Abstract
The development of communications systems demands testing. This paper presents a framework for testing on-the-fly, which relies on the definition of 3 types of tests and on their sequential execution. The ioco conformance relation was considered in order to assign verdicts. A tool prototype is also presented that supports the proposed framework. This tool, named PROFYT, was developed based on the SPIN verifier, and uses communicating FSMs to describe the specification. The test of Conference Protocol implementations was carried out on-the-fly with PROFYT and enabled us to conclude about the benefits of the test methodology proposed.

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A new CMOS VLSI implementation of an asymmetric programmable sigmoid neural activation function, as well as of its derivative, is presented. It consists of two coupled PMOS and NMOS differential pairs with different programmable bias currents that set the upper and lower limits of the sigmoid. The circuit works in the weak inversion region, for low power consumption and exponential envelope, or in strong inversion to achieve higher speeds. The results obtained from the theoretical transfer function, and from the simulations of the circuit implemented in AMI's 0.35 mu m technology, show a very good match.

Abstract
Many real life problems require the classification of items in naturally ordered classes. These problems are traditionally handled by conventional methods for nominal classes, ignoring the order. This paper introduces a new training model for feedforward neural networks, for multiclass classification problems, where the classes are ordered. The proposed model has just one output unit which takes values in the interval [0,1]; this interval is then subdivided into K subintervals (one for each class), according to a specific probabilistic model. A comparison is made with conventional approaches, as well as with other architectures specific for ordinal data proposed in the literature. The new model compares favourably with the other methods under study, in the synthetic dataset used for evaluation.