Path-finding algorithm for the mobile robot

The path-finding algorithm (modified A*) that guaranties the shortest length of trajectory and gives a result with minimum iterations has been developed and realized. The software that implements this routine has been developed. The check of the system has been performed on different samples

Huntsville air traffic forecast

Evaluation and forecast of Huntsville air traffi

MONETARY POLICY UNDER INFLATION TARGETING: AN INTRODUCTION

This brief review takes stock of the recent literature on monetary policy under inflation targeting and introduces new analytical and empirical research in this field. Six key areas of previous research are reviewed: the practice and optimality of inflation targeting regime features; optimal monetary policy; uncertainty, learning, and monetary policy; transparency, communication, and accountability; asset prices and monetary policy; and economic performance under inflation targeting and in comparison to non-targeting regimes. The review suggests a significant number of open issues that are addressed in 13 new contributions presented at the 2005 Annual Conference of the Central Bank of Chile, which are summarized here.

Rectification from Radially-Distorted Scales

This paper introduces the first minimal solvers that jointly estimate lens distortion and affine rectification from repetitions of rigidly transformed coplanar local features. The proposed solvers incorporate lens distortion into the camera model and extend accurate rectification to wide-angle images that contain nearly any type of coplanar repeated content. We demonstrate a principled approach to generating stable minimal solvers by the Grobner basis method, which is accomplished by sampling feasible monomial bases to maximize numerical stability. Synthetic and real-image experiments confirm that the solvers give accurate rectifications from noisy measurements when used in a RANSAC-based estimator. The proposed solvers demonstrate superior robustness to noise compared to the state-of-the-art. The solvers work on scenes without straight lines and, in general, relax the strong assumptions on scene content made by the state-of-the-art. Accurate rectifications on imagery that was taken with narrow focal length to near fish-eye lenses demonstrate the wide applicability of the proposed method. The method is fully automated, and the code is publicly available at https://github.com/prittjam/repeats.Comment: pre-prin

Обзор современных систем и алгоритмов технического зрения, которые используются на беспилотных летательных аппаратах и наземных мобильных роботах

В роботі наведений огляд сучасних систем та алгоритмів технічного зору, описані їх сильні та слабки сторони, вирішені та невирішені задачи. Окреслений напрямок майбутніх досліджень в цій галузі.In this paper state-of-art of computer vision systems and algorythms is presented. The algorythms advantages and disadvatages, solved and unsolved tasks are discribed. The topics of the future work are circumscribed.В работе приведен обзор современных систем и алгоритмов технического зрения, описаны их сильные и слабые стороны, решенные и нерешенные задачи. Очерчено направление будущих исследований в этой области

The representation of abstract task rules in the human prefrontal cortex

We have previously reported sustained activation in the ventral prefrontal cortex while participants prepared to perform 1 of 2 tasks as instructed. But there are studies that have reported activation reflecting task rules elsewhere in prefrontal cortex, and this is true in particular when it was left to the participants to decide which rule to obey. The aim of the present experiment was to use functional magnetic resonance imaging (fMRI) to find whether there was activation in common, irrespective of the way that the task rules were established. On each trial, we presented a word after a variable delay, and participants had to decide either whether the word was abstract or concrete or whether it had 2 syllables. The participants either decided before the delay which task they would perform or were instructed by written cues. Comparing the self-generated with the instructed trials, there was early task set activation during the delay in the middle frontal gyrus. On the other hand, a conjunction analysis revealed sustained activation in the ventral prefrontal and polar cortex for both conditions. We argue that the ventral prefrontal cortex is specialized for handling conditional rules regardless of how the task rules were established

Modelling of Things on the Internet for the Search by the Human Brain

Part 4: Intelligent Computational SystemsInternational audienceThe Internet has become the main source of information for business and research activities. Despite the value of libraries supported by computational cataloging, there are far more opportunities to retrieve information on the Internet than in paper books. However, when we seek the Internet we get essentially chunks of text with titles and descriptors resulting from search engine’s activity. Albeit some information may contain sensorial or emotional contents, the search results come essentially from algorithmic execution over keywords by relevance. Our brain retrieves information about things in real world by capturing sensorial information and storing it with emotional experience. We can question why things in Internet are not represented in a similar way to human brain. The present research aims to support a new type of search by sensations and emotions in a path to model Things in Internet towards a human-like representation of objects and events, based on lessons learned from the human brain

Associative3D: Volumetric Reconstruction from Sparse Views

This paper studies the problem of 3D volumetric reconstruction from two views of a scene with an unknown camera. While seemingly easy for humans, this problem poses many challenges for computers since it requires simultaneously reconstructing objects in the two views while also figuring out their relationship. We propose a new approach that estimates reconstructions, distributions over the camera/object and camera/camera transformations, as well as an inter-view object affinity matrix. This information is then jointly reasoned over to produce the most likely explanation of the scene. We train and test our approach on a dataset of indoor scenes, and rigorously evaluate the merits of our joint reasoning approach. Our experiments show that it is able to recover reasonable scenes from sparse views, while the problem is still challenging. Project site: https://jasonqsy.github.io/Associative3DComment: ECCV 202

Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings

To what extent are two images picturing the same 3D surfaces? Even when this is a known scene, the answer typically requires an expensive search across scale space, with matching and geometric verification of large sets of local features. This expense is further multiplied when a query image is evaluated against a gallery, e.g. in visual relocalization. While we don't obviate the need for geometric verification, we propose an interpretable image-embedding that cuts the search in scale space to essentially a lookup. Our approach measures the asymmetric relation between two images. The model then learns a scene-specific measure of similarity, from training examples with known 3D visible-surface overlaps. The result is that we can quickly identify, for example, which test image is a close-up version of another, and by what scale factor. Subsequently, local features need only be detected at that scale. We validate our scene-specific model by showing how this embedding yields competitive image-matching results, while being simpler, faster, and also interpretable by humans.Comment: ECCV 202