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Recent progress on human brain science requires developing advanced neural recording system to capture the activity of large neural populations accurately, across a large area of the brain, and over extended periods. Recently proposed distributed neural recording systems with numerous implanted devices require reliably energizing them wirelessly. Random distribution of these mm-sized implants and brain motion place them at different positions and orientations with respect to the power transmitter. Therefore, traditional wireless power transfer techniques fall short of reaching sufficient power for all implants simultaneously, rendering some implants nonfunctional. In this paper, a three-layer power transmitting array with three-phase coil excitation current is introduced, which is capable of producing omnidirectional and homogeneous magnetic field across the volume where the Rx coils are located. The individual coil dimensions in the array is optimized to improve the worst-case scenario in terms of homogeneity, which is further verified by the measurements using a scaled-up prototype system. The measurement results show that the minimum received voltages is improved from 0.34 V for 10-mm side-length hexagonal transmitting coil array to 0.83 V for the optimal case, i.e., 35 mm side-length hexagonal transmitting coil array.

A decoding scheme of the orthogonally concatenated codes with low resource utilizations is proposed. In the optical transport networks (OTN), forward error-correction (FEC) techniques are used to reduce the errors which occur in transmissions. Two-orthogonal-concatenated (TOC) codes are widely used in FEC techniques for their powerful error-correction capabilities based on the iterative decoding procedure. However, the framing structure is complex so the decoding procedure is more difficult than the decoding of in–out concatenated codes. And the powerful error-correction capability relies on the multi-iterative decoding processing, thus how to effectively utilize the hardware resources is a very important problem. Especially when the decoding procedure is implemented in the field programmable gate array (FPGA) devices, effective optimizations are required for the limited resources. In this paper we present an iterative decoding scheme in FPGA with low resource utilizations. As an example, an actual engineering application under the G.975.1 recommendation is given to show the efficiency of the proposed design.

In this paper, a novel particle filter termed the wavelet-based grey particle filter (WG-PF) is proposed to self-estimate the trajectory of a manoeuvring autonomous underwater vehicle (AUV) without prior manoeuvring information. To implement the WG-PF, the particles are sampled by the state transition and grey prediction. The state transition is based on a prior dynamic model, while the grey prediction is a kind of model-free method that predicts the state through historical measurements. Therefore, the WG-PF has the inherent advantages of both model-based and model-free systems. Additionally, the measurement noise covariance is modified by the wavelet transform. Thus, the WG-PF can effectively correct the prior distribution and likelihood function of the particles and then alleviate the sample degeneracy problem. The estimation performances of three filters, the proposed WG-PF, the multiple model particle filter (PF) and the adaptive extended Kalman filter, are evaluated and compared through the experimental data, in which a trajectory plotted by the underwater acoustic positioning sensors is employed as the true trajectory. The grey-prediction-based and wavelet-based PFs are also examined to demonstrate their positive effects in the WG-PF. The presented results show that the WG-PF acquires satisfactory effectiveness, robustness and better estimation accuracy than the other filters.