In this work a new protocol for Voice over IP (VoIP) transmissions in wireless ad-hoc networks is proposed. Distributed architecture is necessary when dealing with dynamic environments, such as ports or battlefields, where creating infrastructures becomes expensive or impossible. Mobile ad-hoc networks (MANETs) are based on a peer-to-peer approach and each node participates in the organization of the whole network. VoIP over MANETs is a challenging issue due to the intrinsic distributed nature of the existing peer-to-peer paradigm. This paper proposes a new protocol, capable of ensuring a quality of service (QoS) level for VoIP calls over a MANET and to manage a large number of calls in the system. Novel metric and utility functions are proposed to perform the best path selection from source to destination nodes, respecting the QoS parameters for VoIP quality. In particular, an objective metric such as R-factor is considered, and a flexibility index is defined in order to maximize the number of acceptable VoIP calls. Performance evaluation shows that the proposed approach led to better network management in terms of admitted calls and respected QoS constraints.

This paper proposes a new method for removing noise and blurring from 3D microscopy images. The main contribution is the definition of a space-variant generating model of a 3-D signal, which is capable to stochastically describe a wide class of 3-D images. Unlike other approaches, the space-variant structure allows the model to consider the information on edge locations, if available. A suitable description of the image acquisition process, including blurring and noise, is then associated to the model. A state-space realization is finally derived, which is amenable to the application of standard Kalman filter as an image restoration algorithm. The so obtained method is able to remove, at each spatial step, both blur and noise, via a linear minimum variance recursive one-shot procedure, which does not require the simultaneous processing of the whole image. Numerical results on synthetic and real microscopy images confirm the merit of the approach.