Geforce gtx 750 ti

Manufactured by NVIDIA

The GeForce GTX 750 Ti is a graphics processing unit (GPU) designed for desktop computers. It is based on the Maxwell architecture and features 640 CUDA cores, a base clock speed of 1020 MHz, and up to 2GB of GDDR5 video memory. The core function of the GeForce GTX 750 Ti is to provide enhanced graphics processing capabilities for a variety of applications, including gaming, video editing, and general-purpose computing.

Automatically generated - may contain errors

Lab products found in correlation

Eyelink 2, by SR Research (1 mentions) I5 6500 cpu, by NVIDIA (1 mentions) Matlab r2017b, by MathWorks (1 mentions)

4 protocols using geforce gtx 750 ti

Visual Stimuli Presentation and Eye Tracking

Check if the same lab product or an alternative is used in the 5 most similar protocols

Stimuli were generated using PsychoPy (Peirce, 2009 ) and saved as a JPEG. They were displayed on a LED monitor (BenQ) driven by an NVIDIA GeForce GTX 750 Ti graphics board at a refresh rate of 60 Hz. The resolution of the monitor was set at 2560 × 1440 pixels, which corresponded to physical dimensions of 708-mm wide by 398-mm high. At a viewing distance of 30 cm, the display occupied a viewing area of 99° horizontally and 67° vertically. Fixation was monitored using an EyeLink II (SR Research Ltd., Mississauga, Ontario, Canada) sampling at 500 Hz in pupil-only mode. Manual responses were collected using a two-button box set with a TTL trigger.

Casteau S, & Smith D.T. (2020). On the link between attentional search and the oculomotor system: Is preattentive search restricted to the range of eye movements?. Attention, Perception & Psychophysics, 82(2), 518-532.

+ Open protocol

+ Expand

E-Nose Optimization through CFD Simulation

Check if the same lab product or an alternative is used in the 5 most similar protocols

CFD is a discipline that uses numerical methods to solve governing equations to discover the laws of various flow phenomena. The designed e-nose system was analyzed by CFD simulation to explore whether the addition of the steady flow plates could improve the detection accuracy of the e-nose. ANSYS 2019 R1 software was used for grid division, model solving, and post-processing for CFD simulation in this study with an Inter (R) Core (TM) i5-6500 CPU @ 3.20 GHz × 4, 16 G memory, NVIDIA GeForce GTX 750 Ti graphics card.

Zou X., Wang C., Luo M., Ren Q., Liu Y., Zhang S., Bai Y., Meng J., Zhang W, & Su S.W. (2022). Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine. Sensors (Basel, Switzerland), 22(8), 2997.

+ Open protocol

+ Expand

Integrated FPGA-based MRI Control System

Check if the same lab product or an alternative is used in the 5 most similar protocols

The FPGA board is the main component in the experimental control system. This is plugged into a Peripheral Component Interconnect (PCI) slot on the motherboard of a control computer, and allows us to: i) generate low power rf pulses which are amplified for coherent spin excitation; ii) generate three independent low-frequency and low-power outputs which are amplified and fed to the gradient coils for spatial information encoding; iii) read in, digitize, down-convert and filter the MR signal emitted by the sample; iv) execute all of the above operations synchronously; and v) communicate bidirectionally with the control computer.
We have programmed our own Graphical User Interface (GUI) in Matlab, where we design pulse sequences, set the individual pulse parameters independently, configure data acquisition and filtering settings, and visualize the received data and reconstructed objects. Additionally, we have written an intermediate layer in C/C++ to interact from the GUI with the provided drivers for the RadioProcessor-G.
To perform image reconstruction we use MATLAB in a PC with an Intel Core i7-7700 CPU (6.65 GHz, 4 main processors, 8 logic processors), an NVIDIA GeForce GTX 750 Ti GPU (640 CUDA cores, 1.4 TFLOPS, 2GB memory) and 32 GB RAM.

Algarín J.M., Díaz-Caballero E., Borreguero J., Galve F., Grau-Ruiz D., Rigla J.P., Bosch R., González J.M., Pallás E., Corberán M., Gramage C., Aja-Fernández S., Ríos A., Benlloch J.M, & Alonso J. (2020). Simultaneous imaging of hard and soft biological tissues in a low-field dental MRI scanner. Scientific Reports, 10, 21470.

+ Open protocol

+ Expand

Deep Neural Network for Image Analysis

Check if the same lab product or an alternative is used in the 5 most similar protocols

The DNN model was developed and tested in Keras 2.2.2 (Chollet and others, 2015) with TensorFlow 1.9.0 (Abadi et al., 2015) backend running on an NVIDIA (GeForce GTX 750 Ti) GPU. All other computations and image analysis were performed by MATLAB R2017b (MathWorks, Inc., Natick, MA, USA).

Moghimi A., Yang C, & Anderson J.A. (2020). Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat. Computers and Electronics in Agriculture., 172.

+ Open protocol

+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Revolutionizing how scientists
search and build protocols!