlo dropout is applied in the training and testing phases. The quantitative results showed that the proposed network acquired the averaged Dice score of 79.46% in comparison with the baseline model 77.93%.

Many models of perceptual performance consist of two components: (1) a representation of the available sensory and memory information, and (2) a Bayes-optimal decision computation. If the model accurately predicts performance, then that finding is often interpreted as evidence for both components of the model. However, it is important and informative to also test heuristic decision rules. Here, we illustrate such analyses for the task of covert visual search, where a target can appear at one of n locations (or be absent) with some prior probability distribution (the "prior map"). The available sensory information can be represented by the signal-to-noise ratio at each retinal location (the "d' map") together with the prior map. It can be shown that the Bayes-optimal decision rule is to multiply the response at each location by the d' at that location, add the log prior for that location, and then pick the location that has the maximum. For a wide range of d' and prior maps we simulated performance for the optimal and various heuristic decision rules. As expected, varying the d' and prior maps has a large effect on performance due to the variation in the available sensory information. However, a wide range of simple decision rules perform almost as well as the Bayes-optimal rule. In other words, in the covert search task, our decision processes can be idiosyncratic and almost completely ignorant of the d' and prior maps and still perform optimally.

High-Definition Maps (HD maps) are essential for the precise navigation and decision-making of autonomous vehicles, yet their creation and upkeep present significant cost and timeliness challenges. The online construction of HD maps using on-board sensors has emerged as a promising solution; however, these methods can be impeded by incomplete data due to occlusions and inclement weather, while their performance in distant regions remains unsatisfying. This paper proposes PriorDrive to address these limitations by directly harnessing the power of various vectorized prior maps, significantly enhancing the robustness and accuracy of online HD map construction. Our approach integrates a variety of prior maps uniformly, such as OpenStreetMap's Standard Definition Maps (SD maps), outdated HD maps from vendors, and locally constructed maps from historical vehicle data. To effectively integrate such prior information into online mapping models, we introduce a Hybrid Prior Representation (HPQuery) that standardizes the representation of diverse map elements. We further propose a Unified Vector Encoder (UVE), which employs fused prior embedding and a dual encoding mechanism to encode vector data. To improve the UVE's generalizability and performance, we propose a segment-level and point-level pre-training strategy that enables the UVE to learn the prior distribution of vector data. Through extensive testing on the nuScenes, Argoverse 2 and OpenLane-V2, we demonstrate that PriorDrive is highly compatible with various online mapping models and substantially improves map prediction capabilities. The integration of prior maps through PriorDrive offers a robust solution to the challenges of single-perception data, paving the way for more reliable autonomous driving.

This article presents a prior data-driven strategy to solve the motion planning problem of autonomous underwater vehicle (AUV) for the target search mission. Compared with the widely studied cases over wide marine area, however, this article focuses on searching static target that exists in nonwide area. First, under the assumption that the nonwide area could be regarded as a combination of lines or curves, we utilize the Parzen windows theory with one-dimensional Gaussian kernels to generate the prior probability map of target based on the previous qualitative data. Then, we use the “global warming” method to extract some valuable subregions quantitatively, which will be visited by AUV in the optimal sequence. Finally, we verify the good performance of our method by an example of underwater pipeline inspection.

Occupational exposure assessment is important in preventing occupational coal worker’s diseases. Methods have been proposed to assess compliance with exposure limits which aim to protect workers from developing diseases. A Bayesian framework with informative prior distribution obtained from historical or expert judgements has been highly recommended for compliance testing. The compliance testing is assessed against the occupational exposure limits (OEL) and categorization of the exposure, ranging from very highly controlled to very poorly controlled exposure groups. This study used a Bayesian framework from historical and expert elicitation data to compare the posterior probabilities of the 95th percentile (P95) of the coal dust exposures to improve compliance assessment and decision-making. A total of 10 job titles were included in this study. Bayesian framework with Markov chain Monte Carlo (MCMC) simulation was used to draw a full posterior probability of finding a job title to an exposure category. A modified IDEA (“Investigate”, “Discuss”, “Estimate”, and “Aggregate”) technique was used to conduct expert elicitation. The experts were asked to give their subjective probabilities of finding coal dust exposure of a job title in each of the exposure categories. Sensitivity analysis was done for parameter space to check for misclassification of exposures. There were more than 98% probabilities of the P95 exposure being found in the poorly controlled exposure group when using expert judgments. Historical data and non-informative prior tend to show a lower probability of finding the P95 in higher exposure categories in some titles unlike expert judgments. Expert judgements tend to show some similarity in findings with historical data. We recommend the use of expert judgements in occupational risk assessment as prior information before a decision is made on current exposure when historical data are unavailable or scarce.

Abstract: The use of Web 2.0 in the language classroom is an area of exploration and interest to many. In recent years, much research has looked at the use of blogs in the assistance of language development, and this paper continues in a similar fashion. One key area where this paper adds to the field however, is that it looks at a Web 2.0 portal specifically aimed at language learners; the portal in question is Lang-8, which in conjunction with being similar to a blogging platform, also provides similar functions to a social networking service. This research exposed 12 Korean participants to Lang-8 as part of a credit-bearing university writing course. The participants made weekly journal entries on Lang-8, and upon completion of the course, were given an anonymous online survey to complete. The survey addressed areas relating to online language journals, corrective feedback, motivation, and learner autonomy. Overall, the participants reported that the use of Lang-8 positively affected their motivation levels, had positive experiences through received varied corrective feedback, and were exposed to a portal that allowed for more autonomous learning.

Carefully planned flight trajectories for Unmanned Aerial Vehicle (UAV) are used to reconstruct high-quality 3D models of real environments, which greatly simplify the process of large-scale scene reconstruction and reduce the computing cost. However, in order to better capture the image of uneven terrain in the strange environment, most state-of-the-art methods need a prior map of the scene, and thus additional pre-processing steps are required before the actual image acquisition. To address this limitation and make unknown landscapes easier to be reconstructed, we propose an algorithm for real-time exploration and reconstruction that does not demand any prior map of the area. And it can quickly generate dense point clouds for the area. The algorithm uses the sparse point cloud generated by Simultaneous Localization and Mapping (SLAM) to guide the UAV movement, in addition it generate pruned dense 3D point clouds by constructing virtual stereo-pair from consecutive frame. Experiments show that the proposed method could generate effective flight trajectories that satisfy to the task, therefore, the 3D scene could be reconstructed quickly with comparable accuracy without prior map information.

BackgroundThe Clarion-Clipperton Fracture Zone (CCFZ) in the Northeast Central Pacific Ocean is a region of heightened scientific and public interest because of its wealth in manganese nodules. Due to a poor ecological understanding at the abyssal seafloor and limited knowledge of the organisms inhabiting this area, huge efforts in alpha taxonomy are required. To predict and manage potential hazards associated with future mining, taxonomy is an essential first step to grasp fundamental ecosystem traits, such as biogeographic patterns, connectivity, and the potential for post-impact recolonization. Amongst samples from the Global Sea Mineral Resources NV exploration area (EA) in the CCFZ an undescribed species of the isopod crustacean family Macrostylidae was discovered. Previously, it has been reported from two other nearby regions, the Institut Français de Recherche pour l’Exploitation de la Mer and BGR EAs. There it was one of the more widely distributed and abundant species of the benthic macrofauna and exhibited geographically structured populations. It nevertheless remained taxonomically undescribed so far.MethodsThe new species is described by means of integrative taxonomy. Morphologically, macro photography, confocal microscopy, scanning electron microscopy and light microscopy were used to describe the species and to get first insights on its phylogenetic origin. Additionally, mitochondrial DNA markers were used to test the morphological allocation of the two dimorphic sexes and juvenile stages, to analyze geographic patterns of genetic differentiation, and to study intra-and inter-species relationships, also in light of previously published population genetics on this species.ResultsThe new species, Macrostylis metallicola spec. nov., is a typical representative of Macrostylidae as recognizable from the fossosoma, prognathous cephalothorax, and styliform uropods. It can be morphologically distinguished from congeners by a combination of character states which include the autapomorphic shape of the first pleopod of the copulatory male. A sexual dimorphism, as expressed by a peculiar sequence of article length-width ratios of the male antennula, indicates a relationship with M. marionae Kniesz, Brandt & Riehl (2018) and M. longipes Hansen (1916) amongst other species sharing this dimorphism. Mitochondrial genetic markers point in a similar direction. M. metallicola appears to be amongst the more common and widely distributed components of the benthic macrofauna in this region which may suggest a resilience of this species to future mining activities because of its apparent potential for recolonization of impacted sites from adjacent areas of particular environmental interest. The genetic data, however, show geographic clustering of its genetic variability, pointing towards a limited potential for dispersal. Local extinction of populations could potentially not be compensated quickly and would mean a loss of genetic diversity of this species.

The Bayesian inference is widely used in many scientific and engineering problems, especially in the linear inverse problems in infinite-dimensional setting where the unknowns are functions. In such problems, choosing an appropriate prior distribution is an important task. Especially when the function to infer has much detail information, such as many sharp jumps, corners, and the discontinuous and nonsmooth oscillation, the so-called total variation-Gaussian (TG) prior is proposed in function space to address it. However, the TG prior is easy to lead the blocky (staircase) effect in numerical results. In this work, we present a fractional order-TG (FTG) hybrid prior to deal with such problems, where the fractional order total variation (FTV) term is used to capture the detail information of the unknowns and simultaneously uses the Gaussian measure to ensure that it results in a well-defined posterior measure. For the numerical implementations of linear inverse problems in function spaces, we also propose an efficient independence sampler based on a transport map, which uses a proposal distribution derived from a diagonal map, and the acceptance probability associated to the proposal is independent of discretization dimensionality. And in order to take full advantage of the transport map, the hierarchical Bayesian framework is applied to flexibly determine the regularization parameter. Finally we provide some numerical examples to demonstrate the performance of the FTG prior and the efficiency and robustness of the proposed independence sampler method.

Extrapolating Survival Data Using Historical Trial–Based a Priori Distributions

In this paper, we study the cooperative autonomous inspection by Unmanned Aerial Vehicles (UAV) swarms in dynamic and uncertain environments. Facing the challenges of spatio-temporally random target events and constrained onboard resources, we propose a distributed online path planning algorithm based on a Hybrid Artificial Potential Field (HAPF). In contrast to existing works relying on a single probability map, we construct a hybrid artificial potential field to integrate a probability map and an exploration map. The probability map, generated via Kernel Density Estimation from historical and real-time data, learns the distribution pattern of events, while the exploration map encourages coverage of unvisited areas. Virtual attractive forces guide UAVs to prioritize high-probability events and explore unknown regions, whereas virtual repulsive forces ensure collision-free cooperation. Moreover, a Hungarian algorithm based dynamic task allocation method is proposed to prevent resource redundancy. To tackle the well-known local minimum problem in potential field, a sticking release mechanism is designed to escape local optima. Simulation results demonstrate that, in comparison to existing algorithms, our method improving the task completion rate by 20 % and shortening the average completion time by 13 %, offering a practical solution for resource-constrained UAV swarms to achieve efficient and safe autonomous inspection in complex and dynamic environments.

Exploration risk evaluation using object-based modeling, an example from the Tertiary fractured play, western Qaidam Basin of China

This project was undertaken as part of the PhD research project of Paul Malone, Pricipal Investigator, Covance plc, Harrogate. Mr Malone approached the photography department for involvement in the study with the aim of settling the current debate on the anatomical and histological features of the distal radioulnar ligaments by capturing the anatomy photographically throughout the process of dissection via a microtome. The author was approached to lead on the photographic protocol as part of her post-graduate certificate training at Staffordshire University.High-resolution digital images of an entire human arm were required, the main area of interest being the distal radioulnar joint of the wrist. Images were to be taken at 40 μm intervals as the specimen was sliced. When microtomy was undertaken through the ligaments images were made at 20 μm intervals. A method of suspending a camera approximately 1 metre above the specimen was devised, together with the preparation for the capture, processing and storage of images.The resulting images were then to be subject to further analysis in the form of 3-Dimensional reconstruction, using computer modelling techniques and software. The possibility of merging the images with sequences obtained from both CT & MRI using image handling software is also an area of exploration, in collaboration with the University of Manchester's Visualisation Centre.