This paper presents the work of investigating and comparing the uses of CRS and QTC Pills
as a tactile sensor material for Robotic Hand application. The materials were tested for their resistivity
characteristics to determine the data reproducibility of the materials. The experiments were conducted
based on three parameters; the supply voltage, the separation gap, and the sensor construction against
the force/ load that had been exerted onto the materials. The results showed that CRS could cover the
lower loading range when compared to QTC Pills. However, the data reproducibility of QTC Pills was
better compared to those of CRS, thus making it more suitable to be used as a material for tactile
sensing application. Therefore, this paper highlights the potential of these two materials as a tactile
sensing transducer, and later, can be used as a useful guideline when designing a tactile sensor with
these materials.
Cassava starch was used as feedstock for production of bioethanol by Saccharomyces cerevisiae. The cassava starch was hydrolyzed using commercial α-amylase and glucoamylase enzymes followed by a batch ethanol fermentation process using saccharified starch slurry. By using 110 g/L of reducing sugar from saccharified starch slurry, the ethanol yield was promising with maximum ethanol concentration of 20.6 g/L recorded after 55 hours of cultivation process. Three different models - the Logistic model, Luedeking-Piret-like equation and Gompertz equation - were used to characterize and explain the cell growth, reducing sugar consumption and production formation, respectively. The kinetic parameters were estimated by fitting the experimental data to the proposed models using non-linear regression analysis. The correlation coefficient r2 values for the Logistic model, Luedeking-Piret-like equation and the Gompertz equation were 0.994, 0.996 and 0.990, respectively. The high correlation coefficient values indicate that the proposed models were able to describe the ethanol fermentation process.
The agricultural industry scenario in many industrialized countries has adopted an image processing system as a solution to automate the grading process in order to provide accurate, reliable, consistent and quantitative information in addition to the large volumes, which human graders are not able to perform. In Malaysia, the grading of palm oil Fresh Fruit Bunches (FFB) is still performed manually through visual inspection using the surface color as the main quality attribute. It is the intention here to introduce an automated grading system for palm oil FFB using a computer assisted photogrammetric methodology which correlate the surface color of fruit bunches, not the fruitlets, to their ripeness and eventually sorts the fruit to two predefined fruit categories. The methodology consists of five main phases, i.e. image acquisition, image pre-processing, image segmentation, calculation of color Digital Numbers (DN) (data manipulation) and finally the classification of ripeness. This computerized photogrammetric image processing technique using MATLAB® package which is integrated to a sorting system differs in various aspects from other digital imaging technique or machine vision system adopted for classifying fruit ripeness. A comprehensive discussion will be presented based on the results achieved through actual fruit testing on the prototype grading system. The main concern was to ensure the reliability of the computerized photogrammetric technique achievable and the system’s mechanism working as intended. The fruit classification ability of the system yields above 90% accuracy and taking not more than 25 seconds to classify and sort each fruit.