A moisture detection of single rice grains using a slim and small open-ended coaxial probe is presented. The coaxial probe is suitable for the nondestructive measurement of moisture values in the rice grains ranging from from 9.5% to 26%. Empirical polynomial models are developed to predict the gravimetric moisture content of rice based on measured reflection coefficients using a vector network analyzer. The relationship between the reflection coefficient and relative permittivity were also created using a regression method and expressed in a polynomial model, whose model coefficients were obtained by fitting the data from Finite Element-based simulation. Besides, the designed single rice grain sample holder and experimental set-up were shown. The measurement of single rice grains in this study is more precise compared to the measurement in conventional bulk rice grains, as the random air gap present in the bulk rice grains is excluded.

Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results.

Determining the Moisture Content (MC) inside rice grain is an important element for grain processesing such as harvesting, storage, quality control and transportation. A microstrip wide-ring sensor and a microstrip coupled-line sensor with low insertion loss were developed to operate at relatively low frequency to determine the MC and relative complex permittivity of rice grain. The complex permittivity for rice grain with various moisture levels was measured by the proposed sensors based on the resonance technique. Calibration equations for measurement of grain MC were obtained and validated with white rice for MC ranging from 10 to 28% wet basis. The coupled-line sensor showed better sensitivity to moisture measurement as compared to the wide-ring sensor. For a 1% change in MC, the changes of resonant frequency for the wide-ring and coupled-line sensors were 4.36 and 10.69 MHz respectively. Meanwhile, the wide-ring sensor had a higher accuracy in MC prediction than the coupled-line sensor. The average errors in moisture prediction for wide-ring and coupled-line sensors were 0.85 and 1.30% respectively.

Two main species of cultivated rice in the world are Oryza sativa (Asian rice) and Oryza glaberrima (African rice). The Oryza sativa species, which is grown worldwide, is far more widely utilized compared with the Oryza glaberrima species, which is grown in West Africa. Recently, the annual rice production has reached almost 480 million tonnes, and this demand is expected to rise to 550 million tonnes in 2035. Thus, this increases the need to characterize and maintain the quality of rice and hence to determine the price of rice appropriately. Obviously, modern technologies that can provide fast and accurate measurement are essential in the large-scale industrial rice processing. In this chapter, several technologies and instruments used for rice processing are reviewed. The principle of the measurement for each technology is briefly described. The strength of this chapter is to introduce the application of microwave technology during rice processing, such as rice dying process, rice moisture detection, broken rice measurement and rice insect control. The pros and cons of the microwave method will be discussed in detail. Hence, some standard test laboratory for monitoring of carbohydrate, protein, fat and trace elements content is also described in this chapter.

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Direct piercing carved wood panels (DPCWP) have been used as part of the wall panel for mosques in this country. Among the earliest used of DPCWP is as in Masjid Sultan Zainal Abidin in 1700s, Kuala Terengganu, Terengganu. It is envisage, the use of DPCWP is to help in achieving good speech intelligibility inside. This is considering DPCWP allowing sound waves to pass through, hence sound reflection to the main prayer is contain. Minimizing sound reflection toward the mosque main area, ensuring optimization of speech intelligibility. DPCWP has the ability to allow sound waves to pass through the panel. This qualifies DPCWP as sound absorber material. This as Sabine, Kutruff and Maekawa definition of sound absorption coefficient. In this paper the sound absorption coefficient of DPCWP with uniform geometric patterns are discussed. The perforation ratios are 31% and 37%. Numerical experiments were conducted using Boundary Element Method (BEM). The measured results were obtained using sound intensity measurements technique. Comparison of sound absorption coefficient obtained through numerical experiment and measured using sound intensity are discussed in details. Analysis of resonance frequencies due to types and sizes of apertures in relation to sound absorption coefficient also highlighted. The measured and numerical results suggest, DPCWP of uniform geometric patterns indeed able to act as good sound absorber. This finding allows DPCWP to be used as sound absorber more effectively in future mosque construction.

The purpose of this work is to develop a spoken language processing system for smart device troubleshooting using human-machine interaction. This system combines a software Bidirectional Long Short Term Memory Cell (BLSTM)-based speech recognizer and a hardware LSTM-based language processor for Natural Language Processing (NLP) using the serial RS232 interface. Mel Frequency Cepstral Coefficient (MFCC)-based feature vectors from the speech signal are directly input into a BLSTM network. A dropout layer is added to the BLSTM layer to reduce over-fitting and improve robustness. The speech recognition component is a combination of an acoustic modeler, pronunciation dictionary, and a BLSTM network for generating query text, and executes in real time with an 81.5% Word Error Rate (WER) and average training time of 45 s. The language processor comprises a vectorizer, lookup dictionary, key encoder, Long Short Term Memory Cell (LSTM)-based training and prediction network, and dialogue manager, and transforms query intent to generate response text with a processing time of 0.59 s, 5% hardware utilization, and an F1 score of 95.2%. The proposed system has a 4.17% decrease in accuracy compared with existing systems. The existing systems use parallel processing and high-speed cache memories to perform additional training, which improves the accuracy. However, the performance of the language processor has a 36.7% decrease in processing time and 50% decrease in hardware utilization, making it suitable for troubleshooting smart devices.

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