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We note here that Feder and Shende [9] mention a centralized strategy for this case that has competitive ratio at most 2 This uses a simple idea involving a convex hull technique The algorithm follows from the following simple observation Consider an embedding of a square graph on a triangular lattice Extract a node on the convex hull of the graph It is easy to see that its neighborhood can be covered with two cliques Hence, if every node in v s neighborhood is already assigned channels from the set [1, , 2 (G, w)], then v can also find channels from this set without conflicting with any neighbors Therefore, the algorithm first constructs an ordering of nodes by computing the convex hull of the nodes, removing a hull node, and repeating this process on the remaining nodes Finally, nodes are assigned channels in the reverse of this ordering.

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I shall brie y describe the most prominent of these Initially, the most visible position was Magda Arnold s, though it quickly was lost in the stream of newcomers Arnold (1960) developed a hybrid phenomenologicalcognitive-physiological theory She starts with the appraisals of events as good or bad, judgments that are unanalyzable and are part of our basic humanness She proceeds from there to the phenomenology of emotional felt tendencies and accompanying bodily states, and concludes by describing the possible neurophysiology behind these processes Also in the 1960s, Sylvan Tomkins (1962 1992), the most consistent defender of the fundamental emotions approach, started presenting his theory Tomkins argued that certain eliciting stimuli feed into innate neural affect programs, which represent primary affects such as fear, anger, sadness, surprise, happiness, and others Each of these primary affects.

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It is clear that 2 (G, w) channels suffice In [38], the authors show that the recoloring greedy strategy has a competitive ratio between 7/3 and 23/8 for reuse distance 3 in hexagon graphs and, thus, for the recoloring case, the borrowing strategy is at least as good as the greedy strategy 453 Arbitrary Graphs Janssen and Kilakos [17] show that for general k-colorable graphs, a generalization of the FPA strategy discussed earlier has a performance ratio of k/2 In what follows, we discuss some specific classes of graphs that have been studied in the context of radio channel assignment These include odd cycles, outerplanar graphs, and graphs derived from hexagon graphs, either by considering arbitrary reuse distance in such graphs, or by considering networks that have a certain amount of overlap between adjacent cells.

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Other interesting classes of graphs are unit disk graphs and graphs that can be drawn in an (r, s)-civilized manner Odd Cycles and Outerplanar Graphs Odd cycles and outerplanar graphs can be multicolored optimally using simple linear time sequential algorithms The chromatic number of an odd cycle with 2m + 1 nodes is max{ (G, w), W/m }, where W is the sum of the weights of all nodes in the cycle The centralized algorithm given in [37] first finds the minimum index k such that 2k+1wi i=1 k (G, w) Such an index must exist, because m satisfies the above property Then the algorithm uses contiguous colors in a cyclic manner from the colors [1, (G, w)] to color nodes 1 to 2k The remaining nodes are colored based on their parity, as in a bipartite graph.

The optimal algorithm for odd cycles is extended in [37] to derive an optimal algorithm to multicolor outerplanar graphs Arbitrary Reuse Distance in Hexagon Graphs We consider the case of arbitrary reuse distance for hexagon graphs The problem of channel assignment in a hexagon graph (G, w) with reuse distance r is equivalent to multicoloring the graph (Gr, w) As stated in Section 44, there exist graphs (Gr, w) that require 5 (Gr, w)/4 channels An upper bound of 6 for the performance ratio of the greedy strategy follows from the observation that the neighborhood of any node in a graph Gr can always be covered with 6 cliques However, a borrowing strategy similar to the one in [9] for reuse distance 3 has a better upper bound.

In particular, as shown in [35] and [45], for a hexagon graph G with reuse distance r, (Gr) = (Gr) = 3r2/4 if r is even and (3r2 + 1)/4.

14421 Introduction Each object in a geographical region generates a timevarying space time signature eld that may be sensed in different modalities, such as acoustic, seismic, or thermal The nodes sample the signature eld spatially, and the density of nodes should be commensurate with the rate of spatial variation in the eld Similarly, the time series from each sensor should be sampled at a rate commensurate with the required bandwidth Thus, the rate of change of the space time signature eld and the nature of the query determines the required space time sampling rate A moving object in a region corresponds to a peak in the spatial signal eld that moves with time Tracking an object corresponds to tracking the location of the spatial peak over time 1442.

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