High performance parallel-DSP computing in model-based spectral estimation

IntroductionThe identification of low frequency variations in blood pressure recordings date back to the mid‐1800's. The mechanism for the low frequency oscillation is associated with intrinsic myogenic activity of smooth muscle cells in resistance arteries. Therefore, local metabolic demand modulates blood flow distribution. Non‐invasive assessment of the microcirculation in dermatology, diabetes, skin‐graft, and burn‐injury patients could facilitate assessment of therapeutic interventions. Eulerian Video Magnification (EVM) software was developed by the Massachusetts Institute of Technology (MIT) to magnify subtle changes in motion and color in video recordings. Using this technology, magnification of a video recording may reveal local changes in the cutaneous human microcirculation.HypothesisWe hypothesize that EVM can be used to assess oscillations in cutaneous blood flow velocity.MethodsIRB approval was obtained to acquire physiological data from healthy graduate students (N = 10). Tissue Doppler was used to measure local changes in blood flow velocity on the forehead. Heart rate was assessed with an ECG and subjects breathed in time to a metronome (15 breaths/min) to normalize the effect of ventilation. Videos were processed using MIT's EVM Matlab code to magnify motion (Phase), or color & motion (Linear). All videos were magnified in the frequency range of interest (0.067–1.67 Hz; 4–100 cycles/min) to enhance human physiological frequencies. Gain was set to 50%; and a Gaussian filter was set to level 4. Phase magnification used a pyramid filtering scheme with octaves (1, ½, ¼) to specify the number of times the video is filtered; while linear magnification utilized chrominance attenuation to reduce color changes created by the magnification process. Red, green, and blue color spectrums extracted from magnified videos were analyzed with a Fast Fourier Transform (FFT) to identify magnified frequencies and power.ResultsPhase magnification (motion) and corresponding FFTs suggest strong correlations to heart rate and respiration rate but not tissue Doppler blood flow. On the other hand, linear magnification (color) and accompanying FFTs were strongly correlated to heart rate (r = 0.987; p = 0.001), respiration rate (r = 0.741; p = 0.014), and local tissue Doppler recordings (r = 0.945; p = 0.001).ConclusionThese preliminary data demonstrate that a video recording of a human subject can be magnified with EVM to assess heart rate, respiration rate, and regional blood flow velocity oscillations in the cutaneous microcirculation. EVM may be a useful non‐invasive modality to evaluate changes in microcirculation autoregulation in human subjects.Support or Funding InformationMidwestern University, Chicago College of Osteopathic Medicine, College of Health Sciences

Application of classical and model-based spectral methods to ophthalmic arterial Doppler signals with uveitis disease

BackgroundThe clinical mitigation of intracranial hypertension due to traumatic brain injury requires timely knowledge of intracranial pressure to avoid secondary injury or death. Noninvasive intracranial pressure (nICP) estimation that operates sufficiently fast at multihour timescales and requires only common patient measurements is a desirable tool for clinical decision support and improving traumatic brain injury patient outcomes. However, existing model-based nICP estimation methods may be too slow or require data that are not easily obtained.ObjectiveThis work considers short- and real-time nICP estimation at multihour timescales based on arterial blood pressure (ABP) to better inform the ongoing development of practical models with commonly available data.MethodsWe assess and analyze the effects of two distinct pathways of model development, either by increasing physiological integration using a simple pressure estimation model, or by increasing physiological fidelity using a more complex model. Comparison of the model approaches is performed using a set of quantitative model validation criteria over hour-scale times applied to model nICP estimates in relation to observed ICP.ResultsThe simple fully coupled estimation scheme based on windowed regression outperforms a more complex nICP model with prescribed intracranial inflow when pulsatile ABP inflow conditions are provided. We also show that the simple estimation data requirements can be reduced to 1-minute averaged ABP summary data under generic waveform representation.ConclusionsStronger performance of the simple bidirectional model indicates that feedback between the systemic vascular network and nICP estimation scheme is crucial for modeling over long intervals. However, simple model reduction to ABP-only dependence limits its utility in cases involving other brain injuries such as ischemic stroke and subarachnoid hemorrhage. Additional methodologies and considerations needed to overcome these limitations are illustrated and discussed.

표본조사에서는 일반적으로 지형학적 범위가 넓거나 흔히 우리가 알고 있는 지형적 범위 즉시 또는 도 단위로 표본설계가 이루어진다. 그러므로 지형학적 범위가 작은 소지역은 충분한 표본의 확보가 불가능하며 따라서 정확한 소지역 통계를 얻는 것은 매우 어렵다. 이러한 문제로 정확한 소지역 통계를 얻기 위한 연구가 활발히 진행되고 있다. 최근 신기일과 이상은(2003)은 공간통계 모형을 이용한 소지역 추정을 연구하였다. 본 논문은 신기일과 이상은(2003)의 공간자기회귀(Spatial Autoregressive: SAR) 모형을 확장한 모형인 베이지안 자기 포아송 모형 (Bayesian Auto-Poisson Model: BAPM)을 이용한 소지역 추정에 관하여 연구하였다. 분석에 사용된 자료는 호주의 1998년 장애인 통계 (Survey of Disability, Aging and Cares:SDAC)이 며 MSE, MB 그러고 회귀 분석을 이용한 편의 분석기법이 비교에 사용되었다. In sample survey sample designs are performed by geographically-based domain such as countries, states and metropolitan areas. However mostly statistics of interests are smaller domain than sample designed domain. Then sample sizes are typically small or even zero within the domain of interest. Shin and Lee(2003) mentioned Spatial Autoregressive(SAR) model in small area estimation model-based method and show the effectiveness by MSE. In this study, Bayesian Auto-Poisson Model is applied in model-based small area estimation method and compare the results with SAR model using MSE ME and bias check diagnosis using regression line. In this paper Survey of Disability, Aging and Cares(SDAC) data are used for simulation studies.

This paper proposes a generator polynomial model-based eye diagram estimation method for the error-correction codes (ECCs), which are the Bose–Chaudhuri–Hocquenghem (BCH) code and the Reed–Solomon (RS) code. The ECCs compensate for the channel loss at the bit level, so most of the systems include the ECC block. The BCH and RS codes are the generator polynomial-based ECCs; this paper proposes the generator polynomial model-based method. The model provides the accurate probability density function of the code words for the BCH and RS code. Thus, the proposed method is capable of achieving the signal integrity analysis for the system level. For verification, we design the BCH and RS encoders. The estimated eye diagrams are nearly the same as that of the transient simulation for the BCH and RS codes. Furthermore, the voltage bathtub curves are the same up to 10−8. For a smaller bit-error rate value, the proposed method only provides the bathtub curve. This paper successfully verifies the eye diagram estimation method for the BCH and RS codes.

The human brain is obviously a complex system, and exhibits rich spatiotemporal dynamics. Among the non-invasive techniques for probing human brain dynamics, electroencephalography (EEG) provides a direct measure of cortical activity with millisecond temporal resolution. Early on, EEG analysis was restricted to visual inspection of EEG records. Since there is no definite criterion evaluated by the experts, visual analysis of EEG signals is insufficient. For example, in the case of dominant alpha activity delta and theta, activities are not noticed. Routine clinical diagnosis needs to analysis of EEG signals. Therefore, some automation and computer techniques have been used for this aim (Guler et al., 2001). Since the early days of automatic EEG processing, representations based on a Fourier transform have been most commonly applied. This approach is based on earlier observations that the EEG spectrum contains some characteristic waveforms that fall primarily within four frequency bands—delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz). Such methods have proved beneficial for various EEG characterizations, but fast Fourier transform (FFT), suffer from large noise sensitivity. Parametric power spectrum estimation methods such as AR, reduces the spectral loss problems and gives better frequency resolution. AR method has also an advantage over FFT that, it needs shorter duration data records than FFT (Zoubir et al., 1998). A powerful method was proposed in the late 1980s to perform time-scale analysis of signals: the wavelet transforms (WT). This method provides a unified framework for different techniques that have been developed for various applications. Since the WT is appropriate for analysis of non-stationary signals and this represents a major advantage over spectral analysis, it is well suited to locating transient events, which may occur during epileptic seizures. Wavelet’s feature extraction and representation properties can be used to analyse various transient events in biological signals. (Adeli et al., 2003) gave an overview of the discrete wavelet transform (DWT) developed for recognising and quantifying spikes, sharp waves and spike-waves. Wavelet transform has been used to analyze and characterise epileptiform discharges in the form of 3-Hz spike and wave complex in patients with absence seizure. Through wavelet decomposition of the EEG records, transient features are accurately captured and localised in both time and frequency context. The capability of this mathematical microscope to analyse different scales of neural rhythms is shown to be a

In this paper, a new design method for small point fast Fourier transform (FFT) processor is proposed in order to satisfy the increased demands for low resource and power consumption in addition to high real-time performance in communication and control systems. Using 64 point FFT with radix-2, decimation in time (DIT) algorithm as the design objective, only an adder block and a multiplier block with pipeline structure and floating point arithmetic are adopted applying this design method. According to different classifications of twiddle factor, corresponding butterfly operations can be implemented to save the total calculation time to only 1100 clock cycles for 64 point FFT. In addition, multiple FFT processor blocks can be integrated for parallel computing to achieve higher data throughput rate. Compared with some other designs of 64 point FFT, the final results show that the proposed design has better real-time performance and lower system level complexity with the same amount of resources.

Image Registration is the process of straightening two or more images of the same scene with testimonial to a particular image. The images are captured from various sensors at different times and at multiple viewpoints. Thus, to get a better picture of any change of a scene/object over a considerable period of time image registration is important. Image registration finds application in medical sciences, remote sensing and in computer vision. This paper presents a detailed review of several approaches which are classified accordingly along with their contributions and drawbacks. The main steps of an image registration procedure are also discussed. Different performance measures are presented that determine the registration quality and accuracy. The scope for the future research are presented as well. Applications based on Fast Fourier Transform (FFT) such as signal and image processing require high computational power, plus the ability to experiment with algorithms. Reconfigurable hardware devices in the form of Field Programmable Gate Arrays (FPGAs) have been proposed as a way of obtaining high performance at an economical price. At present, however, users must program FPGAs at a very low level and have a detailed knowledge of the architecture of the device being used. To try to reconcile the dual requirements of high performance and ease of development, this paper reports on the design and realization of a High-Level framework for the implementation of 1-D and 2-D FFTs for real-time applications. Results show that the parallel implementation of 2-D FFT achieves virtually linear speed-up and real-time performance for large matrix sizes. Finally, an FPGA-based parametrizable environment based on the developed parallel 2-D FFT architecture is presented as a solution for frequency domain image filtering application

PFFTC: An improved fast Fourier transform for the IBM cell broadband engine

Applications based on the fast Fourier transform (FFT), such as signal and image processing, require high computational power, plus the ability to experiment with algorithms. Reconfigurable hardware devices in the form of field programmable gate arrays (FPGAs) have been proposed as a way of obtaining high performance at an economical price. However, users must program FPGAs at a very low level and have a detailed knowledge of the architecture of the device being used. They do not therefore facilitate easy development of, or experimentation with, signal/image processing algorithms. To try to reconcile the dual requirements of high performance and ease of development, this paper reports on the design and realisation of a high level framework for the implementation of 1-D and 2-D FFTs for real-time applications. A wide range of FFT algorithms, including radix-2, radix-4, split-radix and fast Hartley transform (FHT) have been implemented under a common framework in order to enable the system designers to meet different system requirements. Results show that the parallel implementation of 2-D FFT achieves linear speed-up and real-time performance for large matrix sizes. Finally, an FPGA-based parametrisable environment based on 2-D FFT is presented as a solution for frequency-domain image filtering application.

The Burg algorithm for maximum entropy power spectral density estimation is applied to a time series of data obtained from a Michelson interferometer and compared with a standard fast Fourier transform (FFT) estimate for resolution capability. The propane transmittance spectrum was estimated by use of the FFT with a 2(18)-data sample interferogram, giving a maximum unapodized resolution of 0.06 cm(-1). This estimate was then interpolated by zero filling an additional 2(18) points, and the final resolution was taken to be 0.06 cm(-1). Comparison of the maximum entropy method (MEM) estimate with the FFT was made over a 45-cm(-1) region of the spectrum for several increasing record lengths of interferogram data beginning at 2(10). It is found that over this region of 45 cm(-1), the MEM estimate with 2(16) data samples is in close agreement with the FFT estimate using 2(18) samples.

The location and size of the sample volume is of considerable importance in pulsed Doppler blood flow detection and velocity measurement. This study examines the feasibility of using a mechanical waveguide to control the ultrasonic field (and thus the sample volume size) of a flat disk transducer operated in pulse-echo mode. The waveguide controls the axial location of the natural focus and the lateral beam width at the natural focus. Experimental measurements are compared to theoretical results derived from a transient field model of the pulse-echo system. Results confirm that the waveguides offer a potentially useful and inexpensive means of altering the pulse-echo field of a flat disk transducer. [Work supported by NSF Grant No. MSS 9058607.]

Currently, monocular localization as a category of vision-based indoor localization methods has attracted more attention because of its application to indoor navigation and augmented reality. In a typical monocular localization system, the absolute position estimation is utilized to acquire the initial position of the query camera, and then the relative position estimation is employed to achieve subsequent camera positions. However, accumulative errors, i.e., localization drifts, caused by the relative position estimation seriously affect the localization performance. Therefore, a line model that contains a fitted line segment and some visual features is introduced, and the line model is used to seek inliers of the estimated features for the drift estimation. Based on the pre-constructed dense 3D map, a line model-based drift estimation method is presented to monitor accumulative errors. As a switching mechanism, the proposed method determines when the relative position estimation should switch to the absolute position estimation to correct user positions. Compared with the existing monocular localization methods, the proposed drift estimation method significantly reduces the accumulative errors, and the average errors are limited within a desirable range by giving a proper drift threshold. Experimental results demonstrate that the average localization errors of the proposed method are limited within 30 centimeters in various scenes by a 50-centimeters drift threshold.

For underwater vehicles, the state of charge (SOC) ofbattery isoften used to guide the optimal allocation of energy. An accurateSOC estimation can improve work efficiency and reliability of underwatervehicles. Model-based SOC estimation methods are still mainstreamroutes used in practical applications. Hence, accurate battery modelsare highly desirable, which depends not only on the circuit structurebut also on the circuit parameters. Four-parameter identificationalgorithms, offline mechanism-based and least squared (LS) methods,as well as online recursive least-squares with forget factor (FFRLS)and extended Kalman filter (EKF) methods were analyzed in terms ofSOC estimation under three different conditions. The results revealedthat in the case without any disturbance, the predicted SOCs basedon four-parameter identification circuits fitted well with the reference.Moreover, it is remarkable that the LS offline methods work betterthan the FFRLS online routes. In addition, the robustness has alsobeen accessed through the other two conditions, i.e., measurementdata with disturbance and initial SOC value with deviation. The resultsshowed that maximum errors of SOC estimation based on the EKF approachare significantly lower than those of the other methods, and the valuesare 0.51% and 0.20%, respectively. Thus, the circuit model based onthe EKF parameter identification approach possessed a stronger anti-interferenceperformance during the SOC estimation process. This research can providecorresponding theoretical support on ECM parameter identificationfor lithium-ion batteries in underwater vehicles.

This paper proposes a method for estimating the speed and position of unsampled vehicles using sampled data from connected automated vehicles (CAVs). The determination of vehicle speed and position on the road is a challenging and crucial task, as they can effectively reflect traffic flow characteristics and contribute to traffic state estimation and intersection signal timing optimization. Connected automated vehicles have the capability to upload their own trajectory data while also capturing trajectory data of surrounding vehicles through onboard sensors. Therefore, this paper proposes a novel approach to estimate the speed and position of unsampled vehicles. Firstly, using real vehicle trajectory data, the correlation between the velocity of following vehicles and the velocity of leading vehicles under different densities is analyzed, leading to the development of a velocity estimation model incorporating a speed correction factor. Secondly, the correlation between time headway, the rate of change of following vehicle acceleration, and traffic density is examined. To address the issue of heterogeneous behavior in vehicle following described by the Intelligent Driver Model (IDM), a real-time optimization model for estimating vehicle position by optimizing IDM parameters is proposed. The velocity estimation model and the position estimation model are summarized as two nonlinear optimization problems. Finally, the proposed method is validated using actual vehicle trajectory data. Experimental results demonstrate that when the number of connected automated vehicles (CAVs) is 2, the proposed method reduces the average absolute error by 30.73% and the standard deviation of the average absolute error by 42.8% compared to a linear model-based speed estimation method under different density conditions. Compared to a method that estimates vehicle position by calibrating desired gaps, the proposed method reduces the average absolute error by 38.2% and the standard deviation of the average absolute error by 41.7% under different density conditions. Furthermore, the proposed method exhibits good practicality under different CAV penetration rates.