Computer science and engineering

Computer science and engineering

The research in computer science and engineering focuses on two areas: data science and parallel computer systems. The research includes both practical and theoretical aspects of data processing and applications, as well as implementation of such systems.

We study and develop methods and technologies for collecting, representing, storing, processing, and analyzing data in the data science area. We are also interested in how data can be used for prediction, classification, and pattern recognition. Our research is primarily in machine learning, data mining, big data, planning and scheduling, as well as optimization. We apply our methods in the areas decision support, image processing, analysis of historic documents, the health sector, transport and logistics, as well as anomaly and intrusion detection.

In the area of parallel computer systems we study computer systems with multiple processing units (processors), e.g., multi-core systems, distributed systems, cloud based systems, GPUs, and heterogeneous systems. The area includes methods and technologies for design, implementation, and analysis of parallel systems, in order to achieve qualities such as correctness, performance, robustness, scalability, high resource utilization, low energy consumptions. The research includes hardware aspects, as well as system and application software, and the interaction between hardware and software. The application focus is primarily algorithms and methods from the data science area, e.g., algorithms for machine learning, data mining, pattern recognition, and large scale analysis of data images.

The research in computer science and engineering is mainly conducted at the Department of Computer Science and Engineering.

 

Specialisations

Intelligent transportation systems

Research in the field of intelligent transportation systems involves the integration of electronic road tolls and how rail traffic can be re-scheduled when disturbances occur. The focus lies on transportation policy analysis, simulations, development of search algorithms and finding sustainable transportation solutions.

Distributed systems

In the distributed systems field architecture of multiprocessors and data in the cloud (a technology based on the use of computers over the Internet) are studied. Another area is the artificial intelligence and human-machine interaction. The focus is on various cloud services and applications in social networks.

Security

In the security research field machine learning techniques and anomaly detection (finding patterns in data that do not conform to an established “normal” behavior) are used to analyze irregularities in large sets of data. The focus is to identify and correct weaknesses in the police- and defense fields, industrial applications and among end users.

Example of projects

Bonseyes

Bonseyes

The Bonseyes project aims to develop a platform consisting of a Data Marketplace, Deep Learning Toolbox, and Developer Reference Platforms for organizations wanting to adopt Artificial Intelligence in low power IoT devices (“edge computing”), embedded computing systems, or data center servers (“cloud computing”). It will bring about orders of magnitude improvements in efficiency, performance, reliability, security, and productivity in the design and programming of Systems of Artificial Intelligence that incorporate Smart Cyber Physical Systems while solving a chicken-egg problem for organizations who lack access to Data and Models. It’s open software architecture will facilitate adoption of the whole concept on a wider scale.

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BigData@BTH

BigData@BTH

How shall we design future scalable systems for big data analytics in order to achieve a good balance between performance and resource efficiency as well as business value?

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AgileSec – Agile Development of Security Critical Software

AgileSec – Agile Development of Security Critical Software

Today, the software industry is moving to agile development processes. One of the main ideas is that an agile team has sufficient skills to get the job done, and does not have to rely on external experts. In traditional software development, security issues are handled by experts. However, in agile development, security issues should be handled by the team. The agile process must, therefore, be extended with security related quality control and support. Rigorous quality control may, however, reduce productivity. The level of security expertise may differ significantly from one agile team to another, and it is, therefore, important to adapt security related quality control to the security maturity level of the team. In this application, we argue that an accurate estimate of a team's security maturity would be helpful in many ways.

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TRANS-FORM – Smart transfers through unraveling urban form and travel flow dynamics

TRANS-FORM – Smart transfers through unraveling urban form and travel flow dynamics

Smart cities and communities rely on efficient, reliable and robust transport systems. Managing urban public transport systems is becoming increasingly challenging with a pronounced shift towards multiply actors operating in a multi-modal multi-level networks. This calls for the development of an integrated passenger-focused management approach which takes advantage of multiple data sources and state-of-the-art scheduling support.

TRANS-FORM, a cooperation between universities, industrial partners, public authorities and private operators, will develop, implement and test a data driven decision making tool that will support smart planning and proactive and adaptive operations. The tool will integrate new concepts and methods of behavioural modelling, passenger flow forecasting and network state predictions into real-time operations.

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CCC – Center for Computational Criminology

CCC – Center for Computational Criminology

Within the Center for Computational Criminology (CCC) at Blekinge Tekniska Högskola (BTH), research and collaboration have been conducted with law enforcement agencies, mainly the police since 2012. The work was funded by the Swedish Agency for Growth between 2012-2014, by Blekinge Region in 2015 and by Vinnova in 2016 . The purpose of the work of the CCC is to investigate, develop and evaluate automated methods to simplify criminal investigations within law enforcement agencies.

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CONVINcE

CONVINcE

CONVINcE addresses the challenge of reducing the power consumption in IPbased video networks with an end-to-end approach, from the Head End where contents are encoded and streamed to the terminals where they are consumed, embracing the Content Delivery Networks (CDN) and the core and access networks. Energy saving is a key challenge for the European Union and the CONVINcE project contributes to win this challenge. CONVINcE helps the European industry to develop new solutions and products reducing the energy footprint of video delivery networks. The double effect gained is to reduce the energy consumption in Europe and to boost the competiveness of the European industry in the area addressed by the CONVINcE project.

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FLOAT: Real-time decision support for railway traffic management

FLOAT: Real-time decision support for railway traffic management

This project is financed by Trafikverket and is a continuation of the previous research projects OAT+ and EOT (both financed by Trafikverket). Researchers working in the project are currently Ph D student Sai Prashanth Josyula, Post-Doc Omid Gholami and Associate Professor Johanna Törnquist Krasemann, BTH.

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OAT + Re-planning train slots

OAT + Re-planning train slots

With the increasing traffic volumes in many railway networks and reports on capacity deficiencies that result in insufficient punctuality and reliability, the need for efficient disturbance management solutions becomes evident. The Swedish railway network is today fully deregulated with a large amount of different freight and passanger operators that share the same resources and sometimes competing for the access to certain slots and track time. The Swedish railway network is at times heavily saturated in some of the main corridors and despite the flexibility with two-way traffic and often access to double-tracked lines, the system is sensitive to small deviations from the timetable that may cause widely spread disturbances geographically as well as in time.

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OAT – RE-PLANNING TRAIN SLOTS

OAT – RE-PLANNING TRAIN SLOTS

RE-PLANNING TRAIN SLOTS
An R&D-project financed by the Swedish Transport Administration (Trafikverket, also formerly known as Banverket), the municipality of Karlshamn and  Blekinge Institute of Technology (www.bth.se). The project is finalised since March 2007 and its continuation started September 2007, within the project OAT+.

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EOT — EFFICIENT REAL‐TIME RE‐SCHEDULING OF TRAINS DURING DISTURBANCES

EOT — EFFICIENT REAL‐TIME RE‐SCHEDULING OF TRAINS DURING DISTURBANCES

This project is financed by Trafikverket and is a continuation of the previous research projects OAT (Omplanering Av Tåglägen) and OAT+ (both financed by Trafikverket). Part of the project team are mainly Magdalena Grimm and Åke Lundberg at Trafikverket as well as Prof. Håkan Grahn, Dr. Johanna Törnquist Krasemann, Ph D student Syed Muhammad Zeeshan Iqbal and project assistant Sara Solani at Blekinge Institute of Technology.

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Big data

BigData@bth – Scalable resource-efficient systems for big data analytics

Data will be generated at an ever-increasing rate for the foreseeable future. Added value and cost savings can be obtained by analyzing big data streams. The analysis of large data sets requires scalable and high-performance computer systems. In order to stay competitive and to reduce consumption of energy and other resources, the next generation systems for scalable big data analytics need to be more resource-efficient. The research profile, Scalable resource-efficient systems for big data analytics, combines existing expertise in machine learning, data mining, and computer engineering to create new knowledge in the area of scalable resource-efficient systems for big data analytics. The value of the new knowledge will be demonstrated and evaluated in two application areas (decision support systems and image processing).

The needs and interests of our 9 industrial partners are grouped into industrial challenges. Based on these challenges and in cooperation with our partners we have defined initial sub-projects grouped into four research themes:

  • Research theme A: Big data analytics for decision support
  • Research theme B: Big data analytics for image processing
  • Research theme C: Core technologies (machine learning)
  • Research theme D: Foundations and enabling technologies

Funding: The Knowledge Foundation (KK-stiftelsen) 2014-2020

Partners: Arkiv Digital AD, Compuverde, Contribe, Ericsson, Indigo IPEX, Noda Intelligent Systems, Telenor Sverige, Sony Mobile Communications, and Maingate Enterprise Solutions

Contact person
: Håkan Grahn, hakan.grahn@bth.se

CLOUDTEST

CLOUDTEST is a research project on the use of cloud technologies to reduce costs and energy consumption associated with the testing of large telecommunication systems. Today, hundreds of test servers, including at Ericsson, are used to test telecommunications systems. These servers are expensive to purchase and maintain, and consume a lot of energy. The goal of the project is to develop techniques and methods to enable the development of a “cloud” of virtual test servers. The idea is for multiple virtual test servers to share the same physical server and that the test cloud can be used by developers in different places all over the world. This technology will reduce both costs and energy consumption. The project is partly financed by the Swedish Knowledge Foundation (KKS).

BTH is working together with Ericsson Karlskrona and ILT Innovations AB on this CLOUDTEST project, funded by KKS.

Contact person: Lars Lundberg

Efficient rescheduling of train traffic (EOT)

The EOT project aims to develop and evaluate documented support for the traffic controller when rescheduling train traffic, using parallelisation of an optimisation-based search algorithm. In other words, the support aims to provide a number of efficient proposals on how the trains are to run, despite changing conditions.

An important part of the project is to analyse how the suitability of the proposals can be assessed and compared in order to facilitate selection and adjustment of the solution for the traffic controller.

Funder: The Swedish Transport Administration
Contact person: Johanna Törnquist

Computer-based support to increase knowledge about serial crimes

Computer-based support to increase knowledge about crimes of a serial nature.

Funder: The European Development Fund 2012–2014
Collaborative partner: The Swedish Police
Contact person: Niklas Lavesson