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A survey of handover algorithms in DVB-H
Digital Video Broadcasting for Handhelds (DVB-H) is a standard for
broadcasting IP Datacast (IPDC) services to mobile handheld terminals.
Based on the DVB-T standard, DVB-H adds new features such as time
slicing, MPE-FEC, in-depth interleavers, mandatory cell id identifier,
optional 4K-modulation mode and the use of 5 MHz bandwidth in addition
to the usually used 6, 7, or 8 MHz raster. IPDC over DVB-H is proposed
for ETSI to complement the DVB-H standard by combining IPDC and
DVB-H in an end-to-end system. Handover in such unidirectional broadcasting
networks is a novel issue. In the last few years since the birth of
DVB-H technology, great attention has been given to the performance
analysis of DVB-H mobile terminals. Handover is one of the main research
topics for DVB-H in mobile scenarios. Better reception quality and greater
power efficiency are considered to be the main targets of handover
research for DVB-H. New algorithms for different handover stages in
DVB-H have been the subject of recent research and are currently being
studied. Further novel algorithms need to be designed to improve the
mobile reception quality. This article provides a comprehensive survey of
the handover algorithms in DVB-H. A systematic evaluation and categorization
approach is proposed based on the problems the algorithms solve
and the handover stages being focused on. Criteria are proposed and analyzed
to facilitate designing better handover algorithms for DVB-H that
have been identified from the research conducted by the author
Mobile Communication Networks and Digital Television Broadcasting Systems in the Same Frequency Bands – Advanced Co-Existence Scenarios
The increasing demand for wireless multimedia services provided by modern communication systems with stable services is a key feature of advanced markets. On the other hand, these systems can many times operate in a neighboring or in the same frequency bands. Therefore, numerous unwanted co-existence scenarios can occur. The aim of this paper is to summarize our results which were achieved during exploration and measurement of the co-existences between still used and upcoming mobile networks (from GSM to LTE) and digital terrestrial television broadcasting (DVB) systems. For all of these measurements and their evaluation universal measurement testbed has been proposed and used. Results presented in this paper are a significant part of our activities in work package WP5 in the ENIAC JU project “Agile RF Transceivers and Front-Ends for Future Smart Multi-Standard Communications Applications (ARTEMOS)”
Deep learning approach to Fourier ptychographic microscopy
Convolutional neural networks (CNNs) have gained tremendous success in
solving complex inverse problems. The aim of this work is to develop a novel
CNN framework to reconstruct video sequence of dynamic live cells captured
using a computational microscopy technique, Fourier ptychographic microscopy
(FPM). The unique feature of the FPM is its capability to reconstruct images
with both wide field-of-view (FOV) and high resolution, i.e. a large
space-bandwidth-product (SBP), by taking a series of low resolution intensity
images. For live cell imaging, a single FPM frame contains thousands of cell
samples with different morphological features. Our idea is to fully exploit the
statistical information provided by this large spatial ensemble so as to make
predictions in a sequential measurement, without using any additional temporal
dataset. Specifically, we show that it is possible to reconstruct high-SBP
dynamic cell videos by a CNN trained only on the first FPM dataset captured at
the beginning of a time-series experiment. Our CNN approach reconstructs a
12800X10800 pixels phase image using only ~25 seconds, a 50X speedup compared
to the model-based FPM algorithm. In addition, the CNN further reduces the
required number of images in each time frame by ~6X. Overall, this
significantly improves the imaging throughput by reducing both the acquisition
and computational times. The proposed CNN is based on the conditional
generative adversarial network (cGAN) framework. Additionally, we also exploit
transfer learning so that our pre-trained CNN can be further optimized to image
other cell types. Our technique demonstrates a promising deep learning approach
to continuously monitor large live-cell populations over an extended time and
gather useful spatial and temporal information with sub-cellular resolution
Deep learning approach to Fourier ptychographic microscopy
Convolutional neural networks (CNNs) have gained tremendous success in solving complex inverse problems. The aim of this work is to develop a novel CNN framework to reconstruct video sequences of dynamic live cells captured using a computational microscopy technique, Fourier ptychographic microscopy (FPM). The unique feature of the FPM is its capability to reconstruct images with both wide field-of-view (FOV) and high resolution, i.e. a large space-bandwidth-product (SBP), by taking a series of low resolution intensity images. For live cell imaging, a single FPM frame contains thousands of cell samples with different morphological features. Our idea is to fully exploit the statistical information provided by these large spatial ensembles so as to make predictions in a sequential measurement, without using any additional temporal dataset. Specifically, we show that it is possible to reconstruct high-SBP dynamic cell videos by a CNN trained only on the first FPM dataset captured at the beginning of a time-series experiment. Our CNN approach reconstructs a 12800×10800 pixel phase image using only ∼25 seconds, a 50× speedup compared to the model-based FPM algorithm. In addition, the CNN further reduces the required number of images in each time frame by ∼ 6×. Overall, this significantly improves the imaging throughput by reducing both the acquisition and computational times. The proposed CNN is based on the conditional generative adversarial network (cGAN) framework. We further propose a mixed loss function that combines the standard image domain loss and a weighted Fourier domain loss, which leads to improved reconstruction of the high frequency information. Additionally, we also exploit transfer learning so that our pre-trained CNN can be further optimized to image other cell types. Our technique demonstrates a promising deep learning approach to continuously monitor large live-cell populations over an extended time and gather useful spatial and temporal information with sub-cellular resolution.We would like to thank NVIDIA Corporation for supporting us with the GeForce Titan Xp through the GPU Grant Program. (NVIDIA Corporation; GeForce Titan Xp through the GPU Grant Program)First author draf
Measurement campaign on transmit delay diversity for mobile DVB-T/H systems
This article is posted here with permission from IEEE - Copyright @ 2010 IEEEThis paper describes the work carried out by Brunel University and Broadreach Systems (UK) to quantify the advantages that can be achieved if Transmit Delay Diversity is applied to systems employing the DVB standard. The techniques investigated can be applied to standard receiver equipment without modification. An extensive and carefully planned field trial was performed during the winter of 2007/2008 in Uxbridge (UK) to validate predictions from theoretical modeling and laboratory simulations. The transmissions were performed in the 730 MHz frequency band with a DVB-T/H transmitter and a mean power of 18.4 dBW. The impact of the transmit antenna separation and the MPE-FEC was also investigated. It is shown that transmit delay diversity significantly improves the quality of reception in fast fading mobile broadcasting application
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