This abstract has been withdrawn

By the characteristics of its origin, appearance of certain elements of valley relief, as well as by its basic shape and geographical position, the Neretva valley is the unique morphological phenomenon in the central Dinarids. Seasonal phytocenological tests have been performed on several sites in Blagaj using Blaun-Blanquet method. During the field visits we have established that there is a large number of various plant species (86) in this area, belonging to different systematic categories, especially to autochthonic therapeutic, edible, aromatic and endemic plant species. On the basis of bioindicator values of the vascular flora in regard to the vegetation and eco-system degradation level (primary P, secundary S, and tertiary T bioindicators), it has been established that the vegetation in Blagaj area is endangered due to numerous problems caused by human activities. Most of the human activities lead towards rapid extinction of rare and ecologically specialized species as well as towards the fragmentation of their habitats. The level of manifestation of such activities brings into question the very survival of these interesting habitats. This paper offers data about the present condition of eco-system in Blagaj area along with proposals for measures to preserve and manage it sustainably.

In network development problems can be encountered considering plans and projects that will provide satisfying QoS for different circumstances, different types of users, and different traffic flow profiles. It is necessary to develop satisfying mathematical model which will provide connections between QoS and network parameters. In this paper we will look into traffic with variable intensity which can effectively be described using Markov Modular Poisson Processes (MMPP). We will offer new analytical model, and using graphs we will show losses function depending on network parameters.

In this paper, a wavelet packet-based method is used for detection of abnormal respiratory sounds. The sound signal is divided into segments, and a feature vector for classification is formed using the results of the search for the best wavelet packet decomposition. The segments are classified as containing crackles, wheezes or normal lung sounds, using Learning Vector Quantization. The method is tested using a small set of real patient data which was also analysed by an expert observer. The preliminary results are promising, although not yet good enough for clinical use.

Respiratory sounds are composed of various events: normal and so-called adventitious sounds. These phenomena present a wide range of characteristics which make difficult their analysis with a single technique. Adapted time-frequency and time-scale techniques allow to fit best, under constraints, the accuracy of analysis of a time segmentation and, by the way, make feasible the study of complex signals. We present here new approaches based only on the wavelet packet decomposition to segment respiratory sounds.

Wheezes are abnormal sounds which are known to be relevant to Chronic Obstructive Pulmonary Diseases (COPD). The analysis of such signals is especially useful in patient monitoring or pharmacology. Respiratory sounds are dependent on the flow and the volume. Furthermore, they can be the result of a complex mixture of events. The analysis of lung sounds can be greatly improved with time-frequency techniques because these methods highlight the evolution of the spectra of events. In this paper, we present the application of the Adaptive Local Trigonometric Decomposition (ALTD) to lung sound analysis. This analysis provides an optimal representation of the signal in the time-frequency domain with a lattice which is adapted in time. In our work, the parameterization of the ALTD is studied for the detection of wheezing phenomena.

The analysis of respiratory sounds highlights the limits of commonly used techniques as a huge variety of sounds can be observed (stationary or nonstationary and of different durations) which can have themselves a great variability. New approaches have been developed in order to associate the acoustic phenomena to the respiratory flow and volume. We present here another approach only based on the wavelet packet decomposition to segment respiratory sounds.

Wavelet packet based methods are used for detection of abnormal respiratory sounds. The associated signal is divided into segments, and a feature vector for classification is formed using the results of the search for the best wavelet packet decomposition. The classification is performed using learning vector quantization.

The authors present an application of the Malvar's wavelet transform to infant's respiratory sounds. They first introduce the acoustical analysis of infantile respiratory sounds. Then, they describe the Adaptive Local Trigonometric Transform they use to operate a segmentation of the signal. The authors also present the results obtained with physiological signals, and they show that their approach allows to highlight symptomatic events called stridors among a set of consecutive events.<<ETX>>

Vector quantization is a powerful way to compress images, rarely used in present compression schemes. We present here the use of this technique on subband images, by means on an entropy contrained quantization algorithm. The codebooks are predetermined on a set of images to speed up transmission. The outline of the method is presented, as well as some experimental results.