Conference Publications
Permanent URI for this collection
Browse
Recent Submissions
1 - 5 of 77
-
PublicationTest frequency optimization using single factor ANOVA for capacitive oil palm fruit ripeness sensor(IOP Publishing Ltd., 2020)The traditional method of palm oil fruit ripeness grading requires manual human observation imposing time consumption. This paper describes the work to determine best frequency gritted for capacitive ripeness grading. To find the ripeness, the frequency response of ripe and unripe fruitlet in between capacitive plates is evaluated. Parallel plate that is connected to resistor in series is used as the sensor. The function generator is used to give a sinusoidal wave frequency or known as Vin that gives input from 20 kHz to 300 kHz and the oscilloscope is used to obtain the output reading which is the difference in value of the Vpp. From the result, it is observed that there is difference between ripe and unripe fruitlet also the frequency of 100 kHz and below shows that the reading can be differentiate between fruitlet. After the frequency past the 100 kHz the reading is hard to differentiate before at one point the graph will become static even the weight of each fruitlet is difference.
-
PublicationEffect of Tryptone Concentration on Cyclodextrin Glucanotranferase (CGTase) excretion and cell lysis of immobilized recombinant Escherichia coli(IOP Publishing Ltd, 2020)The recombinant enzyme excretion into the culture medium provides significant advantages over cytoplasmic expression. Nevertheless, the problems encountered during the excretion of recombinant enzyme are the plasmid instability and occurrence of cell lysis. Various attempts have been made to improve the recombinant enzyme excretion and plasmid stability with the low occurrence of cell lysis. The approaches include the modification of the nitrogen sources in the medium such as tryptone, the use of cell immobilization system and lowering the induction temperature. In the present study, the effects of different tryptone concentrations (1, 5, 10, 20 and 30 g/L) as nitrogen source in super optimal broth (SOB) medium on CGTase excretion and plasmid stability as well as cell lysis of the immobilized cell were studied. The recombinant E. coli was immobilized on polyvinylidene fluoride polymer (PVDF) hollow fiber membrane. The immobilized cells were expressed using 0.011 mM IPTG at 25°C, 200 rpm of agitation rate and pH 8.8 for 24 h of post induction time. The use of low tryptone concentration (5 g/l) produced high CGTase excretion (758.64 U/ml) and increased the plasmid stability (86% increment) with reduction of cell lysis (90% reduction) in comparison with the initial tryptone concentration (20 g/l). Hence, low concentration of tryptone could reduce the cost for CGTase production due to low amount of tryptone used in the fermentation process.
-
PublicationDevelopment of smart incubator grow system for plant(IOP Publishing Ltd, 2020)In the current industrialized food production network, most food suppliers rely on large, mono-crop farms that grow massive amounts of a single product and ship it around the globe. Food may travel thousands of miles over the course of days, weeks, or even months before it reaches our tables. Not only is this system strain on the environment, and on the cost to the consumer, but it also affects the quality of the food we put in our bodies. The objective of this project is to design and develop a smart incubator grow system for plants by developing the open source hardware and software platforms for sensor-controlled hydroponic and aeroponic agriculture systems. Inside of this smart incubator, climate variables such as carbon dioxide, air temperature, humidity, dissolved oxygen, potential hydrogen, electrical conductivity, root-zone temperature, and more can be controlled and monitored. Usage specifications such as operational energy, water use, and mineral consumption can also be monitored and adjusted through electrical meters, flow sensors, and controllable chemical doses throughout the growth period. The complete set of conditions throughout a growth cycle produces unique phenotypic expressions, or physical qualities in different plants. Plants grown under different conditions may vary in colour, size, texture, growth rate, yield, flavour, and nutrient density. The system can be monitor and control through wireless connectivity technology
-
PublicationA switchless pentagon-shaped reconfigurable antenna for radar applications(IOP Publishing, 2020)This paper proposes a switchless pentagon-shaped microstrip patch antenna for radar and radionavigation applications. The antenna is built on Rogers RT5880 substrate with five rectangular radiating elements on top. Five ports have been set up to operate at 13.5 GHz resonant frequency. Besides having reflection coefficient below -10 dB, the antenna also offers high gain when about 8.29 dB is achieved. The proposed antenna also has a bi-directional radiation pattern with 360° of beam steering.
-
PublicationDetection of Parkinson’s Disease (PD) based on speech recordings using machine learning techniques( 2020)There are some neurodegenerative diseases which are unable to cure such as Parkinson's disease (PD) and Hungtinton's disease due to the death of certain parts in the brain that is affecting older adult. PD is an appalling neurodegenerative health disorder that linked to the nervous system which exert influence on motor functions. PD also often known as idiopathic disorder, environmental and genetic factors related, and the causes of PD remain unidentified. To diagnose PD, the clinicians are required to take the history of brain condition for the patient and undergoes various of motor skills examination. Accurate detection of PD plays a crucial role in aiding and providing proper treatment to the patients. Nowadays, there has been recent interest in studying speech-based PD diagnosis. Extracted acoustic attributes are the most important requirement to predict the PD. The experiment was conducted on speech recording dataset consisting of 240 samples. This work studies on the feature selection method, Least Absolute Shrinkage and Selection Operator (LASSO) with multiple machine learnings such as Random Forest (RF), Deep Neural Network (DNN), Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) as the classifier. Throughout this research, train test split method and k-fold cross validation were implemented to evaluate the performance of the classifiers. Through LASSO, Support Vector Machine Grid Search Cross Validation (SVM GSCV) outperformed other 7 models with 100.00 % accuracy, 97.87 % for recall, 65.00 % for specificity and 97.10 % of AUC for 10-fold cross validation. Finally, Graphical User Interface (GUI) was developed and validated through the prediction over UCI speech recording dataset which achieved 96.67 % accuracy for binary classification with 30 samples.