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: Scientific Reports

Article Title: Phase-based fast 3D high-resolution quantitative T 2 MRI in 7 T human brain imaging

doi: 10.1038/s41598-022-17607-z

Figure Lengend Snippet: Human whole-brain T 2 maps with a 0.85 mm isotropic voxel. ( a ) without denoising, ( b ) with denoising, based on DnCNN model for Gaussian noise removal. Arrows point to the cerebellum region, which especially benefits from denoising. Top row, Sagittal and Coronal planes. Bottom two rows, six slices of the Axial plane, at 10 mm intervals.

Article Snippet: To provide even higher robustness following the reduced SNR of the high-resolution datasets, we also incorporated denoising based on a DnCNN deep-learning network (provided in MATLAB, The Mathworks, Natick MA, for Gaussian noise removal).

Techniques: