Sökning: "multi-device"
Visar resultat 1 - 5 av 8 uppsatser innehållade ordet multi-device.
1. Outlier Robustness in Server-Assisted Collaborative SLAM : Evaluating Outlier Impact and Improving Robustness
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : In order to be able to perform many tasks, autonomous devices need to understand their environment and know where they are in this environment. Simultaneous Localisation and Mapping (SLAM) is a solution to this problem. LÄS MER
2. Designing digital instructions for setting up multi-device services
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Digital services are increasingly offered across multiple devices for a single user. A challenge for users is that multi-device services are more complex to use. While instructions are supposed to facilitate ease of use of the services, poorly designed instructions become obstacles themselves. LÄS MER
3. An Open and Nonproprietary Decentralized Messaging Protocol : Operating Entirely on the Internet Computer Blockchain
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Even when end-to-end encryption is used in centralized messaging services, problems related to security, privacy, availability, and transparency remain. These problems can be avoided or reduced by using a decentralized architecture. LÄS MER
4. User access control platform based on simple IoT household devices
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : The Internet of Things (IoT) industry has been thriving for the past few years, especially in the household field. The proposal of smart homes has enabled a increasing number of IoT products to enter people’s daily lives. However, the access control in smart homes has gradually grown up to be a prominent problem. LÄS MER
5. Random projections in a distributed environment for privacy-preserved deep learning
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : The field of Deep Learning (DL) only over the last decade has proven useful for increasingly more complex Machine Learning tasks and data, a notable milestone being generative models achieving facial synthesis indistinguishable from real faces. With the increased complexity in DL architecture and training data, follows a steep increase in time and hardware resources required for the training task. LÄS MER