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2020 – today
- 2024
[c59]Lars Lundberg, Alexander Westerhagen, Dragos Ilie, Håkan Grahn, Bo Granbom:
Dynamic Forward Error Correction Coding to Avoid Detection in Airborne Tactical Networks. ICMCIS 2024: 1-7- [c58]Jim Ahlstrand, Martin Boldt, Anton Borg, Håkan Grahn:
Predicting B2B Customer Churn using a Time Series Approach. IDSTA 2024: 44-51 - [c57]Christoffer Åleskog, Håkan Grahn, Anton Borg:
A Comparative Study on Simulation Frameworks for AI Accelerator Evaluation. IPDPS (Workshops) 2024: 321-328 - 2023
- [j30]Dragos Ilie
, Håkan Grahn, Lars Lundberg, Alexander Westerhagen, Bo Granbom, Anders Höök:
Avoiding Detection by Hostile Nodes in Airborne Tactical Networks. Future Internet 15(6): 204 (2023) - [c56]Christian Nordahl, Veselka Boeva, Håkan Grahn:
MultiStream EvolveCluster. Canadian AI 2023 - [c55]Veronica Sundstedt, Veselka Boeva, Hans-Jürgen Zepernick, Prashant Goswami, Abbas Cheddad, Kurt Tutschku, Håkan Grahn, Emiliano Casalicchio, Markus Fiedler, Emilia Mendes, Shahrooz Abghari, Yan Hu, Valeria Garro, Thi My Chinh Chu, Lars Lundberg, Patrik Arlos:
HINTS: Human-Centered Intelligent Realities. SAIS 2023: 9-17 - [c54]Jim Ahlstrand, Martin Boldt, Anton Borg, Håkan Grahn:
Preliminary Results on the use of Artificial Intelligence for Managing Customer Life Cycles. SAIS 2023: 68-76 - [e1]Håkan Grahn, Anton Borg, Martin Boldt:
35th Annual Workshop of the Swedish Artificial Intelligence Society, SAIS 2023, Karlskrona, Sweden, June 12-13, 2023. Linköping Electronic Conference Proceedings 199, Linköping University Electronic Press 2023, ISBN 978-91-8075-274-9 [contents] - 2022
- [j29]Lars Lundberg, Håkan Grahn:
Research Trends, Enabling Technologies and Application Areas for Big Data. Algorithms 15(8): 280 (2022) - [j28]Christoffer Åleskog, Håkan Grahn, Anton Borg:
Recent Developments in Low-Power AI Accelerators: A Survey. Algorithms 15(11): 419 (2022) - [j27]Christian Nordahl, Veselka Boeva, Håkan Grahn, Marie Persson Netz:
EvolveCluster: an evolutionary clustering algorithm for streaming data. Evol. Syst. 13(4): 603-623 (2022) - 2021
- [j26]Stefan Petersson, Håkan Grahn, Jim Rasmusson:
Blind Correction of Lateral Chromatic Aberration in Raw Bayer Data. IEEE Access 9: 99455-99466 (2021) - [j25]Lars Lundberg, Håkan Grahn, Valeria Cardellini, Andreas Polze, Sogand Shirinbab:
Editorial to the Special Issue on Big Data in Industrial and Commercial Applications. Big Data Res. 26: 100244 (2021) - [j24]Eva García-Martín, Niklas Lavesson, Håkan Grahn, Emiliano Casalicchio, Veselka Boeva:
Energy-aware very fast decision tree. Int. J. Data Sci. Anal. 11(2): 105-126 (2021) - 2020
- [j23]Huseyin Kusetogullari, Amir Yavariabdi, Abbas Cheddad, Håkan Grahn, Johan Hall:
ARDIS: a Swedish historical handwritten digit dataset. Neural Comput. Appl. 32(21): 16505-16518 (2020) - [c53]Florian Westphal, Håkan Grahn, Niklas Lavesson:
Representative Image Selection for Data Efficient Word Spotting. DAS 2020: 383-397 - [c52]Shahrooz Abghari, Veselka Boeva, Jens Brage, Håkan Grahn:
Multi-view Clustering Analyses for District Heating Substations. DATA 2020: 158-168
2010 – 2019
- 2019
- [j22]Eva García-Martín, Crefeda Faviola Rodrigues, Graham D. Riley, Håkan Grahn:
Estimation of energy consumption in machine learning. J. Parallel Distributed Comput. 134: 75-88 (2019) - [c51]Florian Westphal, Niklas Lavesson, Håkan Grahn:
A Case for Guided Machine Learning. CD-MAKE 2019: 353-361 - [c50]Shahrooz Abghari, Veselka Boeva, Jens Brage, Christian Johansson, Håkan Grahn, Niklas Lavesson:
Higher Order Mining for Monitoring District Heating Substations. DSAA 2019: 382-391 - [c49]Christian Nordahl, Veselka Boeva, Håkan Grahn, Marie Persson Netz:
Profiling of Household Residents' Electricity Consumption Behavior Using Clustering Analysis. ICCS (5) 2019: 779-786 - [c48]Florian Westphal, Niklas Lavesson, Håkan Grahn:
Learning Character Recognition with Graph-Based Privileged Information. ICDAR 2019: 1163-1168 - [p1]Julia Sidorova, Oliver Rosander, Lars Sköld, Håkan Grahn, Lars Lundberg:
Finding a Healthy Equilibrium of Geo-demographic Segments for a Telecom Business: Who Are Malicious Hot-Spotters? Machine Learning Paradigms 2019: 187-196 - 2018
- [j21]Florian Westphal, Håkan Grahn, Niklas Lavesson:
Efficient document image binarization using heterogeneous computing and parameter tuning. Int. J. Document Anal. Recognit. 21(1-2): 41-58 (2018) - [c47]Florian Westphal, Niklas Lavesson, Håkan Grahn:
Document Image Binarization Using Recurrent Neural Networks. DAS 2018: 263-268 - [c46]Eva García-Martín, Niklas Lavesson, Håkan Grahn, Emiliano Casalicchio, Veselka Boeva:
Hoeffding Trees with Nmin Adaptation. DSAA 2018: 70-79 - [c45]Florian Westphal, Håkan Grahn, Niklas Lavesson:
User Feedback and Uncertainty in User Guided Binarization. ICDM Workshops 2018: 403-410 - [c44]Shahrooz Abghari, Veselka Boeva, Niklas Lavesson, Håkan Grahn, Selim Ickin, Jörgen Gustafsson:
A Minimum Spanning Tree Clustering Approach for Outlier Detection in Event Sequences. ICMLA 2018: 1123-1130 - [c43]Eva García-Martín, Niklas Lavesson, Håkan Grahn, Emiliano Casalicchio, Veselka Boeva:
How to Measure Energy Consumption in Machine Learning Algorithms. Nemesis/UrbReas/SoGood/IWAISe/GDM@PKDD/ECML 2018: 243-255 - [i2]Max Danielsson, Håkan Grahn, Thomas Sievert, Jim Rasmusson:
Comparing Two Generations of Embedded GPUs Running a Feature Detection Algorithm. CoRR abs/1806.04859 (2018) - [i1]Eva García-Martín, Niklas Lavesson, Håkan Grahn, Emiliano Casalicchio, Veselka Boeva:
Hoeffding Trees with nmin adaptation. CoRR abs/1808.01145 (2018) - 2017
- [j20]Jan Kasper Martinsen, Håkan Grahn, Anders Isberg:
Combining thread-level speculation and just-in-time compilation in Google's V8 JavaScript engine. Concurr. Comput. Pract. Exp. 29(1) (2017) - [c42]Eva García Martín, Niklas Lavesson, Håkan Grahn:
Identification of Energy Hotspots: A Case Study of the Very Fast Decision Tree. GPC 2017: 267-281 - [c41]Christian Nordahl, Marie Persson, Håkan Grahn:
Detection of Residents' Abnormal Behaviour by Analysing Energy Consumption of Individual Households. ICDM Workshops 2017: 729-738 - [c40]Julia Sidorova, Lars Lundberg, Oliver Rosander, Håkan Grahn, Lars Skold:
Recommendations for marketing campaigns in telecommunication business based on the footprint analysis: Who is a good client? IISA 2017: 1-6 - [c39]Abbas Cheddad, Huseyin Kusetogullari, Håkan Grahn:
Object recognition using shape growth pattern. ISPA 2017: 47-52 - [c38]Eva García Martín, Niklas Lavesson, Håkan Grahn, Veselka Boeva:
Energy Efficiency in Machine Learning: A position paper. SAIS 2017: 137:008 - 2016
- [c37]Max Danielsson, Thomas Sievert, Håkan Grahn, Jim Rasmusson:
Feature Detection and Description using a Harris-Hessian/FREAK Combination on an Embedded GPU. ICPRAM 2016: 517-525 - [c36]Stefan Petersson, Håkan Grahn, Jim Rasmusson:
Color Demosaicing Using Structural Instability. ISM 2016: 541-544 - [c35]Huseyin Kusetogullari, Håkan Grahn, Niklas Lavesson:
Handwriting image enhancement using local learning windowing, Gaussian Mixture Model and k-means clustering. ISSPIT 2016: 305-310 - 2015
- [c34]Eva García Martín, Niklas Lavesson, Håkan Grahn:
Energy Efficiency in Data Stream Mining. ASONAM 2015: 1125-1132 - [c33]Lars Lundberg, Håkan Grahn, Dragos Ilie, Christian Melander:
Cache Support in a High Performance Fault-Tolerant Distributed Storage System for Cloud and Big Data. IPDPS Workshops 2015: 537-546 - [c32]Eric Nilsson, Daniel Aarno, Erik Carstensen, Håkan Grahn:
Accelerating Graphics in the Simics Full-System Simulator. MASCOTS 2015: 150-153 - 2014
- [j19]Jan Kasper Martinsen, Håkan Grahn, Anders Isberg:
Heuristics for Thread-Level Speculation in Web Applications. IEEE Comput. Archit. Lett. 13(2): 77-80 (2014) - [j18]Jan Kasper Martinsen, Håkan Grahn, Anders Isberg:
The Effects of Parameter Tuning in Software Thread-Level Speculation in JavaScript Engines. ACM Trans. Archit. Code Optim. 11(4): 46:1-46:25 (2014) - [c31]Stefan Petersson, Håkan Grahn:
Improving image quality by SSIM based increase of run-length zeros in GPGPU JPEG encoding. ACSSC 2014: 1714-1718 - [c30]Jan Kasper Martinsen, Håkan Grahn, Anders Isberg:
An argument for thread-level speculation and just-in-time compilation in the Google's V8 JavaScript engine. Conf. Computing Frontiers 2014: 25:1-25:2 - [c29]Jan Kasper Martinsen, Håkan Grahn, Anders Isberg, Henrik Sundstrom:
Reducing Memory in Software-Based Thread-Level Speculation for JavaScript Virtual Machine Execution of Web Applications. HPCC/CSS/ICESS 2014: 181-184 - 2013
- [j17]Jan Kasper Martinsen, Håkan Grahn, Anders Isberg:
Using Speculation to Enhance JavaScript Performance in Web Applications. IEEE Internet Comput. 17(2): 10-19 (2013) - [c28]Hussein Muzahim Aziz, Markus Fiedler, Håkan Grahn, Lars Lundberg:
Compressing video based on region of interest. EUROCON 2013: 1645-1652 - 2012
- [j16]Hussein Muzahim Aziz, Markus Fiedler, Håkan Grahn, Lars Lundberg:
Eliminating the effect of freezing frames on user perceptive by using a time interleaving technique. Multim. Syst. 18(3): 251-262 (2012) - [j15]Claes Wohlin, Aybüke Aurum, Lefteris Angelis, Laura Phillips, Yvonne Dittrich, Tony Gorschek, Håkan Grahn, Kennet Henningsson, Simon Kågström, Graham Low, Per Rovegard, Piotr Tomaszewski, Christine Van Toorn, Jeff Winter:
The Success Factors Powering Industry-Academia Collaboration. IEEE Softw. 29(2): 67-73 (2012) - [c27]Syed Muhammad Zeeshan Iqbal, Håkan Grahn, Johanna Törnquist Krasemann:
A Parallel Heuristic for Fast Train Dispatching during Railway Traffic Disturbances: Early Results. ICORES 2012: 405-414 - [c26]Hussein Muzahim Aziz, Markus Fiedler, Håkan Grahn, Lars Lundberg:
Adapting the Streaming Video Based on the Estimated Position of the Region of Interest. SITIS 2012: 1-7 - 2011
- [c25]Håkan Grahn, Niklas Lavesson, Mikael Hellborg Lapajne, Daniel Slat:
CudaRF: A CUDA-based implementation of Random Forests. AICCSA 2011: 95-101 - [c24]Jan Kasper Martinsen, Håkan Grahn:
A methodology for evaluating JavaScript execution behavior in interactive web applications. AICCSA 2011: 241-248 - [c23]Jan Kasper Martinsen, Håkan Grahn, Anders Isberg:
A Comparative Evaluation of JavaScript Execution Behavior. ICWE 2011: 399-402 - [c22]Jan Kasper Martinsen, Håkan Grahn:
Thread-level speculation as an optimization technique in Web Applications - Initial results. SIES 2011: 83-86 - [c21]Moris Behnam, Farhang Nemati, Thomas Nolte, Håkan Grahn:
Towards an efficient approach for resource sharing in real-time multiprocessor systems. SIES 2011: 99-102 - 2010
- [j14]Syed Muhammad Zeeshan Iqbal, Yuchen Liang, Håkan Grahn:
ParMiBench - An Open-Source Benchmark for Embedded Multiprocessor Systems. IEEE Comput. Archit. Lett. 9(2): 45-48 (2010) - [j13]Håkan Grahn:
Transactional memory. J. Parallel Distributed Comput. 70(10): 993-1008 (2010) - [j12]Håkan Grahn:
Acknowledgment to special issue reviewers. J. Parallel Distributed Comput. 70(10): 1085 (2010) - [c20]Hussein Muzahim Aziz, Markus Fiedler, Håkan Grahn, Lars Lundberg:
Streaming video as space - divided sub-frames over wireless networks. WMNC 2010: 1-6
2000 – 2009
- 2009
- [j11]Simon Kågström, Håkan Grahn, Lars Lundberg:
The Design and Implementation of Multiprocessor Support for an Industrial Operating System Kernel. Int. J. Comput. Their Appl. 16(1): 59-68 (2009) - [j10]Håkan Grahn:
Special Issue of the Journal of Parallel and Distributed Computing: Transactional Memory. J. Parallel Distributed Comput. 69(9): 814 (2009) - [c19]Hussein Muzahim Aziz, Håkan Grahn, Lars Lundberg:
Eliminating the freezing frames for the mobile user over unreliable wireless networks. Mobility Conference 2009 - 2008
- [c18]Sajid Hussain, Håkan Grahn:
Tracking Data Structures Coherency in Animated Ray Tracing: Kalman and Wiener Filters Approach. ISVC (1) 2008: 1102-1114 - 2007
- [j9]Piotr Tomaszewski, Lars Lundberg, Håkan Grahn:
Improving Fault Detection in Modified Code - A Study from the Telecommunication Industry. J. Comput. Sci. Technol. 22(3): 397-409 (2007) - [j8]Piotr Tomaszewski, Jim Håkansson, Håkan Grahn, Lars Lundberg:
Statistical models vs. expert estimation for fault prediction in modified code - an industrial case study. J. Syst. Softw. 80(8): 1227-1238 (2007) - [c17]Sajid Hussain, Håkan Grahn:
Fast kd -Tree Construction for 3D-Rendering Algorithms Like Ray Tracing. ISVC (2) 2007: 681-690 - [c16]Simon Kågström, Håkan Grahn, Lars Lundberg:
Cibyl: an environment for language diversity on mobile devices. VEE 2007: 75-82 - 2006
- [j7]Simon Kågström, Håkan Grahn, Lars Lundberg:
The application kernel approach - a novel approach for adding SMP support to uniprocessor operating systems. Softw. Pract. Exp. 36(14): 1563-1583 (2006) - [c15]Piotr Tomaszewski, Jim Håkansson, Lars Lundberg, Håkan Grahn:
The Accuracy of Fault Prediction in Modified Code - Statistical Model vs. Expert Estimation. ECBS 2006: 334-343 - [c14]Piotr Tomaszewski, Håkan Grahn, Lars Lundberg:
A Method for an Accurate Early Prediction of Faults in Modified Classes. ICSM 2006: 487-496 - 2005
- [c13]Simon Kågström, Håkan Grahn, Lars Lundberg:
Automatic low overhead program instrumentation with the LOPI framework. Interaction between Compilers and Computer Architectures 2005: 82-93 - [c12]Piotr Tomaszewski, Lars Lundberg, Håkan Grahn:
Increasing the Efficiency of Fault Detection in Modified Code. APSEC 2005: 421-430 - [c11]Frans Mårtensson, Håkan Grahn, Michael Mattsson:
Evaluating Software Quality Attributes of Communication Components in an Automated Guided Vehicle System. ICECCS 2005: 550-558 - [c10]Simon Kågström, Håkan Grahn, Lars Lundberg:
Experiences from Implementing Multiprocessor Support for an Industrial Operating System Kernel. RTCSA 2005: 365-368 - 2004
- [j6]Håkan Grahn, Per Stenström:
A comparative evaluation of hardware-only and software-only directory protocols in shared-memory multiprocessors. J. Syst. Archit. 50(9): 537-561 (2004) - [c9]Simon Kågström, Lars Lundberg, Håkan Grahn:
A Novel Method for Adding Multiprocessor Support to a Large and Complex Uniprocessor Kernel. IPDPS 2004 - 2001
- [j5]Magnus Broberg, Lars Lundberg, Håkan Grahn:
Performance Optimization Using Extended Critical Path Analysis in Multithreaded Programs on Multiprocessors. J. Parallel Distributed Comput. 61(1): 115-136 (2001) - [c8]Magnus Broberg, Lars Lundberg, Håkan Grahn:
An Allocation Strategy Using Shadow-processors and Simulation Technique. PDCS 2001: 387-394 - [c7]Magnus Broberg, Lars Lundberg, Håkan Grahn:
A Tool for Binding Threads to Processors. Euro-Par 2001: 57-61 - 2000
- [j4]Håkan Grahn, Per Stenström:
Comparative Evaluation of Latency-Tolerating and -Reducing Techniques for Hardware-Only and Software-Only Directory Protocols. J. Parallel Distributed Comput. 60(7): 807-834 (2000)
1990 – 1999
- 1999
- [c6]Magnus Broberg, Lars Lundberg, Håkan Grahn:
Visualization and Performance Prediction of Multithreaded Solaris Programs by Tracing Kernel Threads. IPPS/SPDP 1999: 407-413 - 1998
- [c5]Magnus Broberg, Lars Lundberg, Håkan Grahn:
VPPB - A Visualization and Performance Prediction Tool for Multithreaded Solaris Programs. IPPS/SPDP 1998: 770-776 - [c4]Peter S. Magnusson, Fredrik Larsson, Andreas Moestedt, Bengt Werner, Jim Nilsson, Per Stenström, Fredrik Lundholm, Magnus Karlsson, Fredrik Dahlgren, Håkan Grahn:
SimICS/Sun4m: A Virtual Workstation. USENIX ATC 1998 - [c3]Håkan Grahn, Jan Bosch:
Some initial performance characteristics of three architectural styles. WOSP 1998: 197-198 - 1997
- [j3]Per Stenström, Mats Brorsson, Fredrik Dahlgren, Håkan Grahn, Michel Dubois:
Boosting the Performance of Shared Memory Multiprocessors. Computer 30(7): 63-70 (1997) - [c2]Håkan Grahn, Per Stenström:
Relative Performance of Hardware and Software-Only Directory Protocols Under Latency Tolerating and Reducing Techniques. IPPS 1997: 500- - 1996
- [j2]Håkan Grahn, Per Stenström:
Evaluation of a Competitive-Update Cache Coherence Protocol with Migratory Data Detection. J. Parallel Distributed Comput. 39(2): 168-180 (1996) - 1995
- [j1]Håkan Grahn, Per Stenström, Michel Dubois:
Implementation and evaluation of update-based cache protocols under relaxed memory consistency models. Future Gener. Comput. Syst. 11(3): 247-271 (1995) - [c1]Håkan Grahn, Per Stenström:
Efficient Strategies for Software-Only Protocols in Shared-Memory Multiprocessors. ISCA 1995: 38-47
Coauthor Index
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last updated on 2024-11-27 20:27 CET by the dblp team
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