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This performs a bit of magic under the covers It creates an alias of the encode method we created on the Video class and sets it up to create a new task whenever you call it For example, now if we call the following code:

+ l)x(n)

Woo and CS Raghavendra, Power-aware routing in mobile ad hoc networks, in proc ACM/IEEE MO6 ICOM 98, October 1998 [23] Y Ko and NH Vaidya, Location-aided routing (LAR) in mobile ad hoc networks, in Proc ACM/IEEE MCIBCOM 98, October 1998 [24] CR Sin and M Gerla, MACNPR: an asynchronous multimedia multi- hop wireless network, in Proc IEEE INFOCOM 97, March 1997 [25] ZJ Haas and MR Pearlman, The performance of a new routing protocol for the recon gurable wireless networks, in Proc ICC 98, pp 156 160 [26] ZJ Haas and MR Pearlman, Evaluation of the ad-hoc connectivity with the recon gurable wireless networks, in Virginia Tech s Eighth Symp Wireless Personal Communications, 1998, pp 156 160 [27] ZJ Haas and MR Pearlman, The performance of query control schemes for the zone routing protocol, in Proc SIGCOMM 98, pp 167 177.

(2.4.33)

y(n)=y(n)

(2.4.34)

X(k)

L x(n)W~~-l)(n-l)

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[28] P Jacquet, P Muhlethaler and A Qayyum, Optimized link state routing protocol, IETF MANET, Internet Draft, November 1998 http://wwwietfcnrirestonvaus [29] DB Johnson and DA Maltz, Dynamic source routing in ad hoc wireless networking, in Mobile Computing, T Imielinski and H Korth (Eds) Kluwer: Norwell, MA, 1996 [30] J Moy, OSPF version 2, RFC 2178, March 1997 [31] S Murthy and JJ Garcia-Luna-Aceves, A routing protocol for packet radio networks, in Proc ACM Mobile Computing and Networking Conf (MOBICOM 95), pp 86 94..

(2.4.35)

X2(k)

L x2(n)W~~-1)(n-l)

it is the equivalent of calling this code:

(2.4.36) (2.4.37)

Y(k)

Figure 18.3 Network model for agent brokering environment. The SP is informed periodically about the cost changes associated with each of those interconnection points. As a means of competition, all three networks offer the same access to the remote CP. The SP serving a particular client is responsible for identifying the best/optimal connection path (routing) per request of the customer. In 7, QoS routing with multiconstraint has been shown to be an NP-hard problem [34]. For this reason, in this section we additionally consider a genetic algorithm (GA), which presents a good method to handle multiconstraint optimization problems and does not depend on the properties of the problem itself [37, 38]. The remaining issue is how the underlying agent architecture may interact with the genetic algorithm itself. It is assumed that an SP may host a particular kind of agent [named broker agent (BrkA)] which is in charge of identifying the optimal path to manage a speci c connection request. The interaction between a BrkA and the algorithm may occur according to the following strategies: (1) The BrkA is able to execute the algorithm in run-time upon the request from the PC client. (2) The BrkA sends a request to a network node where the genetic algorithm can be executed. The optimal path is then sent back to the BrkA, which activates the setup procedure. (3) A set of optimal paths for different pairs of PC client and CPs is stored in a database (eventually distributed), which is accessed from the BrkA to retrieve the more convenient path to satisfy the speci c request. Once the connection is established, the genetic algorithm can be re-executed in order to identify a more convenient path, if the case.

= X 2(k)X(k)

We can extend to the case of three-dimensional convolution with three indices as needed for the discrete dipole approximation. Let N x , Ny and N z points, respectively, in X, fJ and z directions with N x , Ny and N z all equal to powers of 2. Let

y(n, m, l)

L L L g(n - n ' , m - m ' , l-l')x(n' , m ' , l')

(2.4.38)

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