Subbands Research Articles

A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic EEG signals

Alcoholism is a critical disorder related to the central nervous system, caused due to repeated and excessive consumption of alcohol. The electroencephalogram (EEG) signals are used to depict brain activities. It can also be employed for diagnosis of subjects consuming excessive alcohol. In this study, we have developed an automated system for the classification of alcoholic and normal EEG signals using a recently designed duration-bandwidth product (DBP), optimized three-band orthogonal wavelet filter bank (TBOWFB), and log-energy (LE). First, we obtain sub-bands (SBs) of EEG signals using the TBOWFB. Then, we use logarithms of the energies of the SBs as the discriminating features which are fed to the least square support vector machine (LS-SVM) for the discrimination of normal and alcoholic EEG signals. We have achieved a classification accuracy (CA) of 97.08%, with ten-fold cross validation strategy. The proposed model presents a promising performance, and therefore it can be used in a practical setup to assist the medical professionals in the diagnosis of alcoholism using EEG signals automatically.

Resource Allocation and Interference Management for D2D-Enabled DL/UL Decoupled Het-Nets

In this paper, resource allocation and interference mitigation are investigated for heterogeneous networks where the lowest tier consists of device-to-device (D2D) cells. In order to alleviate dead-zone problem, we first consider downlink/uplink (DL/UL) decoupling user association and quantify its capability on interference management and network-wide D2D performance enhancement. Second, we propose an UL fractional frequency reuse scheme where subband (SB) bandwidths are adaptively determined based on: 1) user equipment (UE) density; 2) e-node-B (eNB) density; and 3) on/off switching frequency of small cells. Obtained results show that the adaptive method significantly reduces the number of outage users. Thereafter, a novel concatenated bi-partite matching (CBM) method is proposed for joint SB assignment (SA) and resource block allocation (RA) of cellular UEs. Numerical results show that the CBM provides a close performance to exhaustive solution with greatly reduced running time. The CBM is then extended to a centralized mode selection, SA, and RA for D2D cells. Alternatively, we develop offline and online semi-distributed approaches where a D2D-cell can reuse white-list RBs (WRBs), which are not occupied by the adjacent small cells. In the former, D2D-cell members are not aware of intra-cell and inter-cell interference and uniformly distribute their maximum permissible power to WRBs. In the latter, we put D2D sum rate maximization into a convex form by exploiting the proximity gain of D2D UEs. Online distributed solution is then developed by message passing of dual variables and consistency prices. Finally, virtues and drawbacks of the developed approaches are compared and explained.

Array model interpolation and subband iterative adaptive filters applied to beamforming-based acoustic echo cancellation.

In this paper, an evolutionary exposition is given in regard to the enhancing strategies for acoustic echo cancellers (AECs). A fixed beamformer (FBF) is utilized to focus on the near-end speaker while suppressing the echo from the far end. In reality, the array steering vector could differ considerably from the ideal freefield plane wave model. Therefore, an experimental procedure is developed to interpolate a practical array model from the measured frequency responses. Subband (SB) filtering with polyphase implementation is exploited to accelerate the cancellation process. Generalized sidelobe canceller (GSC) composed of an FBF and an adaptive blocking module is combined with AEC to maximize cancellation performance. Another enhancement is an internal iteration (IIT) procedure that enables efficient convergence in the adaptive SB filters within a sample time. Objective tests in terms of echo return loss enhancement (ERLE), perceptual evaluation of speech quality (PESQ), word recognition rate for automatic speech recognition (ASR), and subjective listening tests are conducted to validate the proposed AEC approaches. The results show that the GSC-SB-AEC-IIT approach has attained the highest ERLE without speech quality degradation, even in double-talk scenarios.

Adaptive beamforming for acoustic echo cancellation using measured array models and subband filtering

Acoustic echo that can substantially undermine speech quality is one of the key issues must be addressed in practical telecommunication systems. In this paper, an evolutionary exposition is given in regard to the enhancing strategies for acoustic echo cancelers (AEC). On the basis basic adaptive filtering, a highly directional microphone array is designed as a fixed beamformer (FBF) to focus on the near-end speaker and suppress the echo from the far-end as well as noise and interference from the background. Design of the beamformer is based on array models interpolated from measured frequency responses. Subband (SB) filtering with polyphase decomposition is exploited to accelerate the cancellation process with multiple choice of step size in the subbands. To further enhance the canceler, adaptive generalized sidelobe canceler (GSC) can be utilized, which is comprised of a FBF for the near-end speech and an adaptive blocking module for the echo and noise. Objective tests in terms of echo return loss enhancement (ERLE) and perceptual evaluation of speech quality (PESQ) are conducted to compare four algorithms with various combinations of enhancement. The results show that the GSC-SB-AEC approach has attained the highest ERLE and best speech quality, even in a double-talk scenario.

A sub-band-based feature reconstruction approach for robust speaker recognition

Although the field of automatic speaker or speech recognition has been extensively studied over the past decades, the lack of robustness has remained a major challenge. The missing data technique (MDT) is a promising approach. However, its performance depends on the correlation across frequency bands. This paper presents a new reconstruction method for feature enhancement based on the trait. In this paper, the degree of concentration across frequency bands is measured with principal component analysis (PCA). Through theoretical analysis and experimental results, it is found that the correlation of the feature vector extracted from the sub-band (SB) is much stronger than the ones extracted from the full-band (FB). Thus, rather than dealing with the spectral features as a whole, this paper splits full-band into sub-bands and then individually reconstructs spectral features extracted from each SB based on MDT. At the end, those constructed features from all sub-bands will be recombined to yield the conventional mel-frequency cepstral coefficient (MFCC) for recognition experiments. The 2-sub-band reconstruction approach is evaluated in speaker recognition system. The results show that the proposed approach outperforms full-band reconstruction in terms of recognition performance in all noise conditions. Finally, we particularly discuss the optimal selection of frequency division ways for the recognition task. When FB is divided into much more sub-bands, some of the correlations across frequency channels are lost. Consequently, efficient division ways need to be investigated to perform further recognition performance.

Subbanded DSP Architectures Based on Underdecimated Filter Banks for Coherent OFDM Receivers: Overview and recent advances

Subbanded digital signal processing (DSP) with underdecimated (Udeci) filter banks (FBs), is a recent DSP technique whereby the optical channel bandwidth is digitally sliced into multiple spectrally disjoint subbands (SBs) to be processed in parallel. In terms of DSP hardware (HW) architecture, digital subbanding amounts to an alternative mode of parallelizing the receiver signal processing task to multiple slower processors, whereby the parallelization performed in the frequency domain (FD) rather than in the time domain (TD). We show that FD parallelization is especially suited to the long-haul optical fiber channel and present novel receiver DSP structures based on Udeci FBs, providing substantial complexity savings for ultra-high-speed optically coherent transmission.

Depleted semiconductor quantum nanowire in an electrolytic medium

It is shown that electron lateral confinement leading to formation subbands (SBs) can be readily achieved by surrounding a doped semiconductor nanowire in an electrolytic medium. When the wire radius is comparable or less than the extrinsic Debye length, almost complete depletion occurs creating an electron confining exact harmonic oscillator potential. As ground state energy, SB splitting and spatial extent of the ground state wavefunction as well as the Debye length are determined by the material properties, i.e. dielectric constant, effective electron mass and doping density, the conditions under which SB effects would manifest can be clearly specified. It is suggested that the SB formation originating from the above effect could become noticeable in porous nanocrystalline composite SnO 2–ZnO films permeated with an electrolyte.

DSP implementations of sophisticated speech codecs

Hardware implementation aspects of sophisticated speech codecs are addressed. The major points discussed are approaches to implementation of the sophisticated speech codecs, requirements for DSP (digital signal-processing) implementation of the codecs, such as the type of arithmetic processing and the necessity of bit-level specifications, and codec implementation and DSP programming techniques for three specific coding algorithms: 32-kb/s ADPCM (adaptive digital pulse code modulation), 64-kb/s (7 kHz) SB (subband)-ADPCM, and 16-kb/s APC-AB (adaptive predictive coding with adaptive but allocation) codecs. >

A New Fast Approximate Hilbert Transform with Different Applications

A new and fast approximate Hilbert transform based on subband decomposition is presented. This new algorithm is called the subband (SB)-Hilbert transform. The reduction in complexity is obtained for narrow-band signal applications by considering only the band of most energy. Different properties of the SB-Hilbert transform are discussed with simulation examples. The new algorithm is compared with the full band Hilbert transform in terms of complexity and accuracy. The aliasing errors taking place in the algorithm are found by applying the Hilbert transform to the inverse FFT (time signal) of the aliasing errors of the SB-FFT of the input signal. Different examples are given to find the analytic signal using SB-Hilbert transform with a varying number of subbands. Applications of the new algorithm are given in single-sideband amplitude modulation and in demodulating frequency-modulated signals in communication systems.Key Words: Fast Algorithms, Hilbert Transform, Analytic Signal Processing.