Characteristics of the fluorophores samples
Multiexponential transient signals are particularly important due to their occurrences in many natural phenomena and human applications. For instance, it is important in the study of nuclear magnetic resonance (NMR) in medical diagnosis (Cohn-Sfetcu et al., 1975)), relaxation kinetics of cooperative conformational changes in biopolymers (Provencher, 1976), solving system identification problems in control and communication engineering (Prost and Guotte, 1982), fluorescence decay of proteins (Karrakchou et al., 1992), fluorescence decay analysis (Lakowicz, 1999). Several research work have been reported on the analysis of multicomponent transient signals following the pioneer work of Prony in 1795 (Prony, 1975) and Gardner et al. in 1959 (Gardner, 1979). Detailed review of several techniques for multicomponent transient signals’ analysis was recently reported in (Jibia, 2010).
Generally, a multiexponential transient signal is represented by a linear combination of exponentials of the form
There are many problems associated with the analysis of transient signals of the form given in equation (1) due to the nonorthogonal nature of the exponential function. These problems include incorrect detection of the peaks, poor resolution of the estimated decay and inaccurate results for contaminated or closely-related decay rate data as reported in (Salami et.al., 1985). These problems become increasingly difficult when the level of noise is high. Although Gardner transform eliminated the nonorthogonality problem, it introduced error ripples due to short data record and nonstationarity of the preprocessed data. Apart from these problems, analysis of multiexponential signal is computationally intensive and requires efficient tools for its development and implementation in real-time.
To overcome these problems, modification of Gardner transform has been proposed recently with Multiple Signal Classification (MUSIC) superposition modeling technique (Jibia et al., 2008); with minimum norm modeling technique (Jibia and Salami, 2007), with homomorphic deconvolution, with eigenvalues decomposition techniques, and the Singular Value Decomposition (SVD) based-Autoregressive Moving Average (ARMA) modeling techniques (Salami and Sidek, 2000; Jibia, 2009). As reported in (Jibia, 2009), performance comparison of these four modeling techniques has been investigated. Though, all the four techniques were able to provide satisfactory performances at medium and high signal-to-noise ratio (SNR), the SVD-ARMA was reported to have the highest resolution, especially at low SNR.
Hence, the development of SVD-ARMA based algorithm for multiexponential signal analysis using MATLAB software package is examined in this chapter.
MATLAB provides computational efficient platform for the analysis and simulation of complex models and algorithms. In addition, with the aid of inbuilt embedded MATLAB Simulink block, it offers a tool for the integration of developed algorithm/model in an embedded application with little programming efforts as compared to the use of other programming languages (Mathworks, 2008). This functionality is explored in integrating the developed MATLAB-based algorithm into National Instrument (NI) Labview embedded programming tool. Hence, an integrated MATLAB-Labview software interface is proposed for real-time deployment of the algorithm. To this end, the analytical strength of MATLAB together with simplicity and user-friendly benefits of the National Instrument (NI), Labview design platforms are explored in developing an efficient, user-friendly algorithm for the real-time analysis of multiexponential transient signal.
The rest of the chapter is organized as follows. Section 2 provides brief review of techniques for multiexponential signal analysis. The MATLAB algorithm development for the signal analysis is presented in section 3. The development of an integrated MATLAB-Labview real-time software interface is then examined in section 4. Section 5 presents sample real-time data collection together with results and analysis. The chapter is concluded in section 6 with recommendation for future study.
2. Techniques of multiexponential transient signal analysis
Several techniques have been reported for the analysis of transient multiexponential signal. They are classified as: (i) time domain or frequency domain, and (ii) parametric or nonparametric techniques. The main objective of these techniques in analyzing the multiexponentially decaying signals is to estimate the signal parameters as accurately as possible and to get better display of the signal spectra. Time-domain techniques constitute the oldest methods of multiexponential signal analysis prior to the advent of Gardner transformation technique (Gardner et al, 1959; Salami, 1985). Gardner transformation is one of the most important methods of the transient signal analysis based on spectral analysis. Generally, spectral analysis involves transformation of a time-domain signal to a frequency domain so that certain features of the signals that characterized them are easily discerned such as its decay constant,
Among the earliest time-domain technique is the peeling technique. However, this technique produces poor results when contains more than two components. Other time-domain techniques such as Prony’s method and its variants produce better performance than the peeling technique, however they are very sensitive to noise ((Smyth,2002; Salami, 1995). The nonlinear least squares technique (Smyth,2002) is computationally inefficient and the solution sometimes fails to converge, which means the estimate of the signal parameters cannot be accurately obtained.
The nonparametric techniques of spectral analysis are introduced to overcome some limitations of the time-domain techniques. The Gardner transformation technique produces error ripples which obscure the real peaks of the spectrum due to the cutoff points. This technique is good in analyzing signal with high SNR. The fast Fourier transform (FFT) technique, which is an improvement over the original Gardner transformation produces improved resolution. However, the problem of error ripples still exists. The extension of this technique involving the use of digital signal processing and Gaussian filtering is sensitive to noise and its data range has to be limited to get accurate estimates of the signal parameters. Whilst the modified FFT technique is better than the previous methods, it often fails to estimate
Parametric techniques such as Autoregressive (AR), Moving Average (MA) and ARMA models are introduced to the analysis of multiexponential signals to alleviate the drawbacks of the nonparametric techniques. The AR modeling technique requires less computation than the ARMA modeling technique as its model parameters are relatively easy to estimate. However, it is sensitive to noise due to the assumed all-pole model. On the other hand, MA parameters are difficult to estimate and the resultant spectral estimates have poor resolution. Although, the ARMA modeling technique is much better in estimating noisy signal than the AR modeling technique, it requires a lot of computation. A detailed review of these techniques can be obtained in (Jibia, 2009; Salam and Sidek, 2000).
3. MATLAB-based Algorithm development
The systematic process involved in the development of the MATLAB-based SVD-ARMA algorithm for multiexponential transient signal analysis suitable for real-time application is discussed in this section. Apart from performance evaluation and signal generation, the algorithm consists of five major steps: obtaining convolution integral of the exponential signal using modified Gardner transformation; signal interpolation using spline technique; generation of deconvolved data; SVD-ARMA modeling of the deconvolved data; and power spectrum computation to finally estimate the transient signal parameters. A brief summary of the steps involved are as shown in Figure 2, and briefly highlighted as follows:
Generate the required signal,
This involves the conversion of the measured or generated signal and to y(t) and h(t) respectively using modified Gardner transformation (Salami and Sidek, 2003). This yields a convolution integral as subsequently described.
In general, equation (1) is expressed as
where the basis function,
Both sides of equation (3) are multiplied by in the modified Gardner transformation instead of only
where the output function,
The discrete impulse response function,
where the total number of samples,
The discrete-time signal y[n] obtained in (5) consists of non-equally spaced samples which would be difficult to digitally process. A cubic interpolation is therefore applied to y[n] using MATLAB function ‘spline’ to obtain equally spaced samples of y[n].
In this stage, deconvolved data is generated from y[n] using optimally compensated inverse filtering due to its ability to handle noisy signal when compared to conventional inverse filtering (Salami,1995).
Conventionally, this is done by taking the DFT of equation (5) to produce:
The deconvolved data, can be obtained by computing
To overcome this problem, an optimally compensated inverse filtering is introduced. In this approach,
which is referred to as the optimally compensated inverse filtering. It is noted that a small value of
where denotes the fourth order backward difference operator and
Unwanted high frequency noise can still be introduced by this optimally compensated inverse filtering which can make some portion of unusable. Therefore, a good portion of denoted as
where 1 ≤
SVD-ARMA algorithm is applied to f(k) to estimate the signal parameters
Generally, the ARMA model assumes that
where V(k) is the input driving sequence,
One of the most effective procedures for estimating these model parameters is by solving a modified Yule-Walker equation (Kay and Marple 1981). This procedure is subsequently discussed.
Equation (14) is multiplied by
Equation (16) may not hold exactly in practice because both
The estimated AR coefficients are then used to generate the residual error sequences:
from which the actual MA parameters are obtained directly from equation (20). However, MA spectra can be obtained from the DFT of the error sequences,
and the desired power distribution of
Power distribution graph has been used to display the results of multiexponential signal analysis. This is computed from the power spectrum of the resulting output signal from SVD-ARMA modeling method as shown in equation (21).
The efficiency of the algorithm with SVD-ARMA modeling technique in estimating
The Cramer-Rao lower bound will determine whether the estimator is efficient by comparing the variance of the estimator,
A MATLAB-mfile code has been written to implement the steps 1 to 6 described above, details of this have been thoroughly discussed in (Jibia, 2009).
4. Integrated MATLAB-labview for real-time implementation
This section discusses the development of proposed integrated Labview-MATLAB software interface for real-time (RT) implementation of the algorithm for multicomponent signal analysis as described in section 3. Real-time signal analysis is required for most practical applications of multicomponent signal analysis. In this study, reference is made to the application to fluorescence signal analysis. A typical multicomponent signal analyzer comprises optical sensor which is part of the spectrofluorometer system, signal conditioning/data acquisition system, embedded processor that runs the algorithm in real-time, and display/storage devices as shown in Figure 3. National Instrument (NI) real-time hardware and software are considered for the development of this system due to its ease of implementation.
4.1. Labview real-time module and target
The Labview real-time module (RT-software) together with NI sbRIO-9642 (RT-hardware) has been adopted in this study. The Labview Real-Time software module allows for the creation of reliable, real-time applications which are easily downloaded onto the target hardware from Labview GUI programming tool.
The NI sbRIO-9642 is identical in architecture to CompactRIO system, only in a single circuit board. Single-Board RIO hardware features a real-time processor and programmable FPGA just as with CompactRIO, and has several inputs and outputs (I/O) modules as shown in Figure 4.
System development involves graphical programming with Labview on the host Window computer, which is then downloaded and run on real-time hardware target. Since the algorithm has been developed with MATLAB scripts, an integrated approach was adopted in the programme development as subsequently described.
4.2. MATLAB-labview software integration
The developed algorithm with MATLAB scripts was integrated inside Labview for real-time embedded set-up as shown in Figure 5. Labview front-panel and block diagrams were developed with inbuilt Labview math Scripts to run the MATLAB algorithm for the analysis of multicomponent signals. The use of Labview allows for ease of programming, and real-time deployment using the Labview real-time module described in section 4.1. Also, it provides a user friendly software interface for real-time processing of the fluorescence signals.
As shown in Figure 5, the sampled data produced by the spectrofluorometer system are pre-processed by the NI-DAQ Cards. These signals are then read by the embedded real-time software, analyze the signals and display the results in a user friendly manner. The user is prompted to enter the number of samples to be analyzed from the front-panel using the developed SVD-ARMA algorithm.
Generally, the developed algorithm with MATLAB scripts was integrated inside Labview for real-time embedded set-up as follows:
Pre-simulation of the MATLAB algorithm inside embedded MATLAB Simulink block: this requires re-structuring of the codes to be compatible for embedded Simulink implementation, and hence deployment inside Labview MathScript. Figure 7 shows the MATLAB Simulink blocks configuration with embedded MATLAB function together with cross-section of the algorithm. The DATA with time vector is prepared inside the workspace and linked to the model input. Figure 10 (a-f) shows the simulation results with experimental data described in section 5.
Labview programming: Development of Labview front-panel and block diagram is as shown in Figure 4. In the block diagram programming, the Labview MathScript node is employed to integrate the MATLAB codes in the overall Labview programme. The script (Figure 8) invokes the MATLAB software script server to execute scripts written in the MATLAB language syntax
The integrated software interface is evaluated with the real-time fluorescence data collected from a spectrofluorometer.
5. Sample collection, results and discussion
Due to unavailability of spectrofluorometer system that can be directly linked with the set-up, sampled data collected from fluorescence decay experiment conducted using Spectramax Germini XS system were used to test the performance of the integrated system. The schematic diagram of the spectrofluorometer operation is shown in Figure 9, and itemized as follows:
Three intrinsic fluorophores (Acridine Orange; Fluorescein Sodium and Quinine) were used in the experiment. The details of the substances are given in Table 1.
|Acridine Orange||Fluorescein Sodium||Quinine|
Purity by HPLC
Solubility in water
Solutility in ethanol
The simulation results for Acridine orange, Fluorescein Sodium, Quinine, Quinine plus Arcridine, Fluorescein Sodium plus Acridine orange, and Fluorescein Sodium plus Acridine orange plus Quinin in water are shown in Figure 10 (a-f) respectively.
Figure 11 to Figure 13 show the sample of results obtained from the integrated MATLAB-Labview real-time software which has been developed. Both results of simulation and real-time software interface yield accurate estimates of the fluorescence data as shown in Figure 10-Figure 13, and presented in Table 2. The singular values for each of the samples combination are given in Table 3. The results indicate accurate determination of the constituent samples
6. Conclusion/Future study
The development of MATLAB-based algorithm for real-time analysis of multicomponent transient signal analysis based on SVD-ARMA modeling technique has been presented in this chapter. To enhance real-time interface and rapid prototyping on target hardware, complementary benefits of MATLAB and Labview were explored to develop real-time software interface downloadable into single board computer by NI Labview. In the absence of the spectrofluorometer system, the developed user friendly software for real-time deployment was validated with the collected real-time data. The obtained results indicate the effectiveness of the proposed integrated software for practical application of the proposed algorithm.
Future direction of this research will be directed towards development of customized spectrofluorometer sub-system that can be directly integrated to the overall system. This will eventually facilitate direct application of the developed algorithm in practical applications involving transient signal analysis. Also, other algorithms based on homomorphic and eigenvalues decomposition developed by the authors in similar study are to be made available as option on the user interface.
|Mixture||Expected value||SVD-ARMA||Percentage error|
|Acridine Orange + Fluorescein Sodium|