Number Of Subbands Research Articles

New insights to improve resolution and reliability of Raman spectral analysis using higher-density multiscale regression

When paired with sophisticated multivariate calibration methods, spectroscopy techniques stand out conspicuously for high-throughput analysis of complex samples. However, despite the identification capability of multivariate analysis, fluctuant spectral interference and complicated chemometrics procedure often combine to limit conventional spectroscopy techniques. In this regard, we proposed a novel strategy, higher-density multiscale regression (HDMR), to adaptively process Raman spectra for quantitative analysis. HDMR firstly splits the Raman spectra into frequency components at different scales using higher-density discrete wavelet transform (HDDWT), yielding a larger number of subbands to benefit a good separation of spectral bands. Parallel member models constructed with these decomposed components are then fused into a final prediction through partial least square (PLS) reweighting strategy. With HDMR, the pre-treatment process and multivariate calibration are integrated into a unified calibration model for Raman spectra at hand, regardless of its structural characteristics. This would definitely avoid of information leakage in multivariate calibration, thus promoting the application of Raman spectroscopy technique as a general tool for analytical chemistry. To validate the performance of the HDMR, two industrial Raman spectral data sets were investigated to yield challenges representative of those encountered in Raman spectroscopy. This proposed strategy has improved calibration performance through the reweighting way. Satisfactory calibration results suggest that HDMR provides a universal tool for reliable Raman spectral analysis, which may well extend to other spectral data sets.

Rolling element bearing fault diagnosis based on Over-Complete rational dilation wavelet transform and auto-correlation of analytic energy operator

Abstract Local damage in rolling element bearings usually generates periodic impulses in vibration signals. The severity, repetition frequency and the fault excited resonance zone by these impulses are the key indicators for diagnosing bearing faults. In this paper, a methodology based on over complete rational dilation wavelet transform (ORDWT) is proposed, as it enjoys a good shift invariance. ORDWT offers flexibility in partitioning the frequency spectrum to generate a number of subbands (filters) with diverse bandwidths. The selection of the optimal filter that perfectly overlaps with the bearing fault excited resonance zone is based on the maximization of a proposed impulse detection measure “Temporal energy operated auto correlated kurtosis”. The proposed indicator is robust and consistent in evaluating the impulsiveness of fault signals in presence of interfering vibration such as heavy background noise or sporadic shocks unrelated to the fault or normal operation. The structure of the proposed indicator enables it to be sensitive to fault severity. For enhanced fault classification, an autocorrelation of the energy time series of the signal filtered through the optimal subband is proposed. The application of the proposed methodology is validated on simulated and experimental data. The study shows that the performance of the proposed technique is more robust and consistent in comparison to the original fast kurtogram and wavelet kurtogram.

Eigenvalue ratio based blind spectrum sensing algorithm for multiband cognitive radios with relatively small samples

The multiband detection problem in relatively small sample scenarios where the number of subbands is comparable to the number of samples in magnitude is described. Combined with the sequential hypothesis testing, an eigenvalue ratio based method is proposed for the multiband spectrum sensing (MSS). As the distribution of the new statistic when only noise is present can be precisely obtained by using the random matrix theory, the proposed method is able to reliably set the theoretical threshold, and outperforms the traditional MSS methods based on information theoretic criteria and principal component analysis, especially in the cases with small samples. Meanwhile, the proposed method belongs to a blind detection scheme and can be used in cases without prior knowledge of the primary signal, the channel and the noise. Simulation results show the superiority of the proposed method.

Orthogonality-Based Generalized Multicarrier Constant Envelope Multiplexing for DSSS Signals

The new generation global navigation satellite systems will broadcast multiple direct sequence spread spectrum signals on multiple different central frequencies, with complex spreading chip waveforms. There is a strong demand for efficient and flexible multiplexing techniques to combine these signals into a constant envelope composite signal. In this paper, a high efficiency generalized multicarrier joint constant envelope multiplexing (CEM) technique for multilevel DSSS signals is proposed. The basic idea of this technique is adding an auxiliary component into the direct superposition of signals to keep the envelope of the integrated signal constant. Design principle, constraints, and optimization method of the auxiliary component are investigated. An analysis with typical case studies demonstrates the high efficiency and high flexibility of the proposed technique. Compared with existing CEM techniques, the proposed technique has much higher design flexibility in the number of subbands, the number of signal components, power ratio and phase relationship among components, and the spreading chip waveforms. It can be applied to any number of bipolar or multilevel DSSS signals with arbitrary power distribution at one or more subcarrier frequencies. Such high-level design flexibility provides system designers great room in signal scheme optimization for varied navigation applications in the future.

Experimental investigation of multi-band OCT precoding for OFDM-based visible light communications.

In this paper, we propose and experimentally demonstrate a channel-independent multi-band orthogonal circulant matrix transform (MB-OCT) precoding, to efficiently combat the severe frequency-selective fading of visible light communications (VLC). The proposed MB-OCT precoding exhibits an attractive ladder-like signal-to-noise-ratio (SNR) profile, thus can significantly reduce system BER by applying different quadrature amplitude modulation (QAM) level to different sub-bands. The impacts of sub-band number, signal bandwidth, and length of cyclic prefix (CP) on bit error rate (BER) of the VLC system are investigated. We experimentally compare BER performance of the proposed MB-OCT precoding with that of the conventional MB discrete Fourier transform (MB-DFT) precoding and the adaptive-loaded discrete multitone (DMT). The results show that the MB-OCT precoding outperforms the MB-DFT precoding and the single-band case for different data rates. Furthermore, it exhibits reduced implementation complexity and comparable BER performance with the adaptive-loaded DMT. For ~700-Mb/s VLC system with 2-m transmission distance, the BER is reduced from 1.53 × 10-2 to 1.17 × 10-4 by using the proposed MB-OCT precoding.

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of Q and the redundancy parameter (R) of TQWT showed better robustness for entropy computation in the presence of high- and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal formation of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier. We have also achieved classification accuracies of 99% and 98.6% in classifying seizure versus non-seizure EEG signals and the individual three classes, namely normal, seizure-free and seizure EEG signals, respectively. The performance measure of the proposed multi-scale entropy has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.

Tunable-Q Wavelet Transform Based Multivariate Sub-Band Fuzzy Entropy with Application to Focal EEG Signal Analysis

This paper analyses the complexity of multivariate electroencephalogram (EEG) signals in different frequency scales for the analysis and classification of focal and non-focal EEG signals. The proposed multivariate sub-band entropy measure has been built based on tunable-Q wavelet transform (TQWT). In the field of multivariate entropy analysis, recent studies have performed analysis of biomedical signals with a multi-level filtering approach. This approach has become a useful tool for measuring inherent complexity of the biomedical signals. However, these methods may not be well suited for quantifying the complexity of the individual multivariate sub-bands of the analysed signal. In this present study, we have tried to resolve this difficulty by employing TQWT for analysing the sub-band signals of the analysed multivariate signal. It should be noted that higher value of Q factor is suitable for analysing signals with oscillatory nature, whereas the lower value of Q factor is suitable for analysing signals with non-oscillatory transients in nature. Moreover, with an increased number of sub-bands and a higher value of Q-factor, a reasonably good resolution can be achieved simultaneously in high and low frequency regions of the considered signals. Finally, we have employed multivariate fuzzy entropy (mvFE) to the multivariate sub-band signals obtained from the analysed signal. The proposed Q-based multivariate sub-band entropy has been studied on the publicly available bivariate Bern Barcelona focal and non-focal EEG signals database to investigate the statistical significance of the proposed features in different time segmented signals. Finally, the features are fed to random forest and least squares support vector machine (LS-SVM) classifiers to select the best classifier. Our method has achieved the highest classification accuracy of 84.67% in classifying focal and non-focal EEG signals with LS-SVM classifier. The proposed multivariate sub-band fuzzy entropy can also be applied to measure complexity of other multivariate biomedical signals.

A new image watermarking framework based on levels-directions decomposition in contourlet representation

With the development of digital information technologies, robust watermarking framework is taken into real consideration as a challenging issue in the area of image processing, due to the large applicabilities and its utilities in a number of academic and real environments. There are a wide range of solutions to provide image watermarking frameworks, while each one of them is attempted to address an efficient and applicable idea. In reality, the traditional techniques do not have sufficient merit to realize an accurate application. Due to the fact that the main idea behind the approach is organized based on contourlet representation, the only state-of-the-art materials that are investigated along with an integration of the aforementioned contourlet representation in line with watermarking framework are concentrated to be able to propose the novel and skilled technique. In a word, the main process of the proposed robust watermarking framework is organized to deal with both new embedding and de-embedding processes in the area of contourlet transform to generate watermarked image and the corresponding extracted logo image with high accuracy. In fact, the motivation of the approach is that the suggested complexity can be of novelty, which consists of the contourlet representation, the embedding and the corresponding de-embedding modules and the performance monitoring including an analysis of the watermarked image as well as the extracted logo image. There is also a scrambling module that is working in association with levels-directions decomposition in contourlet embedding mechanism, while a decision maker system is designed to deal with the appropriate number of sub-bands to be embedded in the presence of a series of simulated attacks. The required performance is tangibly considered through an integration of the peak signal-to-noise ratio and the structural similarity indices that are related to watermarked image. And the bit error rate and the normal correlation are considered that are related to the extracted logo analysis, as well. Subsequently, the outcomes are fully analyzed to be competitive with respect to the potential techniques in the image colour models including hue or tint in terms of their shade, saturation or amount of gray and their brightness via value or luminance and also hue, saturation and intensity representations, as long as the performance of the whole of channels are concentrated to be presented. The performance monitoring outcomes indicate that the proposed framework is of significance to be verified.

An Investigation of Delayless Subband Adaptive Filtering for Multi-Input Multi-Output Active Noise Control Applications

The broadband control of noise and vibration using multi-input, multi-output (MIMO) active control systems has a potentially wide variety of applications. However, the performance of MIMO systems is often limited in practice by high computational demand and slow convergence speeds. In the somewhat simpler context of single-input, single-output broadband control, these problems have been overcome through a variety of methods including subband adaptive filtering. This paper presents an extension of the subband adaptive filtering technique to the MIMO active control problem and presents a comprehensive study of both the computational requirements and control performance. The implementation of the MIMO filtered- $x$ LMS algorithm using subband adaptive filtering is described and the details of two specific implementations are presented. The computational demands of the two MIMO subband active control algorithms are then compared to that of the standard full-band algorithm. This comparison shows that as the number of subbands employed in the subband algorithms is increased, the computational demand is significantly reduced compared to the full-band implementation provided that a restructured analysis filter-bank is employed. An analysis of the convergence of the MIMO subband adaptive algorithm is then presented and this demonstrates that although the convergence of the control filter coefficients is dependent on the eigenvalue spread of the subband Hessian matrix, which reduces as the number of subbands is increased, the convergence of the cost function is limited for large numbers of subbands due to the simultaneous increase in the weight stacking distortion. The performance of the two MIMO subband algorithms and the standard full-band algorithm has then been assessed through a series of time-domain simulations of a practical active control system and it has been shown that the subband algorithms are able to achieve a significant increase in the convergence speed compared to the full-band implementation.

A Subband-Specific Deconvolution Model for MTF Improvement in CT.

The purpose of this research is to achieve uniform spatial resolution in CT (computed tomography) images without hardware modification. The main idea of this study is to consider geometry optics model, which can provide the approximate blurring PSF (point spread function) kernel, which varies according to the distance from X-ray tube to each pixel. The FOV (field of view) was divided into several band regions based on the distance from X-ray source, and each region was deconvolved with different deconvolution kernels. Though more precise calculation for the PSF for deconvolution is possible as the number of subbands increases, we set the number of subbands to 11. 11 subband settings seem to be a balancing point to reduce noise boost, while MTF (modulation transfer function) increase still remains. As the results show, subband-wise deconvolution makes image resolution (in terms of MTF) relatively uniform across the FOV. The results show that spatial resolution in CT images can be uniform across the FOV without using additional equipment. The beauty of this method is that it can be applied to any CT system as long as we know the specific system parameters and determine the appropriate PSF for deconvolution maps of the system. The proposed algorithm shows promising result in improving spatial resolution uniformity while avoiding the excessive noise boost.

Spectral detection with multistage granularity bands using filter bank techniques for CR applications

In this paper, we propose a method to detect unused spectrum using multistage Cosine Modulated Filter Bank (CMFB) technique for Cognitive Radio (CR) applications. The precision of spectral detection is increased when the number of sub-bands in the filter bank is increased, this increases the computational complexity of the filter bank structure. We suggest a multistage filter bank structure to reduce the computational complexity. Spectrum detection is done effectively in filter banks by proper choice of prototype filter with specified sub-band attenuation. The sub-bands detected in coarser granularity bands are sensed with finer granularity bands for precise spectral detection depending on the predefined threshold and probability of false alarm. The remaining sub-bands are not sensed further which in turn reduces the computational complexity. Through simulations it is shown that spectrum detection with multistage granularity bands maintains bandwidth efficiency and reliability in spectrum detection even at low SNR.

A robust video watermarking scheme using sparse principal component analysis and wavelet transform

The extension of internet facility profoundly eases the dissemination of all digital data such as audio, images and videos to the general public. This paper proposes a blind scheme for digital video watermarking. In this paper, a discrete wavelet domain watermarking is adopted for hiding a large amount of data with high security, good invisibility and no loss to the secret message. At first, dual tree complex wavelet transform (DTCWT) is applied on each frame decomposing it into a number of sub-bands. Then, holo entropy of each sub-band is calculated and the maximum entropy blocks are selected. The selected blocks are transformed using sparse principal component analysis (SPCA). The maximum coefficient of the SPCA blocks of each sub-band is quantised using quantisation index modulation (QIM). The proposed video watermarking scheme is analysed through various constraints such as the normalised correlation (NC) and the peak signal to noise ratio (PSNR).

A robust video watermarking scheme using sparse principal component analysis and wavelet transform

The extension of internet facility profoundly eases the dissemination of all digital data such as audio, images and videos to the general public. This paper proposes a blind scheme for digital video watermarking. In this paper, a discrete wavelet domain watermarking is adopted for hiding a large amount of data with high security, good invisibility and no loss to the secret message. At first, dual tree complex wavelet transform (DTCWT) is applied on each frame decomposing it into a number of sub-bands. Then, holo entropy of each sub-band is calculated and the maximum entropy blocks are selected. The selected blocks are transformed using sparse principal component analysis (SPCA). The maximum coefficient of the SPCA blocks of each sub-band is quantised using quantisation index modulation (QIM). The proposed video watermarking scheme is analysed through various constraints such as the normalised correlation (NC) and the peak signal to noise ratio (PSNR).

Frequency Index Modulation for Low Complexity Low Energy Communication Networks

In this paper, we propose a low-complexity multi-user communication system based on frequency index modulation that suits Internet of Things applications. This system aims to reduce the transmitted energy and the peak-to-average-power ratio (PAPR) of orthogonal frequency-division multiplexing (OFDM) systems and to perform without sacrificing data rate in comparison with conventional OFDM. In this design, OFDM-like signals are used to make the implementation of the system easy by the virtue of fast Fourier transform (FFT). In the proposed scheme, an OFDM bandwidth of $N_{\text {FIM}}$ total sub carriers is divided into $N_{B}$ equal number of sub-bands, with $N$ subcarriers in every sub-band. At the transmitter side of each sensor, the outgoing bit stream is divided into two blocks: mapped and modulated. The bits within the mapped block are used to activate the corresponding subcarrier in its predefined sub-band in order to carry the data content of the modulated block, while the other $N-1$ subcarriers are nulled out. At the receiver, the FFT is performed first, and then the square-law envelope detector is applied to estimate the active frequency index to recover the mapped bits, followed by a conventional demodulation process to demodulate the transmitted bits. Once the system is presented and analyzed, energy efficiency, PAPR and complexity are studied to show the features of the proposed scheme. Moreover, we derive closed-form expressions of the bit error rate performance over Rayleigh fading channels and we validate the outcome by simulation results. With the characteristics exhibited in this paper, the proposed system would constitute an excellent candidate for wireless sensor applications where it represents a simpler substitution for frequency-hopping-based architectures, in which the hops carry extra bits.

A distance-driven deconvolution method for CT image-resolution improvement

The purpose of this research is to achieve high spatial resolution in CT (computed tomography) images without hardware modification. The main idea is to consider geometry optics model, which can provide the approximate blurring PSF (point spread function) kernel, which varies according to the distance from the X-ray tube to each point. The FOV (field of view) is divided into several band regions based on the distance from the X-ray source, and each region is deconvolved with a different deconvolution kernel. As the number of subbands increases, the overshoot of the MTF (modulation transfer function) curve increases first. After that, the overshoot begins to decrease while still showing a larger MTF than the normal FBP (filtered backprojection). The case of five subbands seems to show balanced performance between MTF boost and overshoot minimization. It can be seen that, as the number of subbands increases, the noise (STD) can be seen to show a tendency to decrease. The results shows that spatial resolution in CT images can be improved without using high-resolution detectors or focal spot wobbling. The proposed algorithm shows promising results in improving spatial resolution while avoiding excessive noise boost.

Novel Elastic Optical Network Embedding Using Re-Optimized VCAT Framework Accompanied by Hitless PPSM Function

The elastic optical network (EON) provides fine-grained and orthogonal frequency slots for establishing the connection that will realize high-rate data transmission among data centers (DCs). With the implementation of cloud DCs (CDCs) and EON virtualization, achieving EON embedding (EONE) is essential, which includes two components: node mapping from the virtual machine to the CDC and link mapping from the virtual lightpath to fiber link(s). In particular, the link mapping must satisfy some rigid constraints in terms of spectrum continuity, consistency, and nonoverlapping, which may create spectrum fragments over fiber links. Thus, in this paper, we reoptimize the virtual concatenation (VCAT) framework accompanied by the hitless push-pull spectrum movement (PPSM) function when the common link mapping fails. Our design framework integrates spectrum movement with inverse multiplexing such that more EONE requests can be satisfied by performing effective spectrum defragmentation. We also optimize the number of subbands with a constrained buffer size to compensate the differential delay at the receiving end. The problem is formulated using the relaxed integer linear programming (ILP) model, and the corresponding heuristic tailored to four different service-sorting strategies is also designed. The simulation results verify the superiority of our method in terms of increasing the spectrum efficiency and reducing the blocking rate of EONE requests. This paper is the first work to focus on designing a reoptimized VCAT framework accompanied by the hitless PPSM function for the EONE that includes both node and link mapping processes rather than a simple spectrum defragmentation.

Study on intelligibility improvement method based on subband waveform processing focusing on dynamic feature of speech

This paper describes intelligibility improvement method for speech signal based on subband waveform processing. Our approach is based on the observation that clear speech has higher delta-cepstrum value in transient parts between phonemes, and emphasizes delta-cepstrum of input speech by a filter in the cepstral domain which amplifies a particular modulation frequency. However, since this approach generates synthetic sound by using an analysis/synthesis system, quality of the generated sound is sometimes degraded. To prevent this degradation, a subband waveform-based method is introduced. This method divides an input signal into several subband signals by a quadrature mirror filter (QMF) which approximately enables perfect reconstruction of input signal from the subband signals, converts an amplification gain sequence in the cepstral domain into that in the subband-waveform domain, and then multiplies the converted amplification gain sequence to the subband signal on a sample-by-sample basis. Synthetic sounds were generated by the method in the cases where the number of subbands is set to two, four, and eight. We found that the sound that the number of subbands is two includes artificial power fluctuation, and increasing the number of subbands decreases the artificial power fluctuation and makes quality of the generated sound better.

Broadband Cooperative Spectrum Sensing Based on Distributed Modulated Wideband Converter.

The modulated wideband converter (MWC) is a kind of sub-Nyquist sampling system which is developed from compressed sensing theory. It accomplishes highly accurate broadband sparse signal recovery by multichannel sub-Nyquist sampling sequences. However, when the number of sparse sub-bands becomes large, the amount of sampling channels increases proportionally. Besides, it is very hard to adjust the number of sampling channels when the sparsity changes, because its undersampling board is designed by a given sparsity. Such hardware cost and inconvenience are unacceptable in practical applications. This paper proposes a distributed modulated wideband converter (DMWC) scheme innovatively, which regards one sensor node as one sampling channel and combines MWC technology with a broadband cooperative spectrum sensing network perfectly. Being different from the MWC scheme, DMWC takes phase shift and transmission loss into account in the input terminal, which are unavoidable in practical application. Our scheme is not only able to recover the support of broadband sparse signals quickly and accurately, but also reduces the hardware cost of the single node drastically. Theoretical analysis and numerical simulations show that phase shift has no influence on the recovery of frequency support, but transmission loss degrades the recovery performance to a different extent. Nevertheless, we can increase the amount of cooperative nodes and select satisfactory nodes by a different transmission distance to improve the recovery performance. Furthermore, we can adjust the amount of cooperative nodes flexibly when the sparsity changes. It indicates DMWC is extremely effective in the broadband cooperative spectrum sensing network.

Effects of Electrode Layer Band Structure on the Performance of Multilayer Graphene-hBN-Graphene Interlayer Tunnel Field Effect Transistors.

Interlayer tunnel field-effect transistors based on graphene and hexagonal boron nitride (hBN) have recently attracted much interest for their potential as beyond-CMOS devices. Using a recently developed method for fabricating rotationally aligned two-dimensional heterostructures, we show experimental results for devices with varying thicknesses and stacking order of the graphene electrode layers and also model the current-voltage behavior. We show that an increase in the graphene layer thickness results in narrower resonance. However, due to a simultaneous increase in the number of sub-bands and decrease of sub-band separation with an increase in thickness, the negative differential resistance peaks becomes less prominent and do not appear for certain conditions at room temperature. Also, we show that due to the unique band structure of odd number of layer Bernal-stacked graphene, the number of closely spaced resonance conditions increase, causing interference between neighboring resonance peaks. Although this can be avoided with even number of layer graphene, we find that in this case the bandgap opening present at high biases tend to broaden the resonance peaks.

A Low-Complexity Multistage Polyphase Filter Bank For Wireless Microphone Detection in CR

In this paper, we propose a low-complexity multistage polyphase filter bank for the detection and estimation of center frequency of wireless microphone (WM) in television channels for cognitive radios. The detection precision is directly related to the number of subbands (granularity) in the filter bank. This implies that the computational complexity becomes high if the number of stages and granularity of the filter bank structure are increased. The novelty of the proposed method is the estimation of center frequency with higher precision and reduced computational complexity using the centroid method. The merit of the centroid method is that the presence of WM can be detected along with estimation of center frequency in the first stage, if the WM lies partly in one subband and partly in the adjacent subband. This single-stage detection and estimation of WM significantly reduces computational and hardware complexities as well as latency. However, if the WM appears anywhere exclusively within a single subband, the detection process can be completed in the second stage without ambiguity. A mathematical expression for calculating the center frequency of WM from the subband energy (power) using centroid method is also derived and presented. The proposed scheme is analyzed and validated through extensive simulations for the detection of WM. The error in estimation of center frequency in most of the cases is less than 4 % for SNR variations from 0 to ź20 dB.