3708864
A New Systolic Architecture for Modular Division
A new systolic architecture for modular division is proposed in this paper. The architecture is based on a new hardware algorithm for modular division that is extended from the plus-minus algorithm for the greatest common divisor computation. Both the area complexity and the time complexity of the new architecture are linear with respect to the operand bit length. Compared to the architecture using a redundant number representation proposed by Kaihara and Takagi, the new architecture allows a doubled throughput due primarily to the greatly reduced critical path delay, while the area is only about 20 percent larger. Moreover, it is shown that, with a small addition to the control logic, the architecture can also be used to perform Montgomery modular multiplication, thereby efficiently realizing a unified modular multiplier/divider
3703243
A dual-field elliptic curve cryptographic processor based on a systolic arithmetic unit
We proposed a high-performance ECC architecture for generic curves over prime fields based on a systolic arithmetic unit, in [1]. This paper is on an extension and a realization of that architecture. We extend the architecture to support binary fields, and realize the extended architecture, developing a dual-field ECC processor. Further, the processor is implemented by an ASIC. The results on the layouted processor show a high performance, confirming the efficiency of the architecture.
5928745
Beyond Banditron: A Conservative and Efficient Reduction for Online Multiclass Prediction with Bandit Setting Model
In this paper, we consider a recently proposed supervised learning problem, called online multiclass prediction with bandit setting model. Aiming at learning from partial feedback of online classification results, i.e. “true” when the predicting label is right or “false” when the predicting label is wrong, this new kind of problems arouses much of researchers’ interest due to its close relations to real world internet applications and human cognitive procedure. While some algorithms have been brought forward, we propose a novel algorithm to deal with such problems. First, we reduce the multiclass prediction problem to binary based on Conservative one-versus-all others Reduction scheme; Then Online Passive-Aggressive Algorithm is embedded as binary learning algorithm to solve the reduced problem. Also we derive a pleasing cumulative mistake bound for our algorithm and a time complexity bound linear to the sample size. Further experimental evaluation on several real world multiclass datasets including RCV1, MNIST, 20 Newsgroups and USPS shows that our method outperforms the existing algorithms with a great improvement.
1107258
Improving image clustering: An unsupervised feature weight learning framework
We address the problem of feature weight learning for image clustering. In practice, before clustering data, we generally normalize all data features between 0 and 1, because we cannot determine which features are more important. In this paper, we provide a feature weight learning framework for clustering which can obtain the feature weights and cluster labels simultaneously. An alternative optimization algorithm is adopted to solve this problem. Empirical studies on the toy data and real image data demonstrate our algorithm's effectiveness in improving the clustering performance.
39222179
A New ART Neural Networks for Remote Sensing Image Classification
A new ART2A-C algorithm based on fuzzy operators to cluster the remote sensing images and aerials is proposed in this paper. By combining two ART ANNs with higher performance, the traditional ART2A-C is developed with the fuzzy operators introduced in matching rule. Then the proposed method is applied to the classification and the new network is implemented as well as two other existed ARTs respectively. Experimental results show that the new method outperforms the traditional ones.
537363
Change propagation algorithm in a unified feature modeling scheme
Product lifecycle stages are inter-related and mutually constraining. Due to the sequential nature of the product development processes, some constraints or conflicts may emerge in a later stage and require modifications to the decisions made in earlier stages. The iterations between stages are hence unavoidable and must be managed carefully to maintain the consistency, integrity and validity of product information models. Due to the inter- or intra-stage relations, a chain of changes is very likely to occur as the consequence of an initial change. Modeling and maintaining these relations are important in collaborative engineering to evolve the state of the whole product model in a consistent manner. This paper introduces a new method of modeling associative engineering relations in a unified feature modeling scheme and elaborates a change propagation algorithm for the information consistency control among multiple applications of product lifecycle stages. The algorithm is established on a JTMS-based dependency network. Two case studies are used to illustrate the proposed dependency network and change propagation algorithm.
1502133
Accelerating Parent-Child Path Matching in XML
With the rapidly increasing popularity of XML as a data format, there is a large demand for efficient XML structural matching techniques. Normally, data in XML are stored in a tree-like structure where nodes (with data) are located using the path relations in the tree. This paper proposes a coding policy named path code to accelerate an important category of structure matching in XML, namely the Parent-Child paths. A Parent-Child path is the path expression that contains only parent-child relationships. The proposed path code employs a partial prefix Path of XML elements using a special compression technique. Based on the path code, we present a Boosting algorithm, which has low-linear time complexity and little I/O cost, to match Parent-Child paths. In addition, we propose two heuristic policies to optimize the Boosting algorithm further by reducing the memory consumption. Our experiments show that the Boosting algorithm considerably outperforms previous algorithms in run-time Parent-Child path matching.