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The competence in these technology areas include signals and systems, informatics, mathematics, computer science and engineering, forensics, electronics and physics. These competences form the research environment EIS. The program is funded by the University and the Knowledge Foundation with support from Swedish Industry. Through our joint competences we can be an attractive partner and deal with projects where the whole range is treated, from enabling technologies — like low-power technologies and semiconductor sensors — to value-adding IT use, considering user aspects.

In between, system and application aspects are treated, that is intelligent algorithms, application-specific computer architectures and efficient interconnection technologies. Our strategy is to focus on a limited number of application areas in which we get recognised by industry and society as a key player and natural cooperation partner. Currently, the selected application areas are: health technology, traffic and transport systems, process industry, high-performance signal processing applications, experience industry, together with the non-application area of ground breaking electronics.

The School of Information Technology. Doctoral education in Information Technology. Halmstad Colloquium — Halmstad University distinguished speaking series.

Table of contents

How can we better manage all collected data? International cross collaboration to help dementia patients. Victory in research competition for quality assurance of patient data. Research for fewer power outages.

Embedded and Intelligent Systems (EIS) - Högskolan i Halmstad

New testing techniques for safer software development. Working towards safer roads. Halmstad University gives courses in cyber security for other universities.

Testing complex autonomous systems. Human interaction with intelligent vehicles — how do we react and when is it dangerous? Halmstad University team wins Volvo hackathon. A human approach to designing future cities and intelligent cars. Our research is carried out in close collaboration with industrial partners and is largely about development within artificial intelligence AI.

The focus is on creating systems that, as autonomously as possible, can be developed based on real-life data.

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The goal with artificial intelligence AI research and development is to construct systems that behave intelligently. Today it is common to assume that human experts define the task to be performed, what data should be collected, how should it be represented, and what metrics to use for performance evaluation. This means that these systems are designed or programmed, which leads to them breaking when the context changes. Our aim with Aware Intelligent Systems research is to approach the construction of systems that can do life-long learning; systems that require less supervision and can handle surprising situations.

In order to do so, the systems must become more aware and able to learn on their own, to handle events that are unknown at the time of design. Our research focuses on creation of systems that, as autonomously as possible, can construct knowledge from real life data capturing the interaction with the environment. Technology Area Leader: Sepideh Pashami. When large amounts of data are collected and analysed by intelligent systems, new solutions to several of today's societal problems can be developed.

The aim of the research is to, in collaboration with our surrounding society, create conscious and intelligent systems that have the ability to develop themselves. When large amounts of data are collected and analysed by these intelligent systems, new solutions to several of today's societal problems can be developed. For example, recent developments in wearable sensors has inspired a vision of personalised health; modern energy production is becoming more volatile, diverse and distributed; transport efficiency depends on better maintenance and monitoring solutions.

All those areas require novel solutions that build upon available data and require autonomous knowledge creation. The research questions we explore include selecting what data to collect and how to find general and robust representations; how to do semi- autonomous deviation detection, dealing with concept drift and seasonal variations; how to associate events from different data sources; is it possible to explain why certain things have happened.

Aware systems research is a systems science , so there are many interconnected parts and the results need to address several aspects, tying them together.

To enable this, we build demonstrators to showcase what this means, with sets of tools for all levels. General AI is not yet developed, but narrow AI is used in a number of different areas today. For example, in autonomous vehicles and when scanning images on the internet. Machine learning is part of AI where algorithms and computer programs learn by repeating examples. Machine learning is a way to reach narrow AI or ultimately to general AI.

Deep learning is an area within machine learning where algorithms find special features and properties for making decisions on their own. The Technology Area is responsible for carrying through and developing courses within artificial intelligens, image analysis, learning systems, mechatronics systems, signals and systems, and control theory. Education of doctoral students is done within the doctoral education in information technology:. Technology Area Digital Service Innovation.

References - glider bibliography

This implies a focus on how value is created for users, organisations and societies through combining, re-combining and integrating resources into digital services. This implies a focus on how value is created for users, organizations and societies through combining, re-combining and integrating resources into digital services. We also investigate how and why DSI may promote or create improvements in the wellbeing of different societal actors, by studying for example stakeholder involvement in innovation processes, digital service logics and architectures, innovation ecosystems and value network governance.

DSI research in Informatics combines theorizing identifying models, patterns, structures, relations, processes with applied and action-oriented research in co-creation with industrial and public-sector partners, and covers process innovation research focused on stakeholder involvement and value co-creation as well as the intersection between digital services and business. The core competence areas include innovation process knowledge, digital service and business innovation, interaction design and design science.

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  • When replacing a real camera with a pinhole camera, the center of projection is located in the center of the lenses. When studying robot geometry and kinematics, we attached a coordinate frame to each rigid body, e. When considering robot vision, the camera itself represents a rigid body and a coordinate frame should be assigned to it.

    The pose of the camera will be from 5. The zc axis of the camera frame is directed along the optical axis, while the origin of the frame is positioned at the center of projection. We shall choose a right-handed frame where the xc axis is parallel to the rows of the imaging sensor and the yc axis is parallel with its columns. The image plane is in the camera, which is placed behind the center of projection.

    The distance fc between the image and the center of projection is called the focal length. In the camera frame the focal length has a negative value, as the image plane intercepts the negative zc axis. It is more convenient to use the equivalent image plane placed at a positive zc value Figure 5.

    Intelligent Systems, Control and Automation: Science and Engineering |

    The equivalent image plane and the real image plane are symmetrical with respect to the origin of the camera frame. The geometrical properties of the objects are equivalent in both planes, and differ only in the sign. From now on we shall call the equivalent image plane simply the image plane. Also the image plane can be considered as a rigid body to which a coordinate frame should be attached.

    The origin of this frame is placed in the intersection of the optical axis with the image plane. The x and y axes are parallel to the xc and yc axes of the camera frame.

    Robotics (Intelligent Systems, Control and Automation: Science and Engineering)

    In this way the camera has two coordinate frames, the camera frame and the image frame. Let the point P be expressed in the camera frame, while the point 60 5 Robot sensors p represents its projection onto the image plane. It is our aim to find the relations between the coordinates of the point P and the coordinates of its image p. Let us first assume that the point P is located in the yc , zc plane of the camera frame. After the perspective projection of the point Q, its image q falls onto the x axis of the image frame. Equations 5.

    From the matrix equation 5.

    Intelligent Systems, Control and Automation: Science and Engineering

    On the contrary, we cannot calculate the coordinates [xc , yc , zc ]T in the camera frame when only the coordinates [x, y]T in the image frame are known, but not the scaling 5. Equation 5. The calculation of [xc , yc , zc ]T from [x, y]T is called inverse projective mapping. When using a single camera and when having no a priori information about the size of the objects in the robot environment, a unique solution of the inverse problem cannot be found. For the ease of programming it is more convenient to use indices, marking the position of a pixel i.

    We shall use two indices which we shall call index coordinates of a pixel Figure 5. These are the row index and the column index. In the memory, storing the digital image, the row index runs from the top of the image to its bottom, while the column index starts at the left and stops at the right edge of the image. We shall use the u axis for the column indices and the v axis for the row indices. In this way the index coordinate frame u, v belongs to each particular image. The upper left pixel is denoted either by 0, 0 or 1, 1.

    The index coordinates have no measuring units. In the further text we shall find the relation between the image coordinates [x, y]T and the index coordinates [u, v]T. In this case each pixel corresponds to a particular element of the image sensor. We shall assume that the area of the image sensor is rectangular. The origin of the image frame is in the point u0 , v0 of the index frame. The size of a pixel is represented by the pair Dx , Dy.