This study presents a fast adaptive algorithm for noise estimation in non-stationary environments. To make noise estimation adapt quickly to non-stationary noise environments, a robust entropy-based voice activity detection (VAD) is thus required. It is well-known that the entropy-based measure defined in spectral domain is very insensitive to the changing level of nose. To exploit the specific nature of straight lines existing on speech-only spectrogram, the proposed spectrum entropy measurement improved from spectrum entropy proposed by Shen et al. is further presented and is named band-splitting spectrum entropy (BSE). Consequently, the proposed recursive noise estimator including BSE-based VAD can update noise power spectrum accurately even if the noise-level quickly changes.

This study analyzes the absolute stability in P and PD type fuzzy logic control systems with both certain and uncertain linear plants. Stability analysis includes the reference input, actuator gain and interval plant parameters. For certain linear plants, the stability (i.e. the stable equilibriums of error) in P and PD types is analyzed with the Popov or linearization methods under various reference inputs and actuator gains. The steady state errors of fuzzy control systems are also addressed in the parameter plane. The parametric robust Popov criterion for parametric absolute stability based on Lur'e systems is also applied to the stability analysis of P type fuzzy control systems with uncertain plants. The PD type fuzzy logic controller in our approach is a single-input fuzzy logic controller and is transformed into the P type for analysis. In our work, the absolute stability analysis of fuzzy control systems is given with respect to a non-zero reference input and an uncertain linear plant with the parametric robust Popov criterion unlike previous works. Moreover, a fuzzy current controlled RC circuit is designed with PSPICE models. Both numerical and PSPICE simulations are provided to verify the analytical results. Furthermore, the oscillation mechanism in fuzzy control systems is specified with various equilibrium points of view in the simulation example. Finally, the comparisons are also given to show the effectiveness of the analysis method.

This paper presents new approaches for the estimation of range between the preceding vehicle and the experimental vehicle, estimation of vehicle size and its projective size, and dynamic camera calibration. First, our proposed approaches adopt a camera model to transform coordinates from the ground plane onto the image plane to estimate the relative position between the detected vehicle and the camera. Then, to estimate the actual and projective size of the preceding vehicle, we propose a new estimation method. This method can estimate the range from a preceding vehicle to the camera based on contact points between its tires and the ground and then estimate the actual size of the vehicle according to the positions of its vertexes in the image. Because the projective size of a vehicle varies with respect to its distance to the camera, we also present a simple and rapid method of estimating a vehicle's projective height, which allows a reduction in computational time for size estimation in real-time systems. Errors caused by the application of different camera parameters are also estimated and analyzed in this study. The estimation results are used to determine suitable parameters during camera installation to suppress estimation errors. Finally, to guarantee robustness of the detection system, a new efficient approach to dynamic calibration is presented to obtain accurate camera parameters, even when they are changed by camera vibration owing to on-road driving. Experimental results demonstrate that our approaches can provide accurate and robust estimation results of range and size of target vehicles.

This study presents several optimization approaches for the MPEG-2/4 Audio Advanced Coding (AAC) Low Complexity (LC) encoding and decoding processes. Considering the power consumption and the peripherals required for consumer electronics, this study adopts the TI OMAP5912 platform for portable devices. An important optimization issue for implementing AAC codec on embedded and mobile devices is to reduce computational complexity and memory consumption. Due to power saving issues, most embedded and mobile systems can only provide very limited computational power and memory resources for the coding process. As a result, modifying and simplifying only one or two blocks is insufficient for optimizing the AAC encoder and enabling it to work well on embedded systems. It is therefore necessary to enhance the computational efficiency of other important modules in the encoding algorithm. This study focuses on optimizing the Temporal Noise Shaping (TNS), Mid/Side (M/S) Stereo, Modified Discrete Cosine Transform (MDCT) and Inverse Quantization (IQ) modules in the encoder and decoder. Furthermore, we also propose an efficient memory reduction approach that provides a satisfactory balance between the reduction of memory usage and the expansion of the encoded files. In the proposed design, both the AAC encoder and decoder are built with fixed-point arithmetic operations and implemented on a DSP processor combined with an ARM-core for peripheral controlling. Experimental results demonstrate that the proposed AAC codec is computationally effective, has low memory consumption, and is suitable for low-cost embedded and mobile applications.

In this study, we have proposed an efficient automatic multilevel thresholding method for image segmentation. An effective criterion for measuring the separability of the homogenous objects in the image, based on discriminant analysis, has been introduced to automatically determine the number of thresholding levels to be performed. Then, by applying this discriminant criterion, the object regions with homogeneous illuminations in the image can be recursively and automatically thresholded into separate segmented images. The proposed method is fast and effective in analyzing and thresholding the histogram of the image. In order to conduct an equitable comparative performance evaluation of the proposed method with other thresholding methods, a combinatorial scheme is also introduced to properly reduce the computational complexity of performing multilevel thresholding. The experimental results demonstrated that the proposed method is feasible and computationally efficient in automatic multilevel thresholding for image segmentation.

This investigation applies the adaptive fuzzy-neural observer (AFNO) to synchronize a class of unknown chaotic systems via scalar transmitting signal only. The proposed method can be used in synchronization if nonlinear chaotic systems can be transformed into the canonical form of Lur'e system type by the differential geometric method. In this approach, the adaptive fuzzy-neural network (FNN) in AFNO is adopted on line to model the nonlinear term in the transmitter. Additionally, the master's unknown states can be reconstructed from one transmitted state using observer design in the slave end. Synchronization is achieved when all states are observed. The utilized scheme can adaptively estimate the transmitter states on line, even if the transmitter is changed into another chaos system. On the other hand, the robustness of AFNO can be guaranteed with respect to the modeling error, and external bounded disturbance. Simulation results confirm that the AFNO design is valid for the application of chaos synchronization.