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: Wrist redundancy management during pointing tasks remains stable over time and in presence of a visuomotor perturbation

doi: 10.1038/s41598-023-33531-2

Figure Lengend Snippet: ( A ) Average Task-Space Accuracy \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A_{sum}$$\end{document} A sum (along trials and participants) over the 4 days; ( B ) average Thickness (along trials and participants) over the 4 days, neglecting the discarded outliers; ( C ) Shape Index averaged along trials and participants over the 4 days, neglecting outliers; ( D ) correlation between Thickness and Task-Space Accuracy . Pearson’s \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R = 0.62$$\end{document} R = 0.62 with a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p = 3.8 \times 10^{-6}$$\end{document} p = 3.8 × 10 - 6 . Within box-plots ( A – C ), red crosses represent the data labelled as outliers by the MATLAB™ function boxplot , net of neglected outliers on the basis of Thickness analysis.

Article Snippet: Pointing tasks were implemented through an interactive MATLAB TM -based video-game, in which participants could see the pointed target in real-time and control the cursor position with wrist rotations.

Techniques:

Journal: Micromachines

Article Title: Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor Control

doi: 10.3390/mi13081264

Figure Lengend Snippet: Block diagram representation for the closed-loop system including the PID controller.

Article Snippet: The proposed method updates the PID controller gains based on the Simulink TM controller tuning toolbox.

Techniques: Blocking Assay