Effect of landscape structure on genetic structure of the Lesser horseshoe bat (Rhinolophus hipposideros) in Britanny colonies

Some species are difficult to observe and others, need to be not disturbed because of their vulnerability. In response to the difficulty of studying the dispersal behaviors of these species, some areas of biology have been combined in order to access the information despite practical limitations. Here we present the combination of several methodologies from landscape ecology to non-invasive population genetics that allow us to obtain important information on Rinolophus hipposideros, a vulnerable European bat. We genotyped 18 georeferrenced colonies in Brittany (France) from droppings collected in their refuges. We used 6 microsatellite markers in order to obtain the genetic distances between them. On the other hand we calculated Euclidian distances between the refuges occupied by these colonies and some ecological distances with the Pathmatrix module of ArcGis 3.2. We tested hypothesis about the difficulty of dispersal of the species in areas without forest cover or with a low density of hedges. Thanks to the Monmonier algorithm we could infer possible genetic barriers between the colonies and we could compare their location to the presence of landscape barriers (areas with little tree cover). We detected a pattern of isolation by distance that reveals limited dispersal capacities in the species but no pattern linked to ecological distances. We found that some of the neighboring colonies with greater genetic distances between them were located in areas with low density of hedges which could suggest an impact of this landscape element in their movements. Finer studies should allow us to conclude on the need or not of forest cover in the dispersal of this species.

ITVT-07. Robotic alignment module Cirq for navigated brainstem tumor biopsies

BACKGROUND With the incorporation of the robotic alignment module Cirq (Brainlab, Germany) into our neurosurgical armamentarium, we aimed to know our baseline accuracy in stererotactic biopsies. We therefore retrospectively reviewed our data on biopsy accuracy for brain(stem) tumors using the non-robotic alignment instrument Varioguide (Brainlab, Germany). Because of unexpectedly large deviations from the intended target, we sought to improve our registration accuracy when we introduced Cirq. Intraoperative 3D CT with bone fiducials was added to the pre-operative 3D T1 MRI with skin fiducials. This made it possible to compare surgical devices as well as registration methods. AIMS To share our experience with the new robotic alignment module Cirq for navigated brain(stem) tumor biopsies and to evaluate its target accuracy with bone fiducial registration, as compared to the previously used Varioguide with skin fiducial registration. METHODS All patients (0–18 years old) that underwent a brain(stem) biopsy in our institution were included. Over 2018–2020, data were collected retrospectively (cohort Varioguide with 3D T1 MRI registration with skin fiducials). From 2021, data were collected prospectively (cohort Cirq with both 3D T1 MRI registration with skin fiducials and intraoperative CT registration with bone fiducials). For both cohorts, Euclidian distances were calculated between the intended target and the obtained target. For the prospective cohort, registration errors were calculated for bone versus skin fiducials. PRELIMINARY REUSLTS The deviation from the intended target was much smaller in the Cirq cohort versus the Varioguide cohort. Within the Cirq cohort, registration errors were submillimetric for bone fiducial registration as compared to several millimeters for skin fiducial registration. CONCLUSION: The Cirq robotic arm is convenient, safe and highly accurate, especially when combined with intraoperative 3D CT bone fiducial registration. Skin fiducial registration does not offer the level of precision that is mandatory in brainstem tumor biopsies.

An Improved Feature Selection Method Based on Random Forest Algorithm for Wind Turbine Condition Monitoring

Feature selection and dimensionality reduction are important for the performance of wind turbine condition monitoring models using supervisory control and data acquisition (SCADA) data. In this paper, an improved random forest algorithm, namely Feature Simplification Random Forest (FS_RF), is proposed, which is capable of identifying features closely correlated with wind turbine working conditions. The Euclidian distances are employed to distinguish the weight of the same feature among different samples, and its importance is measured by means of the random forest algorithm. The selected features are finally verified by a two-layer gated recurrent unit (GRU) neural network facilitating condition monitoring. The experimental results demonstrate the capacity and effectiveness of the proposed method for wind turbine condition monitoring.

Medical Image Segmentation and Classification Based on Sparse Representation and Dictionary Learnng Algorithms

In this thesis a novel classification approach based on sparse representation framework is proposed. The method finds the minimum Euclidian distance between an input patch (pattern) and atoms (templates) of a learned-base dictionary for different classes to perform the classification task. A mathematical approach is developed to map the sparse representation vector to Euclidian distances. We show that the highest coefficient of the sparse vector is not necessarily a suitable indicator for classification. The proposed algorithm is compared with the conventional Sparse Representation Classification (SRC) framework as well as non-sparse based methods to evaluate its performance. Taking advantage of the introduced classification framework, we then propose a novel fully automated method for the purpose of segmenting different organs in medical images of the human body. Our results demonstrated an acceptable accuracy rate for both classification and the segmentation frameworks. To our knowledge, no other method utilizes sparse representation and dictionary learning techniques in order to segment medical images.

Medical Image Segmentation and Classification Based on Sparse Representation and Dictionary Learnng Algorithms

In this thesis a novel classification approach based on sparse representation framework is proposed. The method finds the minimum Euclidian distance between an input patch (pattern) and atoms (templates) of a learned-base dictionary for different classes to perform the classification task. A mathematical approach is developed to map the sparse representation vector to Euclidian distances. We show that the highest coefficient of the sparse vector is not necessarily a suitable indicator for classification. The proposed algorithm is compared with the conventional Sparse Representation Classification (SRC) framework as well as non-sparse based methods to evaluate its performance. Taking advantage of the introduced classification framework, we then propose a novel fully automated method for the purpose of segmenting different organs in medical images of the human body. Our results demonstrated an acceptable accuracy rate for both classification and the segmentation frameworks. To our knowledge, no other method utilizes sparse representation and dictionary learning techniques in order to segment medical images.

Recognition of Online Handwritten Bangla Characters Using Supervised and Unsupervised Learning Approaches

This paper explores the domain of online handwritten Bangla character recognition by stroke-based approach. The component strokes of a character sample are recognized firstly and then characters are constructed from the recognized strokes. In the current experiment, strokes are recognized by both supervised and unsupervised approaches. To estimate the features, images of all the component strokes are superimposed. A mean structure has been generated from this superimposed image. Euclidian distances between pixel points of a stroke sample and mean stroke structure are considered as features. For unsupervised approach, K-means clustering algorithm has been used whereas six popular classifiers have been used for supervised approach. The proposed feature vector has been evaluated on 10,000-character database and achieved 90.69% and 97.22% stroke recognition accuracy in unsupervised (using K-means clustering) and supervised way (using MLP [multilayer perceptron] classifier). This paper also discusses about merit and demerits of unsupervised and supervised classification approaches.

Spoofing Detection of Civilian UAVs Using Visual Odometry

Spoofing of Unmanned Aerial Vehicles (UAV) is generally carried out through spoofing of the UAV’s Global Positioning System (GPS) receiver. This paper presents a vision-based UAV spoofing detection method that utilizes Visual Odometry (VO). This method is independent of the other complementary sensors and any knowledge or archived map and datasets. The proposed method is based on the comparison of relative sub-trajectory of the UAV from VO, with its absolute replica from GPS within a moving window along the flight path. The comparison is done using three dissimilarity measures including (1) Sum of Euclidian Distances between Corresponding Points (SEDCP), (2) angle distance and (3) taxicab distance between the Histogram of Oriented Displacements (HOD) of these sub-trajectories. This method can determine the time and location of UAV spoofing and bounds the drift error of VO. It can be used without any restriction in the usage environment and can be implemented in real-time applications. This method is evaluated on four UAV spoofing scenarios. The results indicate that this method is effective in the detection of UAV spoofing due to the Sophisticated Receiver-Based (SRB) GPS spoofing. This method can detect UAV spoofing in the long-range UAV flights when the changes in UAV flight direction is larger than 3° and in the incremental UAV spoofing with the redirection rate of 1°. Additionally, using SEDCP, the spoofing of the UAV, when there is no redirection and only the velocity of the UAV is changed, can be detected. The results show that SEDCP is more effective in the detection of UAV spoofing and fake GPS positions.

Fusing Detected Humans in Multiple Perception Sensors Network

A fusion method is proposed to keep a correct number of humans from all humans detected by the robot operating system based perception sensor network (PSN) which includes multiple partially overlapped field of view (FOV) Kinects. To this end, the fusion rules are based on the parallel and orthogonal configurations of Kinects in PSN system. For the parallel configuration, the system will decide whether the detected humans staying in FOV of single Kinect or in overlapped FOV of multiple Kinects by evaluating the angles formed between their locations and Kinect original point on top view (x, z plane) of 3D coordination. Then, basing on the angles, the PSN system will keep the person stay in only one FOV or keep the one with biggest ROI if they stay in overlapped FOV of Kinects. In the case of Kinects with orthogonal configuration, 3D Euclidian distances between detected humans are used to determine the group of humans supported to be same human but detected by different Kinects. Then the system, keep the human with a bigger region of interest (ROI) among this group. The experimental results demonstrate the outperforming of the proposed method in various scenarios. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Variación fenotípica de la langosta espinosa Panulirus argus (Decapoda: Paniluridae) en el Caribe Suroccidental

The spiny lobster Panulirus argus is an important fishery species in the Western Central Atlantic. Changes in the dispersion range through its life cycle and heterogeneous habitats, suggest that P. argus exhibit phenotypic plasticity. However, the morphometric variation of this species is unknown so far, although this information is relevant in evolutionary studies as well as to solve questions of fishery interest. Thus, the aim of this study was to determine whether P. argus exhibit phenotypic variation between sexes, among five geographic origins and three oceanographic conditions of Southwest Caribbean (Colombian archipelago San Andrés, Providencia y Santa Catalina). A total of 193 P. argus adults were submitted to geometric morphometrics using six landmarks that delimit one half of the sternal plate. The differences in sternal plate size were compared with Kruskal-Wallis and Mann-Whitney Tests. The allometric effect was estimated using Multivariate Regression Analysis, the model of allometric slopes was tested by Multivariate analysis of covariance and the sternal plate shape differences was explored using non-parametric comparisons of Euclidian distances and Neighbour Joinnig trees. The results showed that the morphometric variation of sternal plate of this spiny lobster varied according to the gender since the sexual size and shape dimorphisms were significant. In both sexes, the sternal plate shape differed among oceanographic scenarios as it was evidenced by significant differences among Euclidian distances, and the tendency to cluster by North, Centre and South sections of San Andrés archipelago. Additionally, the morphometric variation resulting from phenotypic plasticity to variable ecological contexts may explain the phenotypic differences among genetically similar populations. This information permits to define management units, support the selection of regulatory policies of this fishery and complement the genetic analysis of the species in this Caribbean region.