Video Motion Magnification Using Split Spectrum Processing

Video devinim büyütme, insan gözünün algılayamadığı küçük mertebedeki devinimlerin uygun bir yöntemle büyütülmesi ve bu devinimlerin videoda gözle görülebilir hale getirilmesi işlemidir. Bu algoritmalar, video çerçevelerini hem uzamsal hem de zamansal alanda işleme tabi tutarak minik devinim ve titreşimlerin büyütülüp videoya geri gömülmesi temeline dayanmaktadır. Örneğin, nabız atımının bilekte oluşturduğu devinim, köprü salınımları ve bina titreşimleri gibi algılanması zor olan devinimlerin yanında ses geriçatımı ve optik gibi çeşitli alanlarda da bu yöntemler kullanılmaya başlanmıştır. Bu çalışmada video devinim büyütmede kullanılan Euler yönteminin zamansal işleme katmanında, radar ve sesötesi (ultrasound) gibi sinyal işleme alanlarında uygulanan ve sinyal-gürültü oranını arttıran Bölünmüş İzge İşleme yöntemi kullanılmıştır. Önerilen yöntem ve Euler devinim büyütme yöntemi, yapısal benzerlik indeksi üzerinden karşılaştırılmış ve iyileştirmeler gösterilmiştir.Video motion magnification is the process of enlarging small-scale movements that cannot be detected by the human eye. To achieve this, first, the video frames are processed in both spatially and temporally via magnifying the subtle movements and vibrations and then, these processed frames are embedded back into the video to create visibility. Various applications in different fields are studied, i.e., magnification of pulse motion on the wrist, oscillations of bridges, vibrations of buildings, and sound recovery of trembling surfaces from video. In this study, a well-known signal processing method, namely, split spectrum processing method which is used to increase the signal-to-noise ratio of returning signals in radar and ultrasound, is successfully employed on the temporal processing layer of popular Euler motion amplification technique for video magnification. Our proposed and classical Euler magnification methods are compared in terms of their structural similarity index and improvements are demonstrated

Detection and localization of emitters in the presence of multipath

Yön bulma işlemi kısaca DF (Direction Finding, Yön Bulma) veya AOA (Angle of Arrival, Geliş Açısı) olarak ifade edilmektedir. Antenler, alıcı, sayısal kart ve bilgisayardan oluşan sisteme de DF (Direction Finding, Yön Bulma) sistemi denmektedir. DF sistemi tek olarak sadece vericilerin yönünü tespit edebilmektedir. Eğer amaç vericilerinin konumunun belirlenmesi ise ortamda en az iki DF sistemi bulunması gerekmektedir. Bu çalışmada telsiz vericilerinin sayılarının ve yerlerinin tespiti için yeni bir yöntem önerilmiştir. Telsiz vericilerinin sayısının ve yerinin tespitinde MDL (Minimum Description Length, Minimum Tanımlama Uzunluğu) tabanlı ITC (Information Theoretic Criteria) algoritması ile önce ortamdaki yol sayısının daha sonra MUSIC (Multiple Signal Classification, Çoklu İşaret Sınıflandırma) algoritması ile vericilerin yönlerinin belirlenmesi önemli bir aşamadır. MUSIC spektrumundaki her bir tepe her bir yolun geliş açısına karşılık gelecektir. Fakat çoklu yansımalı ortamlarda bir vericiden birden fazla yoldan işaret yayılacağından DF için ek çalışmalar gerekecektir. MVBF (Minimum Variance Beamforming, Minimum Varyans Huzme Şekillendirme) huzme şekillendirme algoritması kullanılarak her yoldan gelen işaretlerin zaman ekseni verileri elde edilir ve aralarındaki korelasyon katsayıları hesaplanır. Bu korelasyon katsayıları kullanılarak işaretlerin aynı kaynaktan yayılıp yayılmadıklarına karar verilir. Böylece verici sayısı ve onların yönleri tespit edilmektedir. Performans analizleri gerçek zamanlı deneylerle elde edilerek sunulmaktadır. Önerilen tekniğin etkinlik testi, tam yansımasız oda içerisinde iki kaynaklı denemelerle gerçekleştirilmiştir. Dış ortamda da başarılı denemeler yapılmıştır.  Anahtar Kelimeler: DF, dizilim anten, huzme şekillendirme, yön tespit.Direction Finding (DF), or in other words, estimation of Angle of Arrival (AOA), has been an active research area since the beginning of the 20th century. DF has several application areas such as, military applications, GSM with improved channel capacity, localization of illegal transmitters such as TV and radio stations, or, of a mountaineer losing his/her path, in addition to spectrum monitoring.When multiple sources are incident on an antenna array simultaneously, classical DF methods, such as interferometry and Doppler cannot resolve these sources. An effective solution to the problem is to use high-resolution subspace-based algorithms. Multiple Signal Identification and Classification (MUSIC) is the most powerful and widely used subspace-based method. MUSIC is known to successfully resolve two or more sources if they are incoherent. However, in practice, signals often propagate in multipath environments. Thus, a source signal is incident on the array from many paths, which implies that coherent signals at different angles will be received by an array.For source localization with MUSIC in a multipath environment, it is critical to initially detect the number of incident paths. The simplest such algorithms are based on counting the smallest eigenvalues of the array covariance matrix. Information Theoretic Criteria (ITC) is advanced algorithm for path enumeration widely used, such as the Akaike Informatin Criteria (AIC) and Minimum Description Length (MDL). Just as in MUSIC, these ITC algorithms are also affected in a multipath environment. This is due to the fact that signal coherence in a multipath environment alters the eigenstructure of the sensor array's correlation matrix, which in turn affects the performance of both MUSIC and ITC. MDL was proposed for source enumeration in the presence of multipath and signal coherence. A recent solution proposed for the problem of signal coherence is the Spatial Smoothing (SS) preprocessing algorithm. SS essentially removes the effect of coherency between incoming signals in the eigen-structure of the correlation matrix. Therefore, source localization process involves the following steps: First, the array antenna output is processed to form a sample covariance matrix, then SS is applied to this matrix, after which the number of signals are estimated and finally, based on these, MUSIC is used to generate an angle spectrum.It is important to note that in a multipath environ-ment, ITC with spatial smoothing merely detects the number of paths, i.e., the signals originating from sources directly or incident from the reflectors, and then the MUSIC algorithm localizes all these signals in the angle/AOA pseudospectrum. In other words, ITC and/or the MUSIC-AOA pseudospectrum, indicate the number of paths and not the number of independent sources present in a multipath environment. This is a significant problem if the objective is to localize actual independent sources in a multipath environment.In this study, the minimum description length information-theoretic algorithm is used for the joint detection and localization of multiple RF transmitters in a multipath environment is to enumerate the number of paths and then to measure the angle of arrival of each path using an antenna array with a high resolution direction finding algorithm such as MUSIC. Those possible propagation paths are the angles corresponding to the peaks of the MUSIC pseudospectrum. Since more than one path may correspond to a single emitter source, further processing is required. The time domain signals of these paths are then extracted with minimum variance beamforming in order to estimate their corre-lation coefficients with each other. These correlation coefficients are used to decide whether or not these paths correspond to the same emitter. Hence, the number of emitters and their angle of arrivals are jointly estimated. Performance analysis of the method is presented via real-time laboratory experimentation and discussed in this paper. To demonstrate the effectiveness of the proposed technique, experiments with two sources are conducted in an anechoic test chamber.  Keywords: DF, direction finding, array antenna, beamforming, MVBF

Estimation of the spectral exponent of 1/f process corrupted by white noise

Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200

HIGHER ORDER STATISTICS AND SPECTRA ANALYSIS OF SLEEP SPINDLES

In this paper we analyze sleep spindles, observed in EEG recorded from humans during sleep, using both time and frequency domain methods which depend on higher order statistics and spectra. The time domain method combines the use of second and third order correlations to reveal information on the stationarity of periodic spindle rhythms, detecting transitions between multiple activities. The frequency domain method, based on the normalized bispectrum, describes the frequency interactions associated with the second order nonlinearities occuring in the observed EEG. Results for real data are presented. 1