Multiple Subbands Research Articles

An improved multi-band spectral subtraction technique designed for cochlear implant users

In this paper, we propose a novel approach to reduce noise in cochlear implants by utilizing an enhanced multi-band spectral subtraction method. The main objective of this work is to optimize the subtraction factors used in the spectral subtraction process to improve the overall performance of the stimulation strategy in cochlear implants. These implants are designed to decompose incoming audio signals into multiple sub-bands for further processing. However, one of the persistent challenges in cochlear implant technology is maintaining speech intelligibility in noisy environments. To address this issue, our approach involves adapting the subtraction factors dynamically based on common noise patterns observed in real-world scenarios. To assess the effectiveness of this method, we conducted extensive simulations using noisy sentences corrupted by eight distinct types of environmental noise, including street noise, machinery, and crowd chatter. Objective evaluations were performed using established metrics for speech intelligibility and audio quality. The results demonstrate that our proposed method significantly enhances the signal-to-noise ratio, ensuring improved clarity of speech for users of cochlear implants. This approach is highly promising, as evidenced by the encouraging results obtained in our simulations. By enabling more precise signal processing, it contributes to better speech intelligibility and overall user satisfaction.

A Novel Filter Bank and Fourier Transform Convolutional Neural Network for SSVEP Classification

Abstract. Classifying Steady-State Visual Evoked Potentials (SSVEPs) is crucial for enhancing the performance of Brain-Computer Interface (BCI) systems. This study introduces a new method for SSVEP classification within BCI frameworks, called the Filter Bank and Fourier Transform Convolutional Neural Network (FBFCNN). The FBFCNN model effectively extracts key harmonic features by first segmenting SSVEP signals into multiple sub-bands through a filter bank. Subsequently, the Fast Fourier Transform (FFT) is applied to convert these signals from the time domain into the frequency domain. The generated spectrum's real and imaginary components are then fed into a convolutional neural network (CNN). This method improves SSVEP feature extraction and increases classification accuracy by fusing the advantages of CNNs with filter banks. Experimental results, derived from publicly available SSVEP datasets, show that the FBFCNN model surpasses traditional techniques in both accuracy and Information Transfer Rate (ITR). Offering a reliable and efficient solution for real-time brain signal decoding, the FBFCNN model represents a significant advancement in SSVEP-based BCI systems.

Wideband multitarget passive tracking based on belief propagation theory

In passive acoustic monitoring system, information is typically derived from radiated noise signals of targets distributed across multiple subbands. When targets are close in the node field of view, direct processing of the entire frequency band may lead to premature merging of detection results or masking of weaker targets due to differences in energy distribution among the target bands. This situation can degrade the performance of tracking algorithms because the downstream tracking processing cannot compensate for the loss of upstream measurements. This study proposes a novel wideband multitarget tracking algorithm based on belief propagation to address this challenge. Initially, a merged target model is established to simulate the mechanism of measurement generation from multiple targets. Based on this model, merged targets and each subband measurement form a cyclic association structure. This structure is processed using the loopy belief propagation algorithm to prevent the combinatorial explosion. A mixture of belief and mutual information entropy weight is established for each subband to fuse all posterior probability density functions, considering differences in subband energy distribution. Simulation and lake trial results demonstrate that this algorithm substantially improves the tracking capabilities of passive acoustic monitoring system for wideband targets, underscoring its practical application value.

The role of Rashba spin-orbit induced spin textures in the anomalous Josephson effect

This work reports the theoretical investigation into the mechanism underpinning the anomalous Josephson effect within ballistic systems; currently, there is no agreed-upon microscopic mechanism behind the origin of this effect. The prototypical system we study is a ballistic two-dimensional junction containing a two-dimensional Rashba spin-orbit interaction. In this paper, we demonstrate how this two-dimensional Rashba interaction mixes the spins of adjacent transverse subbands, leading to significant spin-asymmetry within the junction. Under an external magnetic field, applied perpendicular to both the axis of transport and the normal vector of the junction, the sinusoidal Josephson current can then experience an anomalous phase shift. The role of this spin mixing in the limit of a single sub-band is initially explored by deriving an analytical expression for the resulting anomalous phase shift. The analysis is then extended to systems with multiple occupied sub-bands; in this later section, starting from a microscopic model, we derive an analytic formula for the resulting anomalous phase shift indicating it is linear in both magnetic field and spin-orbit strength. We then verify and validate all findings by comparing them with numerical results evaluated by a tight-binding model.

Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets

Skin cancer (SC) is an important medical condition that necessitates prompt identification to ensure timely treatment. Although visual evaluation by dermatologists is considered the most reliable method, its efficacy is subjective and laborious. Deep learning-based computer-aided diagnostic (CAD) platforms have become valuable tools for supporting dermatologists. Nevertheless, current CAD tools frequently depend on Convolutional Neural Networks (CNNs) with huge amounts of deep layers and hyperparameters, single CNN model methodologies, large feature space, and exclusively utilise spatial image information, which restricts their effectiveness. This study presents SCaLiNG, an innovative CAD tool specifically developed to address and surpass these constraints. SCaLiNG leverages a collection of three compact CNNs and Gabor Wavelets (GW) to acquire a comprehensive feature vector consisting of spatial–textural–frequency attributes. SCaLiNG gathers a wide range of image details by breaking down these photos into multiple directional sub-bands using GW, and then learning several CNNs using those sub-bands and the original picture. SCaLiNG also combines attributes taken from various CNNs trained with the actual images and subbands derived from GW. This fusion process correspondingly improves diagnostic accuracy due to the thorough representation of attributes. Furthermore, SCaLiNG applies a feature selection approach which further enhances the model’s performance by choosing the most distinguishing features. Experimental findings indicate that SCaLiNG maintains a classification accuracy of 0.9170 in categorising SC subcategories, surpassing conventional single-CNN models. The outstanding performance of SCaLiNG underlines its ability to aid dermatologists in swiftly and precisely recognising and classifying SC, thereby enhancing patient outcomes.

Sparse reconstruction methods for wideband source localization in spherical harmonics domain

Wideband acoustic source localization is a challenging task especially in the presence of noise. Generally wideband source localization is done by averaging the Direction of Arrival (DOA) estimates obtained over multiple frequencies or narrow subbands by the method of frequency smoothing. Localization of multiple wideband sources which are correlated is even more challenging. In this work acoustic source localization under these challenging conditions is addressed. A sparse reconstruction framework is developed for wideband source localization in the Spherical Harmonics (SH) domain. The proposed framework jointly computes both the azimuth and elevation. In contrast to earlier methods of source localization in the SH domain, this work utilizes the expression for the SH coefficients of the amplitude density of the plane wave, to develop the sparse reconstruction framework. An expression for Cramér Rao Lower Bound (CRLB) on the DOA using this framework is also derived for the wideband sources. This CRLB is shown to easily generalize for narrowband sources as well. The performance of the proposed method is compared with MUltiple Signal Classification in SH domain (MUSIC-SH), Steered Response Power in SH domain (SRP-SH), MUSIC with Direct Path Dominance test in SH domain (MUSIC-DPD-SH) and Pressure based Compressive Sensing in SH domain (PCS-SH). The performance is evaluated for correlated narrowband and wideband sources through simulations, conducting experiments in an anechoic chamber and under reverberant conditions. Using the proposed framework, it is shown that correlated narrowband and wideband sources can be resolved reasonably well when compared to conventional methods of acoustic source localization in SH domain.

UWLPIPE: Ultra-wide Bandwidth Low-frequency Pulsar Data Processing Pipeline

For real-time processing of ultra-wide bandwidth low-frequency pulsar baseband data, we designed and implemented an ultra-wide bandwidth low-frequency pulsar data processing pipeline (UWLPIPE) based on the shared ringbuffer and GPU parallel technology. UWLPIPE runs on the GPU cluster and can simultaneously receive multiple 128 MHz dual-polarization VDIF data packets preprocessed by the front-end FPGA. After aligning the dual-polarization data, multiple 128M subband data are packaged into PSRDADA baseband data or multi-channel coherent dispersion filterbank data, and multiple subband filterbank data can be spliced into wideband data after time alignment. We used the Nanshan 26 m radio telescope with the L-band receiver at 964 ∼ 1732 MHz to observe multiple pulsars. Finally, we processed the data using DSPSR software, and the results showed that each subband could correctly fold out the pulse profile, and the wideband pulse profile accumulated by multiple subbands could be correctly aligned.

Underwater acoustic signal denoising based on sparse TQWT and wavelet thresholding

The time-varying characteristics of the underwater environment lead to complicated background noise in collected signals. To reduce the negative impact of noise, this paper proposes the tunable Q-factor wavelet transform (TQWT)-basis pursuit (TQWT-BP)-based wavelet thresholding denoising schemes (TQWT-BP-WT). We commence by decomposing the collected signal into multiple subbands upon using TQWT-BP. Then, subbands are divided into signal subbands and noise subbands by applying the component identification criteria. The wavelet thresholding is invoked to denoise the signal subbands, while noise subbands are discarded. To classify the subbands, we design two criteria based on the coefficients threshold and the correlation coefficients, respectively. The coefficients threshold-based criterion determines the subband components by examining the number of coefficients that satisfy the threshold conditions. By comparing the correlation coefficients between the subbands and original signal, as well as between the subbands and the pre-denoised signal, the correlation coefficient-based method distinguishes the subbands, which avoids setting the criterion threshold. In addition, we investigate the influence of different normalized signal amplitudes decomposed by TQWT-BP, and the design of the normalized scale selection approach. Finally, practical simulation results are provided to verify the performance of our proposed schemes compared to other counterparts.

Non-smooth nonlocal mechanical diode

In this paper, we proposed a new mechanical diode with non-smooth and nonlocal elastic, which support a backflow of the input mechanical energy. It is composed of a nonlinear converter connecting with nonlocal bilinear springs and linear phononic crystal. The asymmetrical stiffness of bilinear springs can be utilized to periodically modulate the stiffness of converter, generate non-reciprocal propagation of mechanical waves, thereby decompose the excitation frequency into multiple sub-bands. Utilizing this characteristic of converter, during the forward propagation of mechanical diodes, it is possible to effectively surpass the cutoff frequency of phononic crystals, ensuring higher energy transmission efficiency and achieving wideband performance of mechanical diodes. Nonlocal connections induce a roton-like dispersion in the wave propagation, resulting in negative group velocities and partial energy backflow. The Runge-Kutta method is utilized to simulate wave propagation and verified by experimental. It is shown that the low-frequency impinging wave is rectified and nearly 100% contrast ratio is observed. Our work provide a new platform for mechanical wave manipulation and energy harvesting.

Manipulation of band gap in 1T-TiSe2 via rubidium deposition

Abstract The 1T-TiSe2 is a two-dimensional charge-density-wave (CDW) material that attracts great interest. A small band gap locates at the Fermi level separating the Ti d-bands and Se p-bands, which makes 1T-TiSe2 a promising candidate for realizing excitonic condensation. Here, we studied the band gap in 1T-TiSe2 using angle-resolved photoemission spectroscopy (ARPES). Instead of only focusing on the in-plane band dispersions, we obtained the detailed band dispersions of both conduction and valance bands along the out-of-plane direction. We found that the conduction and valance bands split into multiple sub-bands in the CDW state due to band folding. As a result, the band gap between the Ti d-bands and Se p-bands reduces to ∼25 meV and becomes a direct gap in the CDW state. More intriguingly, such band gap can be further reduced by the rubidium deposition. The band structure becomes semimetallic in the rubidium-doped sample. Meanwhile, exotic gapless behaviors were observed at the p–d band crossing. Our result characterized the band gap of 1T-TiSe2 in three-dimensional Brillouin zone with unpreceded precision. It also suggests a closing of band gap or a potential band inversion in 1T-TiSe2 driven by rubidium deposition.

Deep progressive feature aggregation network for multi-frame high dynamic range imaging

High dynamic range (HDR) imaging is an important task in image processing that aims to generate well-exposed images in scenes with varying illumination. Although existing multi-exposure fusion methods have achieved impressive results, generating high-quality HDR images in dynamic scenes remains difficult. The primary challenges are ghosting artifacts caused by object motion between low dynamic range images and distorted content in underexposure and overexposed regions. In this paper, we propose a deep progressive feature aggregation network for improving HDR imaging quality in dynamic scenes. To address the issues of object motion, our method implicitly samples high-correspondence features and aggregates them in a coarse-to-fine manner for alignment. In addition, our method adopts a densely connected network structure based on the discrete wavelet transform, which aims to decompose the input features into multiple frequency subbands and adaptively restore corrupted contents. Experiments show that our proposed method can achieve state-of-the-art performance under different scenes, compared to other promising HDR imaging methods. Specifically, the HDR images generated by our method contain cleaner and more detailed content, with fewer distortions, leading to better visual quality.

A sub-band division algorithm for ultra-wide bandwidth pulsar signals based on RFSoC

In order to realize the real-time processing and analysis of astronomical ultra-wide bandwidth signals, this study proposes a sub-band division algorithm based on RFSoC. The algorithm uses Kaiser window to design FIR prototype low-pass filter, adopts critical sampling polyphase filter bank to decompose ultra-wide bandwidth signal into several sub-bands, and encapsulates each sub-band into VDIF data frame and sends it to GPU server. The algorithm is implemented on RFSoC platform, and its effectiveness is verified by simulation and actual observation. The experimental results show that the algorithm can divide the astronomical ultra-wide bandwidth signal into multiple sub-bands in real time, packetize and transmit them to GPU. This research provides reproducible design and project for ultra-wide bandwidth signal sub-band division with low spectrum leakage and aliasing, high data accuracy, and fast processing speed.

Efficient Multi-Sound Source Localization Algorithm for Transformer Faults Based on Polyphase Filters.

Power transformers play a critical role in power systems, and the early detection of their faults and defects, accounting for over 30%, can be achieved through abnormal sound analysis. Sound source localization based on microphone arrays has proven effective in focusing on the troubleshooting scope, preventing potential severe hazards caused by delays in fault removal, and significantly reducing operational and maintenance difficulties and costs. However, existing microphone array-based sound source localization algorithms face challenges in maintaining both accuracy and simplicity and especially suffer from a sharp decrease in performance when dealing with multiple sound sources. This paper presents a multi-sound source localization algorithm for transformer faults based on polyphase filters, integrating the sum-difference monopulse angle measurement technique into the microphone array. Firstly, the signals received from the transformers are divided into multiple subbands using polyphase filters, allowing for multi-source separation and reducing the sampling rate of each subband. Next, the time-domain signals in subbands subject to noise suppression are processed into sum and difference beams. The resulting beam outputs are transformed into frequency-domain signals using the Fast Fourier Transform (FFT), effectively enhancing the signal-to-noise ratio (SNR) for separate sound sources. Finally, each subband undergoes sum-difference monopulse angle measurement in the frequency domain to achieve the high-precision localization of specific faults. The proposed algorithm has been demonstrated to be effective in achieving higher localization accuracy and reducing computational complexity in the presence of actual amplitude-phase errors in microphone arrays. These advantages can facilitate its practical applications. By enabling early targeting of fault sources when abnormalities occur, this algorithm provides valuable assistance to operation and maintenance personnel, thereby enhancing the maintenance efficiency of transformers.

Anomalous enhancement of thermoelectric power factor in multiple two-dimensional electron gas system.

Toward drastic enhancement of thermoelectric power factor, quantum confinement effect proposed by Hicks and Dresselhaus has intrigued a lot of researchers. There has been much effort to increase power factor using step-like density-of-states in two-dimensional electron gas (2DEG) system. Here, we pay attention to another effect caused by confining electrons spatially along one-dimensional direction: multiplied 2DEG effect, where multiple discrete subbands contribute to electrical conduction, resulting in high Seebeck coefficient. The power factor of multiple 2DEG in GaAs reaches the ultrahigh value of ~100 μWcm-1 K-2 at 300 K. We evaluate the enhancement rate defined as power factor of 2DEG divided by that of three-dimensional bulk. The experimental enhancement rate relative to the theoretical one of conventional 2DEG reaches anomalously high (~4) in multiple 2DEG compared with those in various conventional 2DEG systems (~1). This proposed methodology for power factor enhancement opens the next era of thermoelectric research.

AUTOMATED DESIGN OF KORSCH ORTHOSCOPIC ASPHERICAL MIRROR TELESCOPES FOR NANOSATELLITES

This study presents the results of an automated parametric synthesis of a series of orthoscopic aspheric optical systems of mirror telescopes with different focal lengths, built according to the Korsch scheme. In order to ensure compactness of the design, these systems consist of three second-order aspheric mirrors and three folded flat mirrors. The obtained optical systems have a focal length ranging from 480 mm to 960 mm, an angular field of view ranging from 1.2° to 2.4°, an aperture number ranging from 6 to 12, an entrance aperture with a diameter of 80 mm, and an axial length of no more than 170 mm, which is within the dimensions of a 2U CubeSat. The maximum linear size of the image detector's sensitive area is 20 mm in all considered systems. The mirror telescopes do not have chromatic aberrations, enabling the capture of images within the visible spectrum and multiple infrared sub-bands. The provided aberration analysis demonstrates the high image quality of the considered optical systems. Specifically, for a wavelength of 0.546 μm, thediffraction modulation transfer function values both in the meridional and sagittal planes across the entire field exceed 0.53 for a spatial frequency of 30 mm-1, and 0.3 for a spatial frequency of 50 mm-1. The maximum relative distortion in the designed systems is 0.005%, corresponding to an absolute displacement of 0.5 μm. The presented telescopes are capable of covering an observation band of Earth's surface with linear dimensions ranging from 12.6 km to 25.1 km when a satellite track height is 600 km. By applying multi-element image detectors with a pixel size of 5 μm and achieving diffraction-limited image quality in the optical systems, both the geometric and diffraction resolution limits on Earth's surface will not exceed 10 m.

Multi-domain feature joint optimization based on multi-view learning for improving the EEG decoding.

Brain-computer interface (BCI) systems based on motor imagery (MI) have been widely used in neurorehabilitation. Feature extraction applied by the common spatial pattern (CSP) is very popular in MI classification. The effectiveness of CSP is highly affected by the frequency band and time window of electroencephalogram (EEG) segments and channels selected. In this study, the multi-domain feature joint optimization (MDFJO) based on the multi-view learning method is proposed, which aims to select the discriminative features enhancing the classification performance. The channel patterns are divided using the Fisher discriminant criterion (FDC). Furthermore, the raw EEG is intercepted for multiple sub-bands and time interval signals. The high-dimensional features are constructed by extracting features from CSP on each EEG segment. Specifically, the multi-view learning method is used to select the optimal features, and the proposed feature sparsification strategy on the time level is proposed to further refine the optimal features. Two public EEG datasets are employed to validate the proposed MDFJO method. The average classification accuracy of the MDFJO in Data 1 and Data 2 is 88.29 and 87.21%, respectively. The classification result of MDFJO was significantly better than MSO (p < 0.05), FBCSP32 (p < 0.01), and other competing methods (p < 0.001). Compared with the CSP, sparse filter band common spatial pattern (SFBCSP), and filter bank common spatial pattern (FBCSP) methods with channel numbers 16, 32 and all channels as well as MSO, the MDFJO significantly improves the test accuracy. The feature sparsification strategy proposed in this article can effectively enhance classification accuracy. The proposed method could improve the practicability and effectiveness of the BCI system.

Optimization of Channel Segregation-Based Fractional Frequency Reuse for Inter-Cell Interference Coordination in Cellular Ultra-Dense RAN

To cope with ever growing mobile data traffic, we recently proposed a concept of cellular ultra-dense radio access network (RAN). In the cellular ultra-dense RAN, a number of distributed antennas are deployed in the base station (BS) coverage area (cell) and user-clusters are formed to perform small-scale distributed multiuser multi-input multi-output (MU-MIMO) transmission and reception in each user-cluster in parallel using the same frequency resource. We also proposed a decentralized interference coordination (IC) framework to effectively mitigate both intra-cell and inter-cell interferences caused in the cellular ultra-dense RAN. The inter-cell IC adopted in this framework is the fractional frequency reuse (FFR), realized by applying the channel segregation (CS) algorithm, and is called CS-FFR in this paper. CS-FFR divides the available bandwidth into several sub-bands and allocates multiple sub-bands to different cells. In this paper, focusing on the optimization of the CS-FFR, we find by computer simulation the optimum bandwidth division number and the sub-band allocation ratio to maximize the link capacity. We also discuss the convergence speed of CS-FFR in a cellular ultra-dense RAN.

Multi-class semantic segmentation of breast tissues from MRI images using U-Net based on Haar wavelet pooling

MRI images used in breast cancer diagnosis are taken in a lying position and therefore are inappropriate for reconstructing the natural breast shape in a standing position. Some studies have proposed methods to present the breast shape in a standing position using an ordinary differential equation of the finite element method. However, it is difficult to obtain meaningful results because breast tissues have different elastic moduli. This study proposed a multi-class semantic segmentation method for breast tissues to reconstruct breast shapes using U-Net based on Haar wavelet pooling. First, a dataset was constructed by labeling the skin, fat, and fibro-glandular tissues and the background from MRI images taken in a lying position. Next, multi-class semantic segmentation was performed using U-Net based on Haar wavelet pooling to improve the segmentation accuracy for breast tissues. The U-Net effectively extracted breast tissue features while reducing image information loss in a subsampling stage using multiple sub-bands. In addition, the proposed network is robust to overfitting. The proposed network showed a mIOU of 87.48 for segmenting breast tissues. The proposed networks demonstrated high-accuracy segmentation for breast tissue with different elastic moduli to reconstruct the natural breast shape.

Overlapping filter bank convolutional neural network for multisubject multicategory motor imagery brain-computer interface

BackgroundMotor imagery brain-computer interfaces (BCIs) is a classic and potential BCI technology achieving brain computer integration. In motor imagery BCI, the operational frequency band of the EEG greatly affects the performance of motor imagery EEG recognition model. However, as most algorithms used a broad frequency band, the discrimination from multiple sub-bands were not fully utilized. Thus, using convolutional neural network (CNNs) to extract discriminative features from EEG signals of different frequency components is a promising method in multisubject EEG recognition.MethodsThis paper presents a novel overlapping filter bank CNN to incorporate discriminative information from multiple frequency components in multisubject motor imagery recognition. Specifically, two overlapping filter banks with fixed low-cut frequency or sliding low-cut frequency are employed to obtain multiple frequency component representations of EEG signals. Then, multiple CNN models are trained separately. Finally, the output probabilities of multiple CNN models are integrated to determine the predicted EEG label.ResultsExperiments were conducted based on four popular CNN backbone models and three public datasets. And the results showed that the overlapping filter bank CNN was efficient and universal in improving multisubject motor imagery BCI performance. Specifically, compared with the original backbone model, the proposed method can improve the average accuracy by 3.69 percentage points, F1 score by 0.04, and AUC by 0.03. In addition, the proposed method performed best among the comparison with the state-of-the-art methods.ConclusionThe proposed overlapping filter bank CNN framework with fixed low-cut frequency is an efficient and universal method to improve the performance of multisubject motor imagery BCI.

Research on Channelization Techniques of Radio Astronomical Wideband Signal with Oversampled Polyphase Filter Banks

Digital channelization decomposes a wideband signal into multiple adjacent sub-bands using Parallel Technology. Channelization can effectively reduce the pressure on the radio astronomy digital backends system and make wideband signal processing possible. Aiming at the problems of signal attenuation at sub-band edge, spectral leakage and aliasing encountered in wideband signal channelization, algorithms to reduce the problems are studied. We design a Critically Sampled Polyphase Filter Bank (CS-PFB) based on the Finite Impulse Response digital filter with a Hamming Window and systematically analyze the frequency response characteristics of the CS-PFB. Based on the channelized structure of the CS-PFB, an OverSampled Polyphase Filter Bank (OS-PFB) is designed by data reuse, and the filtering frequency response characteristics of CS-PFB and OS-PFB are compared and analyzed. Using the wideband baseband data generated by the CASPSR (Collaboration for Astronomy Signal processing and electronics research Parkes Swinburne Recorder), we implement sub-band division and 16-band output of these data based on the 2× oversampling OS-PFB, and the problem of spectrum inversion in the sub-bands is corrected. After removing 25% of redundant data in the head and tail of each sub-band, we recombine the sub-bands into a wideband. The wideband signal is almost identical to the original observed signal. Therefore, the experimental results show that the OS-PFB can improve the channel response. For the 400 MHz baseband data of J0437-4715, we compare the pulse profile obtained from the original baseband data with the pulse profile obtained after the channelization and recombination. The phase and amplitude information of the pulse profiles are consistent, which verifies the correctness of our channelization algorithm.