Issa M. S. Panahi

A Motion-Tolerant Adaptive Algorithm for Wearable Photoplethysmographic Biosensors

The performance of portable and wearable biosensors is highly influenced by motion artifact. In this paper, a novel real-time adaptive algorithm is proposed for accurate motion-tolerant extraction of heart rate (HR) and pulse oximeter oxygen saturation (SpO2) from wearable photoplethysmographic (PPG) biosensors. The proposed algorithm removes motion artifact due to various sources including tissue effect and venous blood changes during body movements and provides noise-free PPG waveforms for further feature extraction. A two-stage normalized least mean square adaptive noise canceler is designed and validated using a novel synthetic reference signal at each stage. Evaluation of the proposed algorithm is done by Bland-Altman agreement and correlation analyses against reference HR from commercial ECG and SpO2 sensors during standing, walking, and running at different conditions for a single- and multisubject scenarios. Experimental results indicate high agreement and high correlation (more than 0.98 for HR and 0.7 for SpO2 extraction) between measurements by reference sensors and our algorithm.

A review of three PWM techniques

Concerns switching power converters for motor drives. Three commonly used PWM techniques, sinusoidal PWM technique, space vector PWM technique and hysteresis (bang-bang) PWM technique, are discussed in this paper with emphasis on implementation. Experimental results are presented for all three PWM techniques.

Adaptive cancellation of motion artifact in wearable biosensors

The performance of wearable biosensors is highly influenced by motion artifact. In this paper, a model is proposed for analysis of motion artifact in wearable photoplethysmography (PPG) sensors. Using this model, we proposed a robust real-time technique to estimate fundamental frequency and generate a noise reference signal. A Least Mean Square (LMS) adaptive noise canceler is then designed and validated using our synthetic noise generator. The analysis and results on proposed technique for noise cancellation shows promising performance.

Hybrid FxRLS-FxNLMS Adaptive Algorithm for Active Noise Control in fMRI Application

A hybrid adaptive algorithm is developed for an active noise control system that leverages the stability of the filtered-input normalized least mean squares (FxNLMS) adaptive algorithm, with the high convergence speed of the filtered-input recursive least squares (FxRLS) adaptive algorithm. This algorithm is motivated by practical issues in implementing a real-time active noise control system. It leads to fast initial convergence with low, stable steady-state error while being limited by the computational capability of hardware. It gives better convergence speed than either the FxNLMS or FxRLS algorithm individually, lower residual error, and a lower overall computational complexity than the FxRLS algorithm, when appropriate filter lengths are chosen. Experimental results are presented for the implementation of the hybrid algorithm to cancel functional magnetic resonance imaging (fMRI) acoustic noise in an fMRI test-bed.

Comparison of tissue segmentation algorithms in neuroimage analysis software tools

Accurate segmentation of different brain tissues is of much importance in magnetic resonance imaging. This paper presents a comparison of the existing segmentation algorithms that are deployed in the neuroimaging community as part of two widely used software packages. The results obtained in this comparison can be used to select the appropriate segmentation algorithm for the neuroimaging application of interest. In addition to the entire brain area, a comparison is carried out for the subcortical region of the brain in terms of its gray matter composition.

On maximum achievable noise reduction in ANC systems

Active noise control (ANC) is primarily a signal estimation problem. The nature of noise to be canceled, the acoustic paths, and the transfer functions of microphones/loudspeakers place fundamental limits on the performance of ANC systems. In this paper we analyze the performance of ANC systems from the maximum achievable noise attenuation level (NALmax) prospective for different ANC system structures i.e feed-forward, feedback and hybrid. We first derive NALmax for stochastic noise and then generalize the result to accommodate tonal (sinusoidal) noises. These results are invaluable in choosing an ANC design scheme.

A New Delayless Subband Adaptive Filtering Algorithm for Active Noise Control Systems

Subband adaptive filtering (SAF) techniques play a prominent role in designing active noise control (ANC) systems. They reduce the computational complexity of ANC algorithms, particularly, when the acoustic noise is a broadband signal and the system models have long impulse responses. In the commonly used uniform-discrete Fourier transform (DFT)-modulated (UDFTM) filter banks, increasing the number of subbands decreases the computational burden but can introduce excessive distortion, degrading performance of the ANC system. In this paper, we propose a new UDFTM-based adaptive subband filtering method that alleviates the degrading effects of the delay and side-lobe distortion introduced by the prototype filter on the system performance. The delay in filter bank is reduced by prototype filter design and the side-lobe distortion is compensated for by oversampling and appropriate stacking of subband weights. Experimental results show the improvement of performance and computational complexity of the proposed method in comparison to two commonly used subband and block adaptive filtering algorithms.

Self-tuning control of switched reluctance motors for optimized torque per ampere at all operating points

Online self-tuning of control angles of a switched reluctance motor (SRM) is essential to optimize its performance in the presence of manufacturing imperfections. This paper reports an adaptive control scheme to optimize the torque per ampere at low and high speeds using artificial neural networks (ANN). An heuristic optimization technique has been introduced to find the changes in control angles. Using these results, the ANN will update its synaptic weights. Computer simulation has been employed to show the feasibility of this approach. Experimental results are provided to demonstrate the working of the self-tuning control.

An Efficient Feedback Active Noise Control Algorithm Based on Reduced-Order Linear Predictive Modeling of fMRI Acoustic Noise

Functional magnetic resonance imaging (fMRI) acoustic noise exhibits an almost periodic nature (quasi-periodicity) due to the repetitive nature of currents in the gradient coils. Small changes occur in the waveform in consecutive periods due to the background noise and slow drifts in the electroacoustic transfer functions that map the gradient coil waveforms to the measured acoustic waveforms. The period depends on the number of slices per second, when echo planar imaging (EPI) sequencing is used. Linear predictability of fMRI acoustic noise has a direct effect on the performance of active noise control (ANC) systems targeted to cancel the acoustic noise. It is shown that by incorporating some samples from the previous period, very high linear prediction accuracy can be reached with a very low order predictor. This has direct implications on feedback ANC systems since their performance is governed by the predictability of the acoustic noise to be cancelled. The low complexity linear prediction of fMRI acoustic noise developed in this paper is used to derive an effective and low-cost feedback ANC system.

Average field map image template for Echo-Planar image analysis

Magnetic resonance field map images are normally used in characterizing the magnetic field inhomogeneity for distortion correction in Echo-Planar Imaging (EPI) and accurate localization in functional MRI (fMRI). In this paper, the computation and applications of an average field map image template is investigated based on real field maps. The introduced methodology and the obtained field map image templates may be used in EPI and fMRI image analysis, distortion correction, registration, and functional localization when high-resolution field map images are not available for individual datasets. The introduced methodology involves three stages of pre-processing, registration, and spatial normalization. The analysis and results presented in this paper show the impact and usefulness of the investigated methodology in several applications.