In this article, we present an integrated solution to this problem by developing a new approach, iASeq, for jointly analyzing allele-specificity in multiple ChIP-seq datasets.
A unified approach, the novel generalized iterative approach, is proposed for jointly designing the MMSE MIMO processors (including precoders, relay processors and decoders) for these schemes.
Subsequently, a graphical modeling-based approach is proposed for jointly performing modality fusion and temporal context exploitation.
The system model for MIMO relay channel is presented in Section 2. In Section 3, the iterative approach is described for jointly optimizing the source precoder, relay transceiver, and destination equalizer for the MIMO relay channel with the direct link.
To evaluate the performance of the proposed decentralized scheme, a generalized iterative approach (GIA) is developed for jointly designing the precoders, decoders, and relay processor to maximize the true sum mutual information.
For the theoretical advance, a novel iterative approach, named GIA, is proposed for jointly designing the precoders and decoders at the two end nodes and the processor at the relay node.
In this paper, we propose a new integrated approach using the more general sliding window model, for jointly scheduling the demands in time and designing a logical topology that can accommodate all the scheduled demands.
We propose a network optimization approach for CR networks, which aims at jointly determining the transmission power and channel allocation while achieving optimal scheduling and routing through lightweight and distributed computation.
A recent example of such international training initiatives is the distance learning approach for tuberculosis control doctors and nurses jointly developed in 2006 by the International Council of Nurses, the International Hospital Federation, and the World Medical Association.
