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Portable leaf chlorophyll meter, named low-cost chlorophyll meter (LCCM), has been created. This device was created to help farmer determining the health condition of plant based on the greenness level of leaf surface. According to previous studies, leaf greenness with a certain amount of chlorophyll level has a direct correlation with the amount of nitrogen in the leaf that indicates health of the plant and this fact needed to provide an estimate of further measures to keep the plants healthy. Device that enables to measure the leaf color change is soil plant analysis development (SPAD) meter 502 from Konica Minolta but it is relatively expensive. To answer the need of low-cost chlorophyll scanner device, this research conducted experiment using light reflectance as the base mechanism. Reflectance system from LCCM consists of near-infrared light emitting diode (LED) and red LED as light resources and photodiode. The output from both of light resources calculated using normalized difference vegetation index (NDVI) formula as the results fetched and displayed on the smartphone application using Bluetooth communication protocol. Finally, the scanner has been made as well as the Android application named NDVI Reader. The LCCM system which has been tested on 20 sample of cassava leaf with SPAD meter as a variable control showed coefficient of determination 0.9681 and root-mean-square error (RMSE) 0.014.

In this work we present a method for dense reconstruction of anatomical structures using white light endoscopic imagery based on a learning process that estimates a mapping between light reflectance and surface geometry. Our method is unique in that few unrealistic assumptions are considered (i.e., we do not assume a Lambertian reflectance model nor do we assume a point light source) and we learn a model on a per-patient basis, thus increasing the accuracy and extensibility to different endoscopic sequences. The proposed method assumes accurate video-CT registration through a combination of Structure-from-Motion (SfM) and Trimmed-ICP, and then uses the registered 3D structure and motion to generate training data with which to learn a multivariate regression of observed pixel values to known 3D surface geometry. We demonstrate with a non-linear regression technique using a neural network towards estimating depth images and surface normal maps, resulting in high-resolution spatial 3D reconstructions to an average error of 0.53mm (on the low side, when anatomy matches the CT precisely) to 1.12mm (on the high side, when the presence of liquids causes scene geometry that is not present in the CT for evaluation). Our results are exhibited on patient data and validated with associated CT scans. In total, we processed 206 total endoscopic images from patient data, where each image yields approximately 1 million reconstructed 3D points per image.

Low fat composition in newborns exposes them to an immediate risk of increased mortality and morbidity, inhibited physical and cognitive development and to diabetes and obesity diseases in later life. Information about nutritional and dietary status of newborns can be accessed by measuring the amount of fat composition in the body. The functions of subcutaneous fat involve energy storage, thermo-insulation and a physical buffer. Current technologies for newborn body fat monitoring are: a device based on air displacement plethesmography (PeaPod), dual-energy Xray, and underwater weighting. However they are bulky, expensive, immobile, and require technical expertise. We propose an alternative portable measurement system of in-vitro for subcutaneous fat that uses diffuse near-infrared light reflectance measurement system. We also introduce an in-vitro three-layered tissue model mimicking the subcutaneous fat layer in newborns together with a preliminary study to measure fat using dual-wavelength nearinfrared light. Based on the output data from these measurements, we have proposed a suitable transmission and scattering model. This model estimated the amount of reflected light collected by a photodetector after incident light is scattered in several fat layers. Our portable sensor is low cost and does not require training hence it is suitable for mass use in the developing world. It consists of a single LED and two photodetectors (900 nm and 1000 nm). The photodetectors wavelengths were chosen to be sensitive to fat as it exhibits a peak in the wavelength at 930 nm and to water at which exhibits a peak at 980 nm; the latter is used, to remove hydration bias. Results on a porcine tissue model demonstrate differentiation as low as 2 mm fat which is a relevant screening thickness to indicate low percentage body fat.

Optical intrinsic signal imaging (OISI) is a functional neuroimaging technique that measures changes in cortical light reflectance induced in vivo by the change in both cortical absorption and scattering. These changes are spatially correlated with neuronal activity and are due to changes in hemoglobin concentration and cell swelling. Typically, a light source at 630nm illuminates the exposed cortex to emphasize changes in deoxyhemoglobin and CCD camera acquired the reflected light during trial (stimulation). One trial consisted of recording multiple consecutive frames to minimize noise during image acquisition. Unfortunately, during trials processing both good and poor quality images are combined together resulting in an overall degradation of resolution performance. The present study describes the performance evaluation of an algorithm developed to detect and screen out these poor images (outliers) during OISI analysis. Algorithm’s performance was tested on rodent's model and the experimental results highlight the potential of the algorithm for enhancing the resolution of the active area in the final OISI images.

Laparoscopic radical prostatectomy (LRP) has revolutionized the surgical treatment of prostate cancer. This procedure permits complete removal of the prostate and seminal vesicles while minimizing pain and recovery time. However, the laparoscopic approach greatly limits the surgeon's tactile sensation during the procedure. This is particularly true with robot-assisted LRP where no tactile feedback is available forcing the surgeon to rely solely on visual cues. The surgeon and pathologist perform intraoperative frozen section pathologic analysis of a few select tissue fragments, but this is time consuming and costly. Concrete conclusions based on such samples are unreliable as they do not reflect the entire surgical margin status. In this case a conservative approach might dictate removal of more marginal material than necessary, thereby compromising the important nerve-sparing aspects of the procedure. In this study, we demonstrate the feasibility of using multi-modal time-gated optical imaging, i.e. time-resolved light reflectance and auto-fluorescence life-time imaging performed by an ICCD (Intensified Charge-Coupled Device) imaging system to enable clinicians to detect positive tumor margins with high sensitivity and specificity over the prostate. Results from animal experiments present the potential of identifying differences in optical signals between prostate cancer and control tissues. Results also show that the use of classification algorithms can identify cancerous regions with high sensitivity and specificity.

Animal meat products may not be the best choice for many people in the world due to various reasons such as cost, health problems, or religious restrictions. High moisture (40-80%) extrusion technology shows a great promise for texturizing vegetable proteins into fibrous meat alternatives. Soy protein which is healthy, highly nutritious, low in both fat and carbohydrate has been used in high moisture extrusion process to produce meat analogs with well formed fiber that resemble chicken or turkey breast meat. Assessing fiber formation in extruded products is important for controlling extrusion quality in manufacturing process. Although several methods have been studied for quantifying fiber formation in extrudates, their applications for real time quality control in manufacturing process have been challenging. We explored the possibility of applying a nondestructive method based on backscattered reflectance to measure the fiber formation of extruded soy proteins. An image processing method was developed to extract the light reflectance profile at the extrudates' surface. We applied the anisotropic continuous time random walk (CTRW) theory to quantitatively describe the fiber formation in extrudates based on extracted surface reflectance profiles. This method has a potential to be used as a non-destructive, fast, real time quality control tool for products with fibrous structures.

We report the light reflectance of powders of nanocristalline alloys of silver (Ag) and silver oxides (Ag2O), molybdenum and molybdenum oxides (MoO3) and silver-molybdenum alloys (Ag2Mo), (Ag2Mo2) and (Ag2Mo4) for different wavelengths 360 nm - 740 nm. We observed a dependence of the light reflectance with the purity of the silver and molybdenum oxide, and a displacement of the maximum of the reflectance from 450 nm to 550 nm in the presence of the molybdenum oxides.

We present a comparative study of the light reflection in the visible region for different wood species (red cedar, pine, banak, acer negundo and walnut) and different humidity levels for each species. We observed dependence in the percentage of the light reflectance with the humidity and with the species.

This paper describes a 3-D laser scanning imager for visual inspection of mounted devices on printed circuit boards (PCB). A 3-D imager for this application must satisfy the following requirements: (1) It must be fast enough to sense a 250 by 330 mm area in 14 seconds; (2) It must have a measurement resolution of at least 125 gm; (3) It must be capable of measuring height and light intensity simultaneously; and (4) It must have an optical dynamic range of at least 10 4. We developed a wide-area telecentric scanning optical system which meets these requirements. It uses retroreflective triangulation optics and digital signal processing hardware. Our system scans a laser beam over a 256 mm length with a resolution of 125 μm, without scanning distortion. The retroreflection triangulation optics collect light reflected from objects on a printed circuit board and focus the image on a position-sensitive detector (PSD). This system measures the profile of objects with a vertical resolution of 30 μm, within a range of 7.6 mm. The digital signal processing hardware has a dynamic range of 10 4 and obtains range data from the output signals of the PSD. Its processing speed is 1M pixels/s. This hardware enables profile measurement of objects having a wide range of light reflectance (about 3000 times), from black devices to glossy metal, with an accuracy of 0.1 mm. This 3-D imager was used in an automated inspection system for PC board-mounted devices. This system detects missing, misplaced, and incorrectly installed devices with an inspection speed of 0.1 s/device.

Currently within decentralised clinical laboratories there is a need for simple and rapid techniques to detect antigens and antibodies of diagnostic significance. The interaction between antigen and antibody molecules may be extremely specific as a consequence of binding geometries and is a principal phenomenon of antibody-based chemical sensors, i.e. immunosensors. Direct optical monitoring of this interaction is an attractive alternative in the development of immunosensors, because optical sensors can be very sensitive, small in size, safe, and cheap. This paper reviews immunosensors based on optical sensing techniques including such methods like light reflectance (the Brewster angle reflectometry, surface plasmon resonance), and evanescent wave fluorescence. The basic principles and the current state-of-the art are discussed with emphasis on sensitivity, measurement range, shortcomings and problems to be solved. Some comments on miniaturisation and future developments will also be given.