In this paper, online sparse Volterra system identification is proposed. For that purpose, the conventional adaptive projection-based algorithm with weighted l1 balls (APWL1) is revisited for nonlinear system identification, whereby the linear-in-parameters nature of Volterra systems is utilized. Compared with sparsity-aware recursive least squares (RLS) based algorithms, requiring higher computational complexity and showing faster convergence and lower steady-state error due to their long memory in time-invariant cases, the proposed approach yields better tracking capability in time-varying cases due to short-term data dependence in updating the weight. Also, when N is the number of sparse Volterra kernels and q is the number of input vectors involved to update the weight, the proposed algorithm requires O(qN) multiplication complexity and O(Nlog 2N) sorting-operation complexity. Furthermore, sparsity-aware least mean-squares and affine projection based algorithms are also tested.

In this paper, we propose a speech-presence uncertainty estimation to improve the global soft decision-based speech enhancement technique by using the spectral gradient scheme. The conventional soft decision-based speech enhancement technique uses a fixed ratio (Q) of the a priori speech-presence and speech-absence probabilities to derive the speech-absence probability (SAP). However, we attempt to adaptively change Q according to the spectral gradient between the current and past frames as well as the status of the voice activity in the previous two frames. As a result, the distinct values of Q to each frequency in each frame are assigned in order to improve the performance of the SAP by tracking the robust a priori information of the speech-presence in time.

Recently, H-bridge pulse width modulation (PWM) micro-stepping motor drivers have been widely used for 3-D printers, robots, and medical instruments. Differently from a simple PWM motor driver circuit, the H-bridge PWM micro-stepping motor driver circuit can generate radio frequency (RF) electromagnetic interference (EMI) noises of up to several hundred MHz frequencies, due to digital interface circuits and a high-performance CPU. For medical instrument systems, the minimization of EMI noises can assure operating safety and greatly reduce the chance of malfunction between instruments. This work proposes a passive-filter configuration-based circuit design for reducing up-to-several-hundred-MHz EMI noises generated from the H-bridge PWM micro-stepping motor driver circuit. More specifically, the proposed RF EMI reduction approach consists of proper passive filter design, shielding in motor wires, and common ground design in the print circuit board. The proposed passive filter configuration design is validated through the overall reduction of EMI noises at RF band. Finally, the proposed EMI reduction approach is tested experientially through a prototype and about 16 dB average reduction of RF EMI noises is demonstrated.

In this Letter, a robust variable step-size affine-projection subband adaptive filter algorithm (RVSS-APSAF) is proposed, whereby a band-dependent variable step-size is introduced to improve convergence and misalignment performances in impulsive noise environments. Specifically, the weight vector is adaptively updated to achieve robustness against impulsive noises. Finally, the proposed RVSS-APSAF algorithm is tested for system identification in an impulsive noise environment.