Prosjektønsker kan registreres fra 1. april 2023.
Velg hva du ønkser å vise prosjekt for.
Advanced forms of analytics and aritificlai intelligence are becoming increasingly deployed to support the work of healthcare workers. Medical doctors, nurses, and administrative staff either use, or are aided by sophisticated technologies which are posed to radically change the nature of their work. For example, radiologists now rely increasingly more on machine learning techniques to and other applications of AI to diagnose patients, while a lot of procedural and repeptive tasks are being done by machines. The objective of this project is to understand how the nature of work for health practitioners is changing, and what positive and negative consequences they experience.
The European regulations about privacy, the GDPR, is changing people’s understanding and attitude towards data/information they own and those the others own. The ownership of data/information has always been a concern but with GDPR there will be more awareness as well as more obligations related to data collection and sharing.
Vis hele beskrivelsen ]
Today data-monopolies like Facebook, Google, Amazon (FGA) etc. are relying most of their business on the data they collect about people, and this without paying for this data. In the near future, people will conceive information/data as “commodity”, i.e., something that can be sold and bought. For example, with the enforcement of GDPR all people will be able to get the data that companies such as FGA collect about them and then sell it to smaller companies that may use this data for generating new business models.
The topic of this thesis is to develop further the blockchain-based data marketplace that enables data selling and buying while resolving trust and privacy problems. The Data Marketplace uses Ethereum blockchain and smart contracts to allow peers to communicate about the conditions of data sharing and perform the data exchange itself. For example, this platform will make me able to sell my facebook data to a company and provide service to me for bargaining about the price as well as including my privacy preferences and constraints into the deal. The peer-to-peer interaction for monetary and privacy related will be done by agents where agents “machine learn” the preferences of their owners (i.e., the peers). 1 - 2 students.
We can demonstrate the current platform as well as developing use cases to you as an inspiration. Then we can discuss about the way forward.
Co-supervisor: Associate Prof. Pinar Øzturk. https://www.ntnu.edu/employees/pinar
[ Skjul beskrivelse ]
Climbing Mont Blanc (CMB) is a system for training and competitions in energy efficient programming of small processors. CMB has been using a heterogeneous multicore (MPSoC, Exynos from Samsung) in more that five years. The chip is accessed via an Odroid XU3 board that has integrated energy monitors. These boards are no longer produced, and the energy measurements are not as precise as we would like. The goal of the proposed project is to be able to use one or two newer single board computers (SBC) as CMB back-end and the Lynsyn system to achieve more precise energy measurements. The project can be continued as a master thesis project in the spring semester.
The project is available for one student, or a group of two students. They should have a solid background in machine-oriented programming, knowledge of C and preferably also C++. Experience in embedded Linux administration is also an advantage.
The full project text can be found at this link
Nowadays, big tech giants, such as Google, Facebook, Amazon, IBM and Microsoft, are dominating the AI market by offering cloud-based AI solutions and APIs. They are collecting user data in one place through free services and systems, analyze it for insights and resell to third parties, such as advertisement companies. This model is centralized AI, which is working fine now. But in the long-run it could lead to monopolization of the AI market. This could also cause unfair pricing, lack of transparency, interoperability, privacy issues and excluding smaller companies from AI innovation. Fortunately, there is the emergence of a decentralized AI market, born at the intersection of blockchain, on-device AI and edge computing/IoT.
In this project, we will investigate the possibility to build a proof-of-concept of a decentralized AI application through blockchain, such as Ethereum. AI agents will train and learn models from their own data. The decentralized AI application to be developed in this project then can combine multiple algorithms/models (developed by different agents) performing different sub-tasks. One of possible applications is Decentralized Autonomous Cars. 1 - 2 students.
We can demonstrate the current result from previous master project as an inspiration. Then we can discuss about the way forward.
An estimated 275 million tonnes of plastic waste was produced on a global scale in 2010, with 8 million of those tonnes being introduced to the oceans - about 3% of global annual plastics waste. Once the plastic reaches the oceans, it is broken down into smaller particles(micro-plastic) by being exposed to ultra violet (UV) radiation and mechanical abrasion from wave actions .The quantity of plastic waste floating at the ocean surface in 2013 was estimated to be approximately 269,000 tonnes (small macro- to micro-plastic), this estimate does not include plastic in-depth or at the seafloor). The plastic debris can affect the wildlife in multiple ways, such as entanglement- entrapping, encircling, or constricting,ingestion- accidental ingestion or ingestion of prey containing plastic, and interaction- being in contact with plastic debris .It is therefore important to be able to detect and collect the plastic waste in nature,before it reaches the oceans. Once plastic waste has reaches a micro-stadium, it is near impossible to collect it and remove it from the water. An analysis on deep sea locations(range from 1176 to 4843m) showed that there was an average abundance of 1 micro-plastic per 25cm3(particle sizes ranging from 75 to 161μm) 
The AI-task will support the observation and monitoring of plastic items at consumer side, such as cities, beaches..... We will monitor the geographic distribution of different types of plastic items in these areas. The data will be gathered by citizens in form of taking photos of plastic objects. The photos will be taken using an application on smart phones. The application will have an artificial intelligence (deep learning) algorithm specially tailored for recognition of the material type of the plastic in the photos. The combined information about the geographic distribution, plastic type, and the object type (e.g., bottle, bag) pertinent to plastic debris will be collected in a database which in turn will be statistically analyzed and visualized. The smart application will serve as a tool for empowering citizen science and will contribute to the increase of the awareness and collaboration of citizens towards mitigation of plastic pollution problem.
 Hannah Ritchie and Max Roser.Plastic pollution.Our World in Data, 2019.https://ourworldindata.org/plastic-pollution.
 Lisbeth Van Cauwenberghe, Ann Vanreusel, Jan Mees, and Colin R. Janssen.Microplastic pollution in deep-sea sediments.Environmental Pollution, 182:495– 499, 2013.ISSN 0269-7491.doi: https://doi.org/10.1016/j.envpol.2013.08.013. URLhttp://www.sciencedirect.com/science/article/pii/S0269749113004387.13
After enforcement of GDPR (General Data Protection Regulation) (May 2018), all companies and institutions collecting data about individuals are obliged to deliver to people the data they collected about them (e.g., whatever facebook, google, amazon, insurance companies etc collects about me shall deliver the data they collected about me when I asked for it). GDPR will give a chance for people to look into her/his data stored by those companies.
So, what people can do with so much (and rich) data about themselves? It would be super hard for them to analyze, extract insights and even look into the raw data downloaded from Facebook, Google, etc..
This project aims to process and analyse in various ways the data people have about themselves to extract/discover new information about their own habits, preferences, intentions, faults, things they should be more careful about, etc. - which even themselves may not be aware of. This kind of insight may be valuable for the individual themselves, but also for others (e.g, insurance, entertainment companies, banks). The data collection, analysis and visualisation are present in a developing app. Data analysis and machine learning is focus. 1-2 students.
We can demonstrate the current result and the developing app from previous master project as an inspiration. Then we can discuss about the way forward.
Reverse engineering (RE) is the process of discovering features and functionality of a hardware or software system. RE of software is applied where the original source code for a program is missing, proprietary, or otherwise unavailable. Motivation for RE ranges from extending support of legacy software to discovery of security vulnerabilities to creating open source alternatives to proprietary software.
RE usually targets binary programs with a known instruction set architecture (ISA) and executable format. The RE process proceeds by disassembling the binary into assembly code, and where possible decompiling the assembly to yield high-level source code (for example, C source code).
However, in many cases the ISA is either undocumented, unknown, or unavailable. In addition, malware has been shown to use custom virtual machines to avoid detection. Such cases prove extremely time intensive for the reverse engineer. ISA features such as word size, instruction format, register size, and number of physical registers are a prerequisite to disassembly.
This project aims to discover to what extent machine learning can be used to detect ISA features from binaries of unknown provenance, and if so, whether these features can be used to help disassemble the binary program so that instruction and control flow information can be recovered.
Useful experience for the project includes good knowledge of computer architecture and assembly, machine learning (using Python), and a passion for staring at random-looking byte sequences for hours at a time.
Sporing av sau ved hjelp av enkel radioteknologi
Bøndene slipper sauene ut på fritt beite rundt midten av mai og sanker de inn igjen fra september og til snøen legger seg. I denne tiden går sauen fritt i skog og fjell med tilsyn i prinsippet en gang i uka. Sanking av sauene om høsten er en flertrinnsoperasjon som normalt tar et antall uker. Man går oftest gjennom et par runder per hovedbeiteområde hvor de aller fleste sauene lokaliseres og drives hjem til gården. Dernest følger en fase hvor de siste skal lokaliseres og bringes hjem. Denne siste fasen er oftest langvarig og arbeidskrevende da man ofte ikke har noen god formening om hvor sauene befinner seg. Systemet vi skal utvikle har primært til hensikt å effektivisere lokaliseringen av disse siste sauene slik at sauebonden kan spare det meste av det svært tidkrevende søkearbeidet.
Målbildet for operativt system
Vi ønsker å utstyre hver enkelt sau og lam med en meget liten batteridrevet radio. Radioen skal være så liten at den kan inkluderes i sauens øremerke uten at dette økes i areal. Radioen skal ha en rekkevidde på opptil 300 til 500 meter. Radioen skal sende ut et «ping» med jevne mellomrom som kan fanges opp av en drone.
Man benytter en flydrone til å søke etter sauene over et stort areal. Når flydronen mottar et signal fra en eller flere sauer, registrerer den sin egen GPS-posisjon. Vi vet da et sauen befinner seg innenfor en radius av 300 til 500 meter i forhold til dronens posisjon. Det vil også være mulig å måle avstanden til sauen ved å gå over i såkalt «ranging-modus» så snart en har mottatt signal fra sauen. Denne informasjonen lastes ned til en sentral hvor en bonde på et kart vil se dronens rute og hvor den har registrert sau.
Radiodelen av systemet skal baseres på Long Range Bluetooth chip nRF52833 fra Nordic Semiconductor. Ved hjelp av utviklingskit skal en lage en demonstrator for systemet. Hvis tiden tillater det, kan en gjøre feltforsøk som demonstrerer rekkevidden til systemet.
Dette er et tverrfaglig prosjekt som er et samarbeid mellom følgende tre partnere:
• Institutt for data og Informasjonsteknologi, NTNU
• Institutt for elektroniske systemer, NTNU
• Nordic Semiconductor
The project will investigate the possibility of implementing volume rendering techniques directly on a mixed reality headset (e.g. Microsoft HoloLens). Main purpose is to visualize patient-specific ultrasound data and/or computed tomography scans and combine them with geometric models representing structures of interest.