Neural network based optimal placement of base stations in three dimensional plane

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

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Load balancing in Wireless Sensor Networks by Optimal Placement of Base Stations using kd-Tree

International Journal of Computing Communication and Instrumentation Engineering ◽

10.15242/ijccie.iae0316010 ◽

2016 ◽

Vol 3 (1) ◽

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Load Balancing ◽

Base Stations ◽

Wireless Sensor ◽

Optimal Placement

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Automatic method for glaucoma diagnosis using a three-dimensional convoluted neural network

Neurocomputing ◽

10.1016/j.neucom.2020.07.146 ◽

2021 ◽

Vol 438 ◽

pp. 72-83

Author(s):

Nonato Rodrigues de Sales Carvalho ◽

Maria da Conceição Leal Carvalho Rodrigues ◽

Antonio Oseas de Carvalho Filho ◽

Mano Joseph Mathew

Keyword(s):

Neural Network ◽

Three Dimensional ◽

Automatic Method ◽

Glaucoma Diagnosis

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Radio Base Stations and Electromagnetic Fields: GIS Applications and Models for Identifying Possible Risk Factors and Areas Exposed. Some Exemplifications in Rome

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10010003 ◽

2020 ◽

Vol 10 (1) ◽

pp. 3

Author(s):

Cristiano Pesaresi ◽

Davide Pavia

Keyword(s):

Risk Factors ◽

Electromagnetic Fields ◽

Digital Health ◽

Three Dimensional ◽

Health Information System ◽

Medical Geography ◽

Base Stations ◽

Buffer Zones ◽

North East ◽

Gis Applications

This paper—which is contextualized in the discussion on the methodological pluralism and the main topics of medical geography, the complexity theory in geographies of health, the remaking of medical geography and ad hoc systems of data elaboration—focuses on radio base stations (RBSs) as sources of electromagnetic fields, to provide GIS applications and simplifying-prudential models that are able to identify areas that could potentially be exposed to hazard. After highlighting some specific aspects regarding RBSs and their characteristics and summarizing the results of a number of studies concerning the possible effects of electromagnetic fields on health, we have taken an area of north-east Rome with a high population and building density as a case study, and we have provided some methodological and applicative exemplifications for different situations and types of antennas. Through specific functionalities and criteria, drawing inspiration from a precautionary principle, these exemplifications show some particular cases in order to support: possible risk factor identification, surveillance and spatial analysis; correlation analysis between potential risk factors and outbreak of diseases and symptoms; measurement campaigns in heavily exposed areas and buildings; education policies and prevention actions. From an operative viewpoint, we have: conducted some field surveys and recorded data and images with specific geotechnological and geomatics instruments; retraced the routes by geobrowsers and basemaps and harmonized and joined up the materials in a GIS environment; used different functions to define, on aero-satellite images, concentric circular buffer zones starting from each RBS, and geographically and geometrically delimited the connected areas subject to high and different exposure levels; produced digital applications and tested prime three-dimensional models, in addition to a video from a bird’s eye view perspective, able to show the buildings in the different buffer zones and which are subject to a hazard hierarchy due to exposure to an RBS. A similar GIS-based model—reproposable with methodological adjustments to other polluting sources—can make it possible to conceive a dynamic and multiscale digital system functional in terms of strategic planning, decision-making and public health promotion in a performant digital health information system.

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Hardware Acceleration Implementation of Three-Dimensional Convolutional Neural Network on Vector Digital Signal Processors

2020 4th International Conference on Robotics and Automation Sciences (ICRAS) ◽

10.1109/icras49812.2020.9135062 ◽

2020 ◽

Author(s):

Weiwen Chen ◽

Yaohua Wang ◽

Chao Yang ◽

Yong Li

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Three Dimensional ◽

Hardware Acceleration ◽

Digital Signal ◽

Digital Signal Processors ◽

Signal Processors

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Estimation of 6D Object Pose Using a 2D Bounding Box

Sensors ◽

10.3390/s21092939 ◽

2021 ◽

Vol 21 (9) ◽

pp. 2939

Author(s):

Yong Hong ◽

Jin Liu ◽

Zahid Jahangir ◽

Sheng He ◽

Qing Zhang

Keyword(s):

Neural Network ◽

Loss Function ◽

Three Dimensional ◽

Unit Vector ◽

Prediction Algorithm ◽

Computational Time ◽

Bounding Box ◽

Dimensional Unit ◽

Bounding Boxes ◽

Rgb Image

This paper provides an efficient way of addressing the problem of detecting or estimating the 6-Dimensional (6D) pose of objects from an RGB image. A quaternion is used to define an object′s three-dimensional pose, but the pose represented by q and the pose represented by -q are equivalent, and the L2 loss between them is very large. Therefore, we define a new quaternion pose loss function to solve this problem. Based on this, we designed a new convolutional neural network named Q-Net to estimate an object’s pose. Considering that the quaternion′s output is a unit vector, a normalization layer is added in Q-Net to hold the output of pose on a four-dimensional unit sphere. We propose a new algorithm, called the Bounding Box Equation, to obtain 3D translation quickly and effectively from 2D bounding boxes. The algorithm uses an entirely new way of assessing the 3D rotation (R) and 3D translation rotation (t) in only one RGB image. This method can upgrade any traditional 2D-box prediction algorithm to a 3D prediction model. We evaluated our model using the LineMod dataset, and experiments have shown that our methodology is more acceptable and efficient in terms of L2 loss and computational time.

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