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longitudinal coordinate, where zo is the object range. These values are termed the diffraction limits because one need only assume that the Fresnel kernel applies to derive them. The spatial resolution of most imaging systems is worse than the diffraction limit owing to suboptimal sampling and processing of W(Dx, Dy, q, l). Optical superresolution is a complex and profound subject with a long history of mixed success and failure. Concomitant with the microprocessor revolution, work since the mid-1990s has demonstrated modest success in computational resolution enhancement, and there are suggestive ideas that future improvements are possible. To date, however, the impact of computational processing is far greater in enabling systems to achieve metrics approaching the diffraction limit over wider elds, with wider depth of focus, and with greater speci city. There are, however, indications that over the long-term systems violating the conventional diffraction limit may be developed. For researchers at the limits of system performance there are many current opportunities for superresolution studies. These opportunities may be grouped into the following categories: 1. Strategies to increase the resolution for signals measured over bandlimited channels. Examples mentioned below include channel coding and estimation algorithms and multispectral encodings. 2. Strategies to increase the bandpass of optical systems. Examples mentioned below include anomalous diffraction and nonlinear detection. We brie y review these strategies in Sections 10.3.1 and 10.3.2. 10.3.1 Bandlimited Functions Sampled over Finite Support

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Stakeholder 3 has been assessed as being ambivalent about the project, neither supportive nor unsupportive , but eager to receive information about the project, whereas the engagement pro le SHOULD BE for receptiveness to be ambivalent: neither supportive nor nonsupportive This is a situation where the current pro le is quite different from the optimal pro le and will require careful handling from the team Based on each stakeholder s engagement strategy, a communication plan will be developed, consisting of: speci c messages or message forms (reports); how messages will be delivered; by whom; whether formal or informal, written or oral; at what frequency The frequency and regularity of delivery of these messages will vary with the level of support and receptiveness of the stakeholder, the gap between current assessment and optimal assessment, as well as the stage of the project.

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M and it is based on regression data up to and including time i Specifically, &(i) = U M , ( ' W M , ~ , where uvlztis the M-th order regressor at time i and W M , %is the weight-vector solution at time a as well All lattice filters described in this part, as well as all other least-squares adaptive filters described in the previous chapters, operate in this sequential manner Namely, the signal estimates that are available at a particular time instant are based on the regression data up to that same time instant This fact is in contrast to a batch least-squares solution (cf 29), whereby all N 1 regression vectors and reference data are used to generate the signal Ytimates.

One nal step to prepare for communication The nal step before developing the communication plan is to categorise each stakeholder into one of three groups: 1 The current engagement position attitude is equal to the optimal position 2 The current engagement position attitude is greater than the optimal position 3 The current engagement position attitude is less than the optimal position In the rst instance where the current attitude is equal to the optimal position, communication can be maintained at business as usual : the de ned level and frequency of regular reports, meetings and presentations can be safely maintained For the situation where the current attitude is greater than the optimal position, two possible approaches need to be considered, depending on the results of the engagement matrix In Figure 7.

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