Computer vision techniques in seafood quality control

The application of computer vision based quality control has been slowly but steadily gaining importance mainly due to its speed in achieving results and also greatly due to its non- destnictive nature of testing. Besides, in food applications it also does not contribute to contamination. However, computer vision applications in quality control needs the application of an appropriate software for image analysis. Eventhough computer vision based quality control has several advantages, its application has limitations as to the type of work to be done, particularly so in the food industries. Selective applications, however, can be highly advantageous and very accurate.Computer vision based image analysis could be used in morphometric measurements of fish with the same accuracy as the existing conventional method. The method is non-destructive and non-contaminating thus providing anadvantage in seafood processing.The images could be stored in archives and retrieved at anytime to carry out morphometric studies for biologists.Computer vision and subsequent image analysis could be used in measurements of various food products to assess uniformity of size. One product namely cutlet and product ingredients namely coating materials such as bread crumbs and rava were selected for the study. Computer vision based image analysis was used in the measurements of length, width and area of cutlets. Also the width of coating materials like bread crumbs was measured.Computer imaging and subsequent image analysis can be very effectively used in quality evaluations of product ingredients in food processing. Measurement of width of coating materials could establish uniformity of particles or the lack of it. The application of image analysis in bacteriological work was also done

Characterization of human and instrument related measurement errors

Measurement is the act or the result of a quantitative comparison between a given quantity and a quantity of the same kind chosen as a unit. It is generally agreed that all measurements contain errors. In a measuring system where both a measuring instrument and a human being taking the measurement using a preset process, the measurement error could be due to the instrument, the process or the human being involved. The first part of the study is devoted to understanding the human errors in measurement. For that, selected person related and selected work related factors that could affect measurement errors have been identified. Though these are well known, the exact extent of the error and the extent of effect of different factors on human errors in measurement are less reported. Characterization of human errors in measurement is done by conducting an experimental study using different subjects, where the factors were changed one at a time and the measurements made by them recorded. From the pre‐experiment survey research studies, it is observed that the respondents could not give the correct answers to questions related to the correct values [extent] of human related measurement errors. This confirmed the fears expressed regarding lack of knowledge about the extent of human related measurement errors among professionals associated with quality. But in postexperiment phase of survey study, it is observed that the answers regarding the extent of human related measurement errors has improved significantly since the answer choices were provided based on the experimental study. It is hoped that this work will help users of measurement in practice to better understand and manage the phenomena of human related errors in measurement.

MS Measurements

RMS measuring device is a nonlinear device consisting of linear and nonlinear devices. The performance of rms measurement is influenced by a number of factors; i) signal characteristics, 2) the measurement technique used and 3) the device characteristics. RMS measurement is not simple, particularly when the signals are complex and unknown. The problem of rms measurement on high crest-factor signals is fully discussed and a solution to this problem is presented in this thesis. The problem of rms measurement is systematically analized and found to have mainly three types of errors: (1) amplitude or waveform error 2) Frequency error and (3) averaging error. Various rms measurement techniques are studied and compared. On the basis of this study the rms -measurement is reclassified three categories: (1) Wave-form-error-free measurement (2) High-frequncy-error measurement and (3) Low-frequency error-free measurement. In modern digital sampled-data systems the signals are complex and waveform-error-free rms measurement is highly appreciated. Among the three basic blocks of rms measuring device the squarer is the most important one. A squaring technique is selected, that permits shaping of the squarer error characteristic in such a way as to achieve waveform-errob free rms measurement. The squarer is designed, fabricated and tested. A hybrid rms measurement using an analog rms computing device and digital display combines the speed of analog techniques and the resolution and ease of measurement of digital techniques. An A/D converter is modified to perform the square-rooting operation. A 10-V rms voltmeter using the developed rms detector is fabricated and tested. The chapters two, three and four analyse the problems involved in rms measurement and present a comparative study of rms computing techniques and devices. The fifth chapter gives the details of the developed rms detector that permits wave-form-error free rms measurement. The sixth chapter, enumerates the the highlights of the thesis and suggests a list of future projects

Photothermal deflection measurement on heat transport in GaAs epitaxial layers

In this paper, we report the in-plane and cross-plane measurements of the thermal diffusivity of double epitaxial layers of n-type GaAs doped with various concentrations of Si and a p-type Be-doped GaAs layer grown on a GaAs substrate by the molecular beam epitaxial method, using the laser-induced nondestructive photothermal deflection technique. The thermal diffusivity value is evaluated from the slope of the graph of the phase of the photothermal deflection signal as a function of pump-probe offset. Analysis of the data shows that the cross-plane thermal diffusivity is less than that of the in-plane thermal diffusivity. It is also seen that the doping concentration has a great influence on the thermal diffusivity value. Measurement of p-type Be-doped samples shows that the nature of the dopant also influences the effective thermal diffusivity value. The results are interpreted in terms of a phonon-assisted heat transfer mechanism and the various scattering process involved in the propagation of phonons.

Photothermal deflection measurement on heat transport in GaAs epitaxial layers

In this paper, we report the in-plane and cross-plane measurements of the thermal diffusivity of double epitaxial layers of n-type GaAs doped with various concentrations of Si and a p-type Be-doped GaAs layer grown on a GaAs substrate by the molecular beam epitaxial method, using the laser-induced nondestructive photothermal deflection technique. The thermal diffusivity value is evaluated from the slope of the graph of the phase of the photothermal deflection signal as a function of pump-probe offset. Analysis of the data shows that the cross-plane thermal diffusivity is less than that of the in-plane thermal diffusivity. It is also seen that the doping concentration has a great influence on the thermal diffusivity value. Measurement of p-type Be-doped samples shows that the nature of the dopant also influences the effective thermal diffusivity value. The results are interpreted in terms of a phonon-assisted heat transfer mechanism and the various scattering process involved in the propagation of phonons

Photothermal deflection measurement on heat transport in GaAs epitaxial layers

In this paper, we report the in-plane and cross-plane measurements of the thermal diffusivity of double epitaxial layers of n-type GaAs doped with various concentrations of Si and a p-type Be-doped GaAs layer grown on a GaAs substrate by the molecular beam epitaxial method, using the laser-induced nondestructive photothermal deflection technique. The thermal diffusivity value is evaluated from the slope of the graph of the phase of the photothermal deflection signal as a function of pump-probe offset. Analysis of the data shows that the cross-plane thermal diffusivity is less than that of the in-plane thermal diffusivity. It is also seen that the doping concentration has a great influence on the thermal diffusivity value. Measurement of p-type Be-doped samples shows that the nature of the dopant also influences the effective thermal diffusivity value. The results are interpreted in terms of a phonon-assisted heat transfer mechanism and the various scattering process involved in the propagation of phonons

Photoacoustic measurement of transport properties in doped GaAs epitaxial layers

The photoacoustic technique under heat transmission configuration is used to determine the effect of doping on both the thermal and transport properties of p- and n-type GaAs epitaxial layers grown on GaAs substrate by the molecular beam epitaxial method. Analysis of the data is made on the basis of the theoretical model of Rosencwaig and Gersho. Thermal and transport properties of the epitaxial layers are found by fitting the phase of the experimentally obtained photoacoustic signal with that of the theoretical model. It is observed that both the thermal and transport properties, i.e. thermal diffusivity, diffusion coefficient, surface recombination velocity and nonradiative recombination time, depend on the type of doping in the epitaxial layer. The results clearly show that the photoacoustic technique using heat transmission configuration is an excellent tool to study the thermal and transport properties of epitaxial layers under different doping conditions.

Model Diagnostics in the presence of measurement error

The problem of using information available from one variable X to make inferenceabout another Y is classical in many physical and social sciences. In statistics this isoften done via regression analysis where mean response is used to model the data. Onestipulates the model Y = µ(X) +ɛ. Here µ(X) is the mean response at the predictor variable value X = x, and ɛ = Y - µ(X) is the error. In classical regression analysis, both (X; Y ) are observable and one then proceeds to make inference about the mean response function µ(X). In practice there are numerous examples where X is not available, but a variable Z is observed which provides an estimate of X. As an example, consider the herbicidestudy of Rudemo, et al. [3] in which a nominal measured amount Z of herbicide was applied to a plant but the actual amount absorbed by the plant X is unobservable. As another example, from Wang [5], an epidemiologist studies the severity of a lung disease, Y , among the residents in a city in relation to the amount of certain air pollutants. The amount of the air pollutants Z can be measured at certain observation stations in the city, but the actual exposure of the residents to the pollutants, X, is unobservable and may vary randomly from the Z-values. In both cases X = Z+error: This is the so called Berkson measurement error model.In more classical measurement error model one observes an unbiased estimator W of X and stipulates the relation W = X + error: An example of this model occurs when assessing effect of nutrition X on a disease. Measuring nutrition intake precisely within 24 hours is almost impossible. There are many similar examples in agricultural or medical studies, see e.g., Carroll, Ruppert and Stefanski [1] and Fuller [2], , among others. In this talk we shall address the question of fitting a parametric model to the re-gression function µ(X) in the Berkson measurement error model: Y = µ(X) + ɛ; X = Z + η; where η and ɛ are random errors with E(ɛ) = 0, X and η are d-dimensional, and Z is the observable d-dimensional r.v.