370694
Further Results on the Asymptotic Memory Capacity of the Generalized Hopfield Network
This paper presents a further theoretical analysis on the asymptotic memory capacity of the generalized Hopfield network (GHN) under the perceptron learning scheme. It has been proved that the asymptotic memory capacity of the GHN is exactly 2(n− 1), where n is the number of neurons in the network. That is, the GHN of n neurons can store 2(n− 1) bipolar sample patterns as its stable states when n is large, which has significantly improved the existing results on the asymptotic memory capacity of the GHN.
6394511
The capacity of time-delay recurrent neural network for storing spatio-temporal sequences
We investigate the capacity of a type of discrete-time recurrent neural network, called time-delay recurrent neural network, for storing spatio-temporal sequences. By introducing the order of a spatio-temporal sequence, the match law between a time-delay recurrent neural network and a spatio-temporal sequence has been established. It has been proved that the full order time-delay recurrent neural network of l-step feedback is able to learn and memorize (or store) any bipolar (or binary) spatio-temporal sequence of the order k if k≤l, and that the asymptotic memory capacity of the first-order time-delay recurrent neural network of one-step feedback is not less than C1(n)=n+1, where n is the number of processing neurons in the network. Moreover, we substantiate the theoretical results by simulation experiments.
6431369
A Neural Network Approach to Real-Time Pattern Recognition
This paper presents a new neural network approach to real-time pattern recognition on a given set of binary (or bipolar) sample patterns. The perceptive neuron of a binary pattern is defined and constructed as a binary neuron with a neighborhood perceptive field. Letting its hidden units be the respective perceptive neurons of the patterns, a three-layer forward neural network is constructed to recognize these patterns with minimum error probability in a noisy environment. The theoretical and simulation analyses show that the network is effective for pattern recognition and can be under strict real-time constraints.
10273602
Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures
It is well known that the convergence rate of the expectation-maximization (EM) algorithm can be faster than those of convention first-order iterative algorithms when the overlap in the given mixture is small. But this argument has not been mathematically proved yet. This article studies this problem asymptotically in the setting of gaussian mixtures under the theoretical framework of Xu and Jordan (1996). It has been proved that the asymptotic convergence rate of the EM algorithm for gaussian mixtures locally around the true solution is o(e0.5()), where > 0 is an arbitrarily small number, o(x) means that it is a higher-order infinitesimal as x 0, and e() is a measure of the average overlap of gaussians in the mixture. In other words, the large sample local convergence rate for the EM algorithm tends to be asymptotically superlinear when e() tends to zero.
10392733
Pre-routing estimation of shielding for RLC signal integrity
The formula-based K/sub eff/ model is a figure of merit for inductive coupling, and has been used to solve the simultaneous shield insertion and net ordering (SINO) and simultaneous signal and power routing (SPR) problems. The authors first show that the K/sub eff/ model has a high fidelity compared to the SPICE-computed noise under an accurate RLC circuit model. We then develop simple yet accurate formulae to estimate numbers of shields needed by optimal SINO solutions under the K/sub eff/ model. Extensive experiments show that our pre-routing estimation has errors less than 10% compared to solutions given by detailed SINO algorithms. These formulae can be used effectively as a pre-routing congestion estimation for layout planning and synthesis.
22258067
A principled approach for formative Web learning assessment and adaptive tutoring
Web based distance learning is a trend of instruction delivery. One of the most difficult challenges of such a learning mechanism is the assessment of students' learning criteria. It is hard to judge the behavior of a student since the instructor is separated spatially and temporally from the students. However, it is possible to rely on some Web based tools to keep track of a student's course attendance, as well as the navigation behavior of that student. In addition, the navigation behavior of an individual can be compared to those of others. Analysis can be conducted, and an interactive tutorial can be generated to assist the student with a poor score. The paper proposes such a mechanism, as well as its supporting system run on Windows browsers.
2885309
A Smart Ambient Sound Aware Environment for Be Quiet Reminding
This paper presents a prototype of a smart sound aware environment that captures ambient sounds, analyzes sound situations and gives a be-quiet reminder to someone who generates sound exceeding certain volume level and disturbing others in the same site. It shows the microphone net to sense sound data and the speaker net to send be-quiet reminders. All computers are interconnected with a P2P network atop of the TOMSCOP platform. It discusses how sound data is captured and processed, and when a be-quiet reminder decision is made with analyzing contexts of sounds and users in a room. Some preliminary experimental results are given
892651
A genetic algorithm for optimal 3-connected telecommunication network designs
In this paper, a genetic algorithm for three connected telecommunication network designs is proposed. The genetic algorithm uses a new approach to solution representation, in which constraints such as diameter and connectivity constraints can be easily encoded and two-point crossover with the operation of swapping duplicated nodes ensures solutions generated through genetic evolution are all feasible so that both checking of the constraints and repair mechanism can be avoided. Both efficiency and effectiveness are increased due to no checking of the constraints and no repair mechanism conducted. In fact, our design algorithm for three-connected network design can he extended to a high-connectivity network design.
3683549
Mobile Wireless Sensor Network: Architecture and Enabling Technologies for Ubiquitous Computing
A mobile wireless sensor network owes its name to the presence of mobile sink or sensor nodes within the network. The advantages of mobile WSN over static WSN are better energy efficiency, improved coverage, enhanced target tracking and superior channel capacity. In this paper we present and discuss hierarchical multi-tiered architecture for mobile wireless sensor network. This architecture is proposed for the future pervasive computing age. We also elaborate on the impact of mobility on different performance metrics in mobile WSN. A study of some of the possible application scenarios for pervasive computing involving mobile WSN is also presented. These application scenarios will be discussed in their implementation context. While discussing the possible applications, we also study related technologies that appear promising to be integrated with mobile WSN in the ubiquitous computing. With an enormous growth in number of cellular subscribers, we therefore place the mobile phone as the key element in future ubiquitous wireless networks. With the powerful computing, communicating and storage capacities of these mobile devices, the network performance can benefit from the architecture in terms of scalability, energy efficiency and packet delay, etc.