Filter Bank Research Articles

The Strategy of Public Relations Through Corporate Social Responsibility Program to Build a Positive Corporate Image

Corporate Social Responsibility is one of the programs from Public Relations PT Samudera Indonesia Tbk as the social responsibility program for the impact of the environment. The target of this program is RW 11 Kampung Tanjung Priok. The research aims to find out the implementation of the concept of 3P profit, planet, and people, to find out the form of communication channels and communication barriers, and to find out the reason of PT. Samudera Indonesia Tbk chose Kampung Lestari Samudera Indonesia in Corporate Social Responsibility programs to build a positive corporate image. The method of the research was qualitative research with a case studies approach and used the concepts of CSR implementation by Wibisono and the concept of 3P profit, planet, and people by John Elkington. The object of this research is PT. Samudera Indonesia Tbk. The technique used for this research are interviewing, observations, study literature, and documentation. Results of the research showed that at planning stage, Public Relations undertake socializing with RW 11 Kampung Tanjung Priok and making activity plans including make a vision, mission, goals, targets, formulating programs, determining human resources, and providing needs. At implementation stage, the company carried out several activities as installation of Filter Udara, Bank Sampah, and Kebun Gizi. At evaluation stage, Public Relations held an evaluation meeting with Ketua RW 11 and Karang Taruna RW 11 in Kampung Tanjung Priok for the sustainability program, and at the reporting stage, Public Relations make an LPJ report and Karang Taruna make a report of activities results. The communication channels that used by PT. Samudera Indonesia Tbk is a website channel, and the communications barriers that occur are mechanical noise and sematic noise and the company chosen this CSR program as a strategy to build a positive corporate image.

AlexNet-based deep convolutional neural network optimized with group teaching optimization algorithm (GTOA) for paediatric bone age assessment from hand X-ray images

ABSTRACT Bone age assessment is used to diagnose paediatric growth because some types of bone diseases occur in childhood. To overcome these issues, AlexNet-Based Deep Convolutional Neural Network Optimized with the Group Teaching Optimization Algorithm is proposed. First, input images are gathered via RSNA paediatric bone age dataset. These images are preprocessed using Wavelet Packet Transform Cochlear Filter Bank. Then input hand X-ray images’ ROI is segmented using Bayesian fuzzy clustering. Then segmented ROI region is fed to ADCNN that accurately predicts BAA. In general, ADCNN does not divulge any optimization techniques adopted for determining the optimal parameters and ensuring accurate classification. Therefore, the GTOA is used to optimize the ADCNN weight parameters. The proposed approach is done in MATLAB and various performance metrics such as accuracy, F-score, sensitivity, precision, specificity, CCC and CC. The BAA-ADCNN-GTOA method provides higher accuracy 23.75%, 17.97%, 31.65% compared with existing methods, like BAA-CNN-RRNN, BAA-RNN-AF-SFO, BAA-U-Net-CTO- WOA, respectively.

Perfect reconstruction two-channel filter banks on arbitrary graphs

This paper extends the existing theory of perfect reconstruction two-channel filter banks from bipartite graphs to non-bipartite graphs. By generalizing the concept of downsampling/upsampling we establish the frame of two-channel filter bank on arbitrary connected, undirected and weighted graphs. Then the equations for perfect reconstruction of the filter banks are presented and solved under proper conditions. Algorithms for designing orthogonal and biorthogonal banks are given and two typical orthogonal two-channel filter banks are calculated. The locality and approximation properties of such filter banks are discussed theoretically and experimentally.

Generalized matrix spectral factorization with symmetry and applications to symmetric quasi-tight framelets

Factorization of matrices of Laurent polynomials plays an important role in mathematics and engineering such as wavelet frame construction and filter bank design. Wavelet frames (a.k.a. framelets) are useful in applications such as signal and image processing. Motivated by the recent development of quasi-tight framelets, we study and characterize generalized spectral factorizations with symmetry for 2×2 matrices of Laurent polynomials. Applying our result on generalized matrix spectral factorization, we establish a necessary and sufficient condition for the existence of symmetric quasi-tight framelets with two generators. The proofs of all our main results are constructive and therefore, one can use them as construction algorithms. We provide several examples to illustrate our theoretical results on generalized matrix spectral factorization and quasi-tight framelets with symmetry.

A Parallel Solution of Timing Synchronization in High-Speed Remote Sensing Data Transmission

Considering the problem that the timing synchronization calculation in high-speed remote sensing signal reception is complex and it is difficult for it to be parallel, this paper deduces and designs a parallel timing error estimation and correction scheme. This paper presents the design of polyphase DFT filter banks with non-maximum decimation. The feedforward timing error estimation and correction method is then improved to enhance synchronization performance. Finally, an implementation scheme for parallel timing error estimation and correction is proposed using the polyphase filter bank time domain decomposition method and the filter polyphase model. In the estimation module, the parallel implementation structure of the joint second-order and fourth-order cyclic statistics methods is designed, which improves the estimation accuracy. In the correction module, a fractional delay filtering method with higher accuracy is adopted in order to improve the calibration accuracy and reduce the computational complexity. The timing synchronization of a high-speed remote sensing signal with timing error is simulated and verified. The experimental results show that the parallel method proposed in this paper greatly reduces the processing speed of subband data, and has a good synchronization performance, which is close to the theoretical limit in the demodulation error rate. This paper utilizes a multi-phase DFT filter bank architecture to achieve parallel timing synchronization, which presents a novel approach for the future parallel reception of high-speed remote sensing signals.

Avocodo: Generative Adversarial Network for Artifact-Free Vocoder

Neural vocoders based on the generative adversarial neural network (GAN) have been widely used due to their fast inference speed and lightweight networks while generating high-quality speech waveforms. Since the perceptually important speech components are primarily concentrated in the low-frequency bands, most GAN-based vocoders perform multi-scale analysis that evaluates downsampled speech waveforms. This multi-scale analysis helps the generator improve speech intelligibility. However, in preliminary experiments, we discovered that the multi-scale analysis which focuses on the low-frequency bands causes unintended artifacts, e.g., aliasing and imaging artifacts, which degrade the synthesized speech waveform quality. Therefore, in this paper, we investigate the relationship between these artifacts and GAN-based vocoders and propose a GAN-based vocoder, called Avocodo, that allows the synthesis of high-fidelity speech with reduced artifacts. We introduce two kinds of discriminators to evaluate speech waveforms in various perspectives: a collaborative multi-band discriminator and a sub-band discriminator. We also utilize a pseudo quadrature mirror filter bank to obtain downsampled multi-band speech waveforms while avoiding aliasing. According to experimental results, Avocodo outperforms baseline GAN-based vocoders, both objectively and subjectively, while reproducing speech with fewer artifacts.

Research on phase modulation to enhance the feature of high-frequency steady-state asymmetric visual evoked potentials

High-frequency steady-state asymmetric visual evoked potential (SSaVEP) provides a new paradigm for designing comfortable and practical brain-computer interface (BCI) systems. However, due to the weak amplitude and strong noise of high-frequency signals, it is of great significance to study how to enhance their signal features. In this study, a 30 Hz high-frequency visual stimulus was used, and the peripheral visual field was equally divided into eight annular sectors. Eight kinds of annular sector pairs were selected based on the mapping relationship of visual space onto the primary visual cortex (V1), and three phases (in-phase[0º, 0º], anti-phase [0º, 180º], and anti-phase [180º, 0º]) were designed for each annular sector pair to explore response intensity and signal-to-noise ratio under phase modulation. A total of 8 healthy subjects were recruited in the experiment. The results showed that three annular sector pairs exhibited significant differences in SSaVEP features under phase modulation at 30 Hz high-frequency stimulation. And the spatial feature analysis showed that the two types of features of the annular sector pair in the lower visual field were significantly higher than those in the upper visual field. This study further used the filter bank and ensemble task-related component analysis to calculate the classification accuracy of annular sector pairs under three-phase modulations, and the average accuracy was up to 91.5%, which proved that the phase-modulated SSaVEP features could be used to encode high- frequency SSaVEP. In summary, the results of this study provide new ideas for enhancing the features of high-frequency SSaVEP signals and expanding the instruction set of the traditional steady state visual evoked potential paradigm.

Hearing as adaptive cascaded envelope interpolation

The human auditory system is designed to capture and encode sounds from our surroundings and conspecifics. However, the precise mechanisms by which it adaptively extracts the most important spectro-temporal information from sounds are still not fully understood. Previous auditory models have explained sound encoding at the cochlear level using static filter banks, but this vision is incompatible with the nonlinear and adaptive properties of the auditory system. Here we propose an approach that considers the cochlear processes as envelope interpolations inspired by cochlear physiology. It unifies linear and nonlinear adaptive behaviors into a single comprehensive framework that provides a data-driven understanding of auditory coding. It allows simulating a broad range of psychophysical phenomena from virtual pitches and combination tones to consonance and dissonance of harmonic sounds. It further predicts the properties of the cochlear filters such as frequency selectivity. Here we propose a possible link between the parameters of the model and the density of hair cells on the basilar membrane. Cascaded Envelope Interpolation may lead to improvements in sound processing for hearing aids by providing a non-linear, data-driven, way to preprocessing of acoustic signals consistent with peripheral processes.

EEG motor imagery classification using deep learning approaches in naïve BCI users

Motor Imagery (MI)-Brain Computer-Interfaces (BCI) illiteracy defines that not all subjects can achieve a good performance in MI-BCI systems due to different factors related to the fatigue, substance consumption, concentration, and experience in the use. To reduce the effects of lack of experience in the use of BCI systems (naïve users), this paper presents the implementation of three Deep Learning (DL) methods with the hypothesis that the performance of BCI systems could be improved compared with baseline methods in the evaluation of naïve BCI users. The methods proposed here are based on Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM)/Bidirectional Long Short-Term Memory (BiLSTM), and a combination of CNN and LSTM used for upper limb MI signal discrimination on a dataset of 25 naïve BCI users. The results were compared with three widely used baseline methods based on the Common Spatial Pattern (CSP), Filter Bank Common Spatial Pattern (FBCSP), and Filter Bank Common Spatial-Spectral Pattern (FBCSSP), in different temporal window configurations. As results, the LSTM-BiLSTM-based approach presented the best performance, according to the evaluation metrics of Accuracy, F-score, Recall, Specificity, Precision, and ITR, with a mean performance of 80% (maximum 95%) and ITR of 10 bits/min using a temporal window of 1.5 s. The DL Methods represent a significant increase of 32% compared with the baseline methods (p < 0.05). Thus, with the outcomes of this study, it is expected to increase the controllability, usability, and reliability of the use of robotic devices in naïve BCI users.

Two‐dimensional multichannel bandpass filter bank with improved fractional bandwidth and selectivity

AbstractThis letter presents a six‐channel bandpass filter bank created on substrate integrated waveguide (SIW) with improved bandwidth and frequency selectivity on the lower band. Through SIW square cavities and slant slots, a two‐dimensional multichannel bandpass filter bank with a transmission zero constructed with the SIW orthogonal resonant modes is produced. The substrate integrated square cavity approach is used to achieve the propagation of the two orthogonal‐oriented transverse electric modes, TE102 and TE201, which resonate on the same frequency and bandwidth, depending on the location of the feed ports. The bandwidth could be altered by the coupling matrix synthesis and coupling windows. Better transmission and bandpass responses are produced in each channel when the feed ports and coupling gaps are positioned and appropriately adjusted. However, a pair of slot lines are imprinted along each cavity's designated diagonal to guarantee better isolation while attaining an increased fractional bandwidth. A 3 × 3 array of six bandpass filters with a center frequency of 8.65 GHz has been built, simulated, and measured to establish the suggested technique. Low insertion loss, high return loss, high isolation, an improved fractional bandwidth, and a compact device were the proposal's advantages.

A structural characterization of compactly supported OEP-based balanced dual multiframelets

Compared to scalar framelets, multiframelets have certain advantages, such as relatively smaller supports on generators, high vanishing moments, etc. The balancing property of multiframelets is very desired, as it reflects how efficient vector-valued data can be processed under the corresponding discrete multiframelet transform. Most of the literature studying balanced multiframelets is from the point of view of the function setting, but very few approaches are from the aspect of multiframelet filter banks. In this paper, we study structural characterizations of balanced dual multiframelets from the point of view of the Oblique Extension Principle (OEP). The OEP naturally connects framelets with filter banks, which makes it a very handy tool for analyzing the properties of framelets. With the OEP, we shall characterize compactly supported balanced dual multiframelets through the concept of balanced moment correction filters, which is the key notion that will be introduced in our investigation. The results of this paper demonstrate what essential structures a balanced dual multiframelet has in the most general setting, and bring us a more complete picture to understand balanced multiframelets and their underlying discrete multiframelet transforms.

Feeding intensity assessment of aquaculture fish using Mel Spectrogram and deep learning algorithms

Accurately and objectively analyzing fish feeding intensity is essential to guiding feeding and production techniques. Fish feeding intensity in recirculating aquaculture systems (RAS) can be used to indicate a fish's appetite. However, the low efficiency and lack of objectivity of manual observation are problems with current fish feeding intensity assessment processes. Applying acoustic techniques to aquaculture issues is an insufficiently explored area that requires new investigations, particularly into methods that explore temporal information in acoustic spectrograms. With Oplegnathus punctatus as the experimental species, we proposed a fish feeding intensity detection method based on the Mel Spectrogram and MobileNetV3-SBSC lightweight networks. First, the Oplegnathus punctatus feeding sound dataset, which has a total of 3 types—"strong," "medium," and "none," was built. Next, Mel Spectrogram feature maps were extracted using steps including preprocessing, Fast Fourier Transform (FFT), Mel filter bank (MFB), etc. Finally, the MobileNetV3-SBSC lightweight network was used to detect and recognize the obtained feature maps. Experimental results indicated that the proposed MobileNetV3-SBSC model, as compared to the MobileNetV3-S model, improved test accuracy by 4.6% and decreased test loss by 67.4% with only a 0.84% increase in the number of parameters and a 3.08% increase in the model size. Additionally, we have verified that the accuracy of the test set was 59.6%, 53.3%, 83.3%, 85.3%, and 85.9% for groups of 5, 15, 40, 70, and 100 fish, respectively. This study demonstrated that the proposed method is not applicable to a small number of fish, which means that when the numbers of fish are small, changes in the feeding of individual fish would have a significant effect on the whole feeding feature map, leading to negligible changes in feeding features. However, in view of the commonly high aquaculture density, the proposed method can be used to automatically and objectively examine fish feeding, which could provide a theoretical basis and methodological support for further feeding decisions.

Vibration-based looseness identification of bolted structures via quasi-analytic wavelet packet and optimized large margin distribution machine

Bolted joints are the most widely utilized connection types in industries, and therein looseness identification of bolted structures is of great significance to guarantee structural reliability. In this article, a comprehensive study of bolt looseness identification under random excitation is presented. To fulfill this task, this research focuses on three prominent difficulties, including nonstationary signal processing, subtle feature extraction, and robust state classification. First, a novel filter bank structure of quasi-analytic dual-tree complex wavelet packet transform is constructed to analyze the measured vibration response signals, for purpose of capturing subtle feature information. Then, multiple features are extracted from subband signals to capture the variations of dynamic characteristics, and sensitive features are selected by Laplacian score to construct the low-dimensional feature set. Subsequently, a novel classifier with better generalization performance, named large margin distribution machine, is optimized with the wavelet kernel function and the whale optimization algorithm, in order to handle the intrinsic uncertainty related to the looseness states of bolted structures. After feeding the low-dimensional feature set, the proposed classifier is trained to identify looseness states of bolted structures. Finally, experiments of a two-bolt lapped beam under random excitation are conducted to verify the effectiveness of the proposed method, and two typical loading conditions (paired-bolt looseness and single-bolt looseness) are considered. Besides, the superiority of the proposed method is demonstrated by comparing with other analogical methods. This research can provide a promising implement in practical applications of bolt looseness identification under random excitation.

Bandwidth-aware adaptive chirp mode decomposition for railway bearing fault diagnosis

It is a challenging task to accurately diagnose a railway bearing fault since bearing vibration signals are under strong interferences from wheel–rail excitations. The commonly used Kurtogram-based methods are often trapped in components induced by the wheel–rail excitations while adaptive mode decomposition methods are sensitive to input control parameters. To address these issues, based on a recently developed powerful signal decomposition method, that is, adaptive chirp mode decomposition (ACMD), a novel method called bandwidth-aware ACMD (BA-ACMD) is proposed in this article. First, the filter bank property of ACMD is thoroughly analyzed based on Monte-Carlo simulation and then a bandwidth expression with respect to the penalty parameter is first obtained by fitting a power law model. Then, a weighted spectrum trend (WST) method is proposed to partition frequency bands and then guide the parameter determination of ACMD through the integration of the obtained bandwidth expression. In addition, according to the order of magnitude of the WST in each band, the BA-ACMD adopts a recursive framework to extract signal modes one by one. In this way, dominating signal modes related to wheel–rail excitations can be extracted and then subtracted from the vibration signal in advance so that the bearing faults induced signal modes can be successfully identified. Both simulation and experimental validations are conducted showing that BA-ACMD can effectively detect single and compound faults of railway bearings under strong wheel–rail excitations.

Minimax design of two-channel critically sampled graph QMF banks

We address here the issue of designing high spectral selectivity graph filter banks, which require high-degree polynomial approximation. To achieve sharp transition band roll required for high-selectivity, the minimax criterion is used in the design formulation. A constraint optimization method is used for the design process, and a spectral transformation is employed to circumvent the numerical problems that arise with high-degree polynomials. Using the transformation, approximation problem can be recast as an approximation problem with trigonometric polynomials. The transformed constraint optimization problem can be solved using the readily available sequential quadratic programming (SQP) method. High order filters, with low maximum approximation error (MAE) and low maximum reconstruction error (MRE), can be designed with this method. An algorithm is also developed that allows the user to choose design parameters that will give the lowest possible MRE. The method can also be adapted to trade-off between reconstruction quality and spectral selectivity of the resulting filter bank. The suite of techniques presented allow the user to easily shape the spectral response of the filters, to suit the application at hand. Numerous examples, with comparisons, will be presented to demonstrate the versatility of the proposed techniques. Applications to nonlinear approximation and denoising of graph signal will also be considered.

Effect of block size on BER performance of inverse filtering based MIMO FBMC

Filter bank multicarrier (FBMC) is a novel next-generation data transfer technique. FBMC has a large number of benefits over conventional orthogonal frequency division multiplexing (OFDM) techniques. The FBMC scheme suffers from imaginary interference, which degrades the bit error rate (BER) performance at the receiver. In the recent past, inter carrier interference (ICI)-free Alamouti-coded FBMC frame and block repetition schemes for BER improvement were discussed. However, in the analysis, various interfering terms are neglected. In this work to reduce ICI and inter symbol interference (ISI), the concept of inverse filtering is applied while considering other interfering terms. Inverse filtering can be used to reduce the negative impacts of the ICI and ISI. This is determined by mathematical modelling that includes the ICI, ISI, and other interfering parameters. The simulation is performed in MATLABR software. The obtained BER at an SNR of 15 dB is 2.3 × 10−4.The simulation results are also compared with some of the recent methods, and it has been found that the proposed scheme is better in comparison to the considered recent methods.

Quantization noise analysis in FBMC receivers and its effect on the BER performance

This paper analyzes the fixed-point and floating-point roundoff errors in different Filter Bank MultiCarrier (FBMC) receiver implementations. Two different structures are considered: the polyphase network based and frequency spreading based. Theoretical closed-form formulas for the total Quantization Noise Power (QNP) are derived and verified through simulation. The results show the effects of each parameter and signal processing block on the overall QNP. Also, the results show that the total QNP for an arbitrary set of parameters can be determined using the derived results without performing exhaustive simulations. The effects of the chosen bit-resolution on the bit-error rate performance are also analyzed. Furthermore – based on the theoretical results – FBMC receivers can be designed with an optimal number of bits used in the arithmetic units without significantly affecting the receiver performance.

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.

Doubly selective channel estimation and equalization based on ICI/ISI mitigation for OQAM-FBMC systems

Over a doubly selective channel, broadband transmission systems face challenges in channel estimation and equalization. High mobility causes inter-carrier interference (ICI), while multipath transmission induces inter-symbol interference (ISI). In this paper, we present a mitigation method of ICI/ISI for the offset quadrature amplitude-modulated filter bank multi-carrier (OQAM-FBMC) system. It features low inherent imaginary interference (IMI) sensitivity and high efficiency. Specifically, a pilot indices optimization algorithm and a sparse adaptive orthogonal subspace pursuit (SAOSP) algorithm are presented based on the 2-D channel modeling scheme. The guard pilots are first added to mitigate the effect of ICI. Then the index optimization and SAOSP algorithms are applied to achieve a high-accuracy estimation of sparse channel coefficients. In addition, a threshold judgment suboptimal minimum mean square error (MMSE) equalization method is presented based on the variability of the interference power. The method uses normalized interference power thresholds to estimate the ISI dimension and reduce the equalization data, thus mitigating the effect of ISI and achieving efficient equalization. To verify the above methods, single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) models are built. Simulation results indicate a 3-5 dB improvement in channel estimation accuracy. The suboptimal MMSE equalization results are close to the optimal MMSE with about four orders of magnitude reduction in complexity.

Filter-Bank-Enabled Leaky-Wave Antenna Array Technique for Full-Band-Locked Radar System in Stitched Frequency-Space Domain

Inspired by the filter-bank (FB) concept that is normally used for multi-rate signal processing, an FB-enabled array technique of leaky-wave antennas (LWAs) is proposed and studied for creating full-band-locked frequency-scanning radar (FSR) systems in a stitched frequency-space domain. This is mainly for addressing the coupling dilemma between the range and angle resolutions, which is historically and naturally inherited from a conventional FSR. Firstly, the typical frequency-modulated continuous-wave system architecture is selected to exemplify and recall the characteristics of a conventional FSR with emphasis on its resolution coupling. Then, a radar solution featuring a stitched frequency-space domain and depending on an FB-enabled array of LWA channels is introduced for realizing the desired resolution decoupling. With the radar equation, FB-related conditions for obtaining the critical “frequency-space stitching” are analytically derived and converted into relevant design specifications of an LWA array, including engineered beam-scanning functions, beam-crossovers, and phase alignments. To facilitate the practical implementation of such a front-end array technique, a detailed and generalized design flow is developed. Finally, for a simple proof of concept, an FB-enabled two-channel LWA array is modeled, fabricated, and measured. Simulated and measured results are in reasonable agreement; both demonstrate the desired “frequency-space stitching” behavior, i.e., enhanced spectrum bandwidth and widened radiation beamwidth. The proposed array solution may be potentially deployed for FSR systems with a full-band-locked beam illumination and a decoupled range-angle resolution, which may present a highly competing scheme against the phased array radar techniques.