816 research outputs found
Adaptive Bit Partitioning for Multicell Intercell Interference Nulling with Delayed Limited Feedback
Base station cooperation can exploit knowledge of the users' channel state
information (CSI) at the transmitters to manage co-channel interference. Users
have to feedback CSI of the desired and interfering channels using
finite-bandwidth backhaul links. Existing codebook designs for single-cell
limited feedback can be used for multicell cooperation by partitioning the
available feedback resources between the multiple channels. In this paper, a
new feedback-bit allocation strategy is proposed, as a function of the delays
in the communication links and received signal strengths in the downlink.
Channel temporal correlation is modeled as a function of delay using the
Gauss-Markov model. Closed-form expressions for bit partitions are derived to
allocate more bits to quantize the stronger channels with smaller delays and
fewer bits to weaker channels with larger delays, assuming random vector
quantization. Cellular network simulations are used to show that the proposed
algorithm yields higher sum-rates than an equal-bit allocation technique.Comment: Submitted to IEEE Transactions on Signal Processing, July 201
Fine-grained Energy and Thermal Management using Real-time Power Sensors
With extensive use of battery powered devices such as smartphones, laptops an
Weaving social Networks
Elfring, T. [Promotor]Bunt, G.G. [Copromotor]van de Tilburg, A. van [Copromotor
Semi-supervised sequence tagging with bidirectional language models
Pre-trained word embeddings learned from unlabeled text have become a
standard component of neural network architectures for NLP tasks. However, in
most cases, the recurrent network that operates on word-level representations
to produce context sensitive representations is trained on relatively little
labeled data. In this paper, we demonstrate a general semi-supervised approach
for adding pre- trained context embeddings from bidirectional language models
to NLP systems and apply it to sequence labeling tasks. We evaluate our model
on two standard datasets for named entity recognition (NER) and chunking, and
in both cases achieve state of the art results, surpassing previous systems
that use other forms of transfer or joint learning with additional labeled data
and task specific gazetteers.Comment: To appear in ACL 201
Visually guided flight in birds using the budgerigar (Melopsittacus undulatus) as a model system
Keywords: Edge detection, Optic flow, Obstacle avoidanc
THERMO-CHEMICAL CONVERSION OF COAL-BIOMASS BLENDS: KINETICS MODELING OF PYROLYSIS, MOVING BED GASIFICATION AND STABLE CARBON ISOTOPE ANALYSIS
The past few years have seen an upsurge in the use of renewable biomass as a source of energy due to growing concerns over greenhouse gas emissions caused by the combustion of fossil fuels and the need for energy independence due to depleting fossil fuel resources. Although coal will continue to be a major source of energy for many years, there is still great interest in replacing part of the coal used in energy generation with renewable biomass. Combustion converts inherent chemical energy of carbonaceous feedstock to only thermal energy. On the other hand, partial oxidation processes like gasification convert chemical energy into thermal energy as well as synthesis gas which can be easily stored or transported using existing infrastructure for downstream chemical conversion to higher value specialty chemicals as well as production of heat, hydrogen, and power.
Devolatilization or pyrolysis plays an important role during gasification and is considered to be the starting point for all heterogeneous gasification reactions. Pyrolysis kinetic modeling is, therefore, an important step in analyzing interactions between blended feedstocks. The thermal evolution profiles of different coal-biomass blends were investigated at various heating rates using thermogravimetric analysis. Using MATLAB, complex models for devolatilization of the blends were solved for obtaining and predicting the global kinetic parameters. Parallel first order reactions model, distributed activation energy model and matrix inversion algorithm were utilized and compared for this purpose. Using these global kinetic parameters, devolatilization rates of unknown fuel blends gasified at unknown heating rates can be accurately predicted using the matrix inversion method.
A unique laboratory scale auto-thermal moving bed gasifier was also designed and constructed for studying the thermochemical conversion of coal-biomass blends. The effect of varying operating parameters was analyzed for optimizing syngas production. In addition, stable carbon isotope analysis using Gas Chromatography-Combustion-Isotope Ratio Mass Spectrometry (GC-C-IRMS) was used for qualitatively and quantitatively measuring individual contributions of coal and biomass feedstocks for generation of carbonaceous gases during gasification. The predictive models utilized and experimental data obtained via these methods can provide valuable information for analyzing synergistic interactions between feedstocks and also for process modeling and optimization
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