High-speed multi-dimensional relative navigation for uncooperative space objects

This work proposes a high-speed Light Detection and Ranging (LIDAR) based navigation architecture that is appropriate for uncooperative relative space navigation applications. In contrast to current solutions that exploit 3D LIDAR data, our architecture transforms the odometry problem from the 3D space into multiple 2.5D ones and completes the odometry problem by utilizing a recursive filtering scheme. Trials evaluate several current state-of-the-art 2D keypoint detection and local feature description methods as well as recursive filtering techniques on a number of simulated but credible scenarios that involve a satellite model developed by Thales Alenia Space (France). Most appealing performance is attained by the 2D keypoint detector Good Features to Track (GFFT) combined with the feature descriptor KAZE, that are further combined with either the H∞ or the Kalman recursive filter. Experimental results demonstrate that compared to current algorithms, the GFTT/KAZE combination is highly appealing affording one order of magnitude more accurate odometry and a very low processing burden, which depending on the competitor method, may exceed one order of magnitude faster computation

The Syntax-IS interface : on the functional discrepancies between Clitic Left Dislocation and ‘Bare Left Dislocation’ in modern Greek

Only in CLLD does the dislocated DP perform the discourse function [Topic] partitioning the utterance into [Topic] + [Comment]; cliticless non-focal dislocated DPs are solely fronted [Ground] information that partition the utterance into [Ground] + [New]. In other words, the two constructions correspond to two different information packaging strategies and grammaticalise two distinct Information Structure interface rules

Optimization of cut surface quality during CNC Plasma Arc Cutting process

Optimization of cut surface quality during CNC plasma-arc cutting (PAC) of mild steel plates is presented. Using design of experiments (DOE) the optimum process parameters are estimated. The process parameters tested include cutting speed, cutting height and arc voltage. Analysis of means (ANOM) and analysis of variances (ANOVA) were used in order for the effect of each parameter on the surface quality to be assessed

Latent Gaussian Count Time Series Modeling

This paper develops theory and methods for the copula modeling of stationary count time series. The techniques use a latent Gaussian process and a distributional transformation to construct stationary series with very flexible correlation features that can have any pre-specified marginal distribution, including the classical Poisson, generalized Poisson, negative binomial, and binomial count structures. A Gaussian pseudo-likelihood estimation paradigm, based only on the mean and autocovariance function of the count series, is developed via some new Hermite expansions. Particle filtering methods are studied to approximate the true likelihood of the count series. Here, connections to hidden Markov models and other copula likelihood approximations are made. The efficacy of the approach is demonstrated and the methods are used to analyze a count series containing the annual number of no-hitter baseball games pitched in major league baseball since 1893

The Syntax-Information Component Interface: On the Properties of V-initial Orders in Greek

In this paper I argue that what regulates the (in-)felicity of the V-initial orders in MG has little to do with the oldness/novelty of referents of lexical items, or with possible restrictions on the syntax of discourse functions per se. Rather, I show that V-initial orders in MG fulfil independent requirements which run on the Syntax-IC Interface

Target recognition for synthetic aperture radar imagery based on convolutional neural network feature fusion

Driven by the great success of deep convolutional neural networks (CNNs) that are currently used by quite a few computer vision applications, we extend the usability of visual-based CNNs into the synthetic aperture radar (SAR) data domain without employing transfer learning. Our SAR automatic target recognition (ATR) architecture efficiently extends the pretrained Visual Geometry Group CNN from the visual domain into the X-band SAR data domain by clustering its neuron layers, bridging the visual—SAR modality gap by fusing the features extracted from the hidden layers, and by employing a local feature matching scheme. Trials on the moving and stationary target acquisition dataset under various setups and nuisances demonstrate a highly appealing ATR performance gaining 100% and 99.79% in the 3-class and 10-class ATR problem, respectively. We also confirm the validity, robustness, and conceptual coherence of the proposed method by extending it to several state-of-the-art CNNs and commonly used local feature similarity/match metrics

Binary morphological shape-based interpolation applied to 3-D tooth reconstruction

In this paper we propose an interpolation algorithm using a mathematical morphology morphing approach. The aim of this algorithm is to reconstruct the $n$-dimensional object from a group of (n-1)-dimensional sets representing sections of that object. The morphing transformation modifies pairs of consecutive sets such that they approach in shape and size. The interpolated set is achieved when the two consecutive sets are made idempotent by the morphing transformation. We prove the convergence of the morphological morphing. The entire object is modeled by successively interpolating a certain number of intermediary sets between each two consecutive given sets. We apply the interpolation algorithm for 3-D tooth reconstruction

IMPROVING THE EDUCATIONAL PRACTICE USING SIMULATIONS IN SCIENCE EDUCATION: THE CONTRIBUTION OF ALTHUSSER’S THEORY ON THE COGNITIVE PROCEDURE

This article discusses the relationship between the theory of Louis Althusser concerning the subjectivity of knowledge and the cognitive process and the effective use of educational computer simulations during Science Education and Science Teaching. Our first aim is to highlight the aspects of the cognitive process – according to Louis Althusser’s theory – that should be considered by teachers when they opt to utilize computer simulations in their classroom teaching in the subject of Science and Physics. Our second aim is to suggest ways in order to overcome the conceptual ambiguity, the misunderstandings and the misconceptions that sometimes students form while using simulation models on the computer. The research question being investigated here is the following: “What kind of learning outcomes might the use of computer simulations have concerning the acquisition and construction knowledge by students in the course of Science and Physics in the light of L. Althusser’s theory and what could teachers do so as to eliminate the potential risks of their use and to achieve better outcomes in the learning procedure?”. The utilization of computer simulations in Science Teaching sometimes make students think that the simulated object or phenomenon is identical in nature with the real one. However, the simulations do not constitute the “real objects” themselves; in contrary, they are the means to come closer to reality in order to study it thoroughly.  Article visualizations

A New Passive 3-D Automatic Target Recognition Architecture for Aerial Platforms

The 3-D automatic target recognition (ATR) has many advantages over its 2-D counterpart, but there are several constraints in the context of small low-cost unmanned aerial vehicles (UAVs). These limitations include the requirement for active rather than passive monitoring, high equipment costs, sensor packaging size, and processing burden. We, therefore, propose a new structure from motion (SfM) 3-D ATR architecture that exploits the UAV's onboard sensors, i.e., the visual band camera, gyroscope, and accelerometer, and meets the requirements of a small UAV system. We tested the proposed 3-D SfM ATR using simulated UAV reconnaissance scenarios and found that the performance was better than classic 3-D light detection and ranging (LIDAR) ATR, combining the advantages of 3-D LIDAR ATR and passive 2-D ATR. The main advantages of the proposed architecture include the rapid processing, target pose invariance, small template size, passive scene sensing, and inexpensive equipment. We implemented the SfM module under two keypoint detection, description and matching schemes, with the 3-D ATR module exploiting several current techniques. By comparing SfM 3-D ATR, 3-D LIDAR ATR, and 2-D ATR, we confirmed the superior performance of our new architecture