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In image processing, leaf shape recognition requires a huge database of similar images. Normal leaf image database construction requires more time and space. On the other hand, mobile offline applications are not able to contain huge image database with high pixel ratio. To solve this problem, mainly in medicinal plant identification, small leaf descriptors are necessary to completely provide the required plant information. Presently, the Botanists use shape reference table to recognize the following shapes of the leaf: ovate, cordate, elliptical, oblong, lanceolate and linear. As the shape numbers are invariant under scale, rotation and translation, which is highly desirable property for object recognition and chain code techniques preserve data by allowing large data reduction. Hence, in the proposed Shape Descriptor Algorithm for Medicinal Plant Identification (SDAMPI), we developed a descriptor to resolve the pixel selection issue of Freeman chain code and generate a unique leaf shape number. This leaf shape digital descriptor will act as a reference table for medicinal plant leaf shape identification. The performance of the proposed descriptor is evaluated through Jaccard similarity index graph and Levenshtein distance (LD) graph. From the results, it is confirming that, SDAMPI descriptor can detect Medicinal plant leaf shapes more accurately than existing methods. [ABSTRACT FROM AUTHOR]

Efficient and low power utilizing clock synchronization is a challenging task for a wireless-sensor network (WSN). Therefore, it is crucial to design a light weight clock synchronization protocols for these networks. An adaptive clock offset prediction model for WSN is proposed in this paper that exchanges fewer synchronization messages to improve the accuracy and efficiency. Timing information required is collected by setting a small WSN set up to investigate the soil condition to control the irrigation in agriculture. The networks investigate soils moisture, temperature, humidity, and pressure content along with the sensors clock offset. First, the prediction model perceives the existing sensor clock offset to observe the clock characteristics and delay. Then, a Gaussian function is applied for adjusting the parameters weight of the observed value in the prediction model. The system results demonstrate that the proposed adaptive non-linear Gaussian regression synchronization model utilizes 20% less energy as consumed by time sync protocol for sensor-network and reference broadcast synchronization Protocol. It also reduces the synchronization error with respect to root-mean-square error (RMSE) by 24.85% as compared to linear prediction synchronization with RMSE 28.72% in terms of accuracy. [ABSTRACT FROM AUTHOR]

Numerous industrial processes are based on the application of phenolic compounds such as Bisphenol-A (BPA), which is used as a monomer and additive in plastics, resins, and coatings manufacturing. Many phenolic compounds are highly toxic and responsible for high pollution in food, water, and soil. The endocrine disrupting activities of BPA makes it an emerging environmental pollutant. The most common source of BPA release into the environment is from plastic packaging. This leakage from plastic packaging is facilitated by heat and pH conditions which speeds up the hydrolysis of the ester bonds. In this paper, we report the estimation of BPA concentration in ethanol, using nanostructured manganese dioxide as a receptor using a non-faradaic electrochemical impedance spectroscopy. An equivalent electrical circuit model has been proposed for electrochemical impedance data of BPA solutions in pure ethanol. A linear relationship of impedance response with the concentration over a range from 1 nM– 62.5~\mu \textM is observed. The detection limit is estimated around to be 0.66~\mu \textM . During the measurement, it has been taken care that the electrodes must give excellent reproducibility. [ABSTRACT FROM PUBLISHER]

A novel hand held micro-electrode sensor based on a label free impedance spectroscopy is fabricated for the detection of adulteration in milk. The results showed remarkable impedance change for different concentrations of starch and detergent in the milk sample in the frequency range of 0.05–5 kHz. The impedance of reference milk sample at resonant frequency 4.75 kHz was found to be 8.01E $+ 02\Omega $ and that of model milk sample adulterated with 10% (w/v) detergent was found to be 56% of the original value while the impedance of milk sample adulterated with 10% (w/v) starch was found to be ~160% of the original value of 8.01E $+ 02\Omega $ . Also, it was found that the change in the concentration of starch and detergent in milk samples changes the overall impedance of the sensor remarkably. The measured impedance values were inversely proportional to the concentration of detergent and directly proportional to the concentration of starch in the model milk samples. The observed trend is explained and highlights the selectivity and sensitivity of the device. The device requires only 0.2 mL of the sample and gives response within 10 s. The real and simulated data were fitted in an ac equivalent circuit model of the device operation. [ABSTRACT FROM PUBLISHER]

Railway track monitoring is a challenging task to avoid railway accidents due to track failures. There are lot many issues involved in the railway accidents but the major involvement is the use of defective railway tracks. The aim of this paper is to present a novel visual inspection technique for detection, marking, and visualization of defected portion in railway tracks. In this paper, maximally stable extremal region technique is used to identify and visualize the geometrical features of the defected regions on the rail head surface in railway track images. Here, three classes of surface defects have been taken. The results are very promising and comprise all the aspects. [ABSTRACT FROM PUBLISHER]