Tampere University of Technology

About: Tampere University of Technology is a based out in . It is known for research contribution in the topics: Laser & Context (language use). The organization has 6802 authors who have published 19787 publications receiving 431793 citations. The organization is also known as: Tampereen teknillinen yliopisto.

3GPP LTE traffic offloading onto WiFi Direct

Abstract: Mobile network operators are struggling to handle the rapidly increasing amounts of data traffic on their networks. As a result, 3GPP is currently investigating possible scenarios for device-to-device (D2D) offloading in LTE networks. However, it remains unclear what kinds of gains can be expected from in-band D2D solutions. By comparison, the already available WiFi Direct D2D technology enables offloading onto unlicensed bands at data rates generally higher than infrastructure cellular links. In this paper1, we study the performance of WiFi Direct as a prominent technology for D2D communications in urban environments. We also discuss some of the potential performance gains for WiFi Direct communications in the presence of 3GPP LTE network-level management. Our extensive simulation study of LTE traffic offloading onto WiFi Direct covers a range of D2D loads and interference levels. We focus on the case where the D2D overlay network is assisted by the cellular infrastructure network; or more specifically, where the cellular network supplies its clients with service discovery information to facilitate the establishment of D2D sessions.

Analysis of human-body blockage in urban millimeter-wave cellular communications

Abstract: The use of extremely high frequency (EHF) or millimeter-wave (mmWave) band has attracted significant attention for the next generation wireless access networks. As demonstrated by recent measurements, mmWave frequencies render themselves quite sensitive to “blocking” caused by obstacles like foliage, humans, vehicles, etc. However, there is a dearth of analytical models for characterizing such blocking and the consequent effect on the signal reliability. In this paper, we propose a novel, general, and tractable model for characterizing the blocking caused by humans (assuming them to be randomly located in the environment) to mmWave propagation as a function of system parameters like transmitter-receiver locations and dimensions, as well as density and dimensions of humans. Moreover, the proposed model is validated using a ray-launcher tool. Utilizing the proposed model, the blockage probability is shown to increase with human density and separation between the transmitter-receiver pair. Furthermore, the developed analysis is shown to demonstrate the existence of a transmitter antenna height that maximizes the received signal strength, which in turn is a function of the transmitter-receiver distance and their dimensions.

Excessive aggregation of membrane proteins in the Martini model

Abstract: The coarse-grained Martini model is employed extensively to study membrane protein oligomerization. While this approach is exceptionally promising given its computational efficiency, it is alarming that a significant fraction of these studies demonstrate unrealistic protein clusters, whose formation is essentially an irreversible process. This suggests that the protein-protein interactions are exaggerated in the Martini model. If this held true, then it would limit the applicability of Martini to study multi-protein complexes, as the rapidly clustering proteins would not be able to properly sample the correct dimerization conformations. In this work we first demonstrate the excessive protein aggregation by comparing the dimerization free energies of helical transmembrane peptides obtained with the Martini model to those determined from FRET experiments. Second, we show that the predictions provided by the Martini model for the structures of transmembrane domain dimers are in poor agreement with the corresponding structures resolved using NMR. Next, we demonstrate that the first issue can be overcome by slightly scaling down the Martini protein-protein interactions in a manner, which does not interfere with the other Martini interaction parameters. By preventing excessive, irreversible, and non-selective aggregation of membrane proteins, this approach renders the consideration of lateral dynamics and protein-lipid interactions in crowded membranes by the Martini model more realistic. However, this adjusted model does not lead to an improvement in the predicted dimer structures. This implicates that the poor agreement between the Martini model and NMR structures cannot be cured by simply uniformly reducing the interactions between all protein beads. Instead, a careful amino-acid specific adjustment of the protein-protein interactions is likely required.

An observer theory for time delay systems

Abstract: A theory of Luenberger type of observers for time-delay systems based on spectral decomposition techniques is developed. The stability, of the observer is rigorously established and the observer equations are reduced to an integro-differential form. The computational aspects are discussed and the difficulty is traced to that of computing eigenvalues of delay differential equations. Formal structural similarities of Kalman filters for delay systems and the observer developed here are indicated.

Robust detection of periodic time series measured from biological systems.

Abstract: Periodic phenomena are widespread in biology. The problem of finding periodicity in biological time series can be viewed as a multiple hypothesis testing of the spectral content of a given time series. The exact noise characteristics are unknown in many bioinformatics applications. Furthermore, the observed time series can exhibit other non-idealities, such as outliers, short length and distortion from the original wave form. Hence, the computational methods should preferably be robust against such anomalies in the data. We propose a general-purpose robust testing procedure for finding periodic sequences in multiple time series data. The proposed method is based on a robust spectral estimator which is incorporated into the hypothesis testing framework using a so-called g-statistic together with correction for multiple testing. This results in a robust testing procedure which is insensitive to heavy contamination of outliers, missing-values, short time series, nonlinear distortions, and is completely insensitive to any monotone nonlinear distortions. The performance of the methods is evaluated by performing extensive simulations. In addition, we compare the proposed method with another recent statistical signal detection estimator that uses Fisher's test, based on the Gaussian noise assumption. The results demonstrate that the proposed robust method provides remarkably better robustness properties. Moreover, the performance of the proposed method is preferable also in the standard Gaussian case. We validate the performance of the proposed method on real data on which the method performs very favorably. As the time series measured from biological systems are usually short and prone to contain different kinds of non-idealities, we are very optimistic about the multitude of possible applications for our proposed robust statistical periodicity detection method. The presented methods have been implemented in Matlab and in R. Codes are available on request. Supplementary material is available at: http://www.cs.tut.fi/sgn/csb/robustperiodic/ .