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manip() exits with this unhandled exception, stack unwinding destroys any object of class type local to the function manip() and created before the function mathFunc() is called The local object localPtr is destroyed before stack unwinding proceeds further up in the chain of function calls, releasing the free store memory referred to by localPtr and preventing a memory leak This is why we say that the C++ exception handling process respects the programming technique known as quot;resource acquisition is initialization; resource release is destruction" If a resource is implemented as a class, and the actions to acquire the resource are encapsulated in the class constructor, and the actions to release the resource are encapsulated in the class destructor, as in our PTR class, an object of such a class type that is local to a function is destroyed automatically if the function exits with an unhandled exception Any actions that must take place to release acquired resources therefore are not skipped by stack unwinding when these actions are encapsulated in a class destructor called for local objects You may remember the auto_ptr facility introduced in Section 84 and defined in the C++ standard library This facility behaves very much like our PTR class It is a facility that encapsulates the acquisition of the free store memory within its constructors and the release of this memory within its destructor Using auto_ptr when allocating a single object on the free store guarantees that the free store memory is properly released when a compound statement or a function exits with an unhandled exception during stack unwinding Exception Specifications Using an exception specification, a function declaration may specify the set of exceptions the function may throw directly or indirectly An exception specification is a guarantee that the function will not throw any exception not listed in the exception specification Exception specifications are first introduced in Section 114 There are a few things that we need to mention regarding exception specifications and class types First, exception specifications can be specified on class member functions just as they can for nonmember functions As with nonmember functions, an exception specification on a member function declaration follows the function parameter list For example, the class bad_alloc from the C++ standard library is defined such that its member functions have an empty exception specification throw() This indicates that its member functions are guaranteed not to throw any exception:. qr code excel generator QR Code Excel Barcode Add-In - Create 2D QR Code Images in MS ...
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For some purpose, users want to make a set of PDF .Related: Barcode Printing Word SDK, SSRS Barcode Generation Library, Barcode Generating .NET SDK Additionally it would be possible for an attacker (either external or internal) to modify the measured distance by jamming the mutual communication of the nodes and then replaying these signals with higher or lower signal strengths The adversary can attempt to change the underlying propagation physics, say by introducing an absorbing barrier between the transmitter and the receiver This would result in signal attenuation, leading to signi cantly lower received signal strength (RSS) As a result the receiver might conclude that it is further from the transmitter than it actually is Ambient noise introduced by the adversary as well as changes in the environment would also affect the signal strength measurements Given the several easy ways in which an adversary can attack a SS based locationing system, the above approaches have not been used so far in the design of secure locationing systems for ad hoc networks Another novel localization approach that leverages signal strength measurement has been recently proposed [148] This is called the radio interferometric positioning system (RIPS) RIPS proposes to use two transmitters which are transmitting at frequencies that are nearly equal The interference caused by these two signals results in a composite signal that has a low frequency and whose envelope signal can be measured using the RSSI indicator on low precision RF chips RIPS proposes to then use two receivers to calculate the relative phase offset of the composite signal at the two receivers This relative phase offset between the two receivers depends only on the four distances between the two transmitters and two receivers By using relative phase offsets calculated at different frequencies, RIPS provides a method to calculate the relative positions of the transmitters and the receivers The security aspects of RIPS need to be investigated, however 72114 Other Techniques Other techniques can also be designed such as the angle-of-arrival (AoA) techniques or the time-difference-of-arrival (TDoA) techniques AoA systems estimate the location based on measurements of the angle of arrival This requires hardware that is capable of measuring the angle at which a signal arrives APS [149] is a system where the AoA technique is used to measure relative angle between two nodes, which is then used to calculate the distance between them using triangulation The TDoA technique consists of determining the times taken by the same signal to reach different points This is typically made use of in cellular networks where the signal from the mobile device is received at three different stationary base stations, each of which notes the time at which they observe the signal The time difference in arrival of the signal at the three synchronized base stations is then used to estimate the location of the mobile device In TDoA systems an internal attacker can send the signals at different times to the three base stations while fooling these base stations into assuming that they are the same signal thereby resulting in false location estimation 7212 Range-Independent Localization Techniques The other category of ranging schemes is the range-independent technique, which is a ess accurate but more costeffective alternative to the range-dependent approach The characteristic of range-independent localization schemes is that, when using these schemes the system nodes can determine their location without measuring any property related to physical distance such as time, angle, or power Since the nodes do not have to perform any measurements there is typically no need for any special hardware16 We next look at some schemes that belong to this family. Early results of the application of the PSO to type I environments (refer to Section A9) with small spatial severity showed that the PSO has an implicit ability to track changing optima [107, 228, 385] Each particle progressively converges on a point on the line that connects its personal best position with the global best position [863, 870] The trajectory of a particle can be described by a sinusoidal wave with diminishing amplitude around the global best position [651, 652] If there is a small change in the location of an optimum, it is likely that one of these oscillating particles will discover the new, nearby optimum, and will pull the other particles to swarm around the new optimum However, if the spatial severity is large, causing the optimum to be displaced outside the radius of the contracting swarm, the PSO will fail to locate the new optimum due to loss of diversity In such cases mechanisms need to be employed to increase the swarm diversity Consider spatial changes where the value of the optimum remains the same after the change, ie f (x (t)) = f (x (t + 1)), with x (t) = x (t + 1) Since the tness remains the same, the global best position does not change, and remains at the old optimum Similarly, if f (x (t)) > f (x (t+1)), assuming minimization, the global best position will also not change Consequently, the PSO will fail to track such a changing minimum This problem can be solved by re-evaluating the tness of particles at time t + 1 and updating global best and personal best positions However, keep in mind that the same problem as discussed above may still occur if the optimum is displaced outside the radius of the swarm One of the goals of optimization algorithms for dynamic environments is to locate the optimum and then to track it The self-adaptation ability of the PSO to track optima (as discussed above) assumes that the PSO did not converge to an equilibrium state in its rst goal to locate the optimum When the swarm reaches an equilibrium (ie converged to a solution), vi = 0 The particles have no momentum and the contributions from the cognitive and social components are zero The particles will remain in this stable state even if the optimum does change In the case of dynamic environments, it is possible that the swarm reaches an equilibrium if the temporal severity is low (in other words, the time between consecutive changes is large) It is therefore important to read the literature on PSO for dynamic environments with this aspect always kept in mind The next aspects to consider are the in uence of particle memory, velocity clamping and the inertia weight The question to answer is: to what extent do these parameters and characteristics of the PSO limit or promote tracking of changing optima These aspects are addressed next: Each particle has a memory of its best position found thus far, and velocity is adjusted to also move towards this position Similarly, each particle retains information about the global best (or local best) position When the nvironment changes this information becomes stale If, after an environment change, particles are still allowed to make use of this, now stale, information, they are. 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