195908742
Threshold network synthesis and optimization and its application to nanotechnologies
We propose an algorithm for efficient threshold network synthesis of arbitrary multioutput Boolean functions. Many nanotechnologies, such as resonant tunneling diodes, quantum cellular automata, and single electron tunneling, are capable of implementing threshold logic efficiently. The main purpose of this work is to bridge the current wide gap between research on nanoscale devices and research on synthesis methodologies for generating optimized networks utilizing these devices. While functionally-correct threshold gates and circuits based on nanotechnologies have been successfully demonstrated, there exists no methodology or design automation tool for general multilevel threshold network synthesis. We have built the first such tool, threshold logic synthesizer (TELS), on top of an existing Boolean logic synthesis tool. Experiments with 56 multioutput benchmarks indicate that, compared to traditional logic synthesis, up to 80.0% and 70.6% reduction in gate count and interconnect count, respectively, is possible with the average being 22.7% and 12.6%, respectively. Furthermore, the synthesized networks are well-balanced structurally. The novelty of this work lies in the introduction of the first comprehensive synthesis methodology and tool for general multilevel threshold logic design.
9661168
Synthesis of majority and minority networks and its applications to QCA, TPL and SET based nanotechnologies
In this paper, we present a methodology for efficient majority/minority network synthesis of arbitrary multioutput Boolean functions. Many emerging nanoscale technologies, such as quantum cellular automata (QCA), tunneling phase logic (TPL), and single electron tunneling (SET), are capable of implementing majority or minority logic very efficiently. However, there exists no comprehensive methodology or design automation tool for general multilevel majority/minority network synthesis. We have built the first such tool, majority logic synthesizer (MALS), on top of an existing Boolean logic synthesis tool. We have performed experiments with 40 MCNC benchmarks. They indicate that up to 68.0% reduction in gate count is possible when utilizing majority logic, with the average reduction being 21.9%, compared to traditional logic synthesis, in which two-input AND/OR gates in the circuit are converted to majority gates.
17591841
State encoding of finite-state machines targeting threshold and majority logic based implementations with application to nanotechnologies
In this paper, we address the problem of state encoding of finite-state machines (FSMs) targeting threshold and majority logic based implementations, which have applications in nanotechnologies. Previous work on state encoding is based on Boolean logic. These methods try to optimize either the literal count or sum-of-product (SOP) terms count, which are more relevant for CMOS technology based circuits. As progress is made in the material and physical understanding of nanoscale devices, functionally-correct nanoscale circuits based on threshold and majority gates are being successfully demonstrated. However, there exists no methodology or design automation tool for state encoding targeting threshold and majority gate based implementations. We propose such a methodology based on an evolutionary algorithm. Experimental results indicate that large reductions in gate count, area and interconnect count are possible, compared to some traditional state encoding methods.
1852867
Semantic repository modeling in image database
This work is about content based image database retrieval, focusing on developing a classification based methodology to address semantics-intensive image retrieval. With self organization map based image feature grouping, a visual dictionary is created for color, texture, and shape feature attributes, respectively. Labeling each training image with the keywords in the visual dictionary, a classification tree is built. Based on the statistical properties of the feature space we define a structure, called /spl alpha/-semantics graph, to discover the hidden semantic relationships among the semantic repositories embodied in the image database. With the /spl alpha/-semantics graph, each semantic repository is modeled as a unique fuzzy set to explicitly address the semantic uncertainty and the semantic overlap existing among the repositories in the feature space. A retrieval algorithm combining the classification tree with the fuzzy set models to deliver semantically relevant image retrieval is provided. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and outperforms a state-of-the-art content based image retrieval system in the literature both in effectiveness and efficiency.
15964861
Exploiting the cognitive synergy between different media modalities in multimodal information retrieval
This is a position paper reporting an on-going collaboration project between SUNY Binghamton, USA, and Waseda University, Japan, on multimodal information retrieval through exploiting the cognitive synergy across the different modalities of the information, to facilitate an effective retrieval. Specifically, we focus on image retrieval in the applications where imagery data appear along with collateral text. It is noted that these applications are ubiquitous. We have proposed the synergistic indexing scheme (SIS) to explicitly exploit the synergy between the information of imagery and text modalities. Since the synergy we have exploited between the information of imagery and text modalities is subjective and depends on specific cognitive context, we call this type of synergy as cognitive synergy. We have reported part of the empirical evaluation and are in the process of fully implementing the SIS prototype for an extensive evaluation.
10950819
An Efficient Sequential RBF Network for Gene Expression-Based Multi-category classification
This paper presents a fast and efficient sequential learning method for RBF networks that can perform classification directly for multi-category cancer diagnosis problems based on microarray data. The recently developed algorithm, referred to as Fast Growing And Pruning-RBF (FGAP-RBF) can perform incremental learning on the future data directly. No training of all the previous data is needed. This character can reduce the learning complexity and improve the learning efficiency and is greatly favored in the real implementation of a gene expression-based cancer diagnosis system. We have evaluated FGAP-RBF algorithm on a benchmark multi-category cancer diagnosis problem based on microarray data, namely GCM dataset. The results indicate that compared with the results available in literature FGAP-RBF algorithm produces a higher classification accuracy with reduced training time and implementation complexity.
3159534
FAST: Toward more effective and efficient image retrieval
This paper focuses on developing a Fast And Semantics-Tailored (FAST) image retrieval methodology. Specifically, the contributions of FAST methodology to the CBIR literature include: (1) development of a new indexing method based on fuzzy logic to incorporate color, texture, and shape information into a region-based approach to improving the retrieval effectiveness and robustness; (2) development of a new hierarchical indexing structure and the corresponding hierarchical, elimination-based A* retrieval (HEAR) algorithm to significantly improve the retrieval efficiency without sacrificing the retrieval effectiveness; it is shown that HEAR is guaranteed to deliver a logarithm search in the average case; (3) employment of user relevance feedback to tailor the effective retrieval to each user's individualized query preference through the novel indexing tree pruning (ITP) and adaptive region weight updating (ARWU) algorithms. Theoretical analysis and experimental evaluations show that FAST methodology holds great promise in delivering fast and semantics-tailored image retrieval in CBIR.
1951197
A probabilistic semantic model for image annotation and multi-modal image retrieval
This paper addresses automatic image annotation problem and its application to multi-modal image retrieval. The contribution of our work is three-fold. (1) We propose a probabilistic semantic model in which the visual features and the textual words are connected via a hidden layer which constitutes the semantic concepts to be discovered to explicitly exploit the synergy among the modalities. (2) The association of visual features and textual words is determined in a Bayesian framework such that the confidence of the association can be provided. (3) Extensive evaluation on a large-scale, visually and semantically diverse image collection crawled from Web is reported to evaluate the prototype system based on the model. In the proposed probabilistic model, a hidden concept layer which connects the visual feature and the word layer is discovered by fitting a generative model to the training image and annotation words through an Expectation-Maximization (EM) based iterative learning procedure. The evaluation of the prototype system on 17,000 images and 7736 automatically extracted annotation words from crawled Web pages for multi-modal image retrieval has indicated that the proposed semantic model and the developed Bayesian framework are superior to a state-of-the-art peer system in the literature.