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one function satisfying these constraints, we must have g(t) = f (t) This provides an explicit representation of f (t) in terms of its samples A general function has an in nite number of degrees of freedom the value of the function at every point can be chosen independently The Nyquist Shannon sampling theorem shows that a bandlimited function has only 2W degrees of freedom per second The values of the function at the sample points can be chosen independently, and this speci es the entire function If a function is bandlimited, it cannot be limited in time But we can consider functions that have most of their energy in bandwidth W and have most of their energy in a nite time interval, say (0, T ) We can describe these functions using a basis of prolate spheroidal functions.

We do not go into the details of this theory here; it suf ces to say that there are about 2T W orthonormal basis functions for the set of almost timelimited, almost bandlimited functions, and we can describe any function within the set by its coordinates in this basis The details can be found in a series of papers by Landau, Pollak, and Slepian [340, 341, 500] Moreover, the projection of white noise on these basis vectors forms an iid Gaussian process The above arguments enable us to view the bandlimited, time-limited functions as vectors in a vector space of 2T W dimensions Now we return to the problem of communication over a bandlimited channel Assuming that the channel has bandwidth W , we can represent both the input and the output by samples taken 1/2W seconds apart.

Sub UpdateProgress(Pct) With UserForm1 .FrameProgress.Caption = Format(Pct, 0% ) .LabelProgress.Width = Pct * (.FrameProgress.Width - 10) .Repaint End With End Sub

Each of the input samples is corrupted by noise to produce the corresponding output sample Since the noise is white and Gaussian, it can be shown that each noise sample is an independent, identically distributed Gaussian random variable If the noise has power spectral density N0 /2 watts/hertz and bandwidth W hertz, the noise has power N0 2W = N0 W and each of the 2W T noise 2 samples in time T has variance N0 W T /2W T = N0 /2 Looking at the input as a vector in the 2T W -dimensional space, we see that the received signal is spherically normally distributed about this point with covariance N0 2 I Now we can use the theory derived earlier for discrete-time Gaussian channels, where it was shown that the capacity of such a channel is C= P 1 log 1 + 2 N bits per transmission (9.

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and the deployment of such biological prevention strategies will bene t from involvement of health psychologists to develop acceptable distribution and adherence strategies, and address beliefs and fears regarding the common perception that governments and scientists cannot be trusted We also expect continued collaboration among biomedical and behavioral scientists and practitioners in the prevention and treatment of HIV disease We expect that greater use will be made of the teachable moments that occur in HIV C&T, STD, and primary care settings In such settings, we expect increased use of brief screening measures (eg, Gerbert et al, 1998) and timelimited interventions (Boekeloo et al, 1999; Kalichman, 1996) Advances in computer technology will make it easier for practitioners to offer such screening, and to make available brief, of ce-based interventions that require little professional time (through videotape, CD-ROM, DVD, and emerging technologies).

Event handler actions onClipEvent (load) onClipEvent (unload) onClipEvent (enterFrame) onClipEvent (mouseDown) onClipEvent (mouseUp) onClipEvent (mouseMove) onClipEvent (keyDown) onClipEvent (keyUp) onClipEvent (data) Event handler methods onLoad onUnload onEnterFrame onMouseDown onMouseUp onMouseMove onKeyDown onKeyUp onData

Screening will also identify persons at high risk who are most likely to bene t from more intensive, face-to-face interventions that can be delivered by peer educators and health care providers Such a system will be more accessible, affordable, and con dential, and increase the reach of such down- and midstream approaches Continued Collaboration with Community Leaders The involvement of community leaders has been essential to the development and implementation of HIV interventions Collaboration among community groups, practitioners, and scientists will continue Community-based and other grass roots groups will increasingly demand a more powerful voice in planning and decision making regarding prevention and treatment trials Community-based groups and health professionals need to continue what has been a productive and mutually rewarding relationship to disseminate effective interventions to community-based organizations (ie, technology transfer ).