Framework for 4D medical data compression

U ovom radu predložen je novi programski okvir za kompresiju četvero-dimenzionalnih (4D) medicinskih podataka. Arhitektura ovog programskog okvira temelji se na različitim procedurama i algoritmima koji detektiraju vremenske i prostorne zalihosti u ulaznim 4D medicinskim podacima. Pokret kroz vrijeme analizira se pomoću vektora pomaka koji predstavljaju ulazne parametre za neuronske mreže koje se koriste za procjenu pokreta. Kombinacijom segmentacije, pronalaženja odgovarajućih blokova i predikcijom vektora pomaka, zajedno s ekspertnim znanjem moguće je optimirati performanse sustava. Frekvencijska svojstva se analiziraju proširenjem wavelet transformacije na tri dimenzije. Za mirne volumetrijske objekte, moguće je konstruirati različite wavelet pakete s različitim filtrima koji omogućavaju širok raspon analiza frekvencijskih zalihosti. Kombinacijom uklanjanja vremenskih i prostornih zalihosti moguće je postići vrlo visoke omjere kompresije.This work presents a novel framework for four-dimensional (4D) medical data compression architecture. This framework is based on different procedures and algorithms that detect time and spatial (frequency) redundancy in recorded 4D medical data. Motion in time is analyzed through the motion fields that produce input parameters for the neural network used for motion estimation. Combination of segmentation, block matching and motion field prediction along with expert knowledge are incorporated to achieve better performance. Frequency analysis is done through an extension of one dimensional wavelet transformation to three dimensions. For still volume objects different wavelet packets with different filter banks can be constructed, providing a wide range of frequency analysis. With combination of removing temporal and spatial redundancies, very high compression ratio is achieved

Waveform Design for Secure SISO Transmissions and Multicasting

Wireless physical-layer security is an emerging field of research aiming at preventing eavesdropping in an open wireless medium. In this paper, we propose a novel waveform design approach to minimize the likelihood that a message transmitted between trusted single-antenna nodes is intercepted by an eavesdropper. In particular, with knowledge first of the eavesdropper's channel state information (CSI), we find the optimum waveform and transmit energy that minimize the signal-to-interference-plus-noise ratio (SINR) at the output of the eavesdropper's maximum-SINR linear filter, while at the same time provide the intended receiver with a required pre-specified SINR at the output of its own max-SINR filter. Next, if prior knowledge of the eavesdropper's CSI is unavailable, we design a waveform that maximizes the amount of energy available for generating disturbance to eavesdroppers, termed artificial noise (AN), while the SINR of the intended receiver is maintained at the pre-specified level. The extensions of the secure waveform design problem to multiple intended receivers are also investigated and semidefinite relaxation (SDR) -an approximation technique based on convex optimization- is utilized to solve the arising NP-hard design problems. Extensive simulation studies confirm our analytical performance predictions and illustrate the benefits of the designed waveforms on securing single-input single-output (SISO) transmissions and multicasting

Determinants of tourism industry in selected European countries: a smooth partial least squares approach

Various events, such as the global economic crisis, have seriously hampered long-term stable tourism processes with a particular relevance to international visits. In this study, we use the smooth paths of partial least squares (PLS; more specifically its PLS-SVD algorithm) and principal component analysis (PCA) dependent on a time parameter to descriptively examine the multivariate connections of tourism and economic growth during the periods close to the crisis. A novel approach regarding the paths of leading singular values and corresponding singular vectors and describing the maximum covariance strength reveals many practical outputs as time lags and mutual connections between sets of data. From the base of Central European countries analysed here, only Switzerland shows a significant tourism lagged situation, where the results provide relative perceptive conditions to non-residents with stable conditions for domestic tourism. Our findings show great evidence of similar behaviour in the Austria, Slovenia and Poland group as well as the Czech Republic and Slovakia group. Also the Czech Republic and Slovakia are potentially very sensitive to non-resident visits. Germany reveals its strong interconnection to the European economy. On the other hand, in the case of Hungary, simultaneous changes in income and consumer prices form ideal conditions for tourism