Restoration of images corrupted by Gaussian and uniform impulsive noise

Many approaches to image restoration are aimed at removing either Gaussian or uniform impulsive noise. This is because both types of degradation processes are distinct in nature, and hence they are easier to manage when considered separately. Nevertheless, it is possible to find them operating on the same image, which produces a hard damage. This happens when an image, already contaminated by Gaussian noise in the image acquisition procedure, undergoes impulsive corruption during its digital transmission. Here we propose a principled method to remove both types of noise. It is based on a Bayesian classification of the input pixels, which is combined with the kernel regression framework.     

Ballistic missile detection via micro-Doppler frequency estimation from radar return

The spinning motion of a ballistic missile (BM), which is a kind of micro-Doppler motion, induces additional frequency modulation on the returned radar signal. This modulation period in frequency, called the micro-Doppler modulation frequency, is a stable and unique parameter in radar target recognition. This paper first establishes the radar signal model of the spinning missile during flight, and then extracts the micro-Doppler modulation frequency through analysis of the periodic structure of the resulting spectrogram (short-time Fourier transform (STFT)) - i.e., the time-frequency distribution (TFD). In order to be used for BM target recognition we implemented a data-dependent optimal window length for the STFT. We treated the resultant TFD as an image and then used the 2-D DFT/FFT for feature extraction. Experimental results illustrate the validity of the proposed method.     

Adaptive per-user per-object cache consistency management for mobile data access in wireless mesh networks

We propose and analyze an adaptive per-user per-object cache consistency management (APPCCM) scheme for mobile data access in wireless mesh networks. APPCCM supports strong data consistency semantics through integrated cache consistency and mobility management. The objective of APPCCM is to minimize the overall network cost incurred due to data query/update processing, cache consistency management, and mobility management. In APPCCM, data objects can be adaptively cached at the mesh clients directly or at mesh routers dynamically selected by APPCCM. APPCCM is adaptive, per-user and per-object as the decision regarding where to cache a data object accessed by a mesh client is made dynamically, depending on the mesh client’s mobility and data query/update characteristics, and the network’s conditions. We develop analytical models for evaluating the performance of APPCCM and devise a computational procedure for dynamically calculating the overall network cost incurred. We demonstrate via both model-based analysis and simulation validation that APPCCM outperforms non-adaptive cache consistency management schemes that always cache data objects at the mesh client, or at the mesh client’s current serving mesh router for mobile data access in wireless mesh networks.     

A two-stage algorithm for identification of nonlinear dynamic systems

This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of the model that is obtained by the forward stepwise methods, while retaining the computational efficiency. The proposed algorithm first generates an initial model using a forward stepwise procedure. The significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced, resulting in an optimised compact model with significantly improved performance. The main contribution of this paper is that these two stages are performed within a well-defined regression context, leading to significantly reduced computational complexity. The efficiency of the algorithm is confirmed by the computational complexity analysis, and its effectiveness is demonstrated by the simulation results.     

Hardware-software co-synthesis of hard real-time systems with reconfigurable FPGAs

Real-time systems cover a wide application domain. This paper presents an efficient heuristic algorithm for enforcing the schedulability of aperiodic hard real-time tasks arriving simultaneously with precedence constraints and individual deadlines. The proposed co-synthesis algorithm integrates partitioning and non-preemptive scheduling. Reconfigurable FPGAs are incrementally added when schedulability suffers in a uniprocessor system. Initially, a schedule that minimizes the maximum lateness and satisfies the precedence constraints is made. If individual timing constraints are not met in this schedule, some tasks are selected and transferred to dynamically reconfigured FPGAs. The proposed algorithm was implemented and tested with a large number of task graphs with task size as high as 700 nodes. The algorithm could not only achieve schedulability but also could reduce the total completion time of the task graph. Moreover, incremental addition of reconfigurable FPGAs yielded a cost effective solution.     

Pattern identification in dynamical systems via symbolic time series analysis

This paper presents symbolic time series analysis (STSA) of multi-dimensional measurement data for pattern identification in dynamical systems. The proposed methodology is built upon concepts derived from Information Theory and Automata Theory. The objective is not merely to classify the time series patterns but also to identify the variations therein. To achieve this goal, a symbol alphabet is constructed from raw data through partitioning of the data space. The maximum entropy method of partitioning is extended to multi-dimensional space. The resulting symbol sequences, generated from time series data, are used to model the dynamical information as finite state automata and the patterns are represented by the stationary state probability distributions. A novel procedure for determining the structure of the finite state automata, based on entropy rate, is introduced. The diversity among the observed patterns is quantified by a suitable measure. The efficacy of the STSA technique for pattern identification is demonstrated via laboratory experimentation on nonlinear systems.     

Adaptive backstepping control of uncertain systems with unknown input time-delay

In this paper, we establish the robustness of adaptive controllers designed using the standard backstepping technique with respect to unmodeled dynamics involving unknown input time delay. While noting that some results on robust stabilization of non-minimum phase systems using the backstepping technique are available, we realize that the standard adaptive backstepping technique has only been shown applicable to unknown minimum phase systems. Another significance of our result is to enable the class of systems stablizable by adaptive backstepping controllers to cross the boundary of minimum phase systems, since systems with input time delay belong to non-minimum phase systems. Moreover, the L₂ and L_∞ norms of the system output are also established as functions of design parameters. This implies that the transient system performance can be adjusted by choosing suitable design parameters.     

A content-based system for human identification based on bitewing dental X-ray images

This paper presents a system for assisting in human identification using dental radiographs. The goal of the system is to archive antemortem (AM) dental images and enable content-based retrieval of AM images that have similar teeth shapes to a given postmortem (PM) dental image. During archiving, the system classifies the dental images to bitewing, periapical, and panoramic views. It then segments the teeth and the bones in the bitewing images, separates each tooth into the crown and the root, and stores the contours of the teeth in the database. During retrieval, the proposed system retrieves from the AM database the images with the most similar teeth to the PM image based on Hausdorff distance measure between the teeth contours. Experiments on a small database show that our method is effective for dental image classification and teeth segmentation, provides good results for separating each tooth into crown and root, and provides a good tool for human identification.     

Global exponential synchronization of fuzzy cellular neural networks with delays and reaction-diffusion terms

The global exponential synchronization for a class of fuzzy cellular neural networks with delays and reaction-diffusion terms is discussed. Some new sufficient conditions are obtained by using the Lyapunov functional method, many real parameters and inequality techniques. The result is also easy to check and plays an important role in the design and application of globally exponentially synchronization. Finally, an example is given to verify our results.     

Extracting the optimal dimensionality for local tensor discriminant analysis

Supervised dimensionality reduction with tensor representation has attracted great interest in recent years. It has been successfully applied to problems with tensor data, such as image and video recognition tasks. However, in the tensor-based methods, how to select the suitable dimensions is a very important problem. Since the number of possible dimension combinations exponentially increases with respect to the order of tensor, manually selecting the suitable dimensions becomes an impossible task in the case of high-order tensor. In this paper, we aim at solving this important problem and propose an algorithm to extract the optimal dimensionality for local tensor discriminant analysis. Experimental results on a toy example and real-world data validate the effectiveness of the proposed method.