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.
1032181
An automatic test pattern generation framework for combinational threshold logic networks
We propose an automatic test pattern generation (ATPG) framework for combinational threshold networks. The motivation behind this work lies in the fact that many emerging nanotechnologies, such as resonant tunneling diodes (RTDs) and quantum cellular automata (QCA), implement threshold logic. Consequently, there is a need to develop an ATPG methodology for this type of logic. We have built the first automatic test pattern generator and fault simulator for threshold logic, which has been integrated on top of an existing computer-aided design (CAD) tool. These exploit new fault collapsing techniques we have developed for threshold networks. We perform fault modeling to show that many cuts and shorts in RTD-based threshold gates are equivalent to stuck-at faults at the inputs and output of the gate. Experimental results with the MCNC benchmarks indicate that test vectors were found for all testable stuck-at faults in their threshold network implementations.
16225700
QSAR Models for the Prediction of Binding Affinities to Human Serum Albumin Using the Heuristic Method and a Support Vector Machine
The binding affinities to human serum albumin for 94 diverse drugs and drug-like compounds were modeled with the descriptors calculated from the molecular structure alone using a quantitative structure-activity relationship (QSAR) technique. The heuristic method (HM) and support vector machine (SVM) were utilized to construct the linear and nonlinear prediction models, leading to a good correlation coefficient (R2) of 0.86 and 0.94 and root-mean-square errors (rms) of 0.212 and 0.134 albumin drug binding affinity units, respectively. Furthermore, the models were evaluated by a 10 compound external test set, yielding R2 of 0.71 and 0.89 and rms error of 0.430 and 0.222. The specific information described by the heuristic linear model could give some insights into the factors that are likely to govern the binding affinity of the compounds and be used as an aid to the drug design process; however, the prediction results of the nonlinear SVM model seem to be better than that of the HM.
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.
5969857
A data mining approach to modeling relationships among categories in image collection
This paper proposes a data mining approach to modeling relationships among categories in image collection. In our approach, with 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 α-Semantics Graph, to discover the hidden semantic relationships among the semantic categories embodied in the image collection. With the α-Semantics Graph, each semantic category is modeled as a unique fuzzy set to explicitly address the semantic uncertainty and semantic overlap among the categories in the feature space. The model is utilized in the semantics-intensive image retrieval application. An algorithm using the classification accuracy measures is developed to combine the built classification tree with the fuzzy set modeling method to deliver semantically relevant image retrieval for a given query image. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and the image retrieval prototype system utilizing the derived model is promising both in effectiveness and efficiency.
8326799
Topology-Aware Peer-to-Peer On-demand Streaming
In this paper, we consider large-scale high-bandwidth on-demand media streaming in a dynamic and heterogeneous environment. We present MetaStream, a scalable and distributed content discovery protocol that enables clients across the Internet to self-organize into a topology-aware overlay network, in which they can “cache and relay” a stream among nearby peers. We present the design and implementation of MetaStream and show experimental results obtained in a large-scale emulated environment. Our evaluation shows that MetaStream distributes relaying load among the clients in a topology-aware manner, imposing low link cost to the underlying network and low streaming load on the media server. MetaStream is also highly resilient to node or network failures and is capable of quickly recovering the streaming quality at clients even at high failure rates.
62602047
A self-organizing flock of Condors