文件列表:

Designing_an_Image_Sensor_Array_for_Hardware_Implemented_Neural_Network_Applications.pdf (3791558, 2023-09-18)
csv_to_image_v2_edge_detection.m (679, 2023-09-18)
feedback_1_csv_variable_input.m (5815, 2023-09-18)
feedback_2_16x16_gloabal_shutter_VEC.m (5044, 2023-09-18)
image_sense_1_csv_variable_input.m (4241, 2023-09-18)
image_sense_2_vec_generator.m (7574, 2023-09-18)
image_sense_and_feedback_label_string_generator_16x16.m (10409, 2023-09-18)

# BSc-Project "Designing an Image Sensor Array for Hardware Implemented Neural Network Applications”. Project was submitted as part of the requirements for the bachelor’s degree in the Faculty of Engineering, Bar-Ilan University. For full information see "Designing_an_Image_Sensor_Array_for_Hardware_Implemented_Neural_Network_Applications.pdf" PDF file. This project aimed to design a novel model of weight based current-division-capable photo sensing device (Weight-Based CAPD), implemented within an image sensing array and utilizing spatial connectivity and real-time feedback. Stage 1: Input variables into Virtuoso Masetro simulation using: 1. for Image Sensing: "image_sense_1_csv_variable_input.m" 2. for Image Sensing with real-time feedback: "feedback_1_csv_variable_input.m" Stage 2: Copy into Virtuoso Schematic corresponding connectivity labels between WBCAPD (15x15) array and 4T Pixel (16x16) array (2 layer rectangular pyramid): 1.1. For Image Sensing Simulation: ROW 1-4 'WBCAPD_input_edge_detection_label_names.xls' 1.2. For Image Sensing Simulation with real-time feedback: ROW 6-9 'WBCAPD_input_edge_detection_label_names.xls' 2. Pixel control signal labels: 'pixel_control_16x16.xls' stage 3: Run Simulations and save as CSV files: 1. Image Sense simulation save as: 'is_results.csv' 2. Image Sense with real-time feedback simulation save as: 'fb_results.csv' Stage 4: run "csv_to_image_v2_edge_detection.m" to perform edge detection algorithm

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