This paper aims to present a Lorenz chaotic free-space optical (FSO) communication system (LC-FSO-CS), and to study the impact of FSO channel on a transmitted Lorenz chaotic signal infused by an arbitrary random binary message. It examines and corroborates the LC-FSO-CS by an analytical approach, using autocorrelation and power spectral density in the presence of the channel’s multiplicative noise. To achieve this task, the paper exploits an autocorrelation model for the multiplicative scintillation noise while approximating the transmitted chaotic signal modeled by a quasirandom telegraph signal. The method presented here considers the effect of the additive noise in the FSO channel as negligible, while successfully employing the linear system concepts and logarithmic operation to analyze the influence of scintillation noise on the transmitted chaotic waveform. The emphasis of this work is on last-mile FSO communication with simulation experiments performed and results presented throughout the paper to validate the LC-FSO-CS.
This paper is devoted to addressing the synchronization, and detection of random binary data exposed to inherent channel variations existing in Free Space Optical (FSO) communication systems. This task is achieved by utilizing the identical synchronization methodology of Lorenz chaotic communication system, and its synergetic interaction in adversities imposed by the FSO channel. Moreover, the Lorenz system has been analyzed, and revealed to induce Stochastic Resonance (SR) once exposed to Additive White Gaussian Noise (AWGN). In particular, the resiliency of the Lorenz chaotic system, in light of channel adversities, has been attributed to the success of the proposed communication system. Furthermore, this paper advocates the use of Haar wavelet transform for enhanced detection capability of the proposed chaotic communication system, which utilizes Chaotic Parameter Modulation (CPM) technique for means of transmission.
This paper utilizes a synchronized Lorenz chaotic drive/response system, which uses Haar filtering and appropriate thresholding in order to detect a transmitted random binary message. Using the Lorenz chaotic attractor to obscure the message, the transmission is passed through an Additive White Gaussian (AWG) channel to successfully retrieve the original binary random data. The detection mechanism employs the Haar Wavelet Transform in combating the channel noise. A communication technique using Chaotic Parameter Modulation (CPM) is simulated in Matlab and prototyped on a reconfigurable hardware platform from Xilinx.
The design and modeling of compressive sensing (CS) imagers is difficult due to the complexity and non-linearity of the system and reconstruction algorithm. The Night Vision Integrated Performance Model (NV-IPM) is a linear imaging system design tool that is very useful for complex system trade studies. The custom component generator, included in NV-IPM, will be used to include a recently published theory for CS that links measurement noise, easily calculated with NV-IPM, to the noise of the reconstructed CS image given the estimated sparsity of the scene and the number of measurements as input. As the sparsity will also depend on other factors such as the optical transfer function and the scene content, an empirical relationship will be developed between the linear model within NV-IPM and the non-linear reconstruction algorithm using measured test data. Using the theory, a CS imager varying the number of measurements will be compared to a notional traditional imager.
KEYWORDS: Binary data, Free space optics, Signal to noise ratio, Wavelets, Field programmable gate arrays, Optical filters, Prototyping, Telecommunications, Free space optical communications, Interference (communication)
High bandwidth, fast deployment with relatively low cost implementation are some of the important advantages of free space optical (FSO) communications. However, the atmospheric turbulence has a substantial impact on the quality of a laser beam propagating through the atmosphere. A new method was presented in [1] and [2] to perform bit synchronization and detection of binary Non-Return-to-Zero (NRZ) data from a free-space optical (FSO) communication link. It was shown that, when the data is binary NRZ with no modulation, the Haar wavelet transformation can effectively reduce the scintillation noise. In this paper, we leverage and modify the work presented in [1] in order to provide a real-time streaming hardware prototype. The applicability of these concepts will be demonstrated through providing the hardware prototype using one of the state-of-the-art reconfigurable hardware, namely Field Programmable Gate Arrays, and highly productive high-level design tools such as System Generator for DSP from Xilinx.
KEYWORDS: Signal to noise ratio, Discrete wavelet transforms, Free space optics, Prototyping, Frequency modulation, Interference (communication), Field programmable gate arrays, Wavelets, Fermium, Digital signal processing
Free-Space Optical (FSO) communications is a vital area of research due to its important advantages of providing a very large bandwidth and relatively low cost of implementation. One of the inherent limitations on the quality of an FSO communication link is the degradation of the received beam due to atmospheric turbulence. This paper is concerned with prototyping a wavelet-based algorithm to remove or reduce the effect of the scintillation noise and other unwanted signal on an FSO link that uses analog frequency modulation. The applicability of these concepts will be demonstrated by providing a real-time prototype using reconfigurable hardware, namely Field Programmable Gate Arrays (FPGA), and high-level design tools such as System Generator for DSP from Xilinx. Our proposed prototype was realized on the Virtex-6 FPGA ML605 board using the XC6VLX240T-1FFG1156 device.
This paper is concerned with the development and implementation of a registration and stabilization method in conjunction with airborne imaging applications. We consider the situations for which the camera motion and vibration collectively affect the noisy image sequence. The general routine presented in this work is a combination of two algorithms for global image registration and image stabilization. We use and present experiments with real image sequences to track a moving object in the direction of its motion trajectory.
This paper presents a method for registration of noisy airborne images for the purpose of the detection of moving objects. A new iterative algorithm is developed and presented for the correction of geometrical distortion caused by global motion in a scene. A binary hypotheses test is subsequently established using a likelihood ratio test (LRT) to classify the pixels in the corrected image as either locally moving (object motion) or not moving (stationary). The paper also incorporates the use of the Expectation-Maximization method for estimation of statistical image features needed by the LRT. We use and present experiments with real image sequences to validate the analytical developments.
This paper presents the use of Expectation-Maximization (EM) method for image motion registration in a scene
captured by a moving camera. In [1] we presented a new iterative algorithm for the correction of geometrical
distortion caused by global motion in a scene. A binary hypotheses test was subsequently established to classify the
pixels in the corrected image as either locally moving (object motion) or not moving (stationary). There were some
unknown parameters, such as noise variance and motion variance, in the developments that needed to be estimated.
This paper presents the use of the EM algorithm to estimate these parameters. We present experiments with real
image sequences to validate the analytical developments.
This paper presents methods for motion detection and estimation of objects in a scene
captured by a moving camera. A new iterative algorithm is developed and presented for
the correction of geometrical distortion caused by global motion in a scene. A binary
hypotheses test is subsequently established to classify the pixels in the corrected image as
either locally moving (object motion) or not moving (stationary). The developed method
incorporates estimates of additive white Gaussian noise in all steps and is therefore more
robust than simple change detection method.
KEYWORDS: Signal to noise ratio, Discrete wavelet transforms, Fermium, Frequency modulation, Wavelets, Scintillation, Free space optics, Free space optical communications, Demodulation, Interference (communication)
Atmospheric noise signals are fundamental limitation of free-space optical communications, as the decrease in signal-to-noise ratio reduces the range and/or bandwidth of the link. In this paper we consider the limitations that this imposes, and investigate the use of discrete wavelet transformation (DWT) to overcome them. Simulations are performed to validate the use of the DWT in the demodulation of the analog data in the presence of noise. Results of the experiments are presented.
Based on wavelet transformation and adaptive Wiener filtering, a new method was presented by the authors to perform synchronization and detection of binary data from a free-space optical (FSO) signal. It was shown prevlously that the Haar wavelet with a fixed scale is an excellent choice for this purpose. The output of the filter was zero mean and was closely related to the derivative of the binary data. In this effort, an analysis of the prior work is presented to obtain the probability of bit error using a Bayesian ternary hypotheses testing. The analysis also results in determining optimum thresholds for the detection of binary data. Simulation experiments are performed and presented to validate the results of the theoretical analysis.
KEYWORDS: Signal to noise ratio, Discrete wavelet transforms, Wavelets, Analog electronics, Free space optical communications, Free space optics, Demodulation, Data communications, Interference (communication), Scintillation
Atmospheric scintillation noise is a fundamental limitation of free space optical communications, as the decrease in signal-to-noise ratio reduces the range and/or bandwidth of the link. A technique employing dual wavelengths has previously been demonstrated to be effective in mitigating scintillation noise by using common mode rejection to remove co-channel noise. However, any practical implementation of this technique will include uncorrelated noise, e.g. amplifier and photodetector noise, which will not be removed. In this paper we consider the limitations that this imposes, and investigate the use of discrete wavelet transformation (DWT) to overcome them. Simulations are performed to validate the use of the DWT in the demodulation of the analog data in the presence of noise. Results of the experiments are presented.
A new method is presented to perform bit synchronization and detection of binary nonreturn-to-zero (NRZ) data from a free-space optical (FSO) communication link. Based on the wavelet transformation, a new bandpass filter is developed and implemented. It is shown that the Haar wavelet is an excellent choice for this purpose. The center frequency of this filter is a function of the scale and could be adjusted to adapt to the variation of the channel. The output of the filter is zero mean and is closely related to the derivative of the binary data. The filter has a linear phase; therefore, its output is used for synchronization and detection of the data. Analysis of the method is presented using Fourier transformation. In addition, adaptive Wiener filtering is utilized to reduce the effect of the additive white Gaussian noise in the data. Simulation experiments are performed and presented using real and synthetic data. The results of the experiments indicate that the Haar wavelet transform and adaptive Wiener filtering are robust and effective tools in dealing with FSO data.
Based on the wavelet transformation and adaptive Wiener Filtering, a new method was presented by the authors to perform the synchronization and detection of the binary data from the Free-Space Optical (FSO) signal. It was shown that the Haar wavelet with a fixed scale is an excellent choice for this purpose. The output of the filter is zero-mean and is closely related to the derivative of the binary data. In this effort an analysis of the work in is presented to obtain the probability of bit error using a Bayesian ternary hypotheses testing. The analysis also results in determining optimum thresholds for the detection of binary data.
A new method is presented to perform the synchronization and detection of the binary data from the FSO signal. Based on the wavelet transformation, a new band-pass filter is developed and implemented. It is shown that the Haar wavelet is an excellent choice for this purpose. The center frequency of this filter is a function of the scale and could be adjusted to adapt to the variation of the channel. The output of the filter is zero-mean and is closely related to the derivative of the binary data. The filter has a linear phase; therefore, its output is used for synchronization and detection of the data. Analysis of the method is presented using Fourier transformation. In addition, simulation experiments are performed and presented using the real data. The results of the experiments indicate that the Haar wavelet transform is a robust and effective tool in dealing with FSO data.
An adaptive thresholding method is presented for optimum detection for optical receivers with large multiplicative noise. The technique uses low frequency sampling of the detected current that enables calculation of the bit means and variances and estimation of the optimum detection threshold. The regime in which this holds is when the sampling frequency is lower than the bit rate but higher than atmospheric turbulence frequency content. Simulations are done with data obtained from the NRL Chesapeake Bay Lasercomm Testbed. The results of simulations comparing BER performance versus sample rate and parameter estimation error will be presented. If the system parameters are characterized in advance with reasonable accuracy, the BER obtained will typically be an order of magnitude improvement over the equal variance threshold (depending on the signal to noise ratio).
It has been shown that for optical communication receivers with large, signal-dependant noise components (multiplicative noise), the optimum detection threshold can be derived from a Bayes' Likelihood Ratio Test (LRT); however, the mean and variance of the bit levels must be known to obtain the order of magnitude bit-error-rate (BER) improvement over the typical matched filter type detector which assumes equal variances of the bit levels. In free-space communication systems, atmospheric conditions can cause variations in optical transmission and subsequently in the bit level means and variances. The bit level means and variances must be tracked and estimated and the detection threshold updated at a rate greater than the frequency of atmospheric changes, or the BER performance may actually be worse than that of the equal-variance threshold. Adaptive thresholding methods have been proposed and developed which track the bit means and variances and update the detection threshold to maintain near optimum performance. In this paper, simulated data based on actual optical receiver component characteristics and measured average received power data containing atmospheric turbulence induced fluctuations are used to test the tracking and BER performance of adaptive thresholding algorithms. The results of simulations comparing performance of three adaptive methods, maximum likelihood estimation/prediction, Kalman filter predictor/smoother, and a Least-Mean-Square (LMS) adaptive predictor, will be presented.
KEYWORDS: Expectation maximization algorithms, Image segmentation, Image processing algorithms and systems, Motion estimation, Image compression, Signal to noise ratio, Video, Quantization, Video coding, Algorithm development
Two essential aspects of uncovered-background prediction and motion compensation for image sequence coding are the segmentation of an image frame in a sequence of images into regions of uncovered- and covered-background, moving, and stationary pixels and the estimation of motion parameters. We have developed and investigated a method which simultaneously estimates motion and sequence parameters and provides image segmentation from noisy image sequences. The method segments an image frame in an image sequence into regions of moving, stationary, covered-background, and uncovered-background pixels relative to a reference frame. The basis of our method is the expectation-maximization algorithm for maximum-likelihood estimation. We previously presented our method under assumptions which imposed sever restrictions on the image composition and the object motion and did not consider covered-background pixels. These cases, though unrealistic, were illustrative of the mathematical formulation of our method. In this paper we remove the limiting restrictions on the image composition, and we introduce a region for covered-background pixels.
We previously proposed and demonstrated the feasibility of a method for segmenting an image in a sequence of images into regions of stationary, moving, and uncovered background pixels and simultaneously estimating parameters of each region. The basis of our method is the expectation-maximization (EM) algorithm for maximum-likelihood estimation. We view the intensity difference between image frames as the incomplete data and the intensity difference with the region identifier as the complete data. Our previous work focused primarily on the viability of the method and considered only moving and stationary pixels. In particular, we estimated the DCT coefficients of the motion field for the moving pixels allowing motion- compensated reconstruction of image frames. In this paper we extend our previous formulation to include uncovered background pixels, and we present results showing image segmentation and parameter convergence.
This paper is concerned with the estimation of the image motion field from a pair of consecutive noisy frames. The maximum likelihood principle is invoked for estimating the nonrandom but unknown displacement function. In our developments, we consider processing both of the observed images (jointly) through a 2 X 2 noncausal matrix filter. The design of this matrix filter depends on the assumed values of the parameters for the displacement function. The analysis presented is the extension and generalization of the work originally established by Stuller who studied the problem of maximum likelihood estimation of variable time delay. The developments are specialized to the case for which the motion field is modeled by an affine transformation. Simulations are performed which indicate the validity of the estimator in the presence of noise. Results of the simulations are presented.
Transformation of the image motion vector to a different domain exhibits impressive properties, such as resistance to noise and reduction in computational time and storage requirements. Using the steepest ascent algorithm in a transformed domain, we develop an iterative technique for frame-to-frame image motion estimation. The estimator seeks for the maximum likelihood (ML) estimate of the transformed-coefficients of the motion field. The scheme is implemented using discrete cosine, discrete sine, slant, Hadamard-Walsh, and Haar transforms. Since the motion field is generally slowly-varying, it is shown that by ignoring the higher number transformed-coefficients, substantial improvement in noise reduction is achieved. Simulation experiments are performed to indicate the validity of the analysis for real images in the presence of noise. Results of the simulations are presented.
The generalized maximum likelihood (GML) algorithm is a gradient-based iterative algorithm for frame-to-frame motion estimation. This algorithm tends toward the maximum likelihood estimates of the Karhunen-Loève expansion coefficients of the motion field. The GML algorithm requires the covariance function matrix as a priori knowledge. Determination of the actual motion covariance in a practical situation is a difficult problem; the problem is approached by assuming that the motion vector is modeled by a separable stationary Markov-2 field. Using this model, we relate and compare the GML algorithm to another well-known motion estimator reported by Netravali and Robbins. Simulation experiments are presented that indicate the improvement of the GML algorithm over Netravali's scheme.
A new iterative technique for frame-to-frame image motion estimation is introduced and impleniented. The algorithm presented in this paper is based on the maximum likelihood criterion and is referred to as the GML algorithm. This scheme requires the covariance function matrix of the motion a priori. For this reason a possible motion model will be introduced and implemented. Simulation experiments are presented which investigate the performance of the algorithm in conjunction with real and synthetic images. Key Words : Motion Compensation Maximum Likelihood Covariance Function Markovian Field.
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