This paper provides a novel normalized sign least-mean square (NSLMS) algorithm which updates only a part of the filter coefficients and simultaneously performs sparse updates with the goal of reducing computational complexity. A combination of the partial-update scheme and the set-membership framework is incorporated into the context of L∞-norm adaptive filtering, thus yielding computational efficiency. For the stabilized convergence, we formulate a robust update recursion by imposing an upper bound of a step size. Furthermore, we analyzed a mean-square stability of the proposed algorithm for white input signals. Experimental results show that the proposed low-complexity NSLMS algorithm has similar convergence performance with greatly reduced computational complexity compared to the partial-update NSLMS, and is comparable to the set-membership partial-update NLMS.

By using distributed database systems, many advantages can be obtained such as database management cost, efficiency, and high integrity of systems through allocating fragments to many distributed sites with horizontal/vertical fragmentation of global database schema. To minimize costs, distributed algorithms must be applied so that database fragments are allocated to optimal sites. It is useful to replicate fragments, such as allocating many copies in many sites including load balancing. But there are too many possible combinations of each site and fragment, making it impossible to find a solution in real time, i.e., it is an NP-complete problem. This paper proposes near optimal heuristic algorithms for minimizing cost by defining a cost model based on read and update queries that are requested in many sites. Various factors are applied to the proposed algorithms for sizing efficient network resources that compute database transactions as remote query or update requests for consistency in replicated database systems. For network load balancing, incoming network traffic table is defined in each site. A request transaction from unallocated sites to allocated sites can be accessed properly at any other replicated sites by using the network traffic table. Finally, some experimental results verified the proposed algorithms by comparing actual cases of database allocation.

We propose a novel bias-free adaptive beamformer employing an affine projection algorithm with the optimal regularization parameter. The generalized sidelobe canceller affine projection algorithm suffers from a bias of a weight vectors under the condition of no reference signals for output of an array in the beamforming application. First, we analyze the bias in the algorithm and prove that the bias can be eliminated through a large regularization parameter. However, this causes slow convergence at the initial state, so the regularization parameter should be controlled. Through the optimization of the regularization parameter, the proposed method achieves fast convergence without the bias at the steady-state. Experimental results show that the proposed beamformer not only removes the bias but also achieves both fast convergence and high steady-state output signal-to-interference-plus-noise ratio.