Multirate Processing Research Articles

ECG based apnea detection by multirate processing hybrid of wavelet-empirical decomposition Hjorth features extraction and neural networks.

Sleep Apnea (SA) can cause health complications including heart stroke and neurological disorders. The Polysomnography (PSG) test can detect the severity of sleep disturbance. However, it is expensive and requires a dedicated sleep laboratory and expertise to examine the patients. Therefore, it is not available to a large population in developing countries. This leads to the development of cost-effective and automated patient examination methods for the detection of sleep apnea. This study suggests an approach of using the ECG signals to categorize sleep apnea. In this work, we have devised an original technique of feature space designing by intelligently hybridizing the multirate processing, a mix of wavelet-empirical mode decomposition (W-EMD), modes-based Hjorth features extraction, and Adam-based optimized Multilayer perceptron neural network (MLPNN) for automated categorization of apnea. A publicly available ECG dataset is used for evaluating the performance of the suggested approach. Experiments are performed for four different sub-bands of the considered ECG signals. For each selected sub-band, five "Intrinsic Mode Functions" (IMFs) are extracted. Onward, three Hjorth features: complexity, activity, and mobility are mined from each IMF. In this way, four feature sets are formed based on wavelet-driven selected sub-bands. The performance of optimized MLPNN, for the apnea categorization, is compared for each feature set. Five different evaluation parameters are used to assess the performance. For the same dataset, a systematic comparison with current state-of-the-artwork has been done. Results have shown a classification accuracy of 98.12%.

Arrhythmia classification using multirate processing metaheuristic optimization and variational mode decomposition

The concept of mobile healthcare systems is promising. It is based on the cloud connected wireless biomedical wearables. In this scenario, the compression, processing, transmission and power effectiveness with precision are the key terms. A novel technique is presented for arrhythmia identification by processing the electrocardiogram signals. The solution is based on an effective hybridization of the multirate processing, QRS selection, variational mode decomposition, features mining from Modes, Metaheuristic optimization based features selection, and machine learning algorithms. The MIT-BIH dataset is used for experimentation. Performance of the Butterfly Optimization Algorithm, Manta Ray Foraging Optimization, and Emperor Penguin Optimization algorithms is investigated for features selection. A multi-subjects and multi-class dataset is used for testing the performance of classification by following the 10-fold cross validation strategy. The multirate processing with QRS selection and Metaheuristic optimization dependent features selection bring compression and aptitude for the processing and data transmission efficiencies. The system efficiently incorporates the multirate processing while securing an effective signal reconstruction. The respective compression gains and classification accuracies for the Butterfly Optimization Algorithm, Manta Ray Foraging Optimization, and Emperor Penguin Optimization algorithms are 27-fold, 29.45-fold & 46.29-fold and 99.14%, 99.08% & 98.65%.

Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare

The next generation healthcare systems will be based on the cloud connected wireless biomedical wearables. The key performance indicators of such systems are the compression, computational efficiency, transmission and power effectiveness with precision. The electrocardiogram (ECG) signals processing based novel technique is presented for the diagnosis of arrhythmia. It employs a novel mix of the Level-Crossing Sampling (LCS), Enhanced Activity Selection (EAS) based QRS complex selection, multirate processing, Wavelet Decomposition (WD), Metaheuristic Optimization (MO), and machine learning. The MIT-BIH dataset is used for experimentation. Dataset contains 5 classes namely, “Atrial premature contraction”, “premature ventricular contraction”, “right bundle branch block”, “left bundle branch block” and “normal sinus”. For each class, 450 cardiac pulses are collected from 3 different subjects. The performance of Marine Predators Algorithm (MPA) and Artificial Butterfly Optimization Algorithm (ABOA) is investigated for features selection. The selected features sets are passed to classifiers that use machine learning for an automated diagnosis. The performance is tested by using multiple evaluation metrics while following the 10-fold cross validation (10-CV). The LCS and EAS results in a 4.04-times diminishing in the average count of collected samples. The multirate processing lead to a more than 7-times computational effectiveness over the conventional fix-rate counterparts. The respective dimension reduction ratios and classification accuracies, for the MPA and ABOA algorithms, are 29.59-times & 22.19-times and 98.38% & 98.86%.

Simplified square to hexagonal lattice conversion based on 1-D multirate processing

Hexagonal image sampling and processing are theoretically superior to the most commonly used square lattice based sampling and processing, but due to the lack of commercial image sensors, current research mainly relies on virtually hexagonally sampled data through square to hexagonal lattice conversion, which is a typical 2-D interpolation problem. This paper presents a simplified and efficient square to hexagonal lattice conversion method. The method firstly utilizes the separable nature of the interpolation kernel to simplify the original 2-D interpolation into 1-D interpolation along the horizontal direction only, and then it applies the 1-D multirate technique to further simplify the shift-variant 1-D interpolation into shift-invariant 1-D convolutions. Compared with the original 2-D interpolation version, the proposed method becomes both simple and computationally efficient, and it is also suitable for implementation with parallel processing and hardware. Finally, experiments are performed and the results are consistent with the analysis.

Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification.

The usage of wearable gadgets is growing in the cloud-based health monitoring systems. The signal compression, computational and power efficiencies play an imperative part in this scenario. In this context, we propose an efficient method for the diagnosis of cardiovascular diseases based on electrocardiogram (ECG) signals. The method combines multirate processing, wavelet decomposition and frequency content-based subband coefficient selection and machine learning techniques. Multirate processing and features selection is used to reduce the amount of information processed thus reducing the computational complexity of the proposed system relative to the equivalent fixed-rate solutions. Frequency content-dependent subband coefficient selection enhances the compression gain and reduces the transmission activity and computational cost of the post cloud-based classification. We have used MIT-BIH dataset for our experiments. To avoid overfitting and biasness, the performance of considered classifiers is studied by using five-fold cross validation (5CV) and a novel proposed partial blind protocol. The designed method achieves more than 12-fold computational gain while assuring an appropriate signal reconstruction. The compression gain is 13 times compared to fixed-rate counterparts and the highest classification accuracies are 97.06% and 92.08% for the 5CV and partial blind cases, respectively. Results suggest the feasibility of detecting cardiac arrhythmias using the proposed approach.

Implementation of optimal multi-rate cardiac signal processing on a 1967BH028 DSP from “Milandr” design center in order to analyze heart rate variability

The analysis of heart rate variability in recent years has become very widespread as a tool for versatile diagnostics of the functional state of the human body. The analysis of heart rate variability is associated with processing procedures characterized by high requirements for the speed of the computational element base. These procedures, however, must be performed in real time in an embedded computing system. The article deals with the problem of reducing the number of computational operations and an implementation of heart rate variability on modern processor elements offered by the domestic industry. The aim of the work is to model the optimal structure of multi-rate signal processing in the analysis of heart rate variability and to implement this structure on a digital signal processor with an estimate of processing time and `memory costs. By modeling, it is shown that the developed optimal structure of multi-rate processing allows getting a reliable processing result while reducing computational costs by several hundred thousand times compared to the implementation at the original sampling frequency. The optimal structure is constructed as a two-stage filtering-decimation structure, followed by passing the signal at a reduced sampling rate through a set of analysis filters. The end-to-end decimation factor is 500. The simulation results allow us to proceed to the implementation on the signal processor. The program codes of the main processing stages, including filtration and filtration decimation, have been developed. It is shown that the processing time with high-quality optimization of program codes can reach 10 million clock cycles, which corresponds to 23 ms and fully satisfies the real-time processing requirement, leaving a large margin for implementing additional more complex analysis algorithms on the same processor. The practical significance of the results is that in addition to the proposed method of reducing computational and memory costs, a prototype of a possible device based on one of the most popular domestic signal processors is obtained.

Using Signal Subtraction Operation to Suppress Electro-Acoustic Leakage in Ultrasonic Low-Frequency Dual Element Transducers

It was shown that the use of complex modulated signals with subsequent multi-rate radio engineering processing of ultrasound echo-signals allows the creation of new methods for ultrasonic non-destructive inspection that are inaccessible for conventional devices. An example of using an ultrasonic multifunctional adaptive measuring complex demonstrates the possibility of creating a method for suppressing the parasitic signal of electro-acoustic leakage in low-frequency dual-element transducers by means of a multi-rate signal processing algorithm including the simplest signal subtraction operation.

MIMO Subband Volterra Digital Predistortion for Concurrent Aggregated Carrier Communications

This paper presents a multiple-input multiple-output (MIMO) nonlinear mitigation technique for closely spaced concurrent aggregated carrier systems. The transmitter architecture considers band-limited sources, where the predistorter and signal bandwidth are the same, thus reducing transmitter hardware cost and power consumption. The technique relies on multirate processing and linear filtering and uses the carrier frequencies to isolate the contribution of linear and nonlinear basis functions to the desired bands. This approach can be used with any MIMO model structure. In particular, models are linear in the parameters of low computational complexity. The technique was evaluated using three concurrent carriers of 50 MHz each fed to a Doherty amplifier. The results show significant reduction in the error vector magnitude and improvements for the transmitter efficiency using the proposed compensation technique.

Fast Physically Correct Refocusing for Sparse Light Fields Using Block-Based Multi-Rate View Interpolation.

Digital refocusing has a tradeoff between complexity and quality when using sparsely sampled light fields for low-storage applications. In this paper, we propose a fast physically correct refocusing algorithm to address this issue in a twofold way. First, view interpolation is adopted to provide photorealistic quality at infocus-defocus hybrid boundaries. Regarding its conventional high complexity, we devised a fast line-scan method specifically for refocusing, and its 1D kernel can be 30× faster than the benchmark View Synthesis Reference Software (VSRS)-1D-Fast. Second, we propose a block-based multi-rate processing flow for accelerating purely infocused or defocused regions, and a further 3- 34× speedup can be achieved for high-resolution images. All candidate blocks of variable sizes can interpolate different numbers of rendered views and perform refocusing in different subsampled layers. To avoid visible aliasing and block artifacts, we determine these parameters and the simulated aperture filter through a localized filter response analysis using defocus blur statistics. The final quadtree block partitions are then optimized in terms of computation time. Extensive experimental results are provided to show superior refocusing quality and fast computation speed. In particular, the run time is comparable with the conventional single-image blurring, which causes serious boundary artifacts.

Application and analysis of multi-rate sampled in structural health monitoring system

Huge and abundant measured information from a structural health monitoring system contain a large amount of noise and uncertain information. Multi-rate data processing can reduce data processing time and meet the real-time requirements in health monitoring systems. Multi-rate digital signal processing includes extraction and interpolation. By frequency domain decomposition method multi-rate systems is converted to an equivalent single-rate system, and simulated with simulink of MATLAB. In comparison with the simulation results of multi-rate system and single-rate system, we can conclude that in a multi-rate system the stability margin is increased, system response is accelerated and overshoot is reduced, so the dynamic quality of the system is improved.

Multiple‐input multiple‐output symbol rate signal digital predistorter for non‐linear multi‐carrier satellite channels

A digital predistortion (DPD) scheme is presented for non-linear distortion mitigation in multi-carrier satellite communication channels. The proposed DPD has a multiple-input multiple-output architecture similar to data DPD schemes. However, it enhances the mitigation performance of data DPDs using a multi-rate processing algorithm to achieve spectrum broadening of non-linear operators. Compared to single carrier (single-input single-output) signal (waveform) DPD schemes, the proposed DPD has lower digital processing rate reducing the required hardware cost of the predistorter. The proposed DPD outperforms, in total degradation, both data and signal DPD schemes. Further, it performs closest to a channel bound described by an ideally mitigated channel with limited maximum output power.

Highly effective spectral output through significant improvement in stopband attenuation by sample modification technique in multirate processing

The digital spectral analysis is of prime consideration for effective reception and analysis of signals with improved spectral characteristics with simple techniques. For this purpose, this paper presents a novel technique in digital bandpass filter bank by Modified Kaiser window based finite impulse response method in multirate processing. The finite impulse response method results in better passband and stopband response in addition to precise control over phase response. This novel technique is more effective in the proposed method in the sense that it allows the modification of samples of input signal in order to derive the advantages in respect of selectivity, stopband attenuation, peak output apart from smooth response in spectral output as compared to the existing methods. Further, with improved features of passband characteristic and spectral efficiency, the proposed method of spectral analysis is better suitable for most of real-time applications, especially in digital hearing aids.

Realization of Programmable BPSK Demodulator-Bit Synchronizer using Multirate Processing

This paper presents the design and implementation of programmable BPSK demodulator and bit synchronizer. The demodulator is based on the Costas loop design whereas the bit synchronizer is based on Gardner timing error detector. The advantage of this design is that it offers programmability using multi-rate processing and does not rely on computation of filter coefficients, NCO angle input for each specific data rate and thus avoids computational complexities. The algorithm and its application were verified on Matlab-Simulink and was implemented on ALTERA platform. A 32 kHz BPSK demodulator–bit synchronizer pair catering for data rates from 1 kbps to 8 kbps was implemented. DOI: http://dx.doi.org/10.11591/ijece.v4i3.5561

Multirate Processing Technique for Obtaining Integer and Fractional-Order Derivatives of Low-Frequency Signals

This paper proposes a multirate processing technique for obtaining integer and fractional derivatives of digital signals with an improved signal-to-quantization noise ratio. The technique consists of an oversampling analog-to-digital conversion unit with a dithering process and multistage down sampling process followed by an half-band finite impulse response (FIR) differentiator with high performance cascaded using a decimator with a factor of two. In the cascaded multistage processing, each stage contains a simple half-band low-pass FIR filter followed with decimation by two. The proposed technique uses an extremely small size for each half-band low-pass filter, hence requiring much less memory and computational complexity in comparison with the single-stage process. Computer simulations and experimental data processing demonstrate the effectiveness of the proposed method.

Realization of Programmable BPSK Demodulator-Bit Synchronizer using Multirate Processing

This paper presents the design and implementation of programmable BPSK demodulator and bit synchronizer. The demodulator is based on the Costas loop design whereas the bit synchronizer is based on Gardner timing error detector. The advantage of this design is that it offers programmability using multi-rate processing and does not rely on computation of filter coefficients, NCO angle input for each specific data rate and thus avoids computational complexities. The algorithm and its application were verified on Matlab-Simulink and were implemented on ALTERA platform. A 32 kHz BPSK demodulator–bit synchronizer pair catering for a data rate from 1kbps to 8kbps was implemented.

Highly Selective Digital Filter Bank through Linear Variation of Stopband Attenuation in Multirate Processing by Sample Modification Technique

Digital spectral analysis is the significant factor of consideration by which numerous applications importantly need of effective reception and analysis of signals, that is, the reception of signals is needed with improved spectral characteristics and simple techniques. To meet the above requirements, a novel technique is proposed in digital bandpass filter bank, supported by 'Modified Kaiser window' based Finite impulse response method in Multirate processing followed by Fast Fourier transform. The novel technique influences largely in the proposed method in such a way that it involves the modification of samples of input signal for deriving the advantages in respect of selectivity, stopband attenuation, peak output and constant width cum sharp rise of response apart from smooth spectral output when compared with existing methods. Further, reduction in computational complexity and hardware complexity are the additional features of the proposed method, henceforth; its spectral output is suitable in many of real time applications and moreover advantageous in digital hearing aids. The simulation results are drawn and its performance is compared to elucidate the advantages in the proposed method.

Multirate Cascaded Discrete-Time Low-Pass ΔΣ Modulator for GSM/Bluetooth/UMTS

This paper shows that multirate processing in a cascaded discrete-time ΔΣ modulator allows to reduce the power consumption by up to 35%. Multirate processing is possible in a discrete-time ΔΣ modulator by its adaptibility with the sampling frequency. The power reduction can be achieved by relaxing the sampling speed of the first stage and increasing it appropriately in the second stage. Furthermore, a cascaded ΔΣ modulator enables the power efficient implementation of multiple communication standards.@The advantages of multirate cascaded ΔΣ modulators are demonstrated by comparing the performance of single-rate and multirate implementations using behavioral-level and circuit-level simulations. This analysis has been further validated with the design of a multirate cascaded triple-mode discrete-time ΔΣ modulator. A 2-1 multirate low-pass cascade, with a sampling frequency of 80 MHz in the first stage and 320 MHz in the second stage, meets the requirements for UMTS. The first stage alone is suitable for digitizing Bluetooth and GSM with a sampling frequency of 90 and 50 MHz respectively. This multimode ΔΣ modulator is implemented in a 1.2 V 90 nm CMOS technology with a core area of 0.076 mm2. Measurement results show a dynamic range of 66/77/85 dB for UMTS/ Bluetooth/GSM with a power consumption of 6.8/3.7/3.4 mW. This results in an energy per conversion step of 1.2/0.74/2.86 pJ.

Joint Carrier Synchronization and Equalization Algorithm for Packet-Based OFDM Systems Over the Multipath Fading Channel

In this paper, a joint carrier synchronization and equalization algorithm is presented for orthogonal frequency-division multiplexing (OFDM) systems in the tracking stage. Based on the minimum mean-square-error (MMSE) criterion, the cost function of the joint algorithm is proposed to minimize the mean square error (MSE), namely, the uncoded bit error rate (BER), on each subchannel and to further lower the carrier frequency jitter concurrently. The carrier synchronization scheme with multirate processing is a dual-loop structure, which is composed of outer and inner loops. The outer loop is a frequency-tracking loop that deals with the phase offset, which is induced by the carrier frequency offset (CFO), in the time domain. The inner loop is a phase-tracking loop to cope with the phase distortions, which are caused by the carrier frequency error and the channel phase variation, on each subcarrier in the frequency domain. There is a gain equalization loop to compensate the magnitude distortion on each subchannel. Furthermore, the closed-loop stability of the carrier synchronization loop is particularly explored for the loop delay induced by hardware realization. Many simulations are done for the additive white Gaussian noise (AWGN) and the multipath frequency-selective fading channels to show that the joint algorithm not only accurately estimates and compensates the CFO and the channel impairment but also provides the cost-effective feature compared with the considered algorithms.

Efficient Image Enhancement Algorithm Using Multi-Rate Image Processing

This paper describes an efficient image enhancement method based on the Multi-Scale Retinex (MSR) approach for pre-processing of video applications. The processing amount is drastically reduced to 4 orders less than that of the original MSR, and 1 order less than the latest fast MSR method. For the efficient processing, our proposed method employs multi-stage and multi-rate filter processing which is constructed by a x-y separable and polyphase structure. In addition, the MSR association is effectively implemented during the above multi-stage processing. The method also modifies a weighting function for enhancement to improve color rendition of bright areas in an image. A variety of evaluation results show that the performance of our simplified method is similar to those of the original MSR, in terms of visual perception, contrast enhancement effects, and hue changes. Moreover, experimental results show that pre-processing of the proposed method contributes to clear foreground object separation.

A PLL-Based Multirate Structure for Time-Varying Power Systems Harmonic/Interharmonic Estimation

This paper describes a phase-locked-loop (PLL)-based power systems harmonic estimation algorithm, which uses an analysis filter bank and multirate processing. The filter bank is composed of bandpass filters. The initial center frequency of each filter is purposely chosen to be equal to harmonic frequencies. However, an adaptation strategy makes it possible to track time-varying frequencies as well as interharmonic components. A downsampler device follows the filtering stage, reducing the computational burden, especially because undersampling operations are performed. Finally, the last stage is composed of a PLL estimator which provides estimates for amplitude, phase, and frequency of the input signal. The proposed method improves the accuracy, computational effort, and convergence time of the previous harmonic estimator based on cascade PLL configuration.