Current Issue : January - March Volume : 2016 Issue Number : 1 Articles : 5 Articles
Four Wave Mixing (FWM) based optical signal-processing techniques are\nreviewed. The use of FWM in arithmetical operation like subtraction, wavelength\nconversion and pattern recognition are three key parts discussed in this paper after a brief\nintroduction on FWM and its comparison with other nonlinear mixings. Two different\napproaches to achieve correlation are discussed, as well as a novel technique to realize all\noptical subtraction of two optical signals....
Sensors generate large amounts of spatiotemporal data that\nhave to be stored and analyzed. However, spatiotemporal\ndata still lack the equivalent of a DBMS that would allow\ntheir declarative analysis. We argue that the reason for\nthis is that DBMSs have been built with the assumption\nthat the stored data are the ground truth. This is not the\ncase with sensor measurements, which are merely incomplete\nand inaccurate samples of the ground truth. Based on\nthis observation, we present Plato; an extensible DBMS for\nspatiotemporal sensor data that leverages signal processing\nalgorithms to infer from the measurements the underlying\nground truth in the form of statistical models. These models\nare then used to answer queries over the data. By operating\non the model instead of the raw data, Plato achieves significant\ndata compression and corresponding query processing\nspeedup. Moreover, by employing models that separate the\nsignal from the noise, Plato produces query results of higher\nquality than even the original measurements....
In digital signal processing (DSP), Nyquist-rate sampling completely describes a signal by exploiting\nits bandlimitedness. Compressed Sensing (CS), also known as compressive sampling, is a DSP\ntechnique efficiently acquiring and reconstructing a signal completely from reduced number of\nmeasurements, by exploiting its compressibility. The measurements are not point samples but\nmore general linear functions of the signal. CS can capture and represent sparse signals at a rate\nsignificantly lower than ordinarily used in the Shannon�s sampling theorem. It is interesting to notice\nthat most signals in reality are sparse; especially when they are represented in some domain\n(such as the wavelet domain) where many coefficients are close to or equal to zero. A signal is\ncalled K-sparse, if it can be exactly represented by a basis, 1 N\ni i , and a set of coefficients k x ,\nwhere only K coefficients are nonzero. A signal is called approximately K-sparse, if it can be represented\nup to a certain accuracy using K non-zero coefficients. As an example, a K-sparse signal is\nthe class of signals that are the sum of K sinusoids chosen from the N harmonics of the observed\ntime interval. Taking the DFT of any such signal would render only K non-zero values k x . An example\nof approximately sparse signals is when the coefficients k x , sorted by magnitude, decrease\nfollowing a power law. In this case the sparse approximation constructed by choosing the K largest\ncoefficients is guaranteed to have an approximation error that decreases with the same power law\nas the coefficients. The main limitation of CS-based systems is that they are employing iterative\nalgorithms to recover the signal. The sealgorithms are slow and the hardware solution has become\ncrucial for higher performance and speed. This technique enables fewer data samples than\ntraditionally required when capturing a signal with relatively high bandwidth, but a low information\nrate. As a main feature of CS, efficient algorithms such as 1 -minimization can be used for\nrecovery. This paper gives a survey of both theoretical and numerical aspects of compressive sensing\ntechnique and its applications. The theory of CS has many potential applications in signal processing,\nwireless communication, cognitive radio and medical imaging....
We investigate the detection and localization properties of harmonic tags working at microwave frequencies. A\ntwo-tone interrogation signal and a dedicated signal processing scheme at the receiver are proposed to eliminate\nphase ambiguities caused by the short signal wavelength and to provide accurate distance/position estimation even\nin the presence of clutter and multipath. The theoretical limits on tag detection and localization accuracy are\ninvestigated starting from a concise characterization of harmonic backscattered signals. Numerical results show that\naccuracies in the order of centimeters are feasible within an operational range of a few meters in the RFID UHF band....
A new random drift model and the measured angular rate model of MEMS gyro are presented. Based on such models, signal\nprocessing techniques are used to decrease gyro drift. Kalman filtering equations have been built for static measurement and\ndynamic measurement of the gyro array, which combines ...
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