OVERVIEW

The teaching unit provides basic elements for the design and development of multimedia applications, in the context of the Internet of Things (IoT), through the use of embedded systems and sensors. The reference platform for teaching is the smartphone that will be used for the exercises and for the final project.

AIMS AND CONTENT

LEARNING OUTCOMES

The course tackles the basics of software development (Apps development) on Android mobile devices such as modern smartphones or those related to the so-called "Internet of Things". The addressed topics concern (but they are not limited to) the representation, processing and acquisition of multi-sensor signals including audio, images, radio signal (WiFi) and inertial signals (through the embedded accelerometer of the phone). At the same time, the main signal processing techniques are presented through Machine Learning and DSP algorithms together with the most modern Deep Learning techniques (AI and Neural Networks).

AIMS AND LEARNING OUTCOMES

The teaching unit addresses the issues related to the representation, processing and recovery of multimedia data including sounds, music, graphics, images and videos but also localization information or information coming from sensors such as the accelerometer in order to create modern applications (App).

At the same time, the course presents the main architectural characteristics of modern telecommunications networks in their evolution towards the Internet of the future: integration between fixed and mobile networks, separation between data transport and network control and management, virtualization of network functions, flexibility, programmability, energy efficiency.

Active participation in the proposed training activities (lessons and exercises) and individual study will allow students to:

• understand and use correct terminology to identify the main components and functionalities of multimedia mobile apps implemented on embedded systems;
• analyze the structure and functions of the Internet of Things;
• know and be able to adequately apply the main methods to process heterogeneous signals and data acquired by the sensors of an embedded system and in particular of a smartphone;
• identify and analyze the main components and functionalities of an Android App;
• create Android Apps in the context of the Internet of Things by implementing solutions based on:
  • Activities (or Fragments)
  • Services
  • Broadcast Receivers and Intents
  • HTTP requests (GET, POST)
  • Use of signals acquired from smartphone sensors or external sensors (optional)
  • Implementation of Machine Learning and Signal Processing algorithms

PREREQUISITES

The basic knowledge acquired in the teaching units of FUNDAMENTALS OF ELECTRONIC SYSTEMS PROGRAMMING, EMBEDDED ELECTRONIC SYSTEMS and SIGNALS AND IMAGES PROCESSING AND TRANSMISSION (which takes place partially simultaneously) are useful for effectively addressing the teaching contents.

TEACHING METHODS

Frontal lessons and classroom exercises.
Attendance IS NOT MANDATORY but is strongly recommended.

The teaching unit includes an initial part of frontal teaching during which the main theoretical concepts will be exposed which will then be demonstrated through joint exercises (on the computer and on the smartphone) for the creation of the first Apps preparatory to the development of the final project.

A phase of project development in groups is also planned afterwards.

SYLLABUS/CONTENT

Introduction

• Introduction to mobile application development paradigms.
• Overview of the Android ecosystem.
• Basic concepts of the Internet of Things (IoT).

Android development fundamentals

• Structure of an Android project (Android Studio).
• Activity, Fragment, Intent.
• Lifecycle management.
• UI/UX Design: Layout, Widgets.
• Asynchronous tasks
• Broadcast Receivers

Access to smartphone embedded sensors

• Audio recording and playback.
• Inertial signal acquisition (accelerometer)
• Radio signal acquisition (WiFi, Bluetooth)
• Camera image capture

IoT App integration

• MQTT protocol
• Client (App) - Server paradigm.
• Theoretical outline of Machine Learning and Deep Learning (AI).
• Use and integration of external IoT platforms such as ESP32, Arduino, Raspberry (optional).

Evaluation of the final project

• Development of a practical group project.
• Presentation and discussion of the project.

RECOMMENDED READING/BIBLIOGRAPHY

• A.B. Carlson, P.B. Crilly and J.C. Rutlege,  “Communication Systems”, 4th ed., McGraw-Hill, 2002
• A. V. Oppenheim, R. W. Schafer, “Elaborazione Numerica dei Segnali”, Franco Angeli
• J. Kurose, K. Ross, “Computer Networking: A Top-Down Approach”, 6/E, Addison-Wesley
• S. Tarkoma, M. Siekkinen, E. Lagerspetz, Y. Xiao, "Smartphone Energy Consumption, Modeling and Optimization", Cambridge University Press, 2014
• http://developer.android.com/index.htm
• http://www.sprik.it/guida/Android4_2.pdf
• Additional material will be provided by the lecturers

Students with learning disabilities ("Disturbi Specifici di Apprendimento", DSA) will be able to use specific methods and supports that will be established from time to time in agreement with the DITEN delegate in the University Committee for the inclusion of students with disabilities or DSA.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

Check the website of the BACHELOR DEGREE COURSE in INGEGNERIA ELETTRONICA E TECNOLOGIE DELL'INFORMAZIONE: https://corsi.unige.it/corsi/9273/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Oral presentation of a project developed by the student, operational demo and related technical discussion.

ASSESSMENT METHODS

The main purpose of the exam is to evaluate the capability of the student to reach the learning objectives.

The assessment methods will take into account the following aspects:
 

"Running" code that produces results coherent with the provided specifications (minimum requirement for passing the test);

• The designed app must work correctly and comply with the assigned specifications.

Effectiveness and clarity in the presentation of results

• The student must be able to justify with relevant language each design choice made and all the source code present in the App.

Explanation and justification of the results

• The student must be able to justify each obtained result, illustrating any measurement campaign carried out, the algorithm used, the choices/calibrations of the parameters.

Style and readability of the code produced

• Although with a smaller impact, the “cleanliness of the code”, the respect of the principles of object-oriented programming, the appropriate use of interfaces, the correct separation of source files are evaluated.

Computational efficiency of the programs.

• Although with a smaller impact, the student’s ability to choose appropriate algorithms to effectively solve the problem posed is evaluated.

FURTHER INFORMATION

Further information is available at: http://www.dsp.diten.unige.it/index.php/teachings/multimedia-apps-and-future-internet.