Efficient lifting wavelet transform for microprocessor and VLSI applications

Olkkonen, H.; Olkkonen, Juuso; Pesola, P.

doi:10.1109/lsp.2004.840904

Cited by 21 publications

(16 citation statements)

References 10 publications

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“…These implementations are generally driven by the need to fulfill certain characteristics such as regularity, smoothness and linear phase of the scaling and wavelet filters, as well as perfect reconstruction of the decomposed signals [3].…”

Section: Introductionmentioning

confidence: 99%

A Scalable Wavelet Transform VLSI Architecture for Real-Time Signal Processing in High-Density Intra-Cortical Implants

Oweiss

Mason

Suhail

et al. 2007

IEEE Trans. Circuits Syst. I

113

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Abstract-This paper describes an area and power-efficient VLSI approach for implementing the discrete wavelet transform on streaming multielectrode neurophysiological data in real time. The VLSI implementation is based on the lifting scheme for wavelet computation using the symmlet4 basis with quantized coefficients and integer fixed-point data precision to minimize hardware demands. The proposed design is driven by the need to compress neural signals recorded with high-density microelectrode arrays implanted in the cortex prior to data telemetry. Our results indicate that signal integrity is not compromised by quantization down to 5-bit filter coefficient and 10-bit data precision at intermediate stages. Furthermore, results from analog simulation and modeling show that a hardware-minimized computational core executing filter steps sequentially is advantageous over the pipeline approach commonly used in DWT implementations. The design is compared to that of a B-spline approach that minimizes the number of multipliers at the expense of increasing the number of adders. The performance demonstrates that in vivo real-time DWT computation is feasible prior to data telemetry, permitting large savings in bandwidth requirements and communication costs given the severe limitations on size, energy consumption and power dissipation of an implantable device.Index Terms-B-spline, brain machine interface, lifting, microelectrode arrays, neural signal processing, neuroprosthetic devices, wavelet transform.

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Section: Introductionmentioning

confidence: 99%

A Scalable Wavelet Transform VLSI Architecture for Real-Time Signal Processing in High-Density Intra-Cortical Implants

Oweiss

Mason

Suhail

et al. 2007

IEEE Trans. Circuits Syst. I

113

View full text Add to dashboard Cite

show abstract

“…The integer valued polyphase components (Table I) enable efficient implementation in VLSI and microprocessor circuits. The present paper serves as a framework, since the FD B-spline filter implementation can be adapted in any of the existing BDWT bank, such as the lifting DWT (Olkkonen et al 2005), Daubechies 7/9 and Legall 3/5 wavelet filters (Unser &Blu, 2003). The present idea is highly impacted on the work of Selesnick (2002) The energy (absolute value) of the complex wavelet corresponds to the envelope, which is a smooth function.…”

Section: Discussionmentioning

confidence: 99%

“…The EEG recording method is described in detail in our previous work (Olkkonen et al 2006). The EEG signals were treated using the BDWT bank given in (Olkkonen et al 2005). The FD wavelet coefficients were calculated via (12) using M=4 and N=0,1,2 and 3.…”

Section: Methodsmentioning

confidence: 99%

“…Fig. 2 shows the impulse responses of the BDWT wavelet filter (Olkkonen et al 2005) and the corresponding fractionally delayed wavelet filters for M = 4 and N = 0, 1,2 and 2. The energy (absolute value) of the impulse response is a smooth function, which warrants the shift invariance.…”

Section: Fir Fd Wavelet Filtersmentioning

confidence: 99%

“…In VLSI hardware the BDWT is usually realized via the ladder network-type filter (Sweldens, 1988). Efficient lifting wavelet transform algorithms implemented by integer arithmetic using only register shifts and summations have been developed for VLSI applications (Olkkonen et al 2005). In multi-scale analysis the drawback of the BDWT is the sensitivity of the transform coefficients to a small fractional shift [0,1] τ ∈ in the signal, which disturbs the statistical comparison across different scales.…”

Section: Introductionmentioning

confidence: 99%

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