Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Linear time-invariant Systems01:23

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
Network Function of a Circuit01:25

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

An evolutionary neural network approach for module orientation problems.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2008
Same author

A gradual neural network approach for FPGA segmented channel routing problems.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2008
Same author

A binary Hopfield neural-network approach for satellite broadcast scheduling problems.

IEEE transactions on neural networks·1997
Same author

A gradual neural-network approach for frequency assignment in satellite communication systems.

IEEE transactions on neural networks·1997
Same author

A parallel improvement algorithm for the bipartite subgraph problem.

IEEE transactions on neural networks·1992

Related Experiment Videos

A gradual neural-network algorithm for jointly time-slot/code assignment problems in packet radio networks.

N Funabiki1, J Kitamichi

  • 1Department of Information and Computer Sciences, School of Engineering Science, Osaka University, Toyonaka, Osaka 560, Japan.

IEEE Transactions on Neural Networks
|February 8, 2008
PubMed
Summary
This summary is machine-generated.

A novel gradual neural network (GNN) algorithm optimizes packet radio network communication by jointly assigning time-slots and codes. This approach minimizes time-slots while ensuring conflict-free data transmission, outperforming existing methods.

Related Experiment Videos

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Communications

Background:

  • Existing algorithms independently assign time-slots and codes in packet radio networks.
  • Packet radio networks utilize time/code division multiple access for conflict-free communication.
  • The jointly time-slot/code assignment problem (JTCAP) is crucial for network efficiency.

Purpose of the Study:

  • To introduce a gradual neural network (GNN) algorithm for the jointly time-slot/code assignment problem (JTCAP).
  • To achieve simultaneous assignment of time-slots and codes to communication links.
  • To minimize the number of time-slots required for conflict-free communication.

Main Methods:

  • Developed a gradual neural network (GNN) algorithm to solve the JTCAP.
  • Incorporated constraints on the maximum number of codes and conflict distances between links.
  • Tested the GNN algorithm on 3000 instances with varying network sizes (100-500 nodes, 100-1000 links).

Main Results:

  • The GNN algorithm successfully found time-slot/code assignments minimizing the number of time-slots.
  • Performance was evaluated against a lower bound and a greedy algorithm.
  • GNN demonstrated superior solution quality compared to the greedy algorithm.

Conclusions:

  • The proposed GNN algorithm effectively addresses the JTCAP in packet radio networks.
  • GNN offers a significant improvement in solution quality for time-slot and code assignment.
  • The algorithm provides comparable computation time while enhancing overall network performance.