Expectation propagation (EP) is a powerful algorithm for signal recovery in compressed sensing. This letter proposes correction of a variance message before denoising to improve the performance of EP in the high signal-to-noise ratio (SNR) regime for finite-sized systems. The variance massage is replaced by an observation-dependent consistent estimator of the mean-square error in estimation before denoising. Massive multiple-input multiple-output (MIMO) is considered to verify the effectiveness of the proposed correction. Numerical simulations show that the proposed variance correction improves the high SNR performance of EP for massive MIMO with a few hundred transmit and receive antennas.

In this paper, for convex rectilinear block packing problem, we propose 1) a novel algorithm to obtain a packing based on a given sequence-pair in O(n2) time (conventional method needs O(n3) time), where n is the number of rectangle sub-blocks made from convex blocks, 2) a move operation for Simulated Annealing which is symmetric and can guarantee reachability for the first time, and 3) a method to generate a random adjacent sequence-pair in O(n2) time. By using 1), 2) and 3) together, the time complexity of the inner loop in Simulated Annealing becomes surely O(n2) time. Experimental results show that the proposed algorithm is faster than the conventional ones in practical and the wire length as well as packing area is taken into consideration in the proposed method.

As well as the schedule affects system performance, the control skew, i.e., the arrival time difference of control signals between registers, can be utilized for improving the system performance, enhancing robustness against delay variations, etc. The simultaneous optimization of the control step assignment and the control skew assignment is more powerful technique in improving performance. In this paper, firstly, we prove that, even if the execution sequence of operations which are assigned to the same resource is fixed, the simultaneous optimization problem under a fixed clock period is NP-hard. Secondly, we propose a heuristic algorithm for the simultaneous control step and skew optimization under given clock period, and we show how much the simultaneous optimization improves system performance. This paper is the first one that uses the intentional skew to shorten control steps under a specified clock period. The proposed algorithm has the potential to play a central role in various scenarios of skew-aware high level synthesis.

We propose two modifications of Gaussian processes, which aim to deal with dynamic environments. One is a weight decay method that gradually forgets old data, and the other is a time stamp method that regards the time course of data as a Gaussian process. We show experimental results when these modifications are applied to regression problems in dynamic environments. The weight decay method is found to follow the environmental change by automatically ignoring the past data, and the time stamp method is found to predict linear alteration.