Enhances construction site safety using YOLO for object detection, identifying hazards like workers without helmets or safety vests, and proximity to machinery or vehicles. HDBSCAN clusters safety cone coordinates to create monitored zones. Post-processing algorithms improve detection accuracy.

A pipelined implementation of the MIPS processor featuring hazard detection as well as forwarding

Verilog Implementation of an ARM LEGv8 CPU

A python toolbox for easily searching, downloading & processing remote sensing imagery from various public sources. Includes a Sentinel-1 InSAR processor.

Design, verification and ASIC implementation of a complete RISC-V CPU with: five stages pipeline, forwarding, automatic hazard detection, Branch Target Buffer using LRU replacement policy, absolute value custom instruction.

ARM Processor, Computer Architecture laboratory, University of Tehran

An MIPS pipelined processor with hazard detection for the course VE370 (FA2020) of UM-SJTU JI.

Python GUI-based hazard video labelling

Smart Building Evacuation Ontology

A 32-bit Arm Processor Using Verilog HDL With Hazard Detection, Forwarding Unit, SRAM Memory & A 2-Way Set-Associative Cache.

Design and development of a complete RISC CPU with: five stage pipeline, forwarding, automatic hazard detection, BTB using LRU policy replacement, four-cycle hardware multiplier.

The Hazard Detection and Captioning System uses state-of-the-art models like YOLOv11 for object detection and BLIP-based models for caption generation to analyze driver behavior and identify hazards in video streams.

Computer organisation and architecture assignments

CURSO - Taller de Sistemas de Información Geográfica aplicados a Desarrollo y Ordenamiento Territorial (OT) - SIGE

A paper exploring emotional contagion within the context of crowd simulation.

Verilog Implementation of an ARM LEGv8 CPU

MIPS implementation

ahead of the weather curve

5-stage pipelined 32-bit MIPS processor

The research mainly aims to identify through classification algorithms if one day, based on its climatic features and concentrations of harmful elements in the air, it turns out to be harmful (or not) to the health of citizens in the Milan metropolis. A second prediction model was adopted to predict daily mean PM2.5 values.