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caused the removal of the CEO and CFO from their posts. Such was the nature of corporate investigations. Every archived e-mail and filed document was scrutinized, and even the mildest oddity had the potential to become the crisis of the day. Everyone, from departmental secretaries to senior directors, was consumed by the unfolding events, both real and imagined. Rumors spread like common colds.

Substituting in the value for r (u) in (10.82) and f(0, 0) = r0 , we have:

In this chapter we consider the problem of object detection with the goal of nding new targets entering the scene, and of labeling them uniquely until they get lost because of occlusions, unexpected maneuvers, or motion outside the eld of view. Target detection is a localization task very similar to tracking, however with more severe restrictions and dif culties: Global vs. local search. A detector must search for targets in the full state space, or at least, the visible-from-camera state volume, and provide roughly the initial distribution of targets for tracking. This means that it must solve a global optimization problem [162] of a multiobjective likelihood function in state space, labeling all its relevant peaks as new targets. Instead, the tracking algorithms presented so far update each target distribution only by looking in a neighborhood of its predicted position. Static vs. dynamic data association. For target detection, no prediction is available apart from a possible absolute prior, restricting the search to the most likely areas. Therefore, any visual modality employed cannot make use of dynamic data association, such as the nearest neighbor to the predicted feature, but can only rely on static methods, matching features by using an invariant descriptor. For example, SIFT or SURF keypoints can be used because their descriptor is to some extent invariant to geometric and photometric variations from different views of the object.

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2.5 SEMANTICS (DATA DEFINITION STANDARDS)

This system can be solved recursively, as [ (t , t + ) ] 1 = log e( 1 ( t ,t + ) 2 and 1 [ (t , K ) ] = log e 2

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Key Topics Addressed in This Collaboration and technology Evolution of networks and data access Technology pioneers

Please don t ask me to explain this formula. I haven t a clue!

Now that most of the OS is set up and running, you need to finish that wild partition layout of a logical volume on top of software RAID (/dev/md3). This section is not required, but if you want the ability to add more drive space to your system in the future, this is the way to prepare for it now without some day having to move GBs of data around between drives. With LVM, you simply add more drives to the system (let s hope in the form of more RAID arrays), and add those resources to the logical volume. No reformatting, copying files, or even so much as a reboot (if you re using USB or ieee1394 drives!). LVM or no LVM It s your call. On my system I run SATA drives, which appear as SCSI devices (/dev/sda instead of /dev/hda), but no matter. Setting up a logical volume atop your software RAID-1 mirror /dev/md3 device (if you re using RAID) will look pretty much the same regardless (except when you poke around with fdisk at the raw partition level).

A function can also use an array as an argument. An array is a series of values separated by a comma and enclosed in brackets. The formula below uses the OR function with an array as an argument. The formula returns TRUE if cell A1 contains 1, 3, or 5.

Plaka, T, Ehsani, M R, and Korchinsky, W J, "Determination of Individual Phase Transfer Units, NG and NL, for a 06 m Diameter Distillation Column Sieve Plate: Methylcyclohexane-Toluene System," Chem Eng Res Des, 67, 316-328 (1989) Ponter, A B and Au-Yeung, P H, "Estimating Liquid Film Mass Transfer Coefficients in Randomly Packed Columns," in Handbook of Heat and Mass Transfer, Cheremisinoff, N P (Ed), Gulf Publishing Corp, Houston, TX, Chap 20, Vol II, pp 903-952, 1986 Powers, M F, Vickery, D J, Arehole, A, and Taylor, R, "A Nonequilibrium Stage Model of Multicomponent Separation Processes V Computational Methods for Solving the Model Equations," Comput Chem Eng, 12, 1229-1241 (1988) Prado, M and Fair, J R, "Fundamental Model for the Prediction of Sieve Tray Efficiency," Ind Eng Chem Res, 29, 1031-1042 (1990) Pratt, H R.

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