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I found the RS-232 interface a great thing to hack. It is there to allow remote video card systems to be controlled by the ATM. However, this is a rarely used option. RS232 is extremely well documented but sadly slow. On the other hand, ATMs have really weird connectivity. The NCR ATMs I researched (Personas and 5xxx series) didn t support TCP/IP They had weird protocols like NCR/ISO Async, IBM 3275 Bisync, and a . lot of other very obscure stuff. RS-232 is the only guaranteed way to move lots of data on and off the system. There is a lot of banking-specific hardware in these things. I don t want to fill this article with specs of currency cassettes or mag card canisters. If you are interested, check my references. The only thing of interest is a DES Hardware encryption system.

Interference is unwanted radio frequency energy introduced at the end receiver of the wanted link. Figure 5.26 illustrates the spectrum of a wanted carrier contaminated by an interfering carrier. The receiver with equivalent noise bandwidth BN captures carrier power C, and interference power Ni . The carrier-to-interference power ratio is C/Ni . The amount of interference power into the receiver depends on the overlap of the carrier spectra, as illustrated in Figure 5.26. The receiver noise bandwidth BN is matched to the bandwidth occupied by the wanted carrier with power C. Power C is given by: C = PSDw BN (W) (5.57)

A constraint is an additional rule that the modeler can assign to the diagram to provide special signi cance to the whole diagram or some element(s) of it. Figure 2.17A shows a constraint on the actor-use case relationship. This constraint is that the CollectsClaimInformation use case will be fiteratively exe cutedg implying that other use cases like, say, RegisterClaim will be executed followed by additional execution of CollectInformation. Another

Figure 9.4 The WordNet online query page, the Web front-end for the database, showing some of the relational dimensions that can be explored

locally and wait for the arrival of a mobile sink Multisink scenario is also considered Each sink moves randomly and collects data from encountered sensors in its communication range Collected data are then carried by the sink to a wireless access point (eg, a xed base station) In the case of stochastic sink mobility, energy consumption at sensor side is only due to sink discovery and subsequent data transfer Assume each sink broadcasts a beacon message while moving A straightforward way of sink discovery is to monitor the wireless communication channel Whenever a sensor hears the beacon message it concludes that a sink arrives However, constant channel monitoring is very expensive in energy Chakrabarti et al (2003) show that, if sinks (eg.

Use cases (,,functional. other possibilities being ,,technical. ., .) Classes (,,entity.., compared to, say, , ,boundary. or , . ,controller. .) Node, being stereotyped as ,,device.. Actors which themselves stereotype , ,interface. classes, representing . , ,user.. here. (Other example types being ,,nonhuman.., etc.)

Suppose the outcome of coin ipping is Tails and the day-trader would like to sell all his IBM stocks, then he sends the message Buy IBM to the broker who, knowing that the key this day is Tails, decrypts it as Sell IBM and acts accordingly. B

minority of companies have done nothing (ie can be placed at level 0) The scores range from 0% to 27%, with a median value of 17% The majority of companies place themselves at level 1 (messaging) or level 2 (online marketing) At these levels the companies are exchanging information The numbers vary, but around 53 75% fall in this range, with a median score of 66 69% A further substantial minority of companies place themselves at level 3 (ordering online) or level 4 (paying online) scores here range from 7% to 35% with a median score of 22% This re ects the approximate number of companies who are undertaking some transactions online In most sectors (although not all), there is more evidence of online ordering than there is of online paying.

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, mounted on shuttle buses) move along the regular path, then sensors can predict their arrival after being allowed a learning curve for their movement pattern After discovering a sink, data transfer should also start in an intelligent way If a sensor simply transmits as soon as it discovers the sink, data may not be successfully delivered or may be delivered with many retrials, wasting energy According to Anastasi et al (2007a), message loss probability drops with decreased sensor-sink distance Suppose the sink passes by sensors along straight line To minimize energy consumption, data transfer should take place in the time interval with minimum message loss probability, which is exactly around the minimum sensor-sink distance point From this consideration, Anastasi et al (2007b) proposed an adaptive data transfer protocol In Anastasi et al.