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public Network() { if (start != null) { start.dist = 0; for (Vertex p = start.nextVertex; p != null; p = p.nextVertex) { p.dist = Integer.MAX_VALUE; // infinity } } } public void findShortestPaths() { // implements Dijkstra s Algorithm: for (Vertex v = start; v != null; v = closestVertex()) { for (Edge e = v.edges; e != null; e = e.nextEdge) { Vertex w = e.to; if (!w.done && v.dist+e.weight < w.dist) { w.dist = v.dist+e.weight; w.back = v; } } v.done = true; } } private Vertex closestVertex() { // returns the vertex with minimal dist among those not done: Vertex v = null; int minDist = Integer.MAX_VALUE; for (Vertex w = start; w != null; w = nextVertex) { if (!w.done && w.dist < minDist) { v = w; minDist = w.dist; } } return v; } }

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The first two iterations are shown in Figure 15.24. On the first iteration, the highest priority vertex is x = A because its distance field is 0 and all the others are infinity. Steps 7 10 iterate three times, once for

Design a digital filter to meet the analog filter specifications with a sampling frequency f, = 10 kHz.

In this implementation, we have used a simple search method closestVertex() instead of a priority queue. This is less efficient, running in O(n) time instead of the O(lgn) time that a priority queue would use.

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With a sampling frequency of 10 kHz, the digital filter should have a cutoff frequency w, = 2 n f,./f, = 0.41~ and a transition bandwidth Aw = 2nAf/f, = O.lrr. For a stopband attenuation of 50 dB, we may use a Kaiser window with B = 0.l l02(50 - 8.7) = 4.55 For the length of the window, we have

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each of A s neighbors y = B, C, and D. The values of s computed for these are 0 + 4 = 4, 0 + 6 = 6, and 0 + 1 = 1. Each of these is less than the current (infinite) value of the corresponding distance field, so all three of those values are assigned, and the back pointers for all three neighbors are set to point to A. On the second iteration, the highest priority vertex among those still in the priority queue is x = D with distance field 1. Steps 7 10 iterate three times again, once for each of D s unvisited neighbors: y = B, F, and G. The values of s computed for these are 1 + 4 = 5, 1 + 2 = 3, and 1 + 6 = 7, respectively. Each of these is less than the current value of the corresponding distance field, so all of those values are assigned and the back pointers are set to D. Note how this changes the distance field and pointer in vertex C.

or N = 59. Finally, the unit sample response of the ideal filter that is to be windowed is a low-pass filter with a ~ cutoff frequency w,. = 0 . 4 and a delay N / 2 = 29.5. Therefore,

GRAPH TRAVERSAL ALGORITHMS The paths produced by Dijkstra s algorithm produce a minimal spanning tree for the graph. That is a spanning tree whose total weighted length is minimal for the graph; that is, no other spanning tree has a smaller total length. The spanning tree is formed in a breadth-first manner, by considering the vertices that are adjacent to the current vertex on each iteration. This is one of two general ways to traverse a graph. The breadth-first search algorithm is essentially the same as Dijkstra s algorithm without regard to the distance fields. Algorithm 15.2 The Breadth-First Search (BFS) Algorithm (Preconditions: G = (V,E) is a graph or digraph with initial vertex v0; each vertex has a boolean visited field initialized to false; T is an empty set of edges; L is an empty list of vertices.) (Postcondition: L lists the vertices in BFS order, and T is a BFS spanning tree for G.)

Find the Kaiser window parameters, B and N , to designa low-pass filter with acutofffrequency w, = n / 2 , a stopband ripple 6 = 0.002, and a transition bandwidth no 1a.rger than 0.117. ,

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