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Wearable Smart Camera With Deep Learning Algorithms For Automatic Detecion

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Short Description

Wearable devices are massively entering in our life and they are more and more pushing the interest big electronic producer. Then, today many company are offering wearable "smart" objects to be worn which enable a wide range of application (form sport & fitness, to entrainment, from tracking to health care). A common issue of wearable device that is reducing the appeal of them is the limited lifetime and reduced "smart" capabilities due to limited energy that can be stored in them batteries. This project will design a new smart camera that is wearable and will include a novel Parallel processots (PULP). The student will design both hardware and software, including deep learning algorithms. The Wearable camera has been carefully designed with low power consumption in mind and leveraging a the energy efficient processor. A whole working demostrator is planned to be achieved by the student. This work can also be done in collaboration with armasuisse.


Depending on the applicant's profile and project type, his tasks may involve some of the following:

Prerequisites

(not all need to be met by the single candidate)

  • Experience using the laboratory instrumentation - signal generators, oscilloscopes, DAQ cards, Matlab etc..
  • knowledge of microcontroller programming and PC programming (C/C++, preferably embedded C)
  • basic knowledge or interests on power converters, wireless communication, and circuit design at a components level (IC design is NOT involved)
  • Motivation to build and test a real system
  • PCB Desing or willing to learn it
  • Machine learning and deep learning on PC and microcontroller (or the motivation/interess to learn it)

Detailed Task Description A detailed task description will be worked out right before the project, taking the student's interests and capabilities into account.



Status: Available

  • Looking for Semester and Master Project Students
Supervisors: Michele Magno


Character

35% Theory
45% Implementation
20% Testing

IIS Professor

Luca Benini

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Detailed Task Description

Goals

Practical Details

Results