90043
A new approach to design robust backstepping controller for MIMO nonlinear systems with input unmodeled dynamics
In this paper, a new scheme inspired by the idea of decentralized control methodology is proposed to design output feedback controllers for a class of MIMO nonlinear systems with input unmodeled dynamics. With dynamic normalization and backstepping approach, the destabilizing effects of unmodeled dynamics depending on inputs are compensated and global uniform boundedness of all the signals in the closed loop system is established for sufficiently weak input unmodeled dynamics.
20468001
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 L"2 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.
162530
Connected substructure similarity search
Substructure similarity search is to retrieve graphs that approximately contain a given query graph. It has many applications, e.g., detecting similar functions among chemical compounds. The problem is challenging as even testing subgraph containment between two graphs is NP-complete. Hence, existing techniques adopt the filtering-and-verification framework with the focus on developing effective and efficient techniques to remove non-promising graphs.
 Nevertheless, existing filtering techniques may be still unable to effectively remove many "low" quality candidates. To resolve this, in this paper we propose a novel indexing technique, GrafD-Index, to index graphs according to their "distances" to features. We characterize a tight condition under which the distance-based triangular inequality holds. We then develop lower and upper bounding techniques that exploit the GrafD-Index to (1) prune non-promising graphs and (2) include graphs whose similarities are guaranteed to exceed the given similarity threshold. Considering that the verification phase is not well studied and plays the dominant role in the whole process, we devise efficient algorithms to verify candidates. A comprehensive experiment using real datasets demonstrates that our proposed methods significantly outperform existing methods.
29715580
A high-speed residue-to-binary converter and a scheme for its VLSI implementation
In this paper, a high-speed residue-to-binary converter for the recently introduced moduli set (2/sup k/, 2/sup k/-1, 2/sup k-1/-1) is proposed. Compared to the previous converter based on this moduli set, the proposed one is 40% faster; also, the time-complexity product is improved by 20%. Following the VLSI design flow established by the Canadian Microelectronics Corporation, the proposed converter is implemented in 0.5-micron CMOS technology. Based on this moduli set, layouts of the 8-bit, 16-bit, 32-bit and 64-bit residue-to-binary converters which can be used in further RNS system designs, are generated and simulation results obtained.
14446380
OP-cluster: clustering by tendency in high dimensional space
Clustering is the process of grouping a set of objects into classes of similar objects. Because of unknownness of the hidden patterns in the data sets, the definition of similarity is very subtle. Until recently, similarity measures are typically based on distances, e.g Euclidean distance and cosine distance. We propose a flexible yet powerful clustering model, namely OP-cluster (Order Preserving Cluster). Under this new model, two objects are similar on a subset of dimensions if the values of these two objects induce the same relative order of those dimensions. Such a cluster might arise when the expression levels of (coregulated) genes can rise or fall synchronously in response to a sequence of environment stimuli. Hence, discovery of OP-Cluster is essential in revealing significant gene regulatory networks. A deterministic algorithm is designed and implemented to discover all the significant OP-Clusters. A set of extensive experiments has been done on several real biological data sets to demonstrate its effectiveness and efficiency in detecting coregulated patterns.
17112567
/spl delta/-clusters: capturing subspace correlation in a large data set
Clustering has been an active research area of great practical importance for recent years. Most previous clustering models have focused on grouping objects with similar values on a (sub)set of dimensions (e.g., subspace cluster) and assumed that every object has an associated value on every dimension (e.g., bicluster). These existing cluster models may not always be adequate in capturing coherence exhibited among objects. Strong coherence may still exist among a set of objects (on a subset of attributes) even if they take quite different values on each attribute and the attribute values are not fully specified. This is very common in many applications including bio-informatics analysis as well as collaborative filtering analysis, where the data may be incomplete and subject to biases. In bio-informatics, a bicluster model has recently been proposed to capture coherence among a subset of the attributes. We introduce a more general model, referred to as the /spl delta/-cluster model, to capture coherence exhibited by a subset of objects on a subset of attributes, while allowing absent attribute values. A move-based algorithm (FLOC) is devised to efficiently produce a near-optimal clustering results. The /spl delta/-cluster model takes the bicluster model as a special case, where the FLOC algorithm performs far superior to the bicluster algorithm. We demonstrate the correctness and efficiency of the /spl delta/-cluster model and the FLOC algorithm on a number of real and synthetic data sets.
15644746
RRNS quasi-chaotic coding and its FPGA implementation
In this paper, a new architecture of the redundant residue number system (RRNS) based quasi-chaotic coding is proposed for the secure telecommunication systems and networks. In the proposed architecture, a number of modulo operations required by the existing designs are replaced by binary coding operations to simplify the design. Also, a moduli selection method and a residue-to-binary converter with error-correction capability are proposed to further improve the efficiency of the design specifically for FPGA implementation. The proposed architecture is implemented and tested using Matlab and Xilinx FPGA hardware. The results show that compared to the existing design, the proposed design requires only 80% of the hardware resources while maintaining the same speed. The power consumption is also reduced by 25%.