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Non-dispersive impact technology for powder flow characterization

Majid AMA, Rahiman MHF, Wong TW

Int J Pharm, 2021 Aug 10;605:120786.
PMID: 34111546 DOI: 10.1016/j.ijpharm.2021.120786

This study developed a tester where the powder flow was characterized using a low sample mass (2 g) and impact instead of dispersion mechanism to mitigate test space constraint. An impact chamber was established where the test powder bed of seven lactose grades was weight-impacted to produce impact crater and ejecta, and imaged quantitatively to determine crater profiling signature (crater depth), regional topography (ejecta roughness), Otsu threshold (bed continuity) and edge segmentation (bed deformation). The Hausner ratio (HR) and Carr's index (CI) values of lactose, and their powder dispersion distance and surface area characteristics evaluated by gas-pressurized dispersibility test were examined as reference method. The crater signature profiling and regional topography were correlated to HR, CI, dispersive distance and surface area. A poorer powder flow was characterized by higher values of crater signature profiling, regional topography, HR, CI, and lower dispersive distance and surface area. The crater signature profiling and regional topography values were higher with smaller and rougher lactose particles that were cohesive. The powder impact flow is a viable non-dispersive approach to characterize powder flowability using a small sample mass and test space.
Fatigue effect on cross-talk in mechanomyography signals of extensor and flexor forearm muscles during maximal voluntary isometric contractions

Mohamad Ismail MR, Lam CK, Sundaraj K, Rahiman MHF

J Musculoskelet Neuronal Interact, 2021 12 01;21(4):481-494.
PMID: 34854387

OBJECTIVE: This paper presents the analyses of the fatigue effect on the cross-talk in mechanomyography (MMG) signals of extensor and flexor forearm muscles during pre- and post-fatigue maximum voluntary isometric contraction (MVIC).
METHODS: Twenty male participants performed repetitive submaximal (60% MVIC) grip muscle contractions to induce muscle fatigue and the results were analyzed during the pre- and post-fatigue MVIC. MMG signals were recorded on the extensor digitorum (ED), extensor carpi radialis longus (ECRL), flexor digitorum superficialis (FDS) and flexor carpi radialis (FCR) muscles. The cross-correlation coefficient was used to quantify the cross-talk values in forearm muscle pairs (MP1, MP2, MP3, MP4, MP5 and MP6). In addition, the MMG RMS and MMG MPF were calculated to determine force production and muscle fatigue level, respectively.
RESULTS: The fatigue effect significantly increased the cross-talk values in forearm muscle pairs except for MP2 and MP6. While the MMG RMS and MMG MPF significantly decreased (p<0.05) based on the examination of the mean differences from pre- and post-fatigue MVIC.
CONCLUSION: The presented results can be used as a reference for further investigation of cross-talk on the fatigue assessment of extensor and flexor muscles' mechanic.
Fulltext RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques

Azmi N, Kamarudin LM, Zakaria A, Ndzi DL, Rahiman MHF, Zakaria SMMS, et al.

Sensors (Basel), 2021 Mar 08;21(5).
PMID: 33800174 DOI: 10.3390/s21051875

Seasonal crops require reliable storage conditions to protect the yield once harvested. For long term storage, controlling the moisture content level in grains is challenging because existing moisture measuring techniques are time-consuming and laborious as measurements are carried out manually. The measurements are carried out using a sample and moisture may be unevenly distributed inside the silo/bin. Numerous studies have been conducted to measure the moisture content in grains utilising dielectric properties. To the best of authors' knowledge, the utilisation of low-cost wireless technology operating in the 2.4 GHz and 915 MHz ISM bands such as Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) have not been widely investigated. This study focuses on the characterisation of 2.4 GHz Radio Frequency (RF) transceivers using ZigBee Standard and 868 to 915 MHz UHF RFID transceiver for moisture content classification and prediction using Artificial Neural Network (ANN) models. The Received Signal Strength Indicator (RSSI) from the wireless transceivers is used for moisture content prediction in rice. Four samples (2 kg of rice each) were conditioned to 10%, 15%, 20%, and 25% moisture contents. The RSSI from both systems were obtained and processed. The processed data is used as input to different ANNs models such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest, and Multi-layer Perceptron (MLP). The results show that the Random Forest method with one input feature (RSSI_WSN) provides the highest accuracy of 87% compared to the other four models. All models show more than 98% accuracy when two input features (RSSI_WSN and RSSI_TAG2) are used. Hence, Random Forest is a reliable model that can be used to predict the moisture content level in rice as it gives a high accuracy even when only one input feature is used.
Fulltext Inline 3D Volumetric Measurement of Moisture Content in Rice Using Regression-Based ML of RF Tomographic Imaging

Almaleeh AA, Zakaria A, Kamarudin LM, Rahiman MHF, Ndzi DL, Ismail I

Sensors (Basel), 2022 Jan 05;22(1).
PMID: 35009947 DOI: 10.3390/s22010405

The moisture content of stored rice is dependent on the surrounding and environmental factors which in turn affect the quality and economic value of the grains. Therefore, the moisture content of grains needs to be measured frequently to ensure that optimum conditions that preserve their quality are maintained. The current state of the art for moisture measurement of rice in a silo is based on grab sampling or relies on single rod sensors placed randomly into the grain. The sensors that are currently used are very localized and are, therefore, unable to provide continuous measurement of the moisture distribution in the silo. To the authors' knowledge, there is no commercially available 3D volumetric measurement system for rice moisture content in a silo. Hence, this paper presents results of work carried out using low-cost wireless devices that can be placed around the silo to measure changes in the moisture content of rice. This paper proposes a novel technique based on radio frequency tomographic imaging using low-cost wireless devices and regression-based machine learning to provide contactless non-destructive 3D volumetric moisture content distribution in stored rice grain. This proposed technique can detect multiple levels of localized moisture distributions in the silo with accuracies greater than or equal to 83.7%, depending on the size and shape of the sample under test. Unlike other approaches proposed in open literature or employed in the sector, the proposed system can be deployed to provide continuous monitoring of the moisture distribution in silos.