Symmetric Radial Basis Function Assisted Space-Time Equalisation for Multiple Receive-Antenna Aided Systems

This constribution considers nonlinear space-time equalisation (STE) designed for single-input multiple-output (SIMO) systems. By exploiting the inherent symmetry of the underlying optimal Bayesian STE solution, a novel symmetric radial basis function (RBF) based STE scheme is proposed, which is capable of achieving the optimal Bayesian equalisation performance. The adaptive adjustment of the STE taps of this symmetric RBF (SRBF) based STE can be achieved by estimating the SIMO channel encountered using the classic least mean square channel estimator and computing the optimal RBF centres from the resultant SIMO channel matrix estimate. Our simulation results demonstrate that the performance of this SRBF based STE is robust with respect to the choice of the algorithmic parameters

Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration

State-of-the-art convolutional neural networks are enormously costly in both compute and memory, demanding massively parallel GPUs for execution. Such networks strain the computational capabilities and energy available to embedded and mobile processing platforms, restricting their use in many important applications. In this paper, we push the boundaries of hardware-effective CNN design by proposing BCNN with Separable Filters (BCNNw/SF), which applies Singular Value Decomposition (SVD) on BCNN kernels to further reduce computational and storage complexity. To enable its implementation, we provide a closed form of the gradient over SVD to calculate the exact gradient with respect to every binarized weight in backward propagation. We verify BCNNw/SF on the MNIST, CIFAR-10, and SVHN datasets, and implement an accelerator for CIFAR-10 on FPGA hardware. Our BCNNw/SF accelerator realizes memory savings of 17% and execution time reduction of 31.3% compared to BCNN with only minor accuracy sacrifices.Comment: 9 pages, 6 figures, accepted for Embedded Vision Workshop (CVPRW

Modeling of wide-band MIMO radio channels based on NLoS indoor measurements

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Text Coherence Analysis Based on Deep Neural Network

In this paper, we propose a novel deep coherence model (DCM) using a convolutional neural network architecture to capture the text coherence. The text coherence problem is investigated with a new perspective of learning sentence distributional representation and text coherence modeling simultaneously. In particular, the model captures the interactions between sentences by computing the similarities of their distributional representations. Further, it can be easily trained in an end-to-end fashion. The proposed model is evaluated on a standard Sentence Ordering task. The experimental results demonstrate its effectiveness and promise in coherence assessment showing a significant improvement over the state-of-the-art by a wide margin.Comment: 4 pages, 2 figures, CIKM 201