Filter Bank Approach Research Articles

Wavelet-based Filter Bank Approach for Image Compression and Enhancement in Alzheimer\'s Disease Diagnosis Using Medical Imaging

Abstract: This research presents a wavelet-based filter bank approach for image compression and enhancement in the context of Alzheimer's disease diagnosis using medical imaging. The proposed system leverages the PyWavelets and OpenCV libraries in Python to implement Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) algorithms. The primary objective is to develop an efficient image compression technique while preserving visual quality and enhancing relevant features for improved diagnostic accuracy. The system utilizes filter banks to process wavelet coefficients, enabling selective enhancement of patterns or biomarkers associated with Alzheimer's disease. Results show that the decompressed images closely resemble the originals, with low Mean Squared Error (MSE) and high Peak Signal-to-Noise Ratio (PSNR) values. Visual inspection and quantitative metrics confirm the successful preservation of image quality and structural details. The wavelet coefficients visualization highlights the captured significant features crucial for reconstruction

Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach

Currently, in modern wind farms, the doubly fed induction generator (DFIG) is commonly adopted for its ability to operate at variable wind speeds. Generally, this type of wind turbine is controlled by using two converters, one on the rotor side (RSC) and the other one on the grid side (GSC). However, the control of these two converters depends mainly on current sensors measurements. Nevertheless, in the case of sensor failure, control stability may be compromised, leading to serious malfunctions in the wind turbine system. Therefore, in this article, we will present an innovative diagnostic approach to detect, locate, and isolate the single and/or multiple real-phase current sensors in both converters. The suggested approach uses an extended Kalman filter (EKF) bank structured according to a generalized observer scheme (GOS) and relies on a nonlinear model for the RSC and a linear model for the GSC. The EKF estimates the currents in the converters, which are then compared to sensor measurements to generate residuals. These residuals are then processed in the localization, isolation, and decision blocks to precisely identify faulty sensors. The obtained results confirm the effectiveness of this approach to identify faulty sensors in the abc phases. It also demonstrates its ability to overcome the nonlinearity induced by wind fluctuations, as well as resolves the coupling issue between currents in the fault period.

Subject-independent EEG classification based on a hybrid neural network.

A brain-computer interface (BCI) based on the electroencephalograph (EEG) signal is a novel technology that provides a direct pathway between human brain and outside world. For a traditional subject-dependent BCI system, a calibration procedure is required to collect sufficient data to build a subject-specific adaptation model, which can be a huge challenge for stroke patients. In contrast, subject-independent BCI which can shorten or even eliminate the pre-calibration is more time-saving and meets the requirements of new users for quick access to the BCI. In this paper, we design a novel fusion neural network EEG classification framework that uses a specially designed generative adversarial network (GAN), called a filter bank GAN (FBGAN), to acquire high-quality EEG data for augmentation and a proposed discriminative feature network for motor imagery (MI) task recognition. Specifically, multiple sub-bands of MI EEG are first filtered using a filter bank approach, then sparse common spatial pattern (CSP) features are extracted from multiple bands of filtered EEG data, which constrains the GAN to maintain more spatial features of the EEG signal, and finally we design a convolutional recurrent network classification method with discriminative features (CRNN-DF) to recognize MI tasks based on the idea of feature enhancement. The hybrid neural network proposed in this study achieves an average classification accuracy of 72.74 ± 10.44% (mean ± std) in four-class tasks of BCI IV-2a, which is 4.77% higher than the state-of-the-art subject-independent classification method. A promising approach is provided to facilitate the practical application of BCI.

Dyadic boundary points based empirical wavelet transform for the elimination of eye movement and eye blink-based ocular artifacts from EEG signals

The movement of eyeballs and eye blinks produces ocular artifacts in the electroencephalogram (EEG) signal during recording. It is necessary to filter out these artifacts from the EEG signal for different biomedical applications. The state-of-the-art (SOTA) methods have less denoising performance to filter ocular artifacts from EEG signals. To overcome this, we have proposed a novel filter-bank-based hybrid approach in this paper to eliminate ocular artifacts from EEG signals. The optimal filter-bank based on the dyadic boundary points based empirical wavelet transform (DBPEWT) is introduced for the multiscale decomposition of EEG signal into various sub-band (SB) signals. The Savitzky–Golay-driven total variation (SGDTV) filter is formulated and applied to the contaminated EEG’s low-frequency range sub-band or first sub-band signal to obtain the filtered sub-band signal. The filtered EEG signal is calculated based on the linear combination of the filtered first sub-band signal evaluated from the SGDTV stage with other sub-band signals of the contaminated EEG signal. The filtering performance of the proposed approach is assessed using the mean absolute error (MAE) in the power spectrum (PS) for θ, α, and β-bands of contaminated and filtered EEG signals. The suggested approach is evaluated using the eye-blink, eye-movement-based ocular artifact-contaminated EEG signals from two public databases. Furthermore, the denoising performance of the proposed filter-bank approach is evaluated using the wearable EEG sensor signals from healthy and epilepsy subjects while performing physical activities such as sitting, walking, stairs, and running, respectively. The results reveal that the proposed approach has obtained the average MAE-PS values for (θ, α, and β) bands as (0.156, 0.308, and 0.201), and (0.081, 0.089, and 0.088), respectively for the removal of eye-blink and eye-movement artifacts from EEG signals. The suggested approach has obtained the average MAE-PS values for (θ, α, and β-bands) as (4.555±0.203, 0.057±0.026, 0.062±0.005), and (4.085±0.086, 0.068±0.043, 0.048±0.006) for healthy and epilepsy subjects during running activity to eliminate artifacts from the wearable sensor-based EEG signals. The denoising performance of the proposed approach has been compared with existing methods to remove eye-movement and eye-blink artifacts from EEG signals.

Filter bank approach for enhancement of supervised Canonical Correlation Analysis methods for SSVEP-based BCI spellers.

Canonical correlation analysis (CCA) is one of the most used algorithms in the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) systems due to its simplicity, efficiency, and robustness. Researchers have proposed modifications to CCA to improve its speed, allowing high-speed spelling and thus a more natural communication. In this work, we combine two approaches, the filter-bank (FB) approach to extract more information from the harmonics, and a range of different supervised methods which optimize the reference signals to improve the SSVEP detection. The proposed models are tested on the publicly available benchmark dataset for SSVEP-based BCIs and the results show improved performance compared to the state-of-the-art methods and, in particular, the proposed FBMwayCCA approach achieves the best results with an information transfer rate (ITR) of 134.8±8.4 bits/minute. This study indeed suggests the feasibility of combining the fundamental and harmonic SSVEP components with supervised methods in target identification to develop high-speed BCI spellers.

An Analog Filter Bank-based Circuit for Performing the Adaptive Impedance Matching in PLC Systems

Aiming to bring attention to the necessity of dealing with the dynamics of access impedance in electric power systems, this paper introduces an adaptive impedance matching circuit that is based on an analog filter bank approach. In this sense, it describes a prototype that validates the proposed filter bank approach for improving impedance matching. Numerical results obtained with the detailed prototype operating in the frequency band between 2 and 500 MHz show that the proposed analog filter bank approach helps to improve impedance matching in power line communication (PLC) systems. Also, the numerical results show that the dynamics of impedance matching between two or more PLC transceivers is a difficult task to be accomplished because real-time coordination among them is necessary. Overall, it is shown that the proposed analog filter bank approach constitutes an interesting research direction for improving impedance matching between PLC transceivers and electric power systems.

Novel algorithm for auditory compensation in hearing aids

Background: Auditory Compensation is the primary block of the hearing aid to compensate the loss in the hearing. The amount of amplification required for a patient is provided by the audiologist in the audiogram. Audiogram is a graph indicating the loss in hearing with respect to frequency. Most of the hearing aids available in the market are developed using filter bank method to divide the signal into various bands and then amplify the signal. Key aspects while designing the auditory compensation block are power dissipation, group delay by the filters, signal attenuation and reconstructing the signal. Objectives: The work here targets to design and develop a novel algorithm for low power implementation of auditory compensation block with the help of Short Time Fourier Transform (STFT). Methods: The algorithm is employed to determine the frequency range of audio signal using STFT which is to be selectively amplified as per the audiogram. Verilog language is used to implement the design. The complete ASIC flow up to generation of Graphic Data System (GDSII) files is carried out. Findings: The algorithm implements the auditory compensation block of the hearing aid successfully with low power consumption and increasing the intelligibility of the speech by solving issues present in the existing filter bank approach. Novelty: Presently Filter banks are used to perform auditory compensation; the work carried out is using STFT which is the novelty of the work. Keywords: Hearing loss; hearing aid; audiogram; auditory compensation; STFT

Comparison of Modern Highly Interactive Flicker-Free Steady State Motion Visual Evoked Potentials for Practical Brain-Computer Interfaces.

Motion-based visual evoked potentials (mVEP) is a new emerging trend in the field of steady-state visual evoked potentials (SSVEP)-based brain–computer interfaces (BCI). In this paper, we introduce different movement-based stimulus patterns (steady-state motion visual evoked potentials—SSMVEP), without employing the typical flickering. The tested movement patterns for the visual stimuli included a pendulum-like movement, a flipping illusion, a checkerboard pulsation, checkerboard inverse arc pulsations, and reverse arc rotations, all with a spelling task consisting of 18 trials. In an online experiment with nine participants, the movement-based BCI systems were evaluated with an online four-target BCI-speller, in which each letter may be selected in three steps (three trials). For classification, the minimum energy combination and a filter bank approach were used. The following frequencies were utilized: 7.06 Hz, 7.50 Hz, 8.00 Hz, and 8.57 Hz, reaching an average accuracy between 97.22% and 100% and an average information transfer rate (ITR) between 15.42 bits/min and 33.92 bits/min. All participants successfully used the SSMVEP-based speller with all types of stimulation pattern. The most successful SSMVEP stimulus was the SSMVEP1 (pendulum-like movement), with the average results reaching 100% accuracy and 33.92 bits/min for the ITR.

Inter-subject correlations during natural viewing: A filter-bank approach.

A central question in neuroscience is how the brain processes real-world sensory input. For decades most classical studies focus on carefully controlled artificial stimuli. More recently researchers started to investigate brain activity under more realistic conditions. The main challenge in this setting is the analysis of the complex signals obtained with modern neuroimaging methods in response to natural stimuli. Inter-subject correlations (ISCs) have become a popular paradigm to study brain activation under natural stimulation. The underlying assumption of this analysis is that features of natural stimuli that are perceived and processed by all subjects exposed to the same stimulus result in similar activation patterns across subjects. Higher degrees of realism in stimulation, for instance audiovisual stimulation is more realistic than auditory stimulation, is usually associated with higher ISC values. We can confirm these findings in experiments in which we present a movie stimulus with varying degrees of realism. Extending previous findings we highlight the importance of artifact removal when evaluating ISCs and show that the impact of realism in natural stimulation on ISCs is frequency-dependent. A major challenge associated with this type of analysis is that it can be difficult to attribute the correlation strength to the physiological process of interest. In this study, we demonstrate that ISCs of neural activation as measured by electroencephalogram (EEG) recordings are influenced significantly by non-neural artifacts such as occulograms. Our findings highlight the potential of inter-subject correlations as a biomarker for immersion: If more realistic stimuli consistently lead to higher inter-subject correlations, then inter-subject correlations can serve as a quantitative marker for how engaging audiovisual stimuli are perceived.Clinical relevance- Future research will evaluate if correlation levels among subjects, who are exposed to natural stimuli are affected by neurological diseases such as Alzheimers, Parkinsons, and Schizophrenia among others.

Discriminating Parkinson diseased and healthy people using modified MFCC filter bank approach

In this paper a modified Mel scaled filter bank-based approach to discriminate people suffering from Parkinson disease (PD) in their early stages from healthy people using speech samples is proposed. Parkinson’s disease not only affects the muscular activities of the human body but also affects the speech of the diseased. So, the speech features of Parkinson affected people tend to vary and hence differ from the speech features of healthy people. In this paper, the speech feature used for discriminating the two groups is the Mel frequency cepstral coefficients (MFCC) extracted from speech samples of both the PD and healthy people. The traditional way of computing the MFCC coefficients involves the design of the Mel filter bank. These filters are usually designed according to the auditory or acoustic system of human ear which follows the Mel scale. In this study, modification to this Mel scaled bank of filters is done by varying its bandwidth in the region of interest to compute the feature, MFCC and its performance is then compared with the conventionally designed MFCC filter bank for the said application. The performance is compared in terms of classification accuracy using radial basis network classifier. The results show an improvement of 6.3% in the classification accuracy obtained using the proposed method.

Sampling Associated with a Unitary Representation of a Semi-Direct Product of Groups: A Filter Bank Approach

An abstract sampling theory associated with a unitary representation of a countable discrete non abelian group G, which is a semi-direct product of groups, on a separable Hilbert space is studied. A suitable expression of the data samples, the use of a filter bank formalism and the corresponding frame analysis allow for fixing the mathematical problem to be solved: the search of appropriate dual frames for ℓ 2 ( G ) . An example involving crystallographic groups illustrates the obtained results by using either average or pointwise samples.

Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods.

Single-trial motor imagery classification is a crucial aspect of brain–computer applications. Therefore, it is necessary to extract and discriminate signal features involving motor imagery movements. Riemannian geometry-based feature extraction methods are effective when designing these types of motor-imagery-based brain–computer interface applications. In the field of information theory, Riemannian geometry is mainly used with covariance matrices. Accordingly, investigations showed that if the method is used after the execution of the filterbank approach, the covariance matrix preserves the frequency and spatial information of the signal. Deep-learning methods are superior when the data availability is abundant and while there is a large number of features. The purpose of this study is to a) show how to use a single deep-learning-based classifier in conjunction with BCI (brain–computer interface) applications with the CSP (common spatial features) and the Riemannian geometry feature extraction methods in BCI applications and to b) describe one of the wrapper feature-selection algorithms, referred to as the particle swarm optimization, in combination with a decision tree algorithm. In this work, the CSP method was used for a multiclass case by using only one classifier. Additionally, a combination of power spectrum density features with covariance matrices mapped onto the tangent space of a Riemannian manifold was used. Furthermore, the particle swarm optimization method was implied to ease the training by penalizing bad features, and the moving windows method was used for augmentation. After empirical study, the convolutional neural network was adopted to classify the pre-processed data. Our proposed method improved the classification accuracy for several subjects that comprised the well-known BCI competition IV 2a dataset.

BER analysis of concatenated levels of encoding in GFDM system using labview

<p>All the devices are interconnected each other in digital form, for different applications the input data is encoded for error correcting and detecting purpose. The paper describes the transmission of QAM signals with two level encoded stages, i.e. convolutional and hamming coded GFDM system with 256-point IFFT at transmitter and FFT at the receiver using LABVIEW software. GFDM is a non-orthogonal, digital multicarrier transmission scheme which digitally implements the classical filter bank approach. GFDM transmits a block of frame composed by M time slots with K subcarriers. The higher order QAM is used because of transmitting more data but is less reliable when compared to lower order QAM. Based on GFDM specifications for the IEEE 802.11, latest 5G physical layer standards, the coding is provided by ½ rate encoder at the input side, and Maximum Likelihood decoder at the receiver side is used. The standard convolution code (7, [171, 133]), is used as encoder for the GFDM system. The GFDM complex values are displayed in the front panel, along with FFT and power spectrum is plotted for GFDM signal. The array of input bits and output bits are shown with green colour LED’s. The van de Beek algorithm is used at the receiver for maximum likelihood detection acts as convolutional decoder of GFDM signal. Next the signal is subjected to remove cyclic prefix and zero padding and applied to channel estimation algorithm. The un-equalized data and equalized data graph is shown in the front panel, before and after channel estimation VI. With BER VI available in the LABVIEW the data is normalized and its response is plotted with respect to SNR. BER values for different levels of encoders have shown in table for SNR values. This paper concludes the 32.91% improvement in BER for two levels of concatenated codes.Thus the GFDM signal outperforms the OFDM signal interms of BER for series levels of coding using labVIEW software.</p>

CORE-PI: Non-iterative convolution-based reconstruction for parallel MRI in the wavelet domain.

To develop and test a novel parameter-free non-iterative wavelet domain method for reconstruction of undersampled multicoil MR data. A linear parallel MRI method that operates in the Stationary Wavelet Transform (SWT) domain is proposed. The method is coined COnvolution-based REconstruction for Parallel MRI (CORE-PI). This method computes the SWT of the unknown MR image directly from subsampled k-space measurements, without modifying the RF excitation pulse. It then reconstructs the image using the wavelet filter bank approach, with simple linear computations. The CORE-PI implementation is demonstrated by experiments with a numeric brain phantom and invivo brain scans data, with various wavelet types and high reduction factors. It is compared to the well-known parallel MRI methods GRAPPA and l1-SPIRiT. The experimental results show that CORE-PI is suitable for different 1D Cartesian k-space undersampling schemes, including regular and irregular ones, and for wavelets of different families. CORE-PI accurately reconstructs the SWT coefficients of the unknown MR image; this wavelet-domain decomposition is fully computed despite the k-space undersampling. Furthermore, CORE-PI provides high-quality final reconstructions, with an average NRMSE of 0.013, which is significantly lower than that obtained by GRAPPA and l1-SPIRiT. Moreover, CORE-PI offers significantly faster computation times: the typical CORE-PI runtime is about 60seconds, which is about 20% shorter than that of l1-SPIRiT and 55%-75% shorter than that of GRAPPA. COnvolution-based REconstruction for Parallel MRI advantageously offers: (a) flexible 1D undersampling of a Cartesian k-space, (b) a parameter-free non-iterative implementation, (c) reconstruction performance comparable or better than that of GRAPPA and l1-SPIRiT, and (d) robust fast computations.

Multirate and Filterbank Approaches in Digital Hearing Aid Design: A Review

Hearing is considered as the most important senses of humans because it connects us to the world always. And the communication between humans are mostly by speaking and listening. So hearing loss can have a significant affect on our life, from our work to our connections and enthusiastic prosperity. A hearing aid is an electro-acoustic gadget, which is intended to increase sound, with the point of making speech more comprehensible. The fundamental task of the hearing aid is to selectively amplify sound signals to such an extent that the handled sound matches ones audiogram. The emerging demand for personalized hearing assistants requires the filter bank to be capable of decomposing the sound waves in accordance with the characteristic of the patients hearing loss. In this review paper, the application of multirate frameworks and filter banks for different procedures in personalized hearing assistants are investigated.

Superfast joint demosaicing and super-resolution

The problem of increasing spatial resolution from Bayer images is considered. It is solved locally at each point of the unknown high-resolution image. In each local neighbourhood, subpixel warp and blur kernel are assumed to be the same, which makes it possible to reduce the computational complexity from (n6) to (n2logn). A detailed description of the algorithm and its proof using the apparatus of multilevel matrices are provided. The relation between solutions of the SR problem with different warping parameters is also studied, and it is proven that certain solutions can be derived from solutions with other parameters by using simple transforms. This makes it possible to reduce the amount of memory used for storing filters within a filter bank approach up to 80 times (2× magnification, 3 input frames).

Space Time MUSIC: Consistent Signal Subspace Estimation for Wideband Sensor Arrays

Wide-band Direction of Arrival (DOA) estimation with sensor arrays is an essential task in sonar, radar, acoustics, biomedical and multimedia applications. Many state of the art wide-band DOA estimators coherently process frequency binned array outputs by approximate Maximum Likelihood, Weighted Subspace Fitting or focusing techniques. This paper shows that bin signals obtained by filter-bank approaches do not obey the finite rank narrow-band array model, because spectral leakage and the change of the array response with frequency within the bin create \emph{ghost sources} dependent on the particular realization of the source process. Therefore, existing DOA estimators based on binning cannot claim consistency even with the perfect knowledge of the array response. In this work, a more realistic array model with a finite length of the sensor impulse responses is assumed, which still has finite rank under a space-time formulation. It is shown that signal subspaces at arbitrary frequencies can be consistently recovered under mild conditions by applying MUSIC-type (ST-MUSIC) estimators to the dominant eigenvectors of the wide-band space-time sensor cross-correlation matrix. A novel Maximum Likelihood based ST-MUSIC subspace estimate is developed in order to recover consistency. The number of sources active at each frequency are estimated by Information Theoretic Criteria. The sample ST-MUSIC subspaces can be fed to any subspace fitting DOA estimator at single or multiple frequencies. Simulations confirm that the new technique clearly outperforms binning approaches at sufficiently high signal to noise ratio, when model mismatches exceed the noise floor.

On-line updating of cyclostationary tools for fault detection in rotating machines - the filter bank approach

The theory of cyclostationarity provides many valuable tools for fault detection in rotating machines. Cyclostationarity indicators and bi-frequency maps help to quantify fault-related modulations of vibration signals. High quality and robustness of these tools are achieved at a relatively high computational cost. Thus, industrial application of cyclostationarity is limited to off-line analysis in the post-processing stage. In this paper an on-line updating of cyclostationary tools is investigated alternatively to the moving block approach. As an exemplary cyclostationary tool the cyclic modulation spectrum is involved. The filter bank approach is proposed in order to carry out the sample-by-sample updating. We compare properties of the newly proposed method with the one based on sliding discrete Fourier transform.

Improving GFDM Symbol Error Rate Performance using “Better than Nyquist” Pulse Shaping Filters

Fourth generation (4G) cellular systems have been optimized to provide high data rates and reliable coverage to mobile users. New waveforms at the physical layer are needed. Generalized frequency division multiplexing (GFDM) is a candidate modulation for the fifth generation (5G) standard based on multi-branch multicarrier filter bank approach. A main characteristic of GFDM is its low out of band emission, achieved by means of a flexible time-domain pulse shaping of individual subcarriers. In this paper, the influence of the improved “Better than Nyquist” pulse shaping filters on symbol error rate (SER) performance of the GFDM system in the case of zero forcing (ZF) receiver is investigated. We considered their use in GFDM to evaluate the impact on SER performance in case of 16-QAM transmission over an additive white Gaussian noise channel. Moreover, we also considered the concept of the wavelet for better time-frequency localization of the pulse shaping filters by using the Meyer auxiliary function. Numerical results are reported to demonstrate the superior SER performance achieved.

Multichannel SAR-GMTI in Maritime Scenarios With F-SAR and TerraSAR-X Sensors

This paper explores the ground moving target indication (GMTI) capabilities of the German Aerospace Center’s state-of-the-art airborne (F-SAR) and spaceborne (TerraSAR-X) synthetic aperture radars (SARs) when operating over maritime scenarios. The performance of classical dual channel GMTI techniques, such as displaced phase center antenna (DPCA) and along-track interferometry (ATI), as well as the promising adaptive techniques, like extended DPCA (EDPCA) and imaging space–time adaptive processing (ISTAP) have been analyzed on the basis of experimental acquisitions with both sensors. The objective of the paper is to highlight the limitations and challenges to be considered when processing real, multichannel GMTI data from pioneering SAR sensors for maritime surveillance. Different calibration or channel balancing strategies, on the basis of the digital balancing (DB) method, are studied, considering their impact on SAR-GMTI performance. An adaptive SAR processor, accounting for target kinematics and based on a matched filter bank (MFB) approach, is integrated in the SAR-GMTI processing chain in order to retrieve refocused images of the moving vessels.