Low-complexity Filter Research Articles

Host Subtraction, Filtering and Assembly Validations for Novel Viral Discovery Using Next Generation Sequencing Data.

The use of next generation sequencing (NGS) to identify novel viral sequences from eukaryotic tissue samples is challenging. Issues can include the low proportion and copy number of viral reads and the high number of contigs (post-assembly), making subsequent viral analysis difficult. Comparison of assembly algorithms with pre-assembly host-mapping subtraction using a short-read mapping tool, a k-mer frequency based filter and a low complexity filter, has been validated for viral discovery with Illumina data derived from naturally infected liver tissue and simulated data. Assembled contig numbers were significantly reduced (up to 99.97%) by the application of these pre-assembly filtering methods. This approach provides a validated method for maximizing viral contig size as well as reducing the total number of assembled contigs that require down-stream analysis as putative viral nucleic acids.

Real-time experimental demonstrations of software reconfigurable optical OFDM transceivers utilizing DSP-based digital orthogonal filters for SDN PONs.

Real-time optical OFDM (OOFDM) transceivers with on-line software-controllable channel reconfigurability and transmission performance adaptability are experimentally demonstrated, for the first time, utilizing Hilbert-pair-based 32-tap digital orthogonal filters implemented in FPGAs. By making use of an 8-bit DAC/ADC operating at 2GS/s, an oversampling factor of 2 and an EML intensity modulator, the demonstrated RF conversion-free transceiver supports end-to-end real-time simultaneous adaptive transmissions, within a 1GHz signal spectrum region, of a 2.03Gb/s in-phase OOFDM channel and a 1.41Gb/s quadrature-phase OOFDM channel over a 25km SSMF IMDD system. In addition, detailed experimental explorations are also undertaken of key physical mechanisms limiting the maximum achievable transmission performance, impacts of transceiver's channel multiplexing/demultiplexing operations on the system BER performance, and the feasibility of utilizing adaptive modulation to combat impairments associated with low-complexity digital filter designs. Furthermore, experimental results indicate that the transceiver incorporating a fixed digital orthogonal filter DSP architecture can be made transparent to various signal modulation formats up to 64-QAM.

Spectral estimation for long‐term evolution transceivers using low‐complex filter banks

For mobile user equipments (UEs), a careful power management is essential. Despite this fact, quite an amount of energy is wasted in today's UEs’ analogue (AFEs) and digital frontends (DFEs). These are engineered for extracting the wanted signal from a spectral environment defined in the corresponding communication standards with their extremely tough requirements. These requirements define a worst-case scenario still ensuring reliable communication. In a typical receiving process the actual requirements can be considered as less critical. Knowledge about the actual environmental spectral conditions allows to reconfigure both frontends to the actual needs and to save energy. In this paper, the authors present a highly efficient generic spectrum sensing approach, which allows to collect information about the actual spectral environment of an UE. This information can be used to reconfigure both the AFE and DFE, thus endowing them with increased intelligence. A low-complex multiplier free filter bank extended by an efficient power calculation unit will be introduced. They also present simulation results, which illustrate the performance of the spectrum sensing approach and a complexity comparison with different well-known implementations is given. Furthermore, estimates on the chip area and power consumption based on a 65 nm CMOS technology database are provided, considering the Smarti4G chip as a reference.

Linear-Phase VDF Design With Unabridged Bandwidth Control Over the Nyquist Band

This brief presents a low-complexity linear-phase variable digital filter (VDF) design with tunable lowpass (LP), highpass (HP), bandpass (BP), and bandstop (BS) responses anywhere over the entire Nyquist band. The spectral-parameter-approximation-based VDFs (SPA-VDFs) was designed using the Farrow structure and has advantages of linear phase, lower group delay, and fewer variable multipliers. However, the total gate count and the dynamic range of filter coefficient values of SPA-VDFs significantly increase with the tunable range of cutoff frequency, which limits their usefulness in emerging signal processing and wireless communication applications. In addition, existing VDFs need to update either filter coefficients or need parallel filter structures to obtain variable LP, HP, BP and BS responses. In this brief, a new VDF design is proposed by deftly integrating SPA-VDF with the modified coefficient decimation method (MCDM), and it will be referred to as SPA-MCDM-VDF. The SPA-MCDM-VDF provides LP, HP, BP, and BS responses with unabridged center frequency and bandwidth control over the entire Nyquist band without the need for hardware reimplementation or coefficient update. The complexity comparisons show that the SPA-MCDM-VDF offers substantial savings in gate count, group delay, and number of variable multiplications over other linear-phase VDFs.

Low-Complexity Reconfigurable Fast Filter Bank for Multi-Standard Wireless Receivers

This brief presents a new low-complexity reconfigurable fast filter bank (RFFB) for wireless communication applications such as spectrum sensing and channelization. In RFFB, the bandwidth and center frequency of sub-bands can be varied with high frequency resolution without hardware reimplementation. This is achieved with an improved modified frequency transformation-based variable digital filter (MFT-VDF) at the first stage of the proposed multistage implementation. Existing second-order frequency transformation-based low-pass VDFs have limited cutoff frequency range which is approximately 12.5% of the sampling frequency. The proposed low-pass MFT-VDF offers unabridged control over the cutoff frequency on a wide frequency range thereby, improving the cutoff frequency range of existing VDFs. The design example shows that the RFFB is easy to design and offers substantial savings in gate counts over other filter banks.

Optimal Sharpening of Compensated Comb Decimation Filters: Analysis and Design

Comb filters are a class of low-complexity filters especially useful for multistage decimation processes. However, the magnitude response of comb filters presents a droop in the passband region and low stopband attenuation, which is undesirable in many applications. In this work, it is shown that, for stringent magnitude specifications, sharpening compensated comb filters requires a lower-degree sharpening polynomial compared to sharpening comb filters without compensation, resulting in a solution with lower computational complexity. Using a simple three-addition compensator and an optimization-based derivation of sharpening polynomials, we introduce an effective low-complexity filtering scheme. Design examples are presented in order to show the performance improvement in terms of passband distortion and selectivity compared to other methods based on the traditional Kaiser-Hamming sharpening and the Chebyshev sharpening techniques recently introduced in the literature.

A 0.5 V tunable complex filter for Bluetooth and Zigbee using OTAs

A 12th-order low voltage tunable differential complex filter for bluetooth and Zigbee applications is proposed in this paper. The filter is based on improved controllable transconductors operating ...

Guest Editorial: Advanced Techniques for Efficient Electronic System Design

This special section in Circuits, Systems, and Signal Processing on Electronic System Design (CSSP) contains five papers which present highly interesting state of the art methodologies and circuits relating to different important aspects of electronic system design including the multi-standard communication, digital watermarking, memory integrity detection and protection in embedded systems, information security in systems on chips (SoC), and elliptic curve cryptography (ECC). Channelization is an essential computation-intensive function for multi-standard communication. The paper entitled “A Low-Complexity Uniform and Non-uniform Digital Filter Bank Based on an Improved Coefficient Decimation Method for MultiStandard Communication Channelizers” by Ambede et al. provides an efficient lowcomplexity solution for this important function. In this paper, the authors present a novel filter bank (FB) design technique based on the combination of the conventional coefficient decimation method (CDM) and the recently proposed modified coefficient decimation method. It is shown that the proposed FB has superior stop band and transition band characteristics, and involves significantly lower complexity compared with the conventional CDM-based progressive decimation filter bank (PDFB). The proposed approach provides uniform as well as non-uniform subbands to be used

A class of reconfigurable and low-complexity two-stage Nyquist filters

This paper introduces a class of reconfigurable two-stage Nyquist filters where the Farrow structure realizes the polyphase components of linear-phase finite-length impulse response (FIR) filters. By adjusting the variable predetermined multipliers of the Farrow structure, various linear-phase FIR Nyquist filters and integer interpolation/decimation structures are obtained, online. However, the filter design problem is solved only once, offline. Design examples, based on the reweighted ℓ1-norm minimization, illustrate the proposed method. Savings in the arithmetic complexity are obtained when compared to the reconfigurable single-stage structures.

Efficient Signal Conditioning Techniques for Brain Activity in Remote Health Monitoring Network

This paper proposes several efficient and less complex signal conditioning algorithms for brain signal enhancement in remote healthcare monitoring applications. In clinical environment during electroencephalogram (EEG) recording, several artifacts encounter and mask tiny features underlying brain wave activity. Especially in remote clinical monitoring, low computational complexity filters are desirable. Hence, in our paper, we propose various efficient and computationally simple adaptive noise cancelers for EEG enhancement. These schemes mostly employ simple addition and shift operations, and achieve considerable speed over the other conventional realizations. We have tested the proposed implementations on real brain waves recorded using emotive EEG system. Our experiments show that the proposed realization gives better performance compared with existing realizations in terms of signal to noise ratio, computational complexity, convergence rate, excess mean square error, misadjustment, and coherence.

Sparse ACEKF for phase reconstruction

We propose a novel low-complexity recursive filter to efficiently recover quantitative phase from a series of noisy intensity images taken through focus. We first transform the wave propagation equation and nonlinear observation model (intensity measurement) into a complex augmented state space model. From the state space model, we derive a sparse augmented complex extended Kalman filter (ACEKF) to infer the complex optical field (amplitude and phase), and find that it converges under mild conditions. Our proposed method has a computational complexity of N(z)N logN and storage requirement of O(N), compared with the original ACEKF method, which has a computational complexity of O(NzN(3)) and storage requirement of O(N(2)), where Nz is the number of images and N is the number of pixels in each image. Thus, it is efficient, robust and recursive, and may be feasible for real-time phase recovery applications with high resolution images.

An improved class of multiplierless decimation filters: Analysis and design

In this paper, we present a class of low-complexity decimation filters for oversampled discrete-time signals. The proposed class of filters improves the frequency response of classical comb filters in two respects. First, it introduces extra-attenuation around the so-called folding bands, i.e., frequency intervals whose spurious signals are folded down to baseband during the decimation process. Second, this class reduces the passband distortion via an effective droop-compensator block, thus increasing the passband of the decimation filters. Like comb filters, the proposed class can be realized through multiplierless architectures, which are also discussed thoroughly in the paper. Unlike comb filters, the proposed filters have superior spurious signal rejection and a greatly reduced droop in the signal passband. These features make the proposed filters suitable for multistage decimation applications, such as reconfigurable software radio receivers, as well as for decimating oversampled digital signals produced by @[email protected] A/D converters. The paper discusses several useful techniques for designing the proposed filters in a variety of architectures with emphasis on non-recursive architectures. Design examples are discussed to highlight the key frequency features along with implementation issues aimed at reducing the computational complexity of the filters.

Digital Coherent Receivers for Long-Reach Optical Access Networks

The relative merits of coherent-enabled optical access network architectures are explored, with a focus on achievable capacity, reach and split ratio. We review the progress in implementing the particular case of the ultra dense wavelength division multiplexed (UDWDM) passive optical network (PON), and discuss some challenges and solutions encountered. The applicability of digital signal processing (DSP) to coherent receivers in PONs is shown through the design and implementation of parallelized, low-complexity application-specific digital filters. In this work, we focus on mitigating the impact of local oscillator laser (LO) relative intensity noise (RIN) on receiver sensitivity, and propose an algorithm which compensates for this impairment. This phenomenon is investigated theoretically and then experimentally by evaluating the sensitivity of a coherent receiver incorporating different tunable light sources; a low-RIN external cavity laser (ECL) and a monolithically integrated digital supermode distributed Bragg reflector (DS-DBR) laser. It is shown that the RIN of the signal laser does not significantly contribute to the degradation of the receiver sensitivity. Finally, a 10 Gbit/s coherent PON is demonstrated using a DS-DBR laser as the LO laser. It is found that a receiver sensitivity of -38.8 dBm is achievable assuming the use of hard-decision forward error correction.

Design of Low-Complexity High-Performance Wavelet Filters for Image Analysis

This paper addresses the construction of a family of wavelets based on halfband polynomials. An algorithm is proposed that ensures maximum zeros at ω = π for a desired length of analysis and synthesis filters. We start with the coefficients of the polynomial (x+1)(n) and then use a generalized matrix formulation method to construct the filter halfband polynomial. The designed wavelets are efficient and give acceptable levels of peak signal-to-noise ratio when used for image compression. Furthermore, these wavelets give satisfactory recognition rates when used for feature extraction. Simulation results show that the designed wavelets are effective and more efficient than the existing standard wavelets.

Coefficient relation-based minimax design and low-complexity structure of variable fractional-delay digital filters

Although the coefficient-relation of the even-order sub-filters in the Farrow structure has been revealed and adopted in the weighted-least-squares (WLS) design of even-order finite-impulse-response (FIR) variable fractional-delay (VFD) digital filters in the literature, it has never been exploited in implementing low-complexity VFD filters. This paper aims to(i)propose a low-complexity implementation structure through exploiting the coefficient-relation such that the coefficient-relation is not only utilized in the design process, but also utilized in the low-complexity implementation;(ii)formulate the minimax design of an even-order VFD filter through exploiting the coefficient-relation;(iii)propose a new two-stage scheme called increase-then-decrease scheme for optimizing the sub-filter orders so as to minimize the VFD digital filter complexity.With the above three advances, an even-order VFD filter can not only be designed and but also be implemented efficiently by exploiting the coefficient-relation. We will use a design example to illustrate the low complexity and high design accuracy.

ShRNAPred (version 1.0): An open source and standalone software for short hairpin RNA (shRNA) prediction.

The small hairpin RNAs (shRNA) are useful in many ways like identification of trait specific molecular markers, gene silencing and characterization of a species. In public domain, hardly there exists any standalone software for shRNA prediction. Hence, a software shRNAPred (1.0) is proposed here to offer a user-friendly Command-line User Interface (CUI) to predict ‘shRNA-like’ regions from a large set of nucleotide sequences. The software is developed using PERL Version 5.12.5 taking into account the parameters such as stem and loop length combinations, specific loop sequence, GC content, melting temperature, position specific nucleotides, low complexity filter, etc. Each of the parameters is assigned with a specific score and based on which the software ranks the predicted shRNAs. The high scored shRNAs obtained from the software are depicted as potential shRNAs and provided to the user in the form of a text file. The proposed software also allows the user to customize certain parameters while predicting specific shRNAs of his interest. The shRNAPred (1.0) is open access software available for academic users. It can be downloaded freely along with user manual, example dataset and output for easy understanding and implementation.AvailabilityThe database is available for free at http://bioinformatics.iasri.res.in/EDA/downloads/shRNAPred_v1.0.exe

Low-complexity high-quality adaptive deblocking filter for H.264/AVC system

Blocking artifacts always appear on the reconstructed image, particularly in a low bit-rate video coding system. This paper presents an adaptive offset method to improve image quality for H.264 decoding. First, the histogram statistic is used to analyze the correlation between the offset and the filtering performance. The best filtering performance can mostly be found at the position of three offsets. Second, the best offset can be searched for with the minimum SAE (Summation of Absolute Error) among the three candidates. This algorithm can not only keep low computations, but it can also obtain good filtering quality. The average performance can be improved about 0.25 to 0.45dB (decibels) higher than the original H.264 deblocking filter. The blocky effect on the decoding image can be smoothed in vision.

Post-processing deblocking filter algorithm for various video decoders

This work presents a low-complexity post-processing deblocking filter to reduce the blocking effects. The proposed algorithm can be applied to current block-based video standards without modifications. The proposed algorithm automatically detects blocking effects at 4×4 or 8×8 block boundaries. In the filtering stage, the proposed algorithm provides four filter modes to eliminate the effects of blocking in various frequency regions based on region activity analysis. To improve both subjective and objective qualities, the proposed algorithm also considers the chrominance filter. Experimental results demonstrate that the proposed algorithm provides higher-quality MPEG-4 and H.264/AVC reconstructed video sequences with a lower processing time, than many approaches.

Decoupling Minimax Design of Low-Complexity Variable Fractional-Delay FIR Digital Filters

This paper presents a simple linear programming (LP) technique for designing high-accuracy low-complexity finite-impulse-response (FIR) variable fractional-delay (VFD) digital filters in the minimax error sense. The objective of the minimax design is to minimize the maximum absolute error of the variable frequency response (VFR) of an FIR VFD filter, which is a nonlinear problem and difficult to solve. This paper shows that the minimax design can be approximately decomposed into a pair of separate LP subproblems by decoupling the minimization of the real-part VFR error from that of the imaginary-part error. As a result, the original nonlinear minimax design problem can be easily solved by solving the two LP subproblems separately. To reduce the VFD filter complexity, we also propose a one-by-one increase scheme for optimizing the subfilter orders in the Farrow structure such that a given design specification (maximum absolute error of VFR) can be exactly satisfied. Both even-order and odd-order design examples are given to illustrate that the decoupling minimax method is not only simple, but also can achieve excellent high-accuracy low-complexity FIR VFD filters.

Low-Complexity Nonlinear Adaptive Filter Based on a Pipelined Bilinear Recurrent Neural Network

To reduce the computational complexity of the bilinear recurrent neural network (BLRNN), a novel low-complexity nonlinear adaptive filter with a pipelined bilinear recurrent neural network (PBLRNN) is presented in this paper. The PBLRNN, inheriting the modular architectures of the pipelined RNN proposed by Haykin and Li, comprises a number of BLRNN modules that are cascaded in a chained form. Each module is implemented by a small-scale BLRNN with internal dynamics. Since those modules of the PBLRNN can be performed simultaneously in a pipelined parallelism fashion, it would result in a significant improvement of computational efficiency. Moreover, due to nesting module, the performance of the PBLRNN can be further improved. To suit for the modular architectures, a modified adaptive amplitude real-time recurrent learning algorithm is derived on the gradient descent approach. Extensive simulations are carried out to evaluate the performance of the PBLRNN on nonlinear system identification, nonlinear channel equalization, and chaotic time series prediction. Experimental results show that the PBLRNN provides considerably better performance compared to the single BLRNN and RNN models.