There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Thank you for downloading an introduction to the event related potential technique. Maybe you have knowledge that, people have look hundreds times for their favorite readings like this an introduction to the event related potential technique, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious bugs inside their laptop.

An Introduction To The Event Related Potential Technique

: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

/pdf/nonlinear-time-series-analysis-1j3a35n4y3.pdf

Nonlinear Time Series Analysis.

https://dr.ntu.edu.sg/bitstream/10220/7126/1/36.%20Nonlinear%20channel%20equalization%20for%20wireless%20communication%20systems%20using%20Legendre%20neural%20networks.pdf

Nonlinear channel equalization for wireless communication systems using Legendre neural networks

Vehicular Ad-hoc Network (VANET) is gaining much attention recently because of its many important applications in transportation, to improve road safety, reduce traffic congestion, to enable efficient traffic management etc. However, there are several technical issues to be addressed for its effective deployment. Stability in communication in VANET is difficult to achieve due to rapid network changes. Restoration is inefficient while using traditional protocols based on broadcast storm. In this paper, we propose a new adaptive protocol to improve performance for on road safety alert application in VANET. It can alleviate the broadcast storm problem using adaptive wait-windows and adaptive probability to transmit. Simulation shows that our proposed approach has better performances in terms of number of collision, success rate, and delay, when compared with other existing protocols.

An adaptive alert message dissemination protocol for VANET to improve road safety

In this paper we study QoS based routing algorithm for supporting resource reservation in high-speed Integrated Services Packet Network (ISPN). Recently, this problem was proved to be NP-complete. However, when the considered QoS constraints are bandwidth, delay, delay jitter, and loss free, we have shown that by employing Weighted Fair Queueing (WFQ) service discipline, the complexity of the problem could be reduced to that of shortest path routing without any QoS constraints. Then such a multiple QoS constrained route could be searched in polynomial time. We also present the routing algorithm (called "QoSR/sub BF/"), which is a modified version of Bellman-Ford shortest path algorithm. Simulation results show that QoSR/sub BF/ has better performance compared to the existing QoS routing algorithms.

QoS based routing algorithm in integrated services packet networks

In this paper, we present the result of our study on the application of artificial neural networks (ANNs) for adaptive channel equalization in a digital communication system using 4-quadrature amplitude modulation (QAM) signal constellation. We propose a novel single-layer Legendre functional-link ANN (L-FLANN) by using Legendre polynomials to expand the input space into a higher dimension. A performance comparison was carried out with extensive computer simulations between different ANN-based equalizers, such as, radial basis function (RBF), Chebyshev neural network (ChNN) and the proposed L-FLANN along with a linear least mean square (LMS) finite impulse response (FIR) adaptive filter-based equalizer. The performance indicators include the mean square error (MSE), bit error rate (BER), and computational complexities of the different architectures as well as the eye patterns of the various equalizers. It is shown that the L-FLANN exhibited excellent results in terms of the MSE, BER and the computational complexity of the networks.

Legendre-FLANN-based nonlinear channel equalization in wireless communication system

This paper presents an efficient heuristic algorithm for the channel assignment problem in cellular radio networks. The task is to find channel assignment with minimum frequency bandwidth necessary to satisfy given demands from different nodes in a cellular network. At the same time the interference among calls within the same cell and from different neighboring cells are to be avoided, where interference is specified as the minimum frequency distance to be maintained between channels assigned to a pair of nodes. The simplest version of this problem, where only cochannel interferences are considered, is NP-complete. The proposed algorithm could generate a population of random valid solutions of the problem very fast. The best among them is the optimum or very near to optimum solution. For all problems with known optimal solutions, the algorithm could find them. A statistical estimation of the performance of the proposed algorithm is done. Comparison with other methods show that our algorithm works better than the algorithms that we have investigated.

Goutam Chakraborty

Papers

Nonlinear channel equalization for wireless communication systems using Legendre neural networks

An adaptive alert message dissemination protocol for VANET to improve road safety

QoS based routing algorithm in integrated services packet networks

Legendre-FLANN-based nonlinear channel equalization in wireless communication system

An efficient heuristic algorithm for channel assignment problem in cellular radio networks