Gtx1060

Manufactured by NVIDIA

The NVIDIA GeForce GTX 1060 is a high-performance graphics processing unit (GPU) designed for desktop computers. It is based on NVIDIA's Pascal architecture and features 1,280 CUDA cores, a boost clock speed of up to 1.7 GHz, and 6 GB of GDDR5 video memory. The GTX 1060 is capable of delivering efficient and smooth graphics performance for a variety of applications, including gaming, video editing, and scientific computing.

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Lab products found in correlation

I7 8700, by NVIDIA (1 mentions) Cuda 8, by NVIDIA (1 mentions) I5 4590, by NVIDIA (1 mentions) I7 7700, by NVIDIA (1 mentions) Gtx 1080, by NVIDIA (1 mentions)

Close spelling variants of this product

GeForce GTX1060 3GB Graphics card

10 protocols using gtx1060

AI Model Training and Evaluation

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Model training. Here is the information on experimental environment: Window10, Intel(R) Core(TM) i7-8700 CPU @3.20GHZ processor, RAM 16G, graphics card NVIDIA GTX1060, Python3.7. Model parameters are set as follows. Input image size is 416 × 416. The batch is set to 4, and the label smoothing is set to 0.05. The breakpoint continuation training method is adopted. One breakpoint is set every 350 times, four breakpoints are set, 350 weight files are generated after 350 times of training, and the best weight file is manually selected as the initial weight of the next breakpoint. The total number of training times is 1400 times. During the test, the confidence is set to 0.4, and IOU is set to 0.4.

Evaluation metrics. In this paper, we mainly evaluate the effectiveness of model training in terms of detection accuracy and efficiency. The evaluation metric used is the mean Average Precision (mAP), the average detection accuracy AP of all categories, and the number of image frames per second FPS detected by the algorithm.

Li H., Li B., Liu G., Wen X., Wang H., Wang X., Zhang S., Zhai Z, & Yang W. (2022). A detection and configuration method for welding completeness in the automotive body-in-white panel based on digital twin. Scientific Reports, 12, 7929.

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Single-Molecule Localization Microscopy

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Each frame from an image stack was first box-filtered with the box size of 4 times of the FWHM of a 2D Gaussian PSF. We note that each pixel was weighted by the inverse of its variation during such box-filtering. The low-pass filtered image was then extracted from the raw image, followed by recognition of local maximums. The local maximums from all the frames of the image stack were then submitted for 2D-Gaussian single-PSF fitting.
The 2D-Gaussian single-PSF fitting were performed in GPU (Nvidia GTX 1060, CUDA 8.0) using the Maximum Likelihood Estimation (MLE) algorithm. In brief, the likelihood function at each pixel was built by convolving the Poisson distribution of the shot noise governed by the photons emitted from fluorophores nearby, and the gaussian distribution of the readout noise that characterized by the expectation, variation, and the analog-to-digital conversion factor that pre-calibrated as mentioned above. The fitting accuracy was estimated by Cramér-Rao lower bound (CRLB).

Yin Y., Lee W.T, & Rothenberg E. (2019). Ultrafast data mining of molecular assemblies in multiplexed high-density super-resolution images. Nature Communications, 10, 119.

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Performance Edge VR for Oculus Rift

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Performance Edge VR was developed for the Oculus Rift VR platform, which requires a direct cable connection to a high-performance gaming laptop (minimum specifications: Intel i5-4590 or greater, NVIDIA GTX 1060 video card or greater, 8 GB+ RAM, Windows 10 and compatible video output; used in current study: Alienware Dell 15R3, Intel core i7-7700HQ, CPU@2.8 GHz, NVIDA GTX 1080, 16 GB RAM, Win 10 Pro) and external VR positioning sensors. Respiratory rate measurements and in-application biofeedback were provided by an EquiVital biosensor respiratory harness (Hidalgo, UK) with integrated transmitter (SEM), with the digital signal transmitted wirelessly via Bluetooth to the laptop. Intellectual property relating to Performance Edge VR is owned by The University of Newcastle. If interested in accessing Performance Edge VR application for research purposes, please contact the corresponding author.

Kluge M.G., Maltby S., Walker N., Bennett N., Aidman E., Nalivaiko E, & Walker F.R. (2021). Development of a modular stress management platform (Performance Edge VR) and a pilot efficacy trial of a bio-feedback enhanced training module for controlled breathing. PLoS ONE, 16(2), e0245068.

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W-Net GAN for Seismic Inversion

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The proposed seismic inversion algorithm was implemented in Python (version 3.9). We used PyTorch with CUDA (versions 1.13 and 11.7) for the W-Net GAN model and training code. The training was conducted on a computer running Windows 11 operating system, with Intel® Core™ i7-8750H CPU and NVIDIA® GeForce™ GTX 1060. The average training time of the W-Net GAN was approximately 45 s per epoch for all the synthetic application examples, and 55 s per epoch for the real application example.

Miele R, & Azevedo L. (2024). Physics-informed W-Net GAN for the direct stochastic inversion of fullstack seismic data into facies models. Scientific Reports, 14, 5122.

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Deep Learning-Powered Image Classification

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The work was implemented in Python™ with Keras and TensorFlow in a Windows 10 environment and was run on the Nvidia^® GeForce^® GTX 1060 graphics processing unit (GPU).

Kaplan S., Handelman D, & Handelman A. (2021). Sensitivity of neural networks to corruption of image classification. Ai and Ethics, 1(4), 425-434.

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Deep Learning for Hippocampus Segmentation

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The training of the CNN model was performed using a NVIDIA GTX 1060 GPU and the CUDA 9 toolkit and cuDNN 7.5 with TensorFlow 1.12.1. All pre-processing steps were performed using the visualization toolkit (VTK) and python 2.7. All registrations were performed using the NiftyReg package [30 (link)]. Brain extraction and bias field correction were done using the brain extraction tool (BET), part of the FSL package. All statistical analyses were performed using open-source R and RStudio software packages. The trained model, pre-processing scripts and instructions for using tool are available on https://github.com/snobakht/DeepHarP_Hippocampus/tree/main/DeepHarP.

Nobakht S., Schaeffer M., Forkert N.D., Nestor S., E. Black S, & Barber P. (2021). Combined Atlas and Convolutional Neural Network-Based Segmentation of the Hippocampus from MRI According to the ADNI Harmonized Protocol. Sensors (Basel, Switzerland), 21(7), 2427.

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Transfer Learning on Desktop Hardware

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All implementations of the transfer learning framework proposed in this article were performed on personal desktops with Core i7-8700 (3.20 GHz), 16 GB RAM, NVIDIA GTX1060 6 GB, and 64-bit operating system. The operating system was Windows10 Professional and Python 3.6 with tensorflow and keras. Since the number of samples was not very large, all algorithms were run in a CPU environment.

Zhai N, & Zhou X. (2020). Temperature Prediction of Heating Furnace Based on Deep Transfer Learning. Sensors (Basel, Switzerland), 20(17), 4676.

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VR System for Immersive Neuroscience Research

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The VR system was set up as previously described (Huang et al., 2022 (link)). Specifically, the VR system used in this study (Shanghai VR-Sens Intelligent Technology Co. Ltd., Shanghai, China) consisted of a VR interface, a VR headset, two controllers, and two cameras (Fig. 2). The VR system was linked to a conventional gaming computer (CPU: Intel i7-7700 and GPU: NVIDIA GTX1060). The VR interface offered scenarios in virtual reality. Two tracking cameras were used to precisely determine the location of the VR headset. The two controllers were employed to interact with VR objects. As a data visualization tool, custom software (VR-SENS VR Implant Tutorial Software) was utilized. The VR-compatible files were previously generated computationally.

Huang Y., Hu Y., Chan U., Lai P., Sun Y., Dai J., Cheng X, & Yang X. (2023). Student perceptions toward virtual reality training in dental implant education. PeerJ, 11, e14857.

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CellexalVR: Immersive Research Simulations

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Users require a VR ready workstation/laptop with a suitable graphics card (Steam recommends an NVIDIA GTX1060 or higher) running Windows 10. CellexalVR will work with any SteamVR compatible headset. The HTC Vive and Valve Index were used during development. Compatibility for other VR systems will be added. These VR systems are readily available, and are priced for the home consumer.
CellexalVR was developed on a gaming class workstation comprising an Intel i7 processor, 16GB RAM and an NVIDIA GTX1080 graphics card.

Legetth O., Rodhe J., Lang S., Dhapola P., Wallergård M, & Soneji S. (2021). CellexalVR: A virtual reality platform to visualize and analyze single-cell omics data. iScience, 24(11), 103251.

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Image Preprocessing for U-Net Model

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Image intensity values can change due to not only by different tissue types but also can change due to noise and scanner artifacts. It has been suggested [39] that intensity normalization has a significant role as a preprocessing stage. The purpose of intensity normalization is to uniformized the mean and variance values of image intensities. We used the normalization process to enable us to change the range of pixel intensity values in ranges 0 and 1. More details for normalization are given in [40] . We used simple noise reduction and image smoothing for all images to improve the U-Net input image quality. The denoising, normalization, and resizing processes were run using custom code written in the OpenCV-Python library. Also, all image sizes were changed to 256×256 to reduce the U-Net training time according to our GPU computational capability and memory (NVIDIA GeForce GTX-1060 with 6 GB memory).

Khorasani A., Kafieh R., Saboori M, & Tavakoli M.B. (2022). Glioma segmentation with DWI weighted images, conventional anatomical images, and post-contrast enhancement magnetic resonance imaging images by U-Net. Physical and engineering sciences in medicine, 45(3).

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