Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Creating a platform for the democratisation of Deep Learning in microscopy

One of the major technological success stories of the last decade has been the advent of deep learning (DL), which has touched almost every aspect of modern life after a breakthrough performance in an image detection challenge in 2012. The bioimaging community quickly recognised the prospect of the automated ability to make sense of image data with near-human performance as potentially ground-breaking. In the decade since, hundreds of publications have used this technology to tackle many problems related to image analysis, such as labelling or counting cells, identifying cells or organelles of interest in large image datasets, or removing noise or improving the resolution of images. However, the adoption of DL tools in large parts of the bioimaging community has been slow, and many tools have remained in the hands of developers. In this project, I have identified key barriers which have prevented many bioimage analysts and microscopists from accessing existing DL technology in their field and have, in collaboration with colleagues, developed the ZeroCostDL4Mic platform, which aims to address these barriers. This project is inspired by the observation that the most significant impact technology can have in science is when it becomes ubiquitous, that is, when its use becomes essential to address the community’s questions. This work represents one of the first attempts to make DL tools accessible in a transparent, code-free, and affordable manner for bioimage analysis to unlock the full potential of DL via its democratisation for the bioimaging community

Trajectory prediction of moving objects by means of neural networks

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 1997Includes bibliographical references (leaves: 103-105)Text in English; Abstract: Turkish and Englishviii, 105 leavesEstimating the three-dimensional motion of an object from a sequence of object positions and orientation is of significant importance in variety of applications in control and robotics. For instance, autonomous navigation, manipulation, servo, tracking, planning and surveillance needs prediction of motion parameters. Although "motion estimation" is an old problem (the formulations date back to the beginning of the century), only recently scientists have provided with the tools from nonlinear system estimation theory to solve this problem eural Networks are the ones which have recently been used in many nonlinear dynamic system parameter estimation context. The approximating ability of the neural network is used to identifY the relation between system variables and parameters of a dynamic system. The position, velocity and acceleration of the object are estimated by several neural networks using the II most recent measurements of the object coordinates as input to the system Several neural network topologies with different configurations are introduced and utilized in the solution of the problem. Training schemes for each configuration are given in detail. Simulation results for prediction of motion having different characteristics via different architectures with alternative configurations are presented comparatively

Towards Nootropia : a non-linear approach to adaptive document filtering

In recent years, it has become increasingly difficult for users to find relevant information within the accessible glut. Research in Information Filtering (IF) tackles this problem through a tailored representation of the user interests, a user profile. Traditionally, IF inherits techniques from the related and more well established domains of Information Retrieval and Text Categorisation. These include, linear profile representations that exclude term dependencies and may only effectively represent a single topic of interest, and linear learning algorithms that achieve a steady profile adaptation pace. We argue that these practices are not attuned to the dynamic nature of user interests. A user may be interested in more than one topic in parallel, and both frequent variations and occasional radical changes of interests are inevitable over time. With our experimental system "Nootropia", we achieve adaptive document filtering with a single, multi-topic user profile. A hierarchical term network that takes into account topical and lexical correlations between terms and identifies topic-subtopic relations between them, is used to represent a user's multiple topics of interest and distinguish between them. A series of non-linear document evaluation functions is then established on the hierarchical network. Experiments using a variation of TREC's routing subtask to test the ability of a single profile to represent two and three topics of interest, reveal the approach's superiority over a linear profile representation. Adaptation of this single, multi-topic profile to a variety of changes in the user interests, is achieved through a process of self-organisation that constantly readjusts the profile stucturally, in response to user feedback. We used virtual users and another variation of TREC's routing subtask to test the profile on two learning and two forgetting tasks. The results clearly indicate the profile's ability to adapt to both frequent variations and radical changes in user interests

Smart and Secure Augmented Reality for Assisted Living

Augmented reality (AR) is one of the biggest technology trends which enables people to see the real-life surrounding environment with a layer of virtual information overlaid on it. Assistive devices use this match of information to help people better understand the environment and consequently be more efficient. Specially, AR has been extremely useful in the area of Ambient Assisted Living (AAL). AR-based AAL solutions are designed to support people in maintaining their autonomy and compensate for slight physical and mental restrictions by instructing them on everyday tasks. The discovery of visual attention for assistive aims is a big challenge since in dynamic cluttered environments objects are constantly overlapped and partial object occlusion is also frequent. Current solutions use egocentric object recognition techniques. However, the lack of accuracy affects the system's ability to predict users’ needs and consequently provide them with the proper support. Another issue is the manner that sensitive data is treated. This highly private information is crucial for improving the quality of healthcare services. However, current blockchain approaches are used only as a permission management system, while the data is still stored locally. As a result, there is a potential risk of security breaches. Privacy risk in the blockchain domain is also a concern. As major investigation tackles privacy issues based on off-chain approaches, there is a lack of effective solutions for providing on-chain data privacy. Finally, the Blockchain size has been shown to be a limiting factor even for chains that store simple transactional data, much less the massive blocks that would be required for storing medical imaging studies. To tackle the aforementioned major issues, this research proposes a framework to provide a smarter and more secure AR-based solution for AAL. Firstly, a combination of head-worn eye-trackers cameras with egocentric video is designed to improve the accuracy of visual attention object recognition in free-living settings. A heuristic function is designed to generate a probability estimation of visual attention over objects within an egocentric video. Secondly, a novel methodology for the storage of large sensitive AR-based AAL data is introduced in a decentralized fashion. By leveraging the power of the IPFS (InterPlanetary File System) protocol to tackle the lack of storage issue in the Blockchain. Meanwhile, a blockchain solution on the Secret Network blockchain is developed to tackle the existent lack of privacy on smart contracts, which provides data privacy at both transactional and computational levels. In addition, is included a new off-chain solution encapsulates a governing body for permission management purposes to solve the problem of the lost or eventual theft of private keys. Based on the research findings, that visual attention-object detection approach is applicable to cluttered environments which presents a transcend performance compared to the current methods. This study also produced an egocentric indoor dataset annotated with human fixation during natural exploration in a cluttered environment. Comparing to previous works, this dataset is more realistic because it was recorded in real settings with variations in terms of objects overlapping regions and object sizes. With respect to the novel decentralized storage methodology, results indicate that sensitive data can be stored and queried efficiently using the Secret Network blockchain. The proposed approach achieves both computational and transactional privacy with significantly less cost. Additionally, this approach mitigates the risk of permanent loss of access to the patient on-chain data records. The proposed framework can be applied as an assistive technology in a wide range of sectors that requires AR-based solution with high-precision visual-attention object detection, efficient data access, high-integrity data storage and full data privacy and security

ATHENA Research Book

The ATHENA European University is an alliance of nine Higher Education Institutions with the mission of fostering excellence in research and innovation by facilitating international cooperation. The ATHENA acronym stands for Advanced Technologies in Higher Education Alliance. The partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal, and Slovenia: the University of Orléans, the University of Siegen, the Hellenic Mediterranean University, the Niccolò Cusano University, the Vilnius Gediminas Technical University, the Polytechnic Institute of Porto, and the University of Maribor. In 2022 institutions from Poland and Spain joined the alliance: the Maria Curie-Skłodowska University and the University of Vigo. This research book presents a selection of the ATHENA university partners' research activities. It incorporates peer-reviewed original articles, reprints and student contributions. The ATHENA Research Book provides a platform that promotes joint and interdisciplinary research projects of both advanced and early-career researchers