15012785
An area-reduced scheme for modulo 2/sup n/-1 addition/subtraction
In this paper, we present a versatile area-reduced scheme for modulo 2/sup n/-1 adders and subtracters using a MUX-based increment/decrement algorithm. A FPGA-based comparison of the proposed modulo adder and the conventional modulo adder designs is carried out. The implementation results show that the proposed adder reduces the area close to 30% compared with the modulo adder of Bayoumi et al. The delay and the power are also reduced around 10%. In addition, it is also shown that the proposed design requires less hardware resources than the parallel-prefix modulo adder of Kalampoukas et al. while providing a comparable operation speed.
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.
8024755
On Demand Phenotype Ranking through Subspace Clustering
High throughput biotechnologies have enabled scientists to collect a large number of genetic and phenotypic attributes for a large collection of samples. Computational methods are in need to analyze these data for discovering genotype-phenotype associations and inferring possible phenotypes from genotypic attributes. In this paper, we study the problem of on demand phenotype ranking. Given a query sample, for which only its genetic information is available, we want to predict the possible phenotypes it may have, ranked in descending order of their likelihood. This problem is challenging since genotype-phenotype databases are updated often and explicitly mine and maintain all patterns is impractical. We propose an on-demand ranking algorithm that uses a modified pattern-based subspace clustering algorithm to effectively identify the subspaces where these relevant clusters may reside. Using this algorithm, we can compute the clusters and their prediction significance for any phenotypes on the fly. Our experiments demonstrate the efficiency and effectiveness of our algorithm.
8653895
The Implementation of an Energy Balanced Routing Protocol with Dynamic Power Scaling in TinyOS
In this paper, we study an energy balanced routing protocol based on dynamic power scaling techniques for wireless sensor networks. In the proposed protocol, each node maintains a list of neighboring nodes and their corresponding statistical residual energy information. The energy balanced routing distributes the levels of residue energy evenly throughout the network, while the dynamic power scaling scheme achieves further energy savings by adjusting transmission power to meet signal strength requirement at the receiver. In order to demonstrate the increased energy efficiency of the proposed protocol, we have conducted an experimental study. The results of our experimental study clearly show that the proposed energy balanced routing scheme with dynamic power improves energy efficiency while achieving energy balance as well
206579866
Efficient detection of DDoS attacks with important attributes
DDoS attacks are major threats in current computer networks. However, DDoS attacks are difficult to be quickly detected. In this paper, we introduce a system that only extracts several important attributes from network traffic for DDoS attack detection in real computer networks. We collect a large set of DDoS attack traffic by implementing various DDoS attacks as well as normal data during normal usage. Information Gain and Chi-square methods are used to rank the importance of 41 attributes extracted from the network traffic with our programs. Bayesian networks as well as C4.5 are then employed to detect attacks as well as to determine what size of attributes is appropriate for fast detection. Empirical results show that only using the most important 9 attributes, the detection accuracy remains the same or even has some improvements compared with that of using all the 41 attributes based on Bayesian Networks and C4.5 methods. Only using several attributes also improves the efficiency in terms of attributes constructing, models training as well as intrusion detection.
29222863
An adaptive approach for image encryption and secure transmission over multirate wireless sensor networks
In this paper, we propose an innovative adaptive secure image delivery approach, to achieve effective and efficient image encryption based on an optimal transmission rate in terms of energy efficiency in wireless sensor networks (WSN). Our contribution is three folds—First, the proposed component based selective encryption scheme achieves high image security, while incurring little encryption overhead; furthermore, the proposed adaptive encryption scheme achieve the best security effort within delay bound; finally, the proposed transmission rate optimization improves energy efficiency, and feeds back effective path capacity parameters for adaptive encryption. Simulation results show a significant gain in both energy efficiency and security based on the proposed approach for image secure transmissions in WSN. Copyright # 2007 John Wiley & Sons, Ltd.
12798864
BASS: approximate search on large string databases
In this paper, we study the problem on how to build an index structure for large string databases to efficiently support various types of string matching without the necessity of mapping the substrings to a numerical space (e.g., string B-tree and MRS-index) nor the restriction of in-memory practice (e.g., suffix tree and suffix array). Towards this goal, we propose a new indexing scheme, BASS-tree, to efficiently support general approximate substring match (in terms of certain symbol substitutions and misalignments) in sublinear time on a large string database. The key idea behind the design is that all positions in each string are grouped recursively into a fully balanced tree according to the similarities of the subsequent segments starting at those positions. Each node is labeled with a regular expression that describes the commonality of the substrings indexed through the subtree. Any search can then be properly directed to the portion in the database with a high potential of matching quickly. With the BASS-tree in place, wild card(s) in the query pattern can also be handled in a seamless way. In addition, search of a long pattern can be decomposed into a series of searches of short segments followed by a process to join the results. It has been demonstrated in our experiments that the potential performance improvement brought by BASS-tree is in an order of magnitude over alternative methods.