Filter Bank Research Articles

Signal processing implementation of low-cost target speed detection of CW radar using FPGA

The low-cost Continuous Wave (CW) radar architecture for object speed detection is presented in this research. An FPGA (Field-Programmable Gate Array) processor is utilized to implement the CW radar dependably and optimally. The suggested approach uses a bank of filters made expressly to extract velocity information from radar signals to compute the target velocity. Depending on the needs of the particular application, the acquired data has either undergone additional processing or been presented in an approachable manner. The implemented design is checked in real-time with a chip scope, functionally simulated, and then inspected with lab instruments to compare all simulated and implemented outcomes. Numerous applications, such as industrial automation, surveillance, and automotive systems, where accurate velocity detection is crucial and low-cost design is required, could benefit from this concept.

On design of preprocessing and postprocessing in cascaded-resonator-based harmonic analyzers and filter banks

The cascaded-resonator (CR)-based filter structure provides high attenuation in the stopbands thanks to the serially coupled resonators, which is an advantage in many applications of harmonic analyses and/or filtering. In addition to that, it has good properties provided by its parallel structure. On the other side, there are applications which besides the high stopband attenuation need wider (or even flat-top) passbands, lower group delays, etc. These performances can be modified/improved by preprocessing and postprocessing through reshaping of the primary CR-based filter bank frequency responses. The numerical complexity is also an important aspect where infinite impulse response (IIR) filters provide implementation with a smaller number of numerical operations, but involving nonlinearity of the phase responses and stability control issue. Although most of these issues have been discussed in the previous papers dealing with the design of the CR-based harmonic filtering and/or filter banks, the aim of this paper is to highlight them all together. In addition, some improvements of the previously described design techniques are suggested too.

Automated explainable wavelet-based sleep scoring system for a population suspected with insomnia, apnea and periodic leg movement

Sleep is an integral and vital component of human life, contributing significantly to overall health and well-being, but a considerable number of people worldwide experience sleep disorders. Sleep disorder diagnosis heavily depends on accurately classifying sleep stages. Traditionally, this classification has been performed manually by trained sleep technologists that visually inspect polysomnography records. However, in order to mitigate the labor-intensive nature of this process, automated approaches have been developed. These automated methods aim to streamline and facilitate sleep stage classification. This study aims to classify sleep stages in a dataset comprising subjects with insomnia, PLM, and sleep apnea. The dataset consists of PSG recordings from the multi-ethnic study of atherosclerosis (MESA) cohort of the national sleep research resource (NSRR), including 2056 subjects. Among these subjects, 130 have insomnia, 39 suffer from PLM, 156 have sleep apnea, and the remaining 1731 are classified as good sleepers. This study proposes an automated computerized technique to classify sleep stages, developing a machine-learning model with explainable artificial intelligence (XAI) capabilities using wavelet-based Hjorth parameters. An optimal biorthogonal wavelet filter bank (BOWFB) has been employed to extract subbands (SBs) from 30 seconds of electroencephalogram (EEG) epochs. Three EEG channels, namely: Fz_Cz, Cz_Oz, and C4_M1, are employed to yield an optimum outcome. The Hjorth parameters extracted from SBs were then fed to different machine learning algorithms. To gain an understanding of the model, in this study, we used SHAP (Shapley Additive explanations) method. For subjects suffering from the aforementioned diseases, the model utilized features derived from all channels and employed an ensembled bagged trees (EnBT) classifier. The highest accuracy of 86.8%, 87.3%, 85.0%, 84.5%, and 83.8% is obtained for the insomniac, PLM, apniac, good sleepers and complete datasets, respectively. Using these techniques and datasets, the study aims to enhance sleep stage classification accuracy and improve understanding of sleep disorders such as insomnia, PLM, and sleep apnea.

CD-aware non-orthogonal DFT-precoding for C-band IM/DD multicarrier signals over a 50-km dispersion-uncompensated link

In this paper, a chromatic-dispersion-aware non-orthogonal discrete Fourier transform precoding (CDA-NODFTP) scheme is proposed for CD-constrained intensity-modulation and direct detection (IM/DD) multicarrier-signal transmission systems. The performance of the proposed CDA-NODFTP scheme is experimentally evaluated and compared with conventional DFTP and NODFTP schemes over a 50-km C-band dispersion-uncompensated link, utilizing 90-Gb/s orthogonal frequency division multiplexing (OFDM) and filter bank multicarrier (FBMC) signals. Experimental results show that the proposed CDA-NODFTP with adaptive spectral compression outperforms conventional DFTP, NODFTP, and the non-precoding schemes in terms of bit error rate (BER) performance. Moreover, based on CD response only, the CDA-NODFTP-FBMC exhibits superior performance compared to both CDA-NODFTP-OFDM and adaptive bit and power loading (ABPL) FBMC, achieving receiver sensitivity improvements of over 2 dB at a BER of 3.8 × 10−3.

Spectral efficiency analysis on massive MIMO filter bank

ABSTRACT This article covers the potential of Filter Bank Multicarrier (FBMC) modulation as an alternative to be used in the future 5 G wireless networks in which Massive Multiple-Input Multiple-Output (MIMO) is implemented. The ‘energy efficient Massive Multiple Input Multiple Output (M-MIMO) Filter bank Multi-carrier Modulation (FBMC)’ is necessary and beneficial for the upcoming wireless communiqué system. This is because there is an ever-increasing demand for higher data rates with higher Spectral Efficiency (SE). This paper compares orthogonal frequency division multiplexing (OFDM) with FBMC. This helps to evaluate the Spectral Efficiency (SE) of a Massive MIMO system that performs transmission under a unicellular environment. The variation in Massive MIMO allows equilibrium of the channel because FBMC has extra benefits due to the confinement of the subcarrier in an assigned range. However, the high Peak to Average Power Ratio (PAPR) in an M-MIMO-FBMC system is recognised as a serious problem that causes nonlinear signal deformation and restricts the effectiveness of the Power Amplifier (PA). In this work, to increase the SE in the system, the antenna count is optimised, such that the SNR or SNDR is maximum. For this, the antenna counts are optimised via Self Improved SSO with a Chaotic Map (SISSO-CM).

Filter bank temporally local multivariate synchronization index for SSVEP-based BCI

BackgroundMultivariate synchronization index (MSI) has been successfully applied for frequency detection in steady state visual evoked potential (SSVEP) based brain–computer interface (BCI) systems. However, the standard MSI algorithm and its variants cannot simultaneously take full advantage of the time-local structure and the harmonic components in SSVEP signals, which are both crucial for frequency detection performance. To overcome the limitation, we propose a novel filter bank temporally local MSI (FBTMSI) algorithm to further improve SSVEP frequency detection accuracy. The method explicitly utilizes the temporal information of signal for covariance matrix estimation and employs filter bank decomposition to exploits SSVEP-related harmonic components.ResultsWe employed the cross-validation strategy on the public Benchmark dataset to optimize the parameters and evaluate the performance of the FBTMSI algorithm. Experimental results show that FBTMSI outperforms the standard MSI, temporally local MSI (TMSI) and filter bank driven MSI (FBMSI) algorithms across multiple experimental settings. In the case of data length of one second, the average accuracy of FBTMSI is 9.85% and 3.15% higher than that of the FBMSI and the TMSI, respectively.ConclusionsThe promising results demonstrate the effectiveness of the FBTMSI algorithm for frequency recognition and show its potential in SSVEP-based BCI applications.

Hybrid Brain-Computer Interface Controlled Soft Robotic Glove for Stroke Rehabilitation.

Soft robotic glove controlled by a brain-computer interface (BCI) have demonstrated effectiveness in hand rehabilitation for stroke patients. Current systems rely on static visual representations for patients to perform motor imagination (MI) tasks, resulting in lower BCI performance. Therefore, this study innovatively used MI and high-frequency steady-state visual evoked potential (SSVEP) to construct a friendly and natural hybrid BCI paradigm. Specifically, the stimulation interface sequentially presented decomposed action pictures of the left and right hands gripping a ball, with the pictures flashing at specific stimulation frequencies (left: 34 Hz, right: 35 Hz). Integrating soft robotic glove as feedback, we established a comprehensive "peripheral - central - peripheral" hand rehabilitation system to facilitate the hand rehabilitation of patients. Filter bank common spatial pattern (FBCSP) and filter bank canonical correlation analysis (FBCCA) algorithms were used to identify MI and SSVEP signals, respectively. Additionally, we proposed a novel fusion algorithm to decide the final output of the system. The feasibility of the proposed system was validated through online experiments involving 12 healthy subjects and 9 stroke patients, achieving accuracy rates of 95.83 ± 6.83% and 63.33 ± 10.38, respectively. The accuracy of MI and SSVEP in 12 healthy subjects reached 81.67 ± 15.63% and 95.14 ± 7.47%, both lower than the accuracy after fusion, these results confirmed the effectiveness of the proposed algorithm. The accuracy rate was more than 50% in both healthy subjects and patients, confirming the effectiveness of the proposed system.

Ambisonics’ setup quality assessment through measurements and computations based on ITD and ILD functions using subbands and a Gammatone Filter Bank

Poznan Supercomputing and Networking Center (PSNC) developed an ambisonic installation and workflow as part of audio-visual 8K VR 360° immersive media experiments. This work aimed to investigate the quality of performance of the PSNC setup through both subjective tests as well as simulations providing objective parameters of interaural characteristics in a real-life scenario of PSNC studio. For the objective part, an algorithm for angle estimation has been proposed and computations were performed.

A novel temporal-frequency combination pattern optimization approach based on information fusion for motor imagery BCIs

Motor imagery (MI) stands as a powerful paradigm within Brain-Computer Interface (BCI) research due to its ability to induce changes in brain rhythms detectable through common spatial patterns (CSP). However, the raw feature sets captured often contain redundant and invalid information, potentially hindering CSP performance. Methodology-wise, we propose the Information Fusion for Optimizing Temporal-Frequency Combination Pattern (IFTFCP) algorithm to enhance raw feature optimization. Initially, preprocessed data undergoes simultaneous processing in both time and frequency domains via sliding overlapping time windows and filter banks. Subsequently, we introduce the Pearson-Fisher combinational method along with Discriminant Correlation Analysis (DCA) for joint feature selection and fusion. These steps aim to refine raw electroencephalogram (EEG) features. For precise classification of binary MI problems, an Radial Basis Function (RBF)-kernel Support Vector Machine classifier is trained. To validate the efficacy of IFTFCP and evaluate it against other techniques, we conducted experimental investigations using two EEG datasets. Results indicate a notably superior classification performance, boasting an average accuracy of 78.14% and 85.98% on dataset 1 and dataset 2, which is better than other methods outlined in this article. The study’s findings suggest potential benefits for the advancement of MI-based BCI strategies, particularly in the domain of feature fusion.

Development of a Dynamically Re-Configurable Radio-Frequency Interference Detection System for L-Band Microwave Radiometers.

Real-Time RFI Detection and Flagging (RT-RDF) for microwave radiometers is a versatile new FPGA algorithm designed to detect and flag Radio-Frequency Interference (RFI) in microwave radiometers. This block utilizes computationally-efficient techniques to identify and analyze RF signals, allowing the system to take appropriate measures to mitigate interference and maintain reliable performance. With L-Band microwave radiometry as the main application, this RFI detection algorithm focuses on the Kurtogram and Spectrogram to detect non-Gaussian behavior. To gain further modularity, an FFT-based filter bank is used to divide the receiver's bandwidth into several sub-bands within the band of interest of the instrument, depending on the application. Multiple blanking strategies can then be applied in each band using the provided detection flags. The algorithm can be re-configured in the field, for example with dynamic integration times to support operation in different environments, or configurable thresholds to account for variable RFI environments. A validation and testing campaign has been performed on multiple scenarios with the ARIEL commercial microwave radiometer, and the results confirm the excellent performance of the system.

Narrow band-pass filtered canonical correlation analysis for frequency identification in SSVEP signals

Steady-state visual evoked potentials (SSVEP) are generated in the parieto-occipital regions, accompanied by background noise and artifacts. A strong pre-processing method is required to reduce this background noise and artifacts. This study proposed a narrow band-pass filtered canonical correlation analysis (NBPFCCA) to recognize frequency components in SSVEP signals. The proposed method is tested on the publicly available 40 stimulus frequencies dataset recorded from 35 subjects and 4 class in-house dataset acquired from 10 subjects. The performance of the proposed NBPFCCA method is compared with the standard canonical correlation analysis (CCA) and the filter bank CCA (FBCCA). The mean frequency detection accuracy of the standard CCA is 86.21% for the benchmark dataset, and it is improved to 95.58% in the proposed method. Results indicate that the proposed method significantly outperforms the standard canonical correlation analysis with an increase of 9.37% and 17% in frequency recognition accuracy of the benchmark and in-house datasets, respectively.

RAGE-Net: Enhanced retinal vessel segmentation U-shaped network using Gabor convolution

Extracting vessel morphology from fundus images is pivotal in acquiring pathological insights and enabling early diagnosis of retinal disorders. Manual segmentation of retinal vessels requires a high degree of expertise and is notably time-intensive. Although existing deep learning techniques for retinal vessel segmentation predominantly hinge on U-shaped convolutional neural networks, significant headway has been made, complexities persist in delineating faint, low-contrast vessels amidst noisy backgrounds. To confront these challenges, we propose an innovative U-shaped convolutional neural network fortified with oriented priors, labeled as the Receptive Field Aggregating Gabor Enhance Network (RAGE-Net). We revamp the conventional U-shaped convolutional network with a foundation in Gabor wavelet and Gabor convolutional network, introducing a Gabor Matching Enhance Architecture (GMEA) amalgamated into the U-shaped convolutional network. This architecture comprises two distinct modules. Initially, a Dual-scale Gabor Enhance Module (DGEB) is introduced to bolster vessel continuity and effectively fortify delicate vessels by integrating oriented feature enhancement through Gabor convolution. Subsequently, a Receptive Field Pyramid Module (RPM) is proposed to supplant the escalation of scale count in the Gabor filter bank for vessel alignment, also serving as feature fusion to enhance the network's comprehensive vessel discernment. In comparison to U-Net, our model boasts fewer parameters and surpasses in terms of sensitivity, accuracy, and F1 score on the DRIVE dataset by 3.58%, 0.34%, and 2.29%, respectively. Our model showcases stellar performance across three public datasets: DRIVE, STARE, and CHASE_DB1, with sensitivity values of 0.8172, 0.8126, and 0.8540 and accuracy figures of 0.9708, 0.9725, and 0.9757, respectively. The analysis on three datasets reveals that our model demonstrates distinct strengths in AUC and Se as a result of integrating GMEA, which comprises RPM and DGEB, into U-shaped networks. However, it does not reduce overall accuracy to improve the model's ability to perceive weak contrast and small blood vessels. While maintaining superior Acc, our model also demonstrates advancements in Sp and F1 score, indicating a balanced progression in multiple evaluation metrics.

Automatic classification of multi-carrier modulation signal using STFT spectrogram and deep CNN

In the realm of communication systems, categorizing Multi-Carrier Modulation (MCM) signals without cooperative communication poses a significant technical challenge. In this paper, we introduce a novel approach for accurately categorizing five distinct MCM signals, including Orthogonal Frequency Division Multiplexing (OFDM), Filter Bank Multicarrier (FBMC), Filtered Orthogonal Frequency Division Multiplexing (FOFDM), Windowed Orthogonal Frequency Division Multiplexing (WOLA), and Universal Filtered Multicarrier (UFMC). Each signal is considered with two types of subcarrier waveforms, Quadrature Amplitude Modulation 16 (QAM16) and Quadrature Amplitude Modulation 64 (QAM64), resulting in a total of 10 unique MCM signals for classification. Our proposed methodology leverages Short-Time Fourier Transform (STFT) spectrograms for feature extraction at the frontend, while at the backend, we employ three variants of Convolutional Neural Network (CNN) models; CNN, CNN with Dropout (CNN_d), CNN with both Dropout and L1 Regularization (CNN_dL1) and one deep CNN model; Xception, individually. We aim to provide an efficient and reliable means of categorizing MCM signals, with practical applications in signal processing and communication systems. Extensive simulations demonstrate the effectiveness of our approach, achieving remarkable accuracies. Notably, the Xception model exhibits the highest accuracy among the four models considered. Specifically, we attain an accuracy of 98% at 10 dB SNR using the Xception model. These results underscore the efficacy of our proposed methodology and highlight the potential for its deployment in real-world scenarios.

RESNET34 with Synchrosqueezing Transform for ADHD Disorder Detection Using EEG Signals

It is vital for the development of attention deficit hyperactivity disorders (ADHDs) diagnostic tools that help healthcare practitioners to have automated recognition of ADHDs. Recently, the detection of ADHD using EEG data has attracted a lot of interest due to its apparent rapidity in diagnosis and treatment. This is because the disease seems to be caused by hyperactivity and inattention. This paper presents the method for diagnosing ADHDs based on EEG data, with the nonlinear characteristics being retrieved via the Synchrosqueezing Transform (ST). The ST filter banks are used to break down the 16 channels of EEG signal information into the ideal number of time-frequency subbands. At each stage of the decomposition process, the distinctive feature vectors are assessed using CNN as well as the proposed nonlinear fractal dimensions approach. Using ResNet34, it was discovered that a modified CNN classifier that used a 10-fold cross-validation approach was an efficient classifier for differentiating between normal people and those with ADHD. The suggested method was able to successfully categorize ADHD and normal participants with the best accuracy, as shown by several performance indicators. The statistical study demonstrated that the CNN and the proposed nonlinear feature estimation techniques produce possible characteristics that are acceptable for the automated identification of ADHD. These features may be categorized with good accuracy, sensitivity and specificity. The technique that has been presented is capable of correctly differentiating between participants who have ADHD and those who do not have ADHD with an ultimate precision of 99.5%.

Effect of Line-of-sight Channels on DC-biased Optical Filter Bank Multicarrier Visible Light Communication with Multiple LED Arrays

This paper investigates the impact of propagation delay and channel loss due to the use of multiple LED arrays in visible light communication (VLC) systems based on filter bank multicarrier (FBMC) modulation. FBMC offers greater spectral efficiency, and asynchronous transmission and is a promising alternative scheme to orthogonal frequency division modulation (OFDM). The proposed FBMC model is based on 4-quadrature amplitude modulation (QAM) and 16-QAM formats and uses 100 symbols and 600 input bits per symbol. In this paper, the VLC-FBMC system is designed based on the line-of-sight (LOS) model under the additive white Gaussian noise (AWGN) channel. Comparison analyses between different bit rates in terms of bit error rate (BER), best sampling point, and signal-to-noise ratio (SNR) requirement have been carried out to show the delay and loss effect on communication quality and system performance. The results demonstrate that the proposed FBMC model achieves a bit rate of up to 29.296 Mbit/s with a low BER of 10-3 and less SNR penalty in high QAM formats, demonstrating its potential as a viable alternative to OFDM for future VLC systems.

A Vacuum Waveguide Filter Bank Spectrometer for Far-Infrared Astrophysics

Traditional technologies for far-infrared (FIR) spectroscopy generally involve bulky dispersive optics. Integrated filter bank spectrometers promise more compact designs, but implementations using superconducting transmission line networks become lossy at terahertz frequencies. We describe a novel on-chip spectrometer architecture designed to extend this range. A filter bank spectrometer is implemented using vacuum waveguide etched into a silicon wafer stack. A single trunk line feeds an array of resonant cavities, each coupled to a kinetic inductance detector fabricated on an adjacent wafer. We discuss the design and fabrication of a prototype implementation, initial test results at ambient temperature, and prospects for future development.

A Deep Transfer Learning Model for the Fault Diagnosis of Double Roller Bearing Using Scattergram Filter Bank 1

In this study, a deep transfer learning model was developed using ResNet-101 architecture to diagnose double roller bearing defects. Vibration data were collected for three different load scenarios, including conditions without load, and for five different rotational speeds, ranging from 500 to 2500 RPM. Significantly, the speed condition of 2500 RPM has not previously been investigated, therefore offering a potential avenue for future investigations. This study offers a thorough examination of bearing conditions using multidirectional vibration data collected from accelerometers positioned in both vertical and horizontal orientations. In addition to transfer learning using ResNet-101, four additional models (VGG-16, VGG19, ResNet-18, and ResNet-50) were trained. Transfer learning using ResNet-101 consistently achieved the highest accuracy in all scenarios, with accuracy rates ranging from 90.78% to 99%. Scattergram Filter Bank 1 was used as the image input for training as a preprocessing method to enhance feature extraction. Research has effectively applied transfer learning to improve fault diagnosis accuracy, especially in limited data scenarios. This shows the capability of the method to differentiate between normal and faulty bearing conditions using signal-to-image transformation, emphasizing the potential of transfer learning to augment diagnostic performance in scenarios with limited training data.

LPI Sequences Optimization Method against Summation Detector Based on FFT Filter Bank

Waveform design is a crucial factor in electronic surveillance (ES) systems. In this paper, we introduce an algorithm that designs a low probability of intercept (LPI) radar waveform. Our approach directly minimizes the detection probability of summation detectors based on FFT filter banks. The algorithm is derived from the general quadratic optimization framework, which inherits the monotonic properties of such methods. To expedite overall convergence, we have integrated acceleration schemes based on the squared iterative method (SQUAREM). Additionally, the proposed algorithm can be executed through fast Fourier transform (FFT) operations, enhancing computational efficiency. With some modifications, the algorithm can be adjusted to incorporate spectral constraints, increasing its flexibility. Numerical experiments indicate that our proposed algorithm outperforms existing ones in terms of both intercept properties and computational complexity.

Applications of Brain Wave Classification for Controlling an Intelligent Wheelchair

The independence and autonomy of both elderly and disabled people have been a growing concern in today’s society. Therefore, wheelchairs have proven to be fundamental for the movement of these people with physical disabilities in the lower limbs, paralysis, or other type of restrictive diseases. Various adapted sensors can be employed in order to facilitate the wheelchair’s driving experience. This work develops the proof concept of a brain–computer interface (BCI), whose ultimate final goal will be to control an intelligent wheelchair. An event-related (de)synchronization neuro-mechanism will be used, since it corresponds to a synchronization, or desynchronization, in the mu and beta brain rhythms, during the execution, preparation, or imagination of motor actions. Two datasets were used for algorithm development: one from the IV competition of BCIs (A), acquired through twenty-two Ag/AgCl electrodes and encompassing motor imagery of the right and left hands, and feet; and the other (B) was obtained in the laboratory using an Emotiv EPOC headset, also with the same motor imaginary. Regarding feature extraction, several approaches were tested: namely, two versions of the signal’s power spectral density, followed by a filter bank version; the use of respective frequency coefficients; and, finally, two versions of the known method filter bank common spatial pattern (FBCSP). Concerning the results from the second version of FBCSP, dataset A presented an F1-score of 0.797 and a rather low false positive rate of 0.150. Moreover, the correspondent average kappa score reached the value of 0.693, which is in the same order of magnitude as 0.57, obtained by the competition. Regarding dataset B, the average value of the F1-score was 0.651, followed by a kappa score of 0.447, and a false positive rate of 0.471. However, it should be noted that some subjects from this dataset presented F1-scores of 0.747 and 0.911, suggesting that the movement imagery (MI) aptness of different users may influence their performance. In conclusion, it is possible to obtain promising results, using an architecture for a real-time application.

Iterative feature mode decomposition: a novel adaptive denoising method for mechanical fault diagnosis

Remaining useful life prediction of rolling bearings highly relies on feature extraction of signals. The use of denoising algorithms helps to better eliminate noise and extract features, thereby constructing health indicators to predict remaining useful life. This paper proposes a novel adaptive denoising method based on iterative feature mode decomposition (IFMD) to accurately and efficiently extract fault features. The feature mode decomposition (FMD) employs correlation kurtosis (CK) as the objective function for iterative filter bank updates, enabling rapid identification of fault features. To achieve IFMD, the sparrow search algorithm combines sine-cosine algorithm and cauchy variation (SCSSA) to optimize two key parameters in FMD. During the continuous iteration process of the SCSSA algorithm, filter length and number of modes were determined. IFMD does not require empirical setting of initial parameters. During iterative process, the signal is accurately decomposed and the noise is eliminated. Compared with other optimization algorithms, SCSSA has obvious advantages in iterative rate and global optimization. The envelope spectrum feature energy ratio (ES-FER) is used to select decomposed modes, and the mode with the largest ES-FER is chosen as the optimal mode. Bearing fault diagnosis is realized by envelope spectrum analysis of the optimal mode. The numerical simulations and experimental verifications both validate the effectiveness and superiority of the proposed IFMD in mechanical fault diagnosis.