Systematic Realization of VDGA-Based Comb Filter for Biomedical Signal Processing
Bibliographic record
Abstract
This research article presents the systematic development of a current-mode-based active comb filter designed to mitigate the undesirable PLI (power line interference) and its harmonics contaminating biomedical signals.The filter is designed using the latest currentmode Analog Building Block (ABB), specifically Voltage Differencing Gain Amplifiers (VDGA).This filter effectively suppresses the 50Hz PLI and reduces the odd consecutive harmonics of the 50Hz PLI, including the third harmonic at 150Hz, the fifth harmonic at 250Hz, and the seventh harmonic at 350Hz.In this design, we employ 'n' VDGAs as active components and '2n' capacitors as passive components to suppress 'n' frequencies.The active and passive components used in this filter are significantly fewer in number compared to similar filter designs in existing literature.Moreover, the filter can be electronically tuned for a specific pole frequency and quality factor value using the VDGA's bias currents.It's important to note that the pole frequency and the quality factor can be independently tuned owing to their orthogonal relationship.In simulation, the proposed filter demonstrates a notch depth of -42.9dB and a total harmonic distortion (THD) of -83dB, indicating its effectiveness in attenuating the pole frequency.The filter's design is simulated using a 0.18µm CMOS process technology and the macro-model of the MAX435 IC in the PSPICE simulator to validate its functionality and feasibility.Furthermore, a non-ideal analysis of the filter's performance has been conducted, considering real VDGAs exhibiting nonidealities such as transconductance gain errors and parasitics on their ports.Finally, the overall performance of this filter is compared based on various parameters, including the technology used, the number of active and passive components, supply voltage, the number of pole frequencies attenuated, notch depth, and THD, in comparison with existing comb and notch filters in the literature.The filter's performance and simplicity make it a promising choice in demanding biomedical detection systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".