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An efficient VLSI design of a lossless electrocardiogram (ECG) encoder is proposed for wireless body sensor networks. To save wireless transmission power, a novel lossless encoding algorithm had been created for ECG signal compression. The proposed algorithm consists of a novel adaptive predictor based on fuzzy decision control, and a novel hybrid entropy encoder including both a two-stage Huffman and a Golomb-Rice coding. The VLSI architecture contains only 2.71 K gate counts and its core area is 33 929 μm 2 synthesized by a 0.18 μm CMOS process. Moreover, this design can be operated at 100 MHz processing rate by consuming only 30 μW. It achieves an average compression rate of 2.56 for the MIT-BIH arrhythmia database. Compared with previous low-complexity and highperformance lossless ECG encoder studies, this design has a higher compression rate, lower power consumption and lower hardware cost than other VLSI designs.

A hardware-oriented lossless electrocardiogram compression algorithm is presented for very large-scale integration (VLSI) circuit design. To achieve high performance and low complexity, a novel prediction method based on the fuzzy decision and particle swarm optimiser (PSO) was developed. The accuracy of prediction was advanced efficiently by using the PSO algorithm to find the optimal parameters, which provided 64 situations for the fuzzy decision. Moreover, a novel low-complexity and high-performance entropy-coding algorithm based on Huffman coding was developed, which used one limited Huffman coding to encode a main region and five-region codes to encode the extending regions. The average compression rate of the whole MIT-BIH Arrhythmia database was up to 2.84 by combing the proposed fuzzy-based PSO prediction and Huffman region entropy-coding techniques. The VLSI architecture contained only a 1.9 K gate count and its core area was 5965 μm 2 synthesised using a 90 nm CMOS process. It consumed 201 μW when operating at a 200 MHz processing rate. Compared with previous low-complexity designs, the average compression rate is not only improved by more than 6.4% but also reduced the gate count by at least 8.2%.

This study utilizes genetic algorithm to minimize the condition number of Hermitian matrix of influence coefficient (HMIC) to reduce the computation errors in balancing procedure. Then, the optimal locations of balancing planes and sensors would be obtained as fulfilling optimization. The finite element method is used to determine the steady-state response of flexible rotor-bearing systems. The optimization improves the balancing accuracy, which can be validated by the experiments of balancing a rotor kit.

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