Journal: ACS sensors

Article Title: GPCR signaling measurement and drug profiling with an automated live-cell microscopy system

doi: 10.1021/acssensors.2c01341

Figure Lengend Snippet: A schematic of how the FALCOscope coordinates between the microscope and the liquid handler using the μManager application program interface (API), the OpenTrons API from the OT-1 and an imaging queue system. Custom MATLAB scripts segment cells from the fluorescent images and output a table of measured single cell responses over time along with associated metadata that can be plotted in various ways.

Article Snippet: To overcome this limitation, we created a highly customizable, MATLAB-based platform: FALCOscope (Flexible Automated Liquid-handling Combined Microscope) that coordinates an OpenTrons liquid handler and a fluorescent microscope to automate drug treatments, fluorescence imaging and single cell analysis.

Techniques: Microscopy, Imaging

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: