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So now that you understand how XMLHttpRequest works, you re led to some further questions: What is being sent and received using the XMLHttpRequest functions, and what are the URLs Once you know the answers to these questions, you can write your own code to spoof these requests, and can then interface directly with the Gmail system. The rest of the book relies on this idea. To find out what Gmail is saying to the browser, use a new tool: the packet sniffer. This is a generic term for a range of applications that can listen to raw network traffic, display it on the screen, log it, analyze it, and so on. What you re interested in is watching what your browser is doing in the background: what it is sending, where it is sending it to, and then the replies it is getting. My packet sniffer of choice for this job is Jeremy Elson s Tcpflow, available at www.circlemud.org/~jelson/software/tcpflow/. I use Marc Liyanage s OS X package, which you can download from www.entropy.ch/software/macosx/#tcpflow. Tcpflow is available under the GPL, and can be compiled on most proper computing platforms. Windows users will need to look elsewhere, but the following techniques remain the same.



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plant is in fact exploiting a preexisting bias in the sensory system of the insect the fact that male insects nd female insects attractive. In terms of signal design this process is termed sensory exploitation, the theory that signals evolve to exploit pre-existing sensory biases on the part of the receiver. In a similar way the courtship display of the water mite Neumania papillator is thought to have evolved to exploit the fact that female mites are especially sensitive to the water-borne vibrations emanating from their copepod prey. During the display male mites wave their trembling forelegs as they approach females, replicating the vibrations of the copepod.





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with a WLAN, AFH may introduce more dynamic frequency interferences to neighboring Bluetooth piconets. Performance results in [22] show that by combining LBT and AFH together, the Bluetooth throughput is higher than if using these methods separately, when there are fewer than 70 piconets coexisting with a WLAN. By using power control and LBT, unnecessary power usage is avoided. LBT is used to deal with dynamic interference. AFH is used to deal with static interference. The hybrid method is implemented by adding a few extensions to the MAC layer, and it is compatible with the current Bluetooth speci cation. To summarize this section, we note that most noncollaborative methods are applied in Bluetooth to avoid collision with WLAN because Bluetooth is more vulnerable to CCI if Bluetooth and WLAN coexist. AFH [15] and interferencesource-oriented AFH (ISOAFH) [25] are effective in dealing with WLAN interference but not applicable for multiple colocated Bluetooth piconets. The performance of AFH is also dependent on the update rate of the frequency classi cation to track the channel dynamics [15]. Approaches based on scheduling such as BIAS [15, 17], OLA [18], and master delay MAC scheduling (MDMS) [25] cause delay in the transmission, hence they may not be bandwidth ef cient. Power control methods [19, 20] depend on the accuracy of channel sensing and cannot provide much improvement if the Bluetooth device is very close to the interfering device. Carrier-sensing-based schemes inevitably suffer from the hidden terminal problem [21, 26]. A hybrid method of power control, LBT and AFH proposed in [22] can achieve better performance with added complexity.

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meter, one could also imagine determining whether the mobile lies on the oor or is really handheld, for evaluating if the user is standing up or lying down. A variation in air pressure can be given by the relation DP rgZ, where r is air density, g the acceleration due to gravity, and Z the altitude. Calibration at the bottom oor of the building should allow the calculation of the air density ( r P/rT ) under the speci c current conditions. Some GPS receivers are currently equipped with micro-barometers and allow oor-level determination without ambiguity.

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Adolescent Anger Rating Scale Adolescent Drinking Index Career Assessment Inventory Childhood Autism Rating Scale Children s Depression Inventory Checklist for Child Abuse Evaluation Coping Response Inventory Dementia Rating Scale-2 Eating Disorder Inventory-2 Emotional Problems Scale Goldfarb Fear of Fat Scale Guilford-Zimmerman Temperament Survey Hamilton Depression inventory Hare Psychopathy Checklist-Revised Health Status Questionnaire Occupational Stress Inventory-Revised Parenting Stress Index Personality Assessment Inventory Quality of Life Inventory Santa Clara Strength of Religious Faith Questionnaire State-Trait Anxiety Inventory State-Trait Anger Expression Inventory-2 Suicidal Ideation Questionnaire Trauma Symptom Inventory West Haven-Yale Multidimensional Pain Inventory

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