Serlab
Welcome
Serlab is the signal and information processing research lab at the Department of Signal Processing and Acoustics at Aalto University. It is part of a larger unit – Signal Processing Laboratory.
Our research is organised around these topics:
- Multiantenna/multisensory systems. In this topic, Serlab is closely linked to the labs leaded by professors Visa Koivunen, Esa Ollila, and Risto Wichman.
- Optimisation algorithms and applications in signal processing.
- Data science. In this topic, Serlab is closely linked to the labs leaded by profs. Visa Koivunen and Esa Ollila.
- Radar signal processing. In this topic, Serlab is closely linked to the lab leaded by prof. Visa Koivunen.
- Wireless signal processing. In this topic, Serlab is closely linked to the labs leaded by profs. Visa Koivunen and Risto Wichman.
The major applications are navigation, radar (automotive radar), joint radar-communication, smart phones, large-scale data analysis and data mining, information fusion, image processing, and statistical/machine learning as well as other applications.
Dear Colleagues
Thanks for visiting! This website provides easy access to our group’s research. Please visit our research page: Dr. Sergiy Vorobyov (aalto.fi) for more details. If you are looking for a specific paper please refer to the list of publications: PUBLICATIONS (aalto.fi) or the Google scholar profile: Sergiy A. Vorobyov - Google Scholar.
Dear Students
Thanks for visiting! If you are currently enrolled in one of the courses taught by Sergiy A. Vorobyov, please use Aalto systems to access the corresponding Mycourse’s website.
If you are thinking of applying for a position in the group we are honored by your consideration. Please look at the list of publications: PUBLICATIONS (aalto.fi) to gain a better idea of what we do and how we do it. If you are still interested after reading, please send email to Sergiy A. Vorobyov: [email protected].
Contact infromation
Group members
Latest publications
A new accelerated gradient-based estimating sequence technique for solving large-scale optimization problems with composite structure
A new class of composite objective multistep estimating sequence techniques
Twenty-Five Years of Advances in Beamforming: From convex and nonconvex optimization to learning techniques
Robust Adaptive Beamforming Via Worst-Case SINR Maximization With Nonconvex Uncertainty Sets
Attention Neural Network for Downlink Cell-Free Massive MIMO Power Control
Convolutional Simultaneous Sparse Approximation with Applications to RGB-NIR Image Fusion
Tensor-Based 2D DOA Estimation for L-Shaped Nested Array
Transmit Energy Focusing For Parameter Estimation in Transmit Beamspace Slow-Time MIMO Radar
Decomposed CNN for Sub-Nyquist Tensor-Based 2-D DOA Estimation
Tensorized Neural Layer Decomposition for 2-D DOA Estimation
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- Updated: