34579762
Implementation of FC-1 and FC-2 Layer for Multi-Gigabit Fibre Channel Transport
Fibre Channel (FC) is a high performance, low-latency data transfer technology that dominates today's high-end Storage Area Networks (SAN) market. This paper presents the design and implementation of Fibre Channel FC-1 and part of FC-2 layer for multi- gigabit Fibre Channel applications such as point-to-point and fabric interconnect. For efficient implementation, we have used the protocol-optimized hardware modules, such as on-the-fly CRC checking, frame length verification modules for frame receiving, CRC calculation and EOF generation modules for frame transmitting, to reduce the network latency. We also present an efficient method for buffer-to-buffer credit recovery, the basic idea of our method is to recover lost credits within the local as well as the remote port and avoid blocking of transmitters in long term. In the experiment, we build a 2G FC SCSI initiator using this logic core. The experimental results show that the initiator can reach a maximum throughput of 189.52MB/s for reading a RAMDisk target on the remote computer equipped with a QLA2310FCHBA.
17199895
An Active Storage System for High Performance Computing
Traditionally, active storage devices execute custom application code on large amounts of data by utilizing the unused processing power of the storage nodes. For computation-intensive applications, the performance might be quite low due to insufficient processing power on the storage devices. In this paper we present a reconfigurable computing solution that can provide flexible, high-performance processing capabilities for the storage nodes. We have made a change over the existing active storage framework for object-based storage device by off loading computation-intensive request-driven services to a reconfigurable hardware accelerator. In the experiment, we use two applications (AES encryption and FFT) as the case studies, and the results show that the reconfigurable computing-based system can dramatically increase the performance comparing with the traditional software-based active storage systems.
206504813
Effective Filtering Scheme against RREQ Flooding Attack in Mobile Ad Hoc Networks
In Mobile Ad hoc Networks (MANET), various types of Denial of Service Attacks (DoS) are possible because of the inherent limitations of its routing protocols. The attack of initiating / forwarding fake Route Requests (RREQs) can lead to hogging of network resources and hence denial of service to genuine nodes. This type of attack is hard to detect since malicious nodes mimic normal nodes in all aspects except that they do route discoveries much more frequently than the other nodes. A distrusted filtering mechanism is proposed to mitigate such situations and reduce the loss of throughput. The proposed mechanism could prevent this specific kind of DoS attack and does not use any additional network bandwidth.
9713230
A Direction to Avoid Re-encryption in Cryptographic File Sharing
Almost all cryptographic file sharing systems need reencryption when the sharing was revoked. These systems differ from each other only in the timing of re-encryption. As re-encryption is an expensive operation, it is significant to avoid re-encryption. The purpose of this paper is to advise a direction to avoid re-encryption and facilitate file sharing in cryptographic file sharing system. A Black-box model is set up to achieve this objective. In the model, FPGA or ASIC chips are used to act as the black-box as they have been extensively researched and applied in cryptography. Some applications of FPGA and ASIC in cryptography are detailed in this paper. Their feasibility to be functioned as the black-box is discussed. Also a software implementation on FPGA is attached with tested and effective performance.
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.
9190159
Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM) for Modeling of Gene Network from Time Series Gene Expression Data
Abstract Exploring gene regulatory network is a key topic in molecular biology. In this paper, we present a new dynamic Bayesian network (DBN) framework embedded with structural expectation maximization (SEM) to model gene relationship. It is well-suited for analyzing the time-series data and can deal with cyclical structures that can not be tackled by static Bayesian network. We applied the new method to learning the regulatory network and the metabolic pathway from Saccharomyces Cerevisiae cell cycle gene expression data. The results show that the proposed method is capable of handling missing values in expression data sets, and the inference accuracy can further be improved. Keyword: Microarrays; Gene regulatory networks; Dynamic Bayesian network; Structural expectation maximization 1. Introduction The establishment of gene regulatory network is critical to the understanding of the genetic regulation process. This problem has become an important challenge in recent years. The invention of microarray technology is viewed as a milestone, which helps scientist measure expression levels of thousands of genes simultaneously. Several methods have been presented so far to learn gene network from microarray data, such as Boolean networks [2, 3], differential equations [4, 5], and Bayesian networks [6-8]. Among all of them, Bayesian network based approach has received a lot of attention because of the probabilistic nature of this model. It can be used to learn causal relationship and particularly, combine it with prior knowledge readily. However, there exist a lot of shortcomings for the static version of Bayesian network. First, it is unable to capture the temporal information. Second, it is impossible to model cyclic network, which is often considered to be an accurate description of real gene regulation mechanism. In this paper, we employ dynamic Bayesian network (DBN) [1, 9,10], instead of its earlier static version, to model a gene network with cyclic regulation. In general, the DBN is well-suited for characterizing time-series gene expression data. Owing to the limitation of experimental condition, there are many missing values in the gene expression data sets, which usually have an impact on the inference accuracy. To address this problem, we propose a new DBN model embedded with
34552917
A new dynamic Bayesian network for integrating multiple data in estimating gene networks
We propose a novel method for estimating a gene regulatory network from both gene expression data and transcription factor binding location data. Based on dynamic Bayesian network (DBN) models, our method has two advantages. First, structural expectation maximization algorithm is embedded into a DBN framework, allowing it to learn the network with unknown structure and incomplete data, which cannot be tackled by expectation maximization algorithm. Second, since learning only from gene expression data is not enough to accurately estimate a gene network, we incorporate transcription factor binding location data through a structure prior. We demonstrate the effectiveness of the proposed method by the analysis of Saccharomyces Cerevisiae cell cycle data. The experimental results show that this new approach suits for dealing with missing values. Furthermore, combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from gene expression data alone.
15799724
An On-Off Queue Control Mechanism for Scalable Video Streaming over the IEEE 802.11e WLAN
In this paper, we study the issue of scalable video streaming over IEEE 802.11e EDCA WLANs. Our basic idea is to control the number of "active" nodes on the channel in order to reduce collisions under heavy traffic conditions. Specifically, we propose a distributed on-off queue control (OOQC) mechanism, which is designed to maintain high network throughput while keeping packet loss due to collision as low as possible. A low priority early drop (LPED) method is also employed to drop the packets at the queue according to packet relative priority index (RPI) provided by scalable video coding. Simulation results show that our proposed OOQC scheme significantly outperforms EDCA in received video quality.
1495082
A coordinate SPC model for assuring designated fit quality via quality-oriented statistical tolerancing
On the basis of studies to process quality indices and fit quality indices, the quality-oriented statistical tolerancing is introduced, which provides an interface between fit quality and the process parameter design of mating parts. A statistical mating model via the quality-oriented statistical tolerancing under information-based manufacturing condition is discussed. Compared with traditional mating model for a pair of hole/shaft fit, the statistical mating model is developed for equal batches of hole/shaft fit, and it can achieve a designated fit quality and minimum defective ratio by fully utilizing statistical tolerance zone and the known information of manufactured mating parts via a coordinate SPC model and local network and information system. Thus, production cost for mating parts can be reduced obviously. This new approach provides a possibility and guideline to concurrent design of the process quality, statistical tolerances to assure designated process quality and fit quality for hole and shaft parts.