scholarly journals Three-dimensional multi-source localization of underwater objects using convolutional neural networks for artificial lateral lines

2020 ◽  
Vol 17 (162) ◽  
pp. 20190616 ◽  
Author(s):  
Ben J. Wolf ◽  
Jos van de Wolfshaar ◽  
Sietse M. van Netten
Keyword(s):  
Neural Network ◽  
Source Localization ◽  
Fluid Velocity ◽  
Three Dimensional ◽  
Sensory System ◽  
Three Dimensions ◽  
Hydrodynamic Theory ◽  
Multiple Sources ◽  
Combined System ◽  
Lateral Lines

This research focuses on the signal processing required for a sensory system that can simultaneously localize multiple moving underwater objects in a three-dimensional (3D) volume by simulating the hydrodynamic flow caused by these objects. We propose a method for localization in a simulated setting based on an established hydrodynamic theory founded in fish lateral line organ research. Fish neurally concatenate the information of multiple sensors to localize sources. Similarly, we use the sampled fluid velocity via two parallel lateral lines to perform source localization in three dimensions in two steps. Using a convolutional neural network, we first estimate a two-dimensional image of the probability of a present source. Then we determine the position of each source, via an automated iterative 3D-aware algorithm. We study various neural network architectural designs and different ways of presenting the input to the neural network; multi-level amplified inputs and merged convolutional streams are shown to improve the imaging performance. Results show that the combined system can exhibit adequate 3D localization of multiple sources.

scholarly journals Modeling Gravity-Dependent Plasticity of the Angular Vestibuloocular Reflex With a Physiologically Based Neural Network

2006 ◽  
Vol 96 (6) ◽  
pp. 3349-3361 ◽  
Author(s):  
Yongqing Xiang ◽  
Sergei B. Yakushin ◽  
Bernard Cohen ◽  
Theodore Raphan
Keyword(s):  
Neural Network ◽  
Three Dimensional ◽  
Learning Rule ◽  
Three Dimensions ◽  
Head Orientation ◽  
Model Parameters ◽  
Vestibuloocular Reflex ◽  
Model Predictions ◽  
Physiologically Based ◽  
Head Positions

A neural network model was developed to explain the gravity-dependent properties of gain adaptation of the angular vestibuloocular reflex (aVOR). Gain changes are maximal at the head orientation where the gain is adapted and decrease as the head is tilted away from that position and can be described by the sum of gravity-independent and gravity-dependent components. The adaptation process was modeled by modifying the weights and bias values of a three-dimensional physiologically based neural network of canal–otolith-convergent neurons that drive the aVOR. Model parameters were trained using experimental vertical aVOR gain values. The learning rule aimed to reduce the error between eye velocities obtained from experimental gain values and model output in the position of adaptation. Although the model was trained only at specific head positions, the model predicted the experimental data at all head positions in three dimensions. Altering the relative learning rates of the weights and bias improved the model-data fits. Model predictions in three dimensions compared favorably with those of a double-sinusoid function, which is a fit that minimized the mean square error at every head position and served as the standard by which we compared the model predictions. The model supports the hypothesis that gravity-dependent adaptation of the aVOR is realized in three dimensions by a direct otolith input to canal–otolith neurons, whose canal sensitivities are adapted by the visual-vestibular mismatch. The adaptation is tuned by how the weights from otolith input to the canal–otolith-convergent neurons are adapted for a given head orientation.

scholarly journals Solving Three Dimensions Volterra Integral Equations (TDVIE) via a Neural Network

2019 ◽  
Vol 23 (10) ◽  
pp. 92
Author(s):  
Nahdh S. M. Al-Saif ◽  
Ameen Sh. Ameen ◽  
Ghaith Fadhil Abbas2
Keyword(s):  
Neural Network ◽  
Integral Equations ◽  
Three Dimensional ◽  
Volterra Integral Equations ◽  
Three Dimensions ◽  
Feed Forward Neural Network ◽  
Transform Method ◽  
Linear Transfer Function ◽  
Differential Transform ◽  
Hidden Layer

The aim of this paper  is present a new numerical method for solvingThree Dimensions Volterra Integral Equations using artificial neural network by design multilayer feed forward Neural Network. A multi- layers design in our proposed method consist of a hidden layer having seven hidden units. and one linear output unit. Linear Transfer function used as each unit and using Levenberg- Marquardtalgorithmtraining. Moreover, examples on three- dimensional Volterra integral equations carried out to illustrate the accuracy and the efficiency of the presented method. In addition, some comparisons among proposed method and Shifted Chebyshev Polynomials method and Reduced Differential Transform Method are presented.   http://dx.doi.org/10.25130/tjps.23.2018.176

Three-dimensional odor tracking by Nautilus pompilius

2000 ◽  
Vol 203 (9) ◽  
pp. 1409-1414 ◽  
Author(s):  
J.A. Basil ◽  
R.T. Hanlon ◽  
S.I. Sheikh ◽  
J. Atema
Keyword(s):  
Three Dimensional ◽  
Sensory System ◽  
Three Dimensions ◽  
Odor Source ◽  
Orientation Behavior ◽  
Living Fossil ◽  
Nautilus Pompilius ◽  
Odor Plumes ◽  
Odor Tracking

The ‘living fossil’ Nautilus pompilius is thought to use olfaction as its primary sensory system during foraging, yet neither the organs responsible for olfaction nor the mechanisms or behaviors associated with odor tracking have been subjected to experimentation. Flume testing under dark conditions revealed that Nautilus could consistently detect and follow turbulent odor plumes to the source over distances up to 10 m, exhibiting two types of orientation behavior while sampling in three dimensions. The paired rhinophores were necessary for orientation behavior: when they were temporarily blocked either uni- or bilaterally, Nautilus detected odor but could not track the plume and locate the source. Animals that were tested post-blockage were able to track and locate the source. The role of the 90 thin tentacles remains enigmatic; they seemed to be able to detect odor, but they were not capable of guiding orientation behavior towards a distant odor source. Bilateral chemical sensing by rhinophores in three dimensions may have been the Umwelt of ammonites and belemnites before the evolution of complex eyes and fast locomotion in modern coleoids.

Predicting spatial activity patterns in a neural map from a probabilistic atlas of 3-D reconstructed neurons

1995 ◽  
Vol 53 ◽  
pp. 812-813
Author(s):  
G. Jacobs ◽  
F. Theunissen
Keyword(s):  
Activity Patterns ◽  
Three Dimensional ◽  
Sensory System ◽  
Neural System ◽  
Probabilistic Atlas ◽  
Uniform Sample ◽  
Dimensional Reconstruction ◽  
The Time Domain ◽  
And Function ◽  
Visualization Techniques

In order to understand how the algorithms underlying neural computation are implemented within any neural system, it is necessary to understand details of the anatomy, physiology and global organization of the neurons from which the system is constructed. Information is represented in neural systems by patterns of activity that vary in both their spatial extent and in the time domain. One of the great challenges to microscopists is to devise methods for imaging these patterns of activity and to correlate them with the underlying neuroanatomy and physiology. We have addressed this problem by using a combination of three dimensional reconstruction techniques, quantitative analysis and computer visualization techniques to build a probabilistic atlas of a neural map in an insect sensory system. The principal goal of this study was to derive a quantitative representation of the map, based on a uniform sample of afferents that was of sufficient size to allow statistically meaningful analyses of the relationships between structure and function.

convergent-beam diffraction from surfaces

1987 ◽  
Vol 45 ◽  
pp. 30-33
Author(s):  
J. A. Eades ◽  
A. E. Smith ◽  
D. F. Lynch
Keyword(s):  
Reciprocal Lattice ◽  
Three Dimensional ◽  
Grazing Incidence ◽  
Three Dimensions ◽  
Plane Figure ◽  
Transmission Electron ◽  
Convergent Beam ◽  
Beam Patterns ◽  
Beam Diffraction

It is quite simple (in the transmission electron microscope) to obtain convergent-beam patterns from the surface of a bulk crystal. The beam is focussed onto the surface at near grazing incidence (figure 1) and if the surface is flat the appropriate pattern is obtained in the diffraction plane (figure 2). Such patterns are potentially valuable for the characterization of surfaces just as normal convergent-beam patterns are valuable for the characterization of crystals.There are, however, several important ways in which reflection diffraction from surfaces differs from the more familiar electron diffraction in transmission.GeometryIn reflection diffraction, because of the surface, it is not possible to describe the specimen as periodic in three dimensions, nor is it possible to associate diffraction with a conventional three-dimensional reciprocal lattice.

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

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.

Extension of the Spatial PDI Model to Three Dimensions

1997 ◽  
Vol 84 (1) ◽  
pp. 176-178
Author(s):  
Frank O'Brien
Keyword(s):  
Population Density ◽  
Dimensional Space ◽  
Finite Lattice ◽  
Three Dimensional ◽  
Upper Bounds ◽  
Three Dimensions ◽  
Lower And Upper Bounds ◽  
Density Index ◽  
Three Dimensional Space

The author's population density index ( PDI) model is extended to three-dimensional distributions. A derived formula is presented that allows for the calculation of the lower and upper bounds of density in three-dimensional space for any finite lattice.

scholarly journals Free partition functions and an averaged holographic duality

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Nima Afkhami-Jeddi ◽  
Henry Cohn ◽  
Thomas Hartman ◽  
Amirhossein Tajdini
Keyword(s):  
Partition Function ◽  
Spectral Gap ◽  
Central Charge ◽  
Three Dimensional ◽  
Two Dimensions ◽  
Three Dimensions ◽  
Partition Functions ◽  
General Constraints ◽  
Dimensional Gravity ◽  
Holographic Duality

Abstract We study the torus partition functions of free bosonic CFTs in two dimensions. Integrating over Narain moduli defines an ensemble-averaged free CFT. We calculate the averaged partition function and show that it can be reinterpreted as a sum over topologies in three dimensions. This result leads us to conjecture that an averaged free CFT in two dimensions is holographically dual to an exotic theory of three-dimensional gravity with U(1)c×U(1)c symmetry and a composite boundary graviton. Additionally, for small central charge c, we obtain general constraints on the spectral gap of free CFTs using the spinning modular bootstrap, construct examples of Narain compactifications with a large gap, and find an analytic bootstrap functional corresponding to a single self-dual boson.

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