3D Data Mapping and Real-Time Experiment Control and Visualization in Brain Slices

The LOG-a-TEC testbed is a combined outdoor and indoor heterogeneous wireless testbed for experimentation with sensor networks and machine-type communications, which is included within the Fed4FIRE+ federation. It supports continuous deployment principles; however, it is missing an option to monitor and control the experiment in real-time, which is required for experiment execution under comparable conditions. The paper describes the implementation of the experiment control and monitoring system (EC and MS) as the upgrade of the LOG-a-TEC testbed. EC and MS is implemented within existing infrastructure management and built systems as a new service. The EC and MS is accessible as a new tab in sensor management system portal. It supports several commands, including start, stop and restart application, exit the experiment, flash or reset the target device, and displays the real-time status of the experiment application. When nodes apply Contiki-NG as their operating system, the Contiki-NG shell tool is accessible with the help of the newly developed tool, giving further experiment execution control capabilities to the user. By using the ZeroMQ concurrency framework as a message exchange system, information can be asynchronously sent to one or many devices at the same time, providing a real-time data exchange mechanism. The proposed upgrade does not disrupt any continuous deployment functionality and enables remote control and monitoring of the experiment. To evaluate the EC and MS functionality, two experiments were conducted: the first demonstrated the Bluetooth Low Energy (BLE) localization, while the second analysed interference avoidance in the 6TiSCH (IPv6 over the TSCH mode of IEEE 802.15.4e) wireless technology for the industrial Internet of Things (IIoT).

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Computer Vision Applications of Self-Organizing Neural Networks

Robotic Vision ◽

10.4018/978-1-4666-2672-0.ch008 ◽

2013 ◽

pp. 129-138

Author(s):

José García-Rodríguez ◽

Juan Manuel García-Chamizo ◽

Sergio Orts-Escolano ◽

Vicente Morell-Gimenez ◽

José Antonio Serra-Pérez ◽

...

Keyword(s):

Real Time ◽

Network Models ◽

Suitable Model ◽

Surveillance Systems ◽

Neural Network Models ◽

Neural Gas ◽

3D Data ◽

Computer Vision Applications ◽

Recognition Systems ◽

Self Organizing

This chapter aims to address the ability of self-organizing neural network models to manage video and image processing in real-time. The Growing Neural Gas networks (GNG) with its attributes of growth, flexibility, rapid adaptation, and excellent quality representation of the input space makes it a suitable model for real time applications. A number of applications are presented, including: image compression, hand and medical image contours representation, surveillance systems, hand gesture recognition systems, and 3D data reconstruction.

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ExtSim: A Flexible Data Mapping and Synchronization Middleware for Scientific Visualization in Virtual Worlds

Journal of Virtual Worlds Research ◽

10.4101/jvwr.v2i5.786 ◽

2010 ◽

Vol 2 (5) ◽

Author(s):

Johan Berntsson ◽

Norman Lin ◽

Zoltan Dezso

Keyword(s):

Protein Folding ◽

Real Time ◽

Virtual Worlds ◽

Scientific Computing ◽

General Purpose ◽

Current Approach ◽

Simulation Software ◽

Third Party ◽

Data Mapping ◽

Future Work

In this paper we present a general-purpose middleware, called ExtSim that allows OpenSim to communicate with external simulation software, and to synchronize the in-world representation of the simulator state. We briefly present two projects in ScienceSim where ExtSim has been used; Galaxsee which is an interactive real-time N-body simulation, and a protein folding demonstration, before discussing the merits and problems with the current approach. The main limitation is that we until now only have been limited to a third-party viewer, and a fixed server-client protocol, but we present our work on a new viewer, called 3Di Viewer “Rei”, which opens new possibilities in enhancing both performance and richness of the visualization suitable for scientific computing,. Finally we discuss some ideas we are currently studying for future work.

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Real-time measurement of stimulated 5-hydroxytryptamine release in rat substantia nigra pars reticulata brain slices

Synapse ◽

10.1002/(sici)1098-2396(199701)25:1<93::aid-syn11>3.0.co;2-# ◽

1997 ◽

Vol 25 (1) ◽

pp. 93-102 ◽

Cited By ~ 21

Author(s):

Mahmoud M. Iravani ◽

Zygmunt L. Kruk

Keyword(s):

Substantia Nigra ◽

Real Time ◽

Brain Slices ◽

Time Measurement ◽

Substantia Nigra Pars Reticulata ◽

Real Time Measurement ◽

Rat Substantia Nigra

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3D Face Detection via Reconstruction Over Hierarchical Features for Single Face Situations

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001416550132 ◽

2016 ◽

Vol 30 (04) ◽

pp. 1655013

Author(s):

Bo Yu ◽

Ian Lane ◽

Fang Chen

Keyword(s):

Real Time ◽

Face Detection ◽

Rejection Rate ◽

Input Image ◽

Computationally Efficient ◽

3D Face ◽

3D Data ◽

Single Face ◽

Multiple Challenges ◽

Face Datasets

There are multiple challenges in face detection, including illumination conditions and diverse poses of the user. Prior works tend to detect faces by segmentation at pixel level, which are generally not computationally efficient. When people are sitting in the car, which can be regarded as single face situations, most face detectors fail to detect faces under various poses and illumination conditions. In this paper, we propose a simple but efficient approach for single face detection. We train a deep learning model that reconstructs face directly from input image by removing background and synthesizing 3D data for only the face region. We apply the proposed model to two public 3D face datasets, and obtain significant improvements in false rejection rate (FRR) of 4.6% (from 4.6% to 0.0%) and 21.7% (from 30.2% to 8.5%), respectively, compared with state-of-art performances in two datasets. Furthermore, we show that our reconstruction approach can be applied using 1/2 the time of a widely used real-time face detector. These results demonstrate that the proposed Reconstruction ConNet (RN) is both more accurate and efficient for real-time face detection than prior works.

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