A novel Raman spectrometer is presented in a handheld format. The spectrometer utilizes a temperature-controlled, distributed Bragg reflector diode laser, which allows the instrument to operate in a sequentially shifted excitation mode to eliminate fluorescence backgrounds, fixed pattern noise, and room lights, while keeping the Raman data in true spectral space. The cost-efficient design of the instrument allows rapid acquisition of shifted excitation data with a shift time penalty of less than 2 s. The Raman data are extracted from the shifted excitation spectra using a novel algorithm that is typically three orders of magnitude faster than conventional shifted-excitation algorithms operating in spectral space. The superiority of the instrument and algorithm in terms of background removal and signal-to-noise ratio is demonstrated by comparison to FT-Raman, standard deviation spectra, shifted excitation Raman difference spectroscopy (SERDS), and conventional multiple-shift excitation methods.

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The design and operation of a novel dual-laser excitation Raman instrument is described. The use of two lasers of differing wavelengths allows for a Raman spectrum covering all fundamental modes of vibration to be collected while minimizing fluorescence and allowing for spatial compression of the spectrum on an imaging detector. The use of diode lasers with integrated distributed Bragg reflector gratings facilitates the use of an integrated thermoelectric cooler to allow collection of shifted excitation spectra for both of the lasers, further enhancing the rejection of fluorescence. An example is given, which uses seven excitation wavelengths for each laser to reconstruct the Raman spectrum of a solvent in the presence of a highly fluorescent dye by using a sequentially shifted excitation Raman reconstruction algorithm.

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Visual surveillance systems have gained a lot of interest in the last few years. In this paper, we present a visual surveillance system that is based on the integration of motion detection and visual tracking to achieve better performance. Motion detection is achieved using an algorithm that combines temporal variance with background modeling methods. The tracking algorithm combines motion and appearance information into an appearance model and uses a particle filter framework for tracking the object in subsequent frames. The systems was tested on a large ground-truthed data set containing hundreds of color and FLIR image sequences. A performance evaluation for the system was performed and the average evaluation results are reported in this paper.