10034268
Stability of impulsive infinite delay differential equations
In this work, we consider the stability of impulsive infinite delay differential equations. By using Lyapunov functions and the Razumikhin technique, we get some results that are more general than ones given before. And in using the Razumikhin technique, we use a new technique that has been given by Shunian Zhang; we extend this technique to study impulsive systems. An example is also discussed in this work to illustrate the advantage of the results obtained.
119556859
Eventual practical stability of impulsive differential equations with time delay in terms of two measurements
In this paper we introduce a new stability--eventual practical stability for impulsive differential equations with time delay. By using Lyapunov functions and comparison principle, we will get some criteria of eventual practical stability, eventual practical quasistability and strong eventual practical stability for impulsive differential equations with time delay in terms of two measurements.
205929604
Boundedness of the solutions of impulsive differential systems with time-varying delay
In this paper, we investigate boundedness of the solutions of impulsive functional differential equations with time-varying delay by using Lyapunov functions and Razumikhin techniques. We will see that impulses do contribute to the solutions' boundedness property.
21616351
Palmprint Recognition Using a Novel Sparse Coding Technique
This paper proposes a novel recognition method for palmprints using a new sparse coding (SC) algorithm proposed by us. This algorithm exploited the maximum Kurtosis as the sparse measure criterion, at one time, a fixed variance term of sparse coefficients is used to yield a fixed information capacity. Experimental results show that the feature basis vectors of palmprint images can be successfully extracted by using our SC algorithm. Using the radial basis probabilistic neural network (RBPNN), the classification task can be implemented easily. Finally, compared with methods of principal component analysis (PCA) and the classical SC, simulation results show that our algorithm is indeed efficient and effective in performing palmprint recognition task.
172003
Reconstructing the Evolutionary History of Complex Human Gene Clusters
Clusters of genes that evolved from single progenitors via repeated segmental duplications present significant challenges to the generation of a truly complete human genome sequence. Such clusters can confound both accurate sequence assembly and downstream computational analysis, yet they represent a hotbed of functional innovation, making them of extreme interest. We have developed an algorithm for reconstructing the evolutionary history of gene clusters using only human genomic sequence data. This method allows the tempo of large-scale evolutionary events in human gene clusters to be estimated, which in turn will facilitate primate comparative sequencing studies that will aim to reconstruct their evolutionary history more fully.
24687403
A synthesis scheme for simultaneous scheduling, binding, partitioning and placement with resources operating at multiple voltages
One promising technique to reduce power consumption is to power a chip with multiple supply voltages. However, as noticed by M.C. Johnson and K. Roy (see ACM Trans. Design Auto. Electronic Syst., vol.2, p.227-48, 1997), multiple voltage designs can cause a number of serious layout problems. We have shown that the layout problems can be partially solved by the addition of a partitioning step into the synthesis flow. A more subtle solution to solve the layout problems requires placement also to be included in the design flow. We present a synthesis scheme, following a simulated annealing engine, to minimize power consumption and area with resources operating at multiple voltages under timing constraints. The scheme simultaneously considers many correlated factors, such as scheduling, binding, partitioning and placement, to reduce power consumption due to both functional units and interconnections between and among them. Experiments with a number of DSP benchmarks show that the proposed algorithm can achieve significant reduction in power and area.
1369350
EEG Source Localization for Two Dipoles in the Brain Using a Combined Method
Estimating the correct location of electric current source with the brain from electroencephalographic (EEG) recordings is a challenging analytic and computational problem. Specifically, there is no unique solution and solutions do not depend continuously on the data. This is an inverse problem from EEG to dipole source. In this paper we consider a method combining backpropagation neural network (BPNN) with nonlinear least square (NLS) method for source localization. For inverse problem, the BP neural network and the NLS method has its own advantage and disadvantage, so we use the BPNN to supply the initial value to the NLS method and then get the final result, here we select the Powell algorithm to do the NLS calculating. All these work are for the fast and accurate dipole source localization. The main purpose of using this combined method is to localize two dipole sources when they are locating at the same region of the brain. The following investigations are presented to show that this combined method used in this paper is an advanced approach for two dipole sources localization with high accuracy and fast calculating.
22570791
A Novel Biology-Inspired Virus Detection Model with RVNS
A virus detection model based on artificial immune principles with real-valued negative selection (RVNS) is proposed. Feature vectors of program code are mapped into high dimension real-valued space. The architecture of this model, the formal definitions of self, nonself, antigen, antibody, and gene library are given. Then the process of self-tolerance of variable-sized detectors in real-valued space is discussed in detail. Variable-sized detectors provide concise representation of the detectors derived from benign files, which reduces the size of detectors set and advances the detection rate. The experimental results show that the model can detect obfuscated and firstly unknown virus more effectively than traditional model.