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MacLean, P., 18 Madanes, C., 194, 195, 197, 198 Maddeaux, C., 181 Madden, D. A., 423 Madigan, S., 173 Magdol, L., 299 Magnavita, J., 395 Magruder, B., 380 Mahalik, J. R., 229 Mahoney, M. J., 212 Main, M., 148 Majors, R., 237, 238 Makinen, J., 181 Malamuth, N. M., 181 Malarkey, W. B., 254 Malik, M., 238, 239 Malloy, P. F., 282 Malone, J., 290, 291, 292, 298 Maloney, H. N., 353 Maltz, W., 275, 278 Malyon, A., 373, 375 Manion, I., 181, 444 Mantooth, C., 304 Maracek, J., 230 Marcus, M. D., 205 Margolin, G., 119, 120, 121, 122, 123, 132, 291, 300, 307, 340 Marinelli-Casey, P., 330 Marini, I., 435 Markham, H., 52 Markman, H., 120, 212, 440, 441, 443 Marlatt, G. A., 319, 329 Marlowe, F., 115 Martin, C., 338 Martin, D., 304 Marvin, C., 379 Mascher, J., 238 Maslow, A., 360, 389 Massey, R. F., 400 Massey, S. D., 400 Matsuyuki, M., 231 Matthews, C. R., 381 Mattison, A. M., 377 Maturana, H. R., 362 May, R., 81 McBride, A. B., 230 McCabe, M. P., 434 McCann, M. J., 330 McCarthy, B., 20 McCarthy, E., 20 McClanahan, M., 447 McCollum, E. E., 431 McCrady, B. S., 127, 313, 315, 322, 323, 324, 326, 330, 332 McCrank, E. W., 127 McCullough, M. E., 355 McDaniel, S. H., 213, 255, 257, 258, 268 McFarlane, W. R., 444

Suppose that we intend to evaluate the accuracy of a clustering algorithm A on a set of objects S relative to a collection of classes on S that forms a partition of S. In other words, we wish to determine the extent to which the clustering produced by A coincides with the partition determined by the classes. If the set S is large, the evaluation can be performed by extracting a random sample T from S, applying A to T , and then comparing the clustering partition of T computed by A and the partition of T into the preexisting classes. Let = {C1 , . . . , Cm } be the clustering partition of T and let = {K1 , . . . , Kn } be the partition of T of classes. The evaluation is helped by n m matrix Q, where qij = |Ci Kj | named the confusion matrix. We can use distances associated with the generalized entropy, d ( , ), to evaluate the distinction between these partitions. This was already observed by Rand [11], who proposed as a measure the cardinality of the symmetric difference of the sets of pairs of objects that belong to the equivalences that correspond to the two partitions. Frequently, one uses the conditional entropy

FIGURE 4.1.2 Crosstalk waveform and amplitude derived from lumped-capacitance approximation. (From [5]. # 1984 by IEEE.)

Precision agriculture applications aim at monitoring and controlling the parameters that affect plant growth Various nutrients (zinc, poshphorus, nitrogen, etc), soil moisture, temperature, pH, soil organic matter content, rooting depth, weed pressure, and pathogens have signi cent in uence on food production These soil parameters change within elds; therefore plant growth also varies within a eld The term spatial variability de nes the variability of a parameter within a eld and a precision agriculture application should obtain spatial variability maps of a eld The integrated project GoodFood is a precision agriculture project that aims at assuring the quality and safety of food chain Principally, it is based on the massive use of tiny detection systems capable of being close to the foodstuff and merged in AmI paradigm.

Macroeconomics views the economy from on high, at the national or international level, and deals with the choices that countries face about economic growth and development and about how to best manage their economies to avoid recessions. Macroeconomics also deals with the misery caused by things such as unemployment and inflation. In this part, you find out about monetary and fiscal policy, the Bank of England, the effects of taxation on the economy, and international trade and trade policy.

In GoodFood, high density sensors are deployed in the agricultural area desired to be monitored and wirelessly interconnected, capable of locally implementing computations based on predetermined mathematical models and forwarding information to a remote site The rst GoodFood application scenario was implemented in spring 2005 in a pilot site in a vineyard at Montepaldi farm, a property of the University of Florence Based on MICA motes and TinyOS, a WSN is disseminated in the area and it will primarily be used for detection of insurgence toxins in the vineyard 2722 Scenario characteristics The main characteristic requirements of the GoodFood scenario are network lifetime and power ef ciency, synchronization, security, localization, address and data centric communications, scalability, fault tolerance and node heterogenity In truth, each characteristic is a challenging area for the scenario and requires research in order to provide proper operation.