Dah-Chung Chang

Image contrast enhancement based on a histogram transformation of local standard deviation

The adaptive contrast enhancement (ACE) algorithm, which uses contrast gains (CGs) to adjust the high-frequency components of images, is a well-known technique for medical image processing. Conventionally, the CG is either a constant or inversely proportional to the local standard deviation (ILSD). However, it is known that conventional approaches entail noise overenhancement and ringing artifacts. In this paper, we present a new ACE algorithm that eliminates these problems. First, a mathematical model for the LSD distribution is proposed by extending Hunts image model. Then, the CG is formulated as a function of the LSD. The function, which is nonlinear, is determined by the transformation between the LSD histogram and a desired LSD distribution. Using our formulation, it can be shown that conventional ACEs use linear functions to compute the new CGs. It is the proposed nonlinear function that produces an adequate CG resulting in little noise overenhancement and fewer ringing artifacts. Finally, simulations using some X-ray images are provided to demonstrate the effectiveness of our new algorithm.

Maneuvering target tracking with colored noise

It is known that colored noise may degrade the performance of a tracking algorithm. A common remedy is to model colored noise as an autoregressive (AR) process and apply the measurement difference method. One problem with the approach is that the AR parameters are usually unknown. In this work, we propose a new method to adaptively estimate the AR parameters. It is shown that this method is simple and practically feasible. We incorporate oar method into the interacting multiple model (IMM) tracking algorithm and show that the performance is almost as good as that in the known parameters case.

Feedforward Active Noise Control With a New Variable Tap-Length and Step-Size Filtered-X LMS Algorithm

The fixed tap-length and step-size filtered-X least mean-square (FxLMS) algorithm is conventionally used in active noise control (ANC) systems. A tradeoff between the performance and the convergence rate is a well-known problem due to the choice of the step size. Although the variable-step-size FxLMS algorithms have been proposed for fast convergence, a long tap-length filter is frequently required in order to deal with different environments such that the convergence rate is still subject to a small step size for the long tap length. In this paper, we study a new ANC system with a variable tap-length and step-size FxLMS algorithm. Based on the assumption of an unsymmetric and two-sided exponential decay response model for the ANC control filter, the new FxLMS algorithm has the minimum mean-square deviation for the optimal filter coefficients. In the online secondary path modeling ANC system, simulation results show that the new algorithm with different kind of variable step sizes can provide significant improvements of convergence rate and noise reduction ratio, compared to the fixed-tap-length FxLMS algorithms.

Digital GFSK Carrier Synchronization

Gaussian frequency shift keying (GFSK) modulation is widely used in low data-rate personal communication standards like Bluetooth. For the purpose of low power and low cost, GFSK transceivers are usually designed in the analog domain. To improve bit error rate (BER) performance and the integration capability for diverse and complicated applications, the digital GFSK demodulation technology becomes attractive. A digital GFSK transceiver is studied in this paper where a new and simple carrier synchronizer is proposed. The new carrier synchronizer uses a decision-aided data cancellation algorithm to estimate carrier frequency offset. The complete GFSK transmission simulation is performed and almost ideal BER performance is achieved in the additive white Gaussian noise environment

Feedback median filter for robust preprocessing of glint noise

Glint noise may arise in a target tracking system. The non-Gaussian behavior of glint noise can severely degrade the tracking performance. Measurement preprocessing at the front-end of the tracker is an effective method to reduce glint noise. The preprocessor proposed by Hewer, Martin, and Zeh (1987), which used the computationally intensive M-estimator, may not be suitable for practical implementation. An alternative method employing the median filter is studied here. The median filter is well known for its simplicity and robustness. However, the efficiency of the median filter can be seriously degraded if input samples are not identically distributed. This is what we may encounter in the tracking problem. A feedback median filter is then proposed to overcome this impediment without substantially increasing complexity. Simulations show that the new preprocessor can greatly improve tracking performance in the glint noise environment.

The extended Kalman filtering algorithm for carrier synchronization and the implementation

The extended Kalman filtering algorithm applied for carrier synchronization and the implementation are studied in this paper. The major advantage of the Kalman-based scheme over the conventional phase-locked loop (PLL) is the self-adaptation of loop bandwidth for better compromising the tradeoff between fast acquisition and small phase tracking variation. We have developed the idea to a practical application example of the cable modem downstream receiver. From simulations and implementation results, it is shown that the Kalman-based approach can achieve much shorter acquisition time than the conventional PLL structure. A 64-QAM receiver with a two-state Kalman filtering carrier recovery is designed and verified on Xilinx Spartan-3 FPGA

Design of an OFDM System with Long Frame by the Decision-Aided Channel Tracking Technique

An OFDM system with the decision-aided channel tracking technique for a long frame application is proposed. The OFDM system in wireless LAN applications assumes static channel response in a short frame and it is enough to use two long training symbols for channel estimation. We build a wireless surveillance system which may transmit data symbols in a seamless manner. The channel estimation with the symbol-by-symbol tracking is then required. We show that the proposed channel tracking method outperforms the channel estimation by only preamble in static and time-varying channels. The channel tracking module adds about 18% circuits to the whole system in our design

Spatial-Division Multiplexing MIMO Detection Based on a Modified Layered OSIC Scheme

Spatial-division multiplexing (SDM) provides very high spectral efficiency in multiple-input multiple-output (MIMO) systems. A well-known SDM-MIMO wireless system is vertical Bell Labs layered space-time (V-BLAST) which exhibits a good tradeoff between performance and complexity. Although maximum likelihood detection (MLD) has the optimal performance, its complexity is too high to practice such that some alternatives have been studied. The ordered successive interference cancellation (OSIC) algorithm was proposed for the advantage of high feasibility, however, there is a significant performance gap between MLD and OSIC. Here, we propose a modified layered OSIC algorithm to improve symbol detection in ill-conditioned layers with lower complexity compared to exhaustive search methods. To reduce the number of calculating matrix inversion for optimal ordering, we introduce a modified parallel interference cancellation method with precancellation and postcancellation to replace part of successive interference cancellation, based on evaluating the post-detection signal to noise ratio for each layer. Complexity analysis shows that the proposed algorithm saves about 65% operation of matrix inversion compared to a near-optimal improved layered OSIC algorithm while maintaining the similar bit error rate performance as shown in numerical results.

An automatic modulation classification technique using high-order statistics for multipath fading channels

In the last few years, the Dynamic Spectrum Access (DSA) has been a flexible strategy for wireless spectrum management. In vehicular communications, OFDM is usually used. The detection of dynamically allocated signals is a problem for a receiver with DSA technique. The automatic modulation classifier can recognize and demodulate the dynamically allocated signals in the OFDM system. We design a layered modulation classifier based on high-order statistics to classify the unknown modulated signal. The aim of the classifier is to defeat the unknown channel effect. We show the classification algorithm of signal detection in multipath environments, and discuss the performance of simulation results.

Pilot-Aided Channel Estimation Methods for ICI Reduction in Mobile OFDM Systems

In a time-varying mobile channel, the Doppler effect causes intercarrier interference (ICI) in an OFDM system. Some researches have been found to mitigate the ICI effect in order to improve the system performance. However, ICI reduction algorithms usually require the information of the channel impulse response (CIR) even on each sample. In this paper we study several channel estimation algorithms for CIR estimation, including the frequency-domain and the time-domain approaches. We have shown that the time-domain Kalman filter channel estimator has better performance than others because it can estimate CIR sample by sample Simulation results also show that a proposed ICI mitigation algorithm incorporating the time-domain Kalman channel estimator achieves better BER performances at different mobile speeds.