This paper presents and solves the problems of modeling and designing the necessary capacity and the adequate capacity of accepting input connections serving the calls into the system. The main aim of the research work is finding the optimum number of input server connections while minimizing the number of rejected requests according to a specific maximum number of expected calls in a specific time-interval, i.e. at peak-hour. With the results obtained we wish to model and optimize the planning and the dimensioning of the processing server as well as reduce the costs of this, since hiring an input line actually presents quite a substantial cost. Therefore it is necessary to first determine how many input connections are needed to serve a certain quota of users at a specific moment by using the methods of statistical modeling. On the basis of obtained results we can then assess whether a certain segment has too many or not enough input connections. The objective of the presented multiple-input and multiple-output (MIMO) simulator is to raise the level and the quality of service and at the same time lower the costs of hiring input connections. This paper presents the key segments composing the call and server system (ordinary, lamer and dummy caller model, statistical Gaussian curve of calls distribution, mechanisms of accepting and rejecting calls, management of input connections capacity, random call triggering, etc.). The above-mentioned segments represent the models and the sub-models of the simulator. They have been derived using the methods of statistical modeling. The optimum solution can be found manually or automatically using the method of automation of simulation runs and incrementing/decrementing the parameter of the number of input connections into the system. Searching the optimum number of input connections manually is an entirely empirical method, where the user manually changes the mentioned parameter, and is looking for a scenario in which the result of the simulator regarding the number of rejected calls is minimal. With an automatic search the simulator automatically generates the number of runs with incrementing and decrementing the mentioned parameter in each, and thus automatically finds the optimum solution. This paper also presents an automatic analysis of simulation runs and a statistical final report, which includes a conclusion on the results obtained in different scenarios.

Fingerprint image enhancement is a key step in the Automated Fingerprint Identification System (AFIS). Because of different factors that affect the image, such as skin condition (very dry or moist, damaged or worn down skin, etc.), sensor noise, irregular print on the sensor, etc., the fingerprint image needs to be enhanced so that the structures of ridges and valleys are clearly visible. This paper presents fingerprint image enhancement with oriented linear anisotropic diffusion in the first stage and oriented local ridge compensation in the second stage. To control the process of oriented diffusion we have determined an orientation field from the previously established ridge orientation, which was additionally enhanced. Because the overall image contrast is decreased after the diffusion process, we have enhanced the contrast with block local normalization. In the second stage we have additionally enhanced ridge structure with oriented local ridge compensation. We have compared and combined our proposed algorithm with some of the state-of-the-art algorithms. The results of experiments, done on a public database FVC2004, show efficient fingerprint image enhancement.

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