Distance Signature Research Articles

Limited impact of vector control on the population genetic structure of Glossina fuscipes fuscipes from the sleeping sickness focus of Maro, Chad.

Tsetse flies (genus Glossina) transmit deadly trypanosomes to human populations and domestic animals in sub-Saharan Africa. Some foci of Human African Trypanosomiasis due to Trypanosoma brucei gambiense (g-HAT) persist in southern Chad, where a program of tsetse control was implemented against the local vector Glossina fuscipes fuscipes in 2018 in Maro. We analyzed the population genetics of G. f. fuscipes from the Maro focus before control (T0), one year (T1), and 18 months (T2) after the beginning of control efforts. Most flies captured displayed a local genetic profile (local survivors), but a few flies displayed outlier genotypes. Moreover, disturbance of isolation by distance signature (increase of genetic distance with geographic distance) and effective population size estimates, absence of any genetic signature of a bottleneck, and an increase of genetic diversity between T0 and T2 strongly suggest gene flows from various origins, and a limited impact of the vector control efforts on this tsetse population. Continuous control and surveillance of g-HAT transmission is thus recommended in Maro. Particular attention will need to be paid to the border with the Central African Republic, a country where the entomological and epidemiological status of g-HAT is unknown.

Distance Signatures of Extended and Co-extended Incidence Graphs of Affine Designs

The distance matrix of a connected graph [Formula: see text], denoted by [Formula: see text], is the matrix whose rows and columns are indexed by the vertex set [Formula: see text] such that the [Formula: see text]-entry is [Formula: see text], where [Formula: see text], [Formula: see text]. The distance signature [Formula: see text] of [Formula: see text] is the inertia of [Formula: see text]. In this paper, we determine the distance signature of the extended (co-extended) incidence graph of an affine design. Furthermore, we state that an open Graffiti conjecture is true for the extended (co-extended) incidence graphs of affine designs by investigating the lower bound of the matching number.

Recognition of human emotion transition from video sequence using triangulation induced various centre pairs distance signatures

Human emotion recognition manifests the concept of contextual understanding of human communication in nonverbal form. This fascinates us to monitor the dynamic information of an emotion. In this article, we present an automatic recognition system for observing gradual changes in human emotion available in the form of a video sequence. In our proposed system, we introduce a triangulation mechanism for geometric feature generation from salient landmarks on the face image. Here, well known active appearance model (AAM) is applied to consecutive image frames in a sequence to track all locations of landmark points. In this work, we explore four centre points (Incentre, Circumcentre, Barycentre, and Orthocentre) from all the triangles formed by salient facial points selected in a triplet. Six different geometric distance signatures viz. Incentre–Circumcentre, Incentre–Barycentre, Incentre–Orthocentre, Circumcentre–Barycentre, Circumcentre–Orthocentre, and Barycentre–Orthocentre are extracted accordingly by taking all possible pairs of those four centres into account to signify several ways of the geometric representation of an emotional image sequence. The discriminative merit of each distance signature is analysed independently by employing them separately as input feature sets into a multilayer perceptron (MLP) classifier for recognition of six basic emotions(anger, disgust, fear, happiness, sadness, and surprise). We verify the performance of our proposed system through the experimentation on several publically available benchmark video datasets such as Extended Cohn–Kanade (CK＋), M&M Initiative (MMI), and Multimedia Understanding Group (MUG). Experimental results indicate impressive accuracy of 98.47% for CK＋, 92.57% for MMI, and 98.62% for the MUG dataset on Incentre–Circumcentre pair distance signature. Moreover, to justify the effectiveness of our proposed method we compare our experimental results with other existing states of the art available in the literature.

Comb-based multispectral LiDAR providing reflectance and distance spectra.

Multispectral LiDAR enables joint observations of the 3D geometry and material properties of natural targets by combining ToF-based distance measurements with remote spectroscopy. Established multispectral LiDAR solutions provide mm-level range resolution and reflectance estimates of the target material over some tens of spectral channels. We propose a novel multispectral LiDAR approach based on an ultra-broadband frequency comb that enables enhanced remote spectroscopy by resolving relative delays in addition to reflectance. The spectrally-resolved delay and power measurements are transformed into distance and reflectance spectra by differential observations to a common reference object and adequate system calibration. These distance and reflectance spectra encode material information related to the surface and sub-surface composition and small-scale geometry. We develop the proposed comb-based multispectral LiDAR on an implementation covering the spectral range between 580 nm and 900 nm on 2 different spectral configurations with 7 and 33 channels of different spectral width. The performance assessment of the implemented system demonstrates a distance measurement precision better than 0.1 mm on most channels. Table-top probing results on five material specimens show that both the distance and the reflectance spectra alone enable discrimination of material specimens, while the novel distance signature particularly complements reflectance and increases classification accuracy when the material surface exhibits significant reflectance inhomogeneity. Material classification results using a support vector machine with radial basis function kernel demonstrate the potential of this approach for enhanced material classification by combining both signature dimensions.

Physical Layer Authentication Scheme in Beamspace MIMO Systems

The broadcast nature of wireless communication makes it vulnerable to various security threats such as spoofing attacks. Physical layer (PL) authentication has emerged as a promising and powerful approach to secure future wireless technologies for next-generation communication networks. In this work, we propose a PL authentication scheme against spoofing attacks based on a novel distance signature that exploits the properties of beamspace multiple-input multiple-output (MIMO) channels in millimeter-wave (mmWave) networks. The proposed signature is derived from the positions of the principal components in beamspace channel domain by measuring their displacement from the original point and sorting the distance values in descending order based on the phases of the principal components. In addition, a mutual coupling effect is introduced into the system, which is a hardware property of multiple antenna design. This is then combined with the proposed distance signature to form a hybrid signature that further improves the authentication performance. Simulation results have confirmed the validity and effectiveness of our proposed system in terms of detection rate and false alarm.

Strain rate sensitivity of binary Mg–Gd and Mg–Y solid solutions

The strain rate sensitivity (SRS) of binary Mg–Gd and Mg–Y alloys and the effect of solute on the rate-controlling mechanisms have been studied by analyzing rate changes during tension and compression tests at 78K and 298K. The steady-state SRS evaluated from the slope of Haasen plots at 78K increases with the solute concentration. The reverse behavior is observed at 298K, and the concentrated alloys exhibit the negative SRS. The activation work and the activation distance evaluated from strain rate jump tests at 78K discriminate regimes of plastic flow determined by solute–dislocation and dislocation–dislocation interactions. At the onset of plastic flow, dislocation–solute interactions control the plastic flow properties. This regime of low flow stress is represented by the activation work in the magnitude of 10−2eV and the activation distance in the range 10−2b - 10−1b. The deformation by mechanical twinning occurring early in the compression conforms to these parameters and suggest no fundamental difference in the thermally activated glide of ordinary and twinning dislocations through the field of solute obstacles. The results suggest that by-passing of Gd and Y atoms by basal dislocations occurs by sequential activation of partial dislocations. At higher flow stress, the dislocation–dislocation interactions determine the work-hardening. The thermally activated motion of jogs produced during interactions of basal and prismatic dislocations is thought to be the rate-controlling process. The activation work and the activation distance signatures of this process are W∗∼ 0.4 eV - 0.8 eV and d ∼ 0.5b - 1b, depending on the solute content.

Low‐cost 3D‐Printed smart robotic system for distance signature and authentication

AbstractDelivering a live signature has become laborious for many people in several countries nowadays. Some departments do not accept a scanned signature, which directs people to travel to give a live signature. In this article, a method to design rapid and fluid movements of a universal robot to perform robot writing will be presented for human handwritten signatures. This system will point out the hardware and software design in the real‐time for CNC signature system. The described system can help any person who has a disability and cannot travel or for people who are far away from the department that required a live signature. The technology behind this system is based on the CNC machine pen plotter system. The hardware is a 3D printed part which is consists of seven parts. All parts are printed by 3D printer type “Anet 6.” The CNC machine tools which will be presented in this article are primarily an educational system and can be made with readily available, low‐cost components and single compatible microcontroller and PHPoC Wi‐Fi Shield, acting as a host for CNC software.

A comparison of longitude–latitude and Euclidean distance shape descriptors for determining tree crown shapes derived from LiDAR data

ABSTRACTPrevious studies demonstrate the effectiveness of the Euclidean distance shape descriptor for characterizing individual tree crowns derived from Light Detection and Ranging (LiDAR) data. This paper evaluates the effectiveness of the longitude–latitude shape descriptor for tree crown shape characterization in comparison with the Euclidean distance descriptor using LiDAR data for 43 individual tree crowns. The three-dimensional (3D) shape signatures obtained from longitude–latitude and Euclidean distance measures of the tree crowns are compared with 3D shape signatures of simulated ellipsoid and hemisphere tree crowns. Kolmogorov–Smirnov test (K–S test) and Mean Square Error (MSE) are used to measure the similarity of longitude–latitude and Euclidean distance signatures. The experimental results from all 43 examined trees suggest that the longitude–latitude shape descriptor can determine tree crown shapes with different leaf traits, while Euclidean distance is only able to characterize the shape of evergreen trees. Furthermore, the longitude–latitude shape descriptor, unlike the Euclidean distance shape descriptor, is not sensitive to the number of LiDAR points from the tree crown.

Facial expression recognition using distance and texture signature relevant features

Distance and texture characteristics among the landmark points reflected in human faces are important features in so far as the recognition of human faces is concerned. In this article we consider (i) normalized distance signature obtained from Active Appearance Model (AAM) based grid, (ii) normalized texture signature derived from salient landmarks within the grid, (iii) stability indices arising out of these signatures and (iv) relevant statistical measures as the set of features for training of artificial neural models such as Multilayer Perceptron (MLP), Radial Basis Function Network (RBF), Nonlinear AutoRegressive with eXogenous input (NARX) and Convolutional Neural Network (CNN) to achieve the task of recognition of facial expressions. The Cohn-Kanade (CK+), Japanese Female Facial Expression (JAFFE), MMI and MUG benchmark databases are used to conduct the experiments and the results obtained justify the effectiveness of the proposed procedure. The combined distance-texture (D-T) signature is found to perform convincingly better than the distance signature and texture signature individually. The effectiveness of the proposed technique based on combined D-T signature is substantiated by its extremely encouraging performance compared to other existing arts.

Single- and two-person action recognition based on silhouette shape and optical point descriptors

In this paper, we present action descriptors that are capable of performing single- and two-person simultaneous action recognition. In order to exploit the shape information of action silhouettes, we detect junction points and geometric patterns at the silhouette boundary. The motion information is exploited by using optical flow points. We compute centroid distance signatures to construct the junction points and optical flow-based action descriptors. By taking advantage of the distinct poses, we extract key frames and construct geometric pattern action descriptor, which is based on histograms of the geometric patterns classes obtained by a distance-based classification method. In order to exploit the shape and motion information simultaneously, we follow the information fusion strategy and construct a joint action descriptor by combining geometric patterns and optical flow descriptors. We evaluate the performance of these descriptors on the two widely used action datasets, i.e., Weizmann dataset (single-person actions) and SBU Kinect interaction dataset, clean and noisy versions (two-person actions). The experimental outcomes demonstrate the ability of the individual descriptors to give satisfactory performance on average. It is found that the joint action descriptor shows the best performance among the proposed descriptors due to its high discriminative power and also outperforms state-of-the-art approaches.

Facial expression recognition using distance and shape signature features

• Distance and Shape signatures, individually and combined, appear as new contribution. • Stability indices corresponding to Distance and Shape have been proposed. • Signatures and Stability indices and statistical metrics of relevance form the feature set. • MLP is learnt with this feature set for emotion estimation from face images. • Results obtained thereof are extremely encouraging. Distance and Shape signature features in human faces offer formidable significance in recognizing facial expressions. Identifying appropriate landmarks is a crucial as well as challenging issue in the field of expression recognition of human faces. Appearance model has been found useful to detect the salient landmarks on human faces. These salient landmarks induce a grid on the human face along with the formation of possible triangles joining the grid. Normalized distance and shape signatures are determined from the grid. Distance signature as well as shape signature find respective stability indices which play important role to recognize the facial expressions. Statistical measures such as range, moment, skewness, kurtosis and entropy are calculated from normalized distance and shape signature pair to supplement the feature set. This enhanced feature set is fed into a Multilayer Perceptron (MLP) to arrive at different expression categories encompassing anger, sadness, fear, disgust, surprise and happy. We investigated our proposed system on Cohn-Kanade (CK+), JAFFE, MMI and MUG databases to conduct and validate our experiment and establish its performance superiority over other existing competitors.

The distance signatures of the incidence graphs of affine resolvable designs

In this note, we determined the distance signatures of the incidence matrices of affine resolvable designs. This proves a conjecture by Kohei Yamada.

New Methodology for Translation of Static Sign Symbol to Words in Kannada Language

is the mean for data transfer over a medium to interact their expressions with each other. The common mode of communication is vocal speech conversation. The main modes of communication are constraint to vocally disabled individuals. For the communication of such individuals various means of communication is suggested, which are called as sign language. Aim: The aim of sign language alphabets recognition is to provide an easy, efficient and accurate mechanism for automatic translation of static sign (determined by a certain configuration of hand) to textual version in kannada language. Problem Statement: The work presented in this paper goal to develop a system for automatic translation of static gestures of alphabets in kannada sign language. It maps letters, words and expression of a certain language to a set of hand gestures enabling an in individual to communicate by using hands gestures rather than by speaking. The system capable of recognizing sign language symbols can be used as a means of communication with hard of hearing people. Sign of the deaf individual can be captured, recognized and translated to words in kannada language for the benefit of blind people. Approach: It has been divided into two phases firstly, feature extraction phase which in turn uses histogram technique, Hough and Segmentation to extract hand from the static sign. Secondly classification phase uses neural network for training samples. Extreme points were extracted from the segmented hand using star skeletonization and recognition was performed by distance signature. Results: The proposed method was tested on the dataset captured in the closed environment with the assumption that the user should be in the field of view. This study was performed for five different datasets in varying lighting conditions. Conclusion: The developed system is focused with objective of reducing the communication gap between normal people and vocally disabled.

Elucidation of population connectivity in synanthropic mesopredators: Using genes to define relevant spatial scales for management of raccoons and Virginia opossums

Raccoons (Procyon lotor) and Virginia opossums (Didelphis virginiana) are highly abundant, synanthropic mesocarnivores that can transmit epizootic diseases of critical importance to human and livestock health such as rabies and tuberculosis. Individuals of both species can exhibit large dispersal distances and our understanding of population connectivity for both of these species is limited. Data on population connectivity and potential for disease transmission are needed for each of these species for the development of data-informed management, control, and vaccination programs. Genetic connectivity among populations can be estimated by evaluation of metrics such as of isolation by distance (IBD) signatures and population structure. Such metrics are influenced not only by geographic distance and barriers to dispersal but also by the life-history characteristics of the species in question. We investigated the scale at which these species exhibited evidence of connectivity via gene flow, using samples collected from 9 sites spanning 254 km across north-central, west-central, south-central, and southern Indiana, USA. We evaluated genetic connectivity and signatures of IBD using Bayesian clustering analyses and matrix correlation methods. In the Bayesian model, raccoons were assigned to 3 genetic clusters, whereas Virginia opossums exhibited apparent panmixia. Spatial autocorrelation results indicated a significant positive correlation between genetic and geographic distance for raccoons at distances up to 25 km and for Virginia opossums up to 4 km. Interspecific differences in reproductive biology, social behavior, and dispersal are likely the causes for differences between these 2 species in the spatial partitioning of populations across broad ranges. In addition, geographic features and past glaciation events may be affecting the genetic characteristics of present-day raccoon populations. Improved information regarding interpopulation distance for these 2 synanthropic species may contribute to more effective management and disease-control programs. © 2014 The Wildlife Society.

Factors responsible for spatial population genetic structure in white-spotted char Salvelinus leucomaensis (Pallas)

Using personal data obtained earlier on the spatial population genetic structure of white-spotted char at ten microsatellite loci, an analysis of factors shaping the interpopulation divergence was performed. The primary role of genetic drift in population differentiation over the distribution range was demonstrated, compared to the practically absent role of stepwise mutation process. This result points to the common origin and relative connections between southern and northern population groups. In the majority of populations, no bottleneck effect was detected. Exclusion of the genetically peculiar Primorye population from the analysis resulted in the identification of the isolation by distance signatures among the examined populations. Such an association can be determined by the migratory exchange between the populations, or it could have formed during the historical post-Pleistocene colonization of the range.

An Improved Shape Signature for Shape Representation and Image Retrieval

The Fourier Descriptor (FD) is a powerful tool for shape analysis andmany signatures have been proposed to derive Fourier descriptors. These shapesignatures lack of important information in articulation and part structures ofcomplex shapes. In this study, the Inner-Centroid Distance (ICDs) signaturewhich is based on the Centroid Distance signature and Inner-Distance isdeveloped to overcome the shortcomings of existing signatures. The retrievalperformance is evaluated by using standard shape database and commonly usedperformance measurement. The experimental results demonstrate that the proposedsignature performs better than the comparison algorithm.

Sign Language Recognition in Robot Teleoperation using Centroid Distance Fourier Descriptors

in robot teleoperation system can be done in several ways, including the use of sign language. In this paper, the use of centroid distance Fourier descriptors as hand shape descriptor in sign language recognition from visually captured hand gesture is considered. The sign language adopts the American Sign Language finger spelling. Only static gestures in the sign language are used. To obtain hand images, depth imager is used in this research. Hand image is extracted from depth image by applying threshold operation. Centroid distance signature is constructed from the segmented hand contours as a shape signature. Fourier transformation of the centroid distance signature results in fourier descriptors of the hand shape. The fourier descriptors of hand gesture are then compared with the gesture dictionary to perform gesture recognition. The performance of the gesture recognition using different distance metrics as classifiers is investigated. The test results show that the use of 15 Fourier descriptors and Manhattan distance-based classifier achieves the best recognition rates of 95% with small computation latency about 6.0573 ms. Recognition error is occurred due to the similarity of Fourier descriptors from some gesture. KeywordsGesture, Sign Language, Fingerspelling, CeFD, Fourier Descriptor.

Shape retrieval using combined Fourier features

Centroid-distance signature is one of the classical shape signatures which are used to derive Fourier descriptors. It has the strong ability of capturing the global features of the shape. However, its ability of characterizing the local shape features is poor. To overcome this limitation, a novel shape signature, perimeter area function (PAF), is proposed in this paper. PAF places all the vertexes of the triangle on the shape boundary. Therefore it can finely capture the local shape boundary information. The Fourier features derived from the proposed PAF are combined with the ones derived from the traditional centroid distance signature for shape retrieval. This hybrid scheme presents an accurate shape description which is more suitable for shape retrieval than the traditional Fourier descriptors. This method is tested on the standard MPEG-7 shape database and the proposed leaf shape database using the standard performance evaluation scheme. All the experimental results indicate that the proposed method outperforms the existing Fourier descriptors and Wavelet Fourier descriptor.

Hand Gesture Recognition for Human-Computer Interaction

Problem statement: With the development of ubiquitous computing, current user interaction approaches with keyboard, mouse and pen are not sufficient. Due to the limitation of these devices the useable command set is also limited. Direct use of hands can be used as an input device for providing natural interaction. Approach: In this study, Gaussian Mixture Model (GMM) was used to extract hand from the video sequence. Extreme points were extracted from the segmented hand using star skeletonization and recognition was performed by distance signature. Results: The proposed method was tested on the dataset captured in the closed environment with the assumption that the user should be in the Field Of View (FOV). This study was performed for 5 different datasets in varying lighting conditions. Conclusion: This study specifically proposed a real time vision system for hand gesture based computer interaction to control an event like navigation of slides in Power Point Presentation.

The LongPen™—The World’s First Original Remote Signing Device*

The LongPen is a remote-controlled pen and videoconferencing device conceived by Canadian author Margaret Atwood in 2004 and initially intended to bring "live" author signings to far away locations. The LongPen allows for individually inscribed long distance signatures and writing while maintaining an original record, written with pen and ink. LongPen specimens were compared with control specimens using different speeds, pen pressures, and types of pens. Preliminary indications are that LongPen inscriptions can be identified or associated with their author. Size and form are maintained and artifacts are subtle. Some limitations with respect to the capture of long tapered strokes, delicate connecting strokes, and differences in line width were noted. Factors which may impact forensic handwriting examinations include limited amounts of writing, light pen pressure, date of the writing, type of writing instrument, dimensions of the writing, and failure to consider that the device has been used.