Employing the autoregressive (AR) technique and the principle of displaced phase centre antenna (DPCA) we construct an optimum adaptive DPCA processor for moving target detection from airborne phased arrary radar data collected under nonDPCA conditions. It is fundamentally different from the existing adaptive DPCA which is not optimum. The number of range samples it needs to estimate its parame...

BACKGROUND The tumor proliferative index marker Ki-67 was shown to be associated with clinically significant outcomes in prostate cancer, but its clinical application has limitations due to lack of uniformity and consistency in quantification. Our objective was to compare the measurements obtained with digital image analysis (DIA) versus virtual microscopy (visual scoring (VS)). METHODS To do...

Subspace method identification (SMI) and model reduction for Multivariate Statistical Process Control has been proposed as an improvement to dynamic principal component analysis (DPCA). The linear parametric model structure captures both static and dynamic information from the system. In this paper, an analysis of the dimension reduction capabilities of the subspace approach is provided. It is ...

We develop the Double Principal Component Analysis (DPCA) based on a dual factor structure for high-frequency intraday returns contaminated with microstructure noise. The allows noise in addition to efficient log-prices. construct estimators of factors both log-prices and as well their common components, provide uniform consistency these when number assets sampling frequency go infinity. In Mon...

Dynamic principal component analysis (DPCA) has been widely used in the monitoring of dynamic multivariate processes. In traditional DPCA, the dynamic relationship between process variables are implicit and hard to interpret. To extract explicit latent factors that are dynamically correlated, a new dynamic latent variable model is proposed. The new structure can improve modeling of dynamic data...

Continuous-time Hopfield network has been an important focus of research area since 1980s whose applications vary from image restoration to combinatorial optimization from control engineering to associative memory systems. On the other hand, in wireless communications systems literature, power control has been intensively studied as an essential mechanism for increasing the system performance. ...

Multivariate analysis has become increasingly common in the analysis of multidimensional spectral data. We previously showed that the multivariate analysis technique principal component analysis (PCA) is an excellent method for interpreting the static time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of adsorbed protein films. PCA is an unsupervised pattern recognition technique...

This paper presents signal processing techniques particularly suited for interferometric Synthetic Aperture Sonar (SAS) systems onboard Autonomous Underwater Vehicles (AUV) (or other platforms carrying high grade navigation systems). The signal processing is applied to data collected in a controlled rail experiment at Elba Island, Italy, using a wideband interferometric SAS and an Inertial Navi...

This paper describes a self-motion compensation technique for a near-range synthetic aperture sonar. We are developing a SBSAS (sub-bottom synthetic aperture sonar), intended for chemical ammunition exploration. Since chemical ammunition is about 20 cm diameter×70 cm length in size, and buried in the sedimentary layers, we chose a low frequency (15 kHz) and applied a synthetic aperture techniqu...

The ability to detect and image moving targets, even surrounded by strong clutter on the ground, makes Synthetic Aperture Radar (SAR) more and more important for Ground Moving Target Indication (GMTI). Moving target detection is most commonly performed by GMTI radars based on antenna array solutions without imaging capability while SAR systems do not facilitate detecting the presence of moving ...