Spatial Filtering

The possibility of developing a photochemical setup on the basis of an optically pumped ammonia laser with an intracavity photoreactor is proved. The obtained values of the cavity 'implication' factor γ are comparable with those of intracavity systems based on a CO2 laser. The conditions for achieving the maximum energy in the focusing cavity are determined and the ways to control the shape of its caustic are indicated.

This paper examines the per capita income convergence process among 23 Argentinian provinces over the period 1983-2002. The purpose of this work is to apply new estimation methods following two-step procedure as in Badinger et al. (2004). We combine a spatial filtering of variables to remove the spatial correlation (Getis and Griffith, 2002) and suitable estimators for dynamic panels (using first-differenced and system GMM estimators). Our estimations on filtered variables reveal a conditional convergence process between Argentinian provinces and a positive and significant impact of agglomeration variables on growth rate. Hence, our results show that ignoring spatial distortions due to geographic proximity misleads estimations and underestimates the speed of convergence specifically for provinces which are distant from Buenos Aires. Moreover, we improve estimations of agglomeration effects when spatial autocorrelation is removed.

Laser beam when expanded shows non-uniform illumination due to the presence of dust particles on mirrors and other optical elements. Due to coherence  interference  among the scattered beam is inevitable, leading to 'pits and valleys' some thing like moon's structure!  So cleaning is needed as a prelude to the experiments to follow. This cleaning itself is an elaborate procedure which is discussed and shown. The shear interferometer patterns are shown with out any theory.

The technique of interpolated arrays is applied to ESPRIT-type direction finding methods. The resulting method uses sensor arrays with an arbitrary configuration, thus eliminating the basic restrictive requirement of ESPRIT for two (or more) identical arrays. This approach allows for resolving D+1, while the original ESPRIT method requires at least 2D sensors. Moreover, it is shown that while ESPRIT performs poorly for signals propagating in parallel (or close to parallel) with the array displacement vector, the advocated technique does not exhibit such weakness. Finally, using two subarrays, ESPRIT cannot resolve azimuth and elevation even when the sensors are not collinear. However, the interpolated ESPRIT procedure resolves azimuth and elevation using only a single array. The performance of the original ESPRIT when the sensor locations are perturbed is also discussed and illustrated numerically

In this study, we propose an implementation of the cross-pattern coherence (CroPaC) spatial- filtering technique. This parametric algorithm utilizes the normalized cross-spectral density be- tween signals from microphones of different orders as a criterion for focusing in specific direc- tions and is implemented by using directional microphones placed in the same look direction and have the same magnitude and phase response. A spatial-filter is calculated from a multichan- nel input and is used to assign attenuation values to a coincidentally captured audio signal. The proposed algorithm is simple to implement and offers the capability of coping with interfering sources at different azimuthal locations with or without the presence of diffuse sound. In con- trary to previous techniques, which rely on the performance of the beamformer for source focusing, CroPaC provides additional beamforming capabilities with increased performance at low frequen- cies. Significant improvement is demonstrated over traditional beamforming techniques in terms of noise reduction and focusing capabilities. Sound examples of the algorithm can be fond online in http://www.acoustics.hut.fi/projects/cropac/soundExamples/

The Common Spatial Patterns (CSP) algorithm finds spatial filters that are useful in discriminating different classes of electroencephalogram (EEG) signals such as those corresponding to different types of motor activities. This algorithm is however, sensitive to outliers because it involves the estimation of covariance matrices. Classical sample covariance estimates are easily affected even if a single outlier exists. To improve the CSP algorithm's robustness against outliers, this paper first investigates how multivariate outliers affect the performance of the CSP algorithm. We then propose a modified version of the algorithm whereby the classical covariance estimates are replaced by the robust covariance estimates obtained using Minimum Covariance Determinant (MCD) estimator. Median Absolute Deviation (MAD) is also used to robustly estimate the variance of the projected EEG signals. The results show that the proposed algorithm is able to reduce the influence of the outliers. ...

In motor imagery-based brain computer interfaces (BCI), discriminative patterns can be extracted from the electroencephalogram (EEG) using the common spatial pattern (CSP) algorithm. However, the performance of this spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are commonly used with the CSP algorithm. To address this problem, this paper proposes a novel filter bank common spatial pattern (FBCSP) to perform autonomous selection of key temporal-spatial discriminative EEG characteristics. After the EEG measurements have been bandpass-filtered into multiple frequency bands, CSP features are extracted from each of these bands. A feature selection algorithm is then used to automatically select discriminative pairs of frequency bands and corresponding CSP features. A classification algorithm is subsequently used to classify the CSP features. A study is conducted to assess the performance of a selection of feature selection and classification algorithms for use with the FBCSP. Extensive experimental results are presented on a publicly available dataset as well as data collected from healthy subjects and unilaterally paralyzed stroke patients. The results show that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher cross-validation accuracies compared to prevailing approaches.

This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly (p < 0.01) improved for most of the subjects (ACC ≥ 74.79%), when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry.

Raytheon's Infrared Operations (RIO) has invented and developed a new type of focal plane array with 'Image Processing on-the-chip' named the Adaptive IR Sensor (AIRS). The AIRS FPA is based upon the human retina in that it performs signal processing near the photoreceptors. The AIRS FPA has been reduced to practice and adaptively removes detector and optic temperature drift and 1/f induced fixed pattern noise. This 3rd-generation multi-mode IRFPA, also called a Smart FPA, is a 256 X 256-array format capable of operation in four modes: (1) Direct Injection (DI), (2) Adaptive Non-uniformity Correction (NUC), (3) Motion/Edge Detection, and (4) Subframe Averaging. Nova Research has developed a Modular IR Application Development System (MIRIADS) which is a compact single board camera system that is highly integrated with the dewar assembly. The MIRIADS system coupled with the AIRS Smart FPA results in a very high performance wide field of view IR Sensor and processing system with integrated in one of the smallest packages to date.