Three different image-processing methods based on the time-averaged technique were compared by the electronic speckle pattern interferometry (ESPI) technique for vibration measurement. The three methods are the video-signal-addition method, the video-signal-subtraction method, and the amplitude-fluctuation method. Also, errors introduced by using the zero-order Bessel function directly into the analysis of the fringe pattern were investigated. The video-signal-addition method has been the most generally used ESPI technique for vibration measurement. However, without additional image and/or signal-processing procedures, the fringe pattern obtained directly by the video-signal-addition method is rather difficult to observe. The reason for poor visibility of the experimentally obtained fringe pattern with this method is explained. To increase the fringe pattern's visibility without additional image and/or signal processes, we tried two video-signal-subtraction methods. One of the two methods is the video-signal-subtraction method that has normally been used in the static applications. The other method, called the amplitude-fluctuation method, and its associated theory are reported here.

In this paper, a fast auto-focus microscope system was developed based on the astigmatic method. A collimated infrared laser beam was employed in the infinite-corrected microscope optical axis by the beam splitter and reflected by the sample surface. By embedding an astigmatic lens in the system, the reflected laser beam has different focal lengths in the sagittal and tangential planes. As the microscope's relative distance varies, the reflected laser beam shape also varies and can be detected by an embedded four-quadrant photodiode, i.e., the focus error signal (FES) can be found. Then, a fast auto-focus system can be realized by converting the FES to the microscope's defocus distance. We designed an astigmatic auto-focus system for a 20× objective lens with a ±50 µm working range, and this system could also be used for 10× and 5× objectives with ±200 µm and ±800 µm working ranges, respectively.

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Diffuse reflectance spectroscopy (DRS) has been utilized to study biological tissues for a variety of applications. However, many DRS systems are not designed for handheld use and/or relatively expensive which limit the extensive clinical use of this technique. In this paper, we report a handheld, low-cost DRS system consisting of a light source, optical switch, and a spectrometer, that can precisely quantify the optical properties of tissue samples in the clinical setting. The handheld DRS system was employed to determine the skin chromophore concentrations, absorption and scattering properties of 11 patients with psoriasis. The measurement results were compared to the clinical severity of psoriasis as evaluated by dermatologist using PASI (Psoriasis Area and Severity Index) scores. Our statistical analyses indicated that the handheld DRS system could be a useful non-invasive tool for objective evaluation of the severity of psoriasis. It is expected that the handheld system can be used for the objective evaluation and monitoring of various skin diseases such as keloid and psoriasis.

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Since the need of pedestrian detection at night has gained more and more interest, real-time pedestrian detection system at night has begun to attract significant attention. However, the detection performance of traditional pedestrian detection system at night is still insufficient since region of interests(ROI) generation and feature extraction are designed separately and less relative. This study presents a novel pedestrian detection algorithm and the corresponding system architecture for the real-time pedestrian detection system at night. The presented pedestrian detection algorithm uses novel outline features which have strong representative for the forward direction of pedestrian. A ROI generation method which is closely related to the outline features is presented to improve the detection accuracy. A three-layer back-propagation feed-forward neural network is applied as the classifier. Since the system architecture is efficiently and the calculation of the outline feature values is simple, the presented pedestrian detection system can operate in real-time. Experimental results show that the presented outline features are significantly effective and the detection performance of pedestrian detection system at night is improved.

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