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