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degrees of stimulation or arousal. The more stimuli that must be processed by a person, the higher the load. Environments that are complex, crowded, asymmetrical, novel, unfamiliar, surprising, or random are high load. Environments that are simple, uncrowded, symmetrical, conventional, familiar, unsurprising, or organized are low-load. If the task to be performed is complex or unusual studying technical material, preparing for an exam, or writing an essay the load is great enough that our degree of arousal is fairly high; additional load from the environment will increase stimulation to such a point that the task is avoided. We become distracted, annoyed, or frustrated, and performance falls off sharply. Tasks that are simple or routine writing checks, making a shopping list, or other familiar chores benefit from a mildly stimulating environment. Daydreaming or dozing may result without increased stimulation. This is why such work often fails to be performed in home offices or studies designed for paperwork; instead it is done in kitchens, dining rooms, or living rooms, which have a higher degree of stimulation. The lower the load of the task, the more it requires a high-load setting for optimum performance. Boring tasks are boring because they are unstimulating (simple or overly familiar) and often unpleasant. Within reason, the more stimulation provided, the more pleasant the task becomes. For many, basic housework is monotonous; playing background music increases stimulation, enabling us to complete boring domestic chores.

D Cell (packet): see Asynchronous transfer mode (ATM). Cell delay variation (CDV): 8.6. Cell error ratio (CER): 8.6. Cell error ratio (CER): AP3.4-2. Cell loss ratio (CLR): 8.6, AP3.4-2. Cell switching: See Asynchronous Transfer Mode (ATM). Cell transfer delay (CTD): 8.6. Channel coding: 3.3.5.1. Channel unit (CU): see Single channel per carrier (SCPC). Channelized subsystem (satellite transponder): 6.3.3.3. China satellite system: AN3.3.3. Chinasat satellite system (China): AN3.3.3.2. Chinastar satellites (China): AN3.3.3.3. Circuit switching: 3.3.3. Circular polarization (CP): see Polarization. Civil works (earth station): 7.8.1. Climatic zones: AN1. Clock recovery: 4.2.1, 4.2.4.2, 4.2.5, 5.1. Clock: see Timing, Synchronization and Clock recovery. Closed user group (CUG): 8.2. Code division multiple access (CDMA)*: 1.4.4.2, 5.4, AP5.1-2, AP6.1-2.5. Code excited linear prediction (CELP): 3.3.2. Code rate*: 3.3.5.1, AP3.2. Codec (Coder/Decoder): see Coder or Decoder. Coding gain*: 3.3.5.4, AP3.2; Coding, Coder*: 3.3, 4.2.4, AP5.1. Coherent demodulation: 4.2.1. Coherent PSK (CPSK): 4.2.1. Collection network: 5.6.1 ii). Combiner: see Coupling systems. Common channel signalling (CCS): AP8.1-4, AP8.1-5. Common signalling channel (CSC): see Common channel signalling. Communication equipment (earth station): 7.1.1.4, 7.5, 7.6. Communications system monitoring (CSM).

where Z(t) is Brownian motion (a Wiener process). The solution to equation (2.36) is given by 2 S(t) = S(0)exp ( )t + Z(t) 2

To help identify the source of the runtime error, insert the following Sub procedure:

{=OR(TheName=NameList)}

where SSE is the sum of the squares of errors, n is the number of observations, and k is the number of independent variables. In the example model, where SSE is 174,096, the number of observations is 16 (n), and the number of independent variables or k is 2, Se is: r Se r Se SSE n k 1 174,096 16 2 1

When two or more targets are being tracked simultaneously, they may overlap in some of the camera elds of view, resulting in a mutual occlusion; this causes part of a target with a higher camera depth to disappear from the eld of view, and the missing data should be considered properly by the feature sampling and data association procedures. In particular, we consider pixel- and featurelevel occlusions separately. In the pixel-level case, measurement data are given by images: for example, a color segmentation, based on the respective object statistics, such as histograms or GMMs (Section 4.1), so that each pixel can be labeled according to a target identi er (ID), with 0 representing the background (Fig. 3.10). The same labeling is applied when computing the image expected in a simultaneous target hypothesis or ensemble state, and the two images have to be compared to evaluate the joint residual and its likelihood. Therefore, occlusion is dealt with by the multilabel shadow h (Fig. 3.11), which is unique only when depth information is available in the camera model. Instead, when depth is missing we need to generate a multihypotheses expectation, where overlapping pixels in the occluding shadows are classi ed as either of the two targets, with given probabilities that are usually equal, in the absence of prior information. Therefore, multiple residuals epix are produced and are combined when evaluating the likelihood of the ensemble state s : P (z s ) = 1 [ P ( z s , A ) + P ( z s , B )] 2 (3.9)

thread.start_new_thread(func, args,[,kwargs]) thread.exit() thread.get_thread() thread.allocate_lock()