1,720,988 research outputs found
Lezioni di Teoria dei Segnali (II modulo)
No full textdisponibile all'indirizzo WEB http://infocom/ing.uniroma1.it/gscaran
Lezioni di Teoria dei Segnali (I modulo)
No full textdisponibile all'indirizzo WEB http://infocom/ing.uniroma1.it/gscaran
“l’adornò fuori di terretta con istorie di sua mano molto belle”. Sulla decorazione esterna di villa Chigi alla Lungara
No full textSulla base di nuovi accertamenti sul progetto e l’esecuzione della villa Chigi alla Lungara, una serie di disegni del primo Cinquecento finora ritenuti copia delle fronti esterne dell’edificio, si rivelano essere disegni di progetto che allineano l’aspetto esterno della villa alla monumentalità all’antica dei grandi progetti raffaelleschi nella Roma di Leone X Medici
Three-dimensional-DCT pipe coding
No full textThe paper reports on a video sequence coding method taking advantage of the generic video- communication layout: some moving objects on a still background. The algorithm operates on groups of frames in which the whole digital video sequence is divided, that implies the synchronization requirements' satisfaction and an acceptable level of compatibility with standard video coding (H.261, MPEG, etc.). An analysis of the spatial-temporal continuum, represented by each group of frames, is performed, in order to detect a tridimensional segmentation that identifies the moving objects by means of spatial regions. These regions can spread, as a sort of `pipes,' through the whole group of frames in the temporal direction. Various pipes' construction and coding strategies, including techniques based on object recognition and coding, are allowed. In this work a pipes' identification method based on fixed size moving blocks and their coding by means of a 3D-DCT transform is reported. The above method allows adjacent starting pipes to part themselves, leaving uncoded stripes at their boundaries. The proposed method does not imply the stripes coding, while it minimizes their number and the amount of the artifacts generated by their presentation. As a final topic, the paper reports some considerations on the coding efficiency related to the quality of the reconstructed sequences and on the compatibility characteristics
Glancing at extended reality: an empirical model of 3D animated XR data traffic
No full textSeveral elements in the design of next generation networks, such as user profiling and network slicing, are based on precise models of the traffic load. In this context, recent research has investigated various video traffic classes, while traffic related to extended reality (XR) services remains unexplored. In this paper, we propose an original empirical model of 3D animated XR data, derived by encoding real point clouds with a standard compliant codec. Our proposed approach spans different temporal scales, from minutes to milliseconds aiming to measure different phenomena. Indeed, we firstly analyze the packet size distribution at the lower time scale, and we identify that it is well approximated by heavy tailed Gamma distribution. Then, we demonstrate how this finding can be integrated to model phenomena at the application layer time scale. Specifically, we show how a general semi-hidden Markov model can be used to capture the dynamics of the service session over time as well as the users behaviours. We demonstrate the use of the model by different examples. Taken together, our model results able to capture fine and coarse grained behaviour in 3D XR traffic
Estimation of Number of Sources Impinging on a Uniformly Spaced Linear Array of Sensors
No full textA novel estimation method of number of sources emitting waves on an uniformly spaced linear array of sensors is described. Similarly to well-known existing techniques, it is inspired by certain properties hold by the eigenvalues of the correlation matrix of the received signal. Numerical results show the feasibility of the here presented novel estimator in comparison with popular, Information Theoretic Criteria based estimators
Detection of change by L1-norm principal-component analysis
No full textWe consider the problem of detecting a change in an arbitrary vector process by examining the evolution of calculated data subspaces. In our developments, both the data subspaces and the change identification criterion are novel and founded in the theory of L1-norm principal-component analysis (PCA). The outcome is highly accurate, rapid detection of change in streaming data that vastly outperforms conventional eigenvector subspace methods (L2-norm PCA). In this paper, illustrations are offered in the context of artificial data and real electroencephalography (EEG) and electromyography (EMG) data sequences
Cross-Burg algorithm for single-input two-outputs autoregressive modeling
No full textThis work addresses the Auto-Regressive modeling in Single-Input Two-Outputs (SITO) scenarios, where the lack of input signal diversity prevents application of state-of-the-art multichannel methods. Firstly, we derive a system of Yule-Walker-like equations involving only the cross-correlation of the observations. Then, we leverage the Toeplitz, not Hermitian, structure of the system coefficient matrix to derive an Asymmetric Levinson recursion. Finally, we present a novel lattice based computation of the recursion, named Cross-Burg algorithm. The Cross-Burg lattice is built by two sub-lattices, mutually connected by the reflection coefficients. The Cross-Burg algorithm is inherently robust to uncorrelated additive noise on the two observed channels. Numerical simulations show that the Cross-Burg algorithm outperforms traditional methods in accuracy and noise robustness for SITO-AR modeling and spectral estimation
Cloud-assisted individual l1-PCA face recognition using wavelet-domain compressed images
No full textFace recognition has been an active research field for a long time, and recently new challenges have arisen in designing cloud-assisted face recognition algorithms. In a cloud assisted face recognition system, mobile devices acquire the data images; then, in order to unbind the cloud face recognition algorithm from the particular features extracted at the mobile device, the images are encoded and uploladed into the cloud. In this framework, it is important to understand and control the effect of the image compression stage performed at the mobile device on the performances of the face recognition algorithms realized within the cloud. Here, we analyze the impact of wavelet domain image compression on the Individual Adaptive (IA) L1-PCA subspace computation and assess the performance of a classifier operating on data characterized by increasing compactness and accordingly decreasing accuracy
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