Electron doping evolution of the magnetic excitations in NaFe1−x_{1-x}Cox_xAs

We use time-of-flight (ToF) inelastic neutron scattering (INS) spectroscopy to investigate the doping dependence of magnetic excitations across the phase diagram of NaFe$_{1-x}$Co$_x$As with $x=0, 0.0175, 0.0215, 0.05,$ and $0.11$. The effect of electron-doping by partially substituting Fe by Co is to form resonances that couple with superconductivity, broaden and suppress low energy ($E\le 80$ meV) spin excitations compared with spin waves in undoped NaFeAs. However, high energy ($E> 80$ meV) spin excitations are weakly Co-doping dependent. Integration of the local spin dynamic susceptibility $\chi^{\prime\prime}(\omega)$ of NaFe$_{1-x}$Co$_x$As reveals a total fluctuating moment of 3.6 $\mu_B^2$/Fe and a small but systematic reduction with electron doping. The presence of a large spin gap in the Co-overdoped nonsuperconducting NaFe$_{0.89}$Co$_{0.11}$As suggests that Fermi surface nesting is responsible for low-energy spin excitations. These results parallel Ni-doping evolution of spin excitations in BaFe$_{2-x}$Ni$_x$As$_2$, confirming the notion that low-energy spin excitations coupling with itinerant electrons are important for superconductivity, while weakly doping dependent high-energy spin excitations result from localized moments.Comment: 14 pages, 16 figure

Survey of Object Detection Methods in Camouflaged Image

Camouflage is an attempt to conceal the signature of a target object into the background image. Camouflage detection methods or Decamouflaging method is basically used to detect foreground object hidden in the background image. In this research paper authors presented survey of camouflage detection methods for different applications and areas

Multi-sensor fire detection by fusing visual and non-visual flame features

This paper proposes a feature-based multi-sensor fire detector operating on ordinary video and long wave infrared (LWIR) thermal images. The detector automatically extracts hot objects from the thermal images by dynamic background subtraction and histogram-based segmentation. Analogously, moving objects are extracted from the ordinary video by intensity-based dynamic background subtraction. These hot and moving objects are then further analyzed using a set of flame features which focus on the distinctive geometric, temporal and spatial disorder characteristics of flame regions. By combining the probabilities of these fast retrievable visual and thermal features, we are able to detect the fire at an early stage. Experiments with video and LWIR sequences of lire and non-fire real case scenarios show good results in id indicate that multi-sensor fire analysis is very promising

Video foreground detection based on symmetric alpha-stable mixture models.

Background subtraction (BS) is an efficient technique for detecting moving objects in video sequences. A simple BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. These assumptions restrict the applicability of BS methods to real-time object detection in video. In this paper, we propose an extended cluster BS technique with a mixture of symmetric alpha stable (SS) distributions. An on-line self-adaptive mechanism is presented that allows automated estimation of the model parameters using the log moment method. Results over real video sequences from indoor and outdoor environments, with data from static and moving video cameras are presented. The SS mixture model is shown to improve the detection performance compared with a cluster BS method using a Gaussian mixture model and the method of Li et al. [11]