Journal: Sensors (Basel, Switzerland)

Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

doi: 10.3390/s24030899

Figure Lengend Snippet: ROCK 4C Plus, NVIDIA Jetson Nano, Google Coral, and Intel ® Arria ® 10 SX SoC Development Kit specification summary.

Article Snippet: However, as it is not available for any of the other CPU-based platforms or the MATLAB ® Deep Learning HDL Toolbox TM , only implementations from the FP32 model are considered in this case, with the A10_Performance and A10_Generic architectures [ ].

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

doi: 10.3390/s24030899

Figure Lengend Snippet: NN implementation workflow for the Intel ® Arria ® 10 SX SoC Development Kit device using the MATLAB ® Deep Learning HDL Toolbox TM .

Article Snippet: However, as it is not available for any of the other CPU-based platforms or the MATLAB ® Deep Learning HDL Toolbox TM , only implementations from the FP32 model are considered in this case, with the A10_Performance and A10_Generic architectures [ ].

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

doi: 10.3390/s24030899

Figure Lengend Snippet: Resource usage in Intel ® Arria ® 10 SX SoC Development Kit with MATLAB ® Deep Learning HDL Toolbox TM .

Article Snippet: However, as it is not available for any of the other CPU-based platforms or the MATLAB ® Deep Learning HDL Toolbox TM , only implementations from the FP32 model are considered in this case, with the A10_Performance and A10_Generic architectures [ ].

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces

doi: 10.3390/s24030899

Figure Lengend Snippet: Inference example, Intel ® Arria ® 10 SX SoC Development Kit with Matlab ® Deep Learning HDL Toolbox TM FP32 accelerator: ( a ) expected RIS, ( b ) inferred RIS, ( c ) error when matching the expected RIS and the inferred RIS, and ( d ) error when matching the opposite of the expected RIS and the inferred RIS (errors are shown in red in both ( c , d ), coincidences in green).

Article Snippet: However, as it is not available for any of the other CPU-based platforms or the MATLAB ® Deep Learning HDL Toolbox TM , only implementations from the FP32 model are considered in this case, with the A10_Performance and A10_Generic architectures [ ].

Techniques:

Journal: Biomedical Optics Express

Article Title: Rapid multi-plane phase-contrast microscopy reveals torsional dynamics in flagellar motion

doi: 10.1364/BOE.419099

Figure Lengend Snippet: Inter-plane distance and brightness calibration. (a) Example of the eight images of scattering microspheres recorded by the multi-plane wide-field microscope. In this example, microspheres are in focus in nominal plane #4 (b) Normalized average intensity in each image plane as a function of axial scan position (for axial scan steps of 100 nm). Crosses (for camera 1) and open circles (for camera 2) represent measured data, solid lines are Gaussian fits. (c) Linear fit of positions of intensity maxima from panel (b). The fit yields an average inter-plane distance of 430 nm (for oil immersion). (d) Relative shift of focal plane position in water (blue line) with respect to oil (red line). Open circles are the result of a wave-optical calculation of imaging in water, the blue solid line is a linear fit to this result. It shows that close to the glass interface, we still find a linear relationship between focal plane position and the objective’s axial position, but inter-plane distance in water is by a factor of 0.78 smaller than in oil.

Article Snippet: For noise reduction, a 2D Gaussian kernel (imgaussfilt function in Matlab ) with a standard deviation of σ = 2 pixels ( ∼ 162 nm) is used for low-pass frequency filtering (Fig. S1d in Supplement 1 ).

Techniques: Microscopy, Imaging