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Geforce gtx 1070 gpu

Manufactured by NVIDIA

The GeForce GTX 1070 is a high-performance GPU produced by NVIDIA. It features 1,920 CUDA cores and a base clock speed of 1,506 MHz, with a boost clock speed of 1,683 MHz. The GTX 1070 has 8 GB of GDDR5 video memory with a memory bandwidth of 256 GB/s.

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7 protocols using geforce gtx 1070 gpu

1

Virtual Reality Timed Up and Go Test

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The TUG VR was performed using a virtual reality set including a HTC Vive Head Mounted Display (HMD) (Framerate: 90Hz, 2160×1200 combined pixels, 110° field of view, 470 grams) and a Nvidia Geforce GTX 1070 GPU equipped PC to run the VR software smoothly.
The virtual reality TUG was designed on Unity game engine, simulating the interior of a stationary train bar car (Fig 1). The real chair was placed at the same spot as the virtual train seat and the line on the ground replaced by a blue suitcase to turn around in front of it. The reason for using a bulky piece of luggage is due to the limited viewing angle of the HMD, so participants can see the demarcation without having to bend their head too much [20 ,25 (link)].
Safety was guaranteed by the permanent presence of a physician close to the participant for every try, and by a collaborator dedicated to managing the headset wire, so it never interfered with the participant movements. Moreover, the virtual exploration was set in a larger real zone exempt of any obstacles, and the chair was kept in place by another collaborator.
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2

Mass Spectrometry Data Processing Pipeline

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The software was written in Python 3.8. The key libraries used were Pandas 1.3.1 for data filtering and interface file input/output, AlphaTims 0.3 for loading raw data from the instrument database, scipy 1.6.1 and numpy 1.19.5 for signal processing, ms_deisotope 0.0.22 for spectra deconvolution, and Ray 1.5.2 for parallel processing. The neural network classifier was built with Keras and used TensorFlow 2.5 for the backend. Algorithm prototyping was done in Jupyter notebooks (jupyter-core 4.6.3).
Software validation work was performed on a PC with a 12-core Intel i7 6850K processor and 64 GB of memory running Ubuntu 20.04. An NVIDIA GeForce GTX 1070 GPU was used for the neural network training and inference.
Readers are encouraged to browse the source code in the GitHub repository (DOI 10.5281/zenodo.6513126) for a detailed understanding of the algorithms and implementation approach. The Jupyter notebooks developed to generate the figures in this paper are also available in the repository.
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3

Comprehensive 3D root crown scanning

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DSLR cameras, including Nikon D5300, Cannon EOS750D and EOS450D were used for photography. Agisoft Metashape 1.6.5 standard edition25 with an educational license was used for photogrammetry, and Blender 2.90.126 ,27 with “Mesh: 3D-print toolbox” add-ons was used for 3D analysis. These software packages were installed in a laptop (Intel® Core™ i7-8400 CPU 3.2 GHz, 32 GB RAM and NVIDIA GeForce GTX 1070 GPU), which was used throughout this work. The camera’s parameters were adjusted as follows; the highest F-stop with a small aperture, high depth of field, ISO speed < 400 to prevent image noise, auto white balance, medium image size (< 5 MB) and no flash mode. For photo shooting, the entire CRC was placed on its side exposing the root structure on a green background (120 × 120 cm) with a cardboard box (W × L × H: 12.5 × 12.5 × 34 cm) as a 3D reference object. Photographs were taken by stepping around the root crown to obtain 25–40 images per object. Oculus Quest 2 was used as a virtual reality tool in Medium by Adobe (version 2.4.6.336).
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4

Deep Learning and Robot Control Setup

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For deep learning and robot control we used a Shuttle SZ170R8 equipped with an Intel Core i3 6100 CPU, 16 GB kit Kingston DDR4 2133Mhz, ECC memory and an NVidia GeForce GTX 1070 GPU. Installed software are: Keras 1.2.2, Theano 0.9.0, NumPy 1.11.0, SciPy 0.17.0. For the analysis of the data, raw data for all the series of microinjections was processed in excel. Statistical analysis was done using excel and GraphPad Prism 6 followed by unpaired t-test with Welch’s correction for single comparisons (when applicable). The criterion for statistical significance was P<0.05. Graphs were plotted using GraphPad Prism 6 and error bars on all graphs represent standard deviation.
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5

Adam Optimization for CNN Training

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We trained the model with the Adam optimization algorithm [43] . The network is initialized with a truncated normal distribution function (standard deviation: 0.1). Training is carried out for 7000 iterations with a batch size of 100 images. At every 500 th step the model is applied on a batch of 200 images of the validation set. The initial learning rate is set to 0.1, then an exponential decay at every 25 th step with a 0.96 rate is performed. Implementation, training, validation and testing of the network was performed using Google TensorFlow [44] on a computer with a single Nvidia Geforce GTX 1070 GPU.
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6

Efficient DSCNN Model for Speech Emotion Recognition

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The recommended DSCNN model layout is implemented in python using the scikit-learn package for machine learning and other resources. The spectrograms are generated from each file, 128 × 128 in size. The whole generated spectrograms are divided by an 80%/20% split ratio for training and testing, respectively. The model training process was evaluated on a single NVIDIA GeForce GTX 1070 GPU with 12 GB of on-board memory for the proposed DSCNN model for SER. The model was trained on 50 epochs with a 0.001 learning rate and a decay one later every 10 epochs. The batch size is 128 in the whole training process and the best accuracy was achieved after 49 epochs with 0.3215 lost on training and 0.5462 lost on validation. The model trains in very little time with a reduced model size (34.5 MB), indicating the computational simplicity.
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7

TeraVR Evaluation on High-End PC

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TeraVR was implemented and evaluated on computers with Intel Core i7-7700 CPU @ 3.60 GHz, 64 GB memory, NVIDIA GeForce GTX 1070 GPU, Windows 10 64-bit edition, and HTC Vive as the VR device.
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