Hand, Foot, and Mouth Disease (HFMD) is an outbreak infectious disease that can easily spread among children under the age of five. The most common causative agents of HFMD are enterovirus 71 (EV71) and coxsackievirus A16 (CVA16), but infection caused by EV71 is more associated with fatalities due to severe neurological disorders. The present diagnosis methods rely on physical examinations by the doctors and further confirmation by laboratories detection methods such as viral culture and polymerase chain reaction. Clinical signs of HFMD infection and other childhood diseases such as chicken pox, and allergies are similar, yet the genetics and pathogenicity of the viruses are substantially different. Thus, there is an urgent need for an early screening of HFMD using an inexpensive and user-friendly device that can directly detect the causative agents of the disease. This paper reviews current HFMD diagnostic methods based on various target types, such as nucleic acid, protein, and whole virus. This was followed by a thorough discussion on the emerging sensing technologies for HFMD detection, including surface plasmon resonance, electrochemical sensor, and surface enhanced Raman spectroscopy. Lastly, optical absorption spectroscopic method was critically discussed and proposed as a promising technology for HFMD screening and detection.

Nowadays, water pollution has become a global issue affecting most countries in the world. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. The objective of the review is to study various conventional and modern methods of monitoring water quality to identify the strengths and weaknesses of the methods. The methods include the Internet of Things (IoT), virtual sensing, cyber-physical system (CPS), and optical techniques. In this review, water quality monitoring systems and process control in several countries, such as New Zealand, China, Serbia, Bangladesh, Malaysia, and India, are discussed. Conventional and modern methods are compared in terms of parameters, complexity, and reliability. Recent methods of water quality monitoring techniques are also reviewed to study any loopholes in modern methods. We found that CPS is suitable for monitoring water quality due to a good combination of physical and computational algorithms. Its embedded sensors, processors, and actuators can be designed to detect and interact with environments. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Traditional approaches are more time-consuming and expensive due to the high maintenance of laboratory facilities, involve chemical materials, and are inefficient for on-site monitoring applications. Apart from that, previous monitoring methods have issues in achieving a reliable measurement of water quality parameters in real time. There are still limitations in instruments for detecting pollutants and producing valuable information on water quality. Thus, the review is important in order to compare previous methods and to improve current water quality assessments in terms of reliability and cost-effectiveness.

Milk is a valuable contributor to a healthy diet as it contains nutritional components such as fats, proteins, carbohydrates, calcium, phosphorous and vitamins. This research aimed to differentiate milk from animal, plant and human sources based on light propagation and random-laser properties. Experimental, statistical and theoretical analyses were used. Light propagation in different types of milk such as almond milk, oat milk, soy milk, fresh milk, goat milk and human breast milk was measured using the spectrometry method. Near-IR and visible light transmission through the diluted milk samples were compared. Soy milk and fresh milk have the highest absorbance and fluorescence of light, respectively, due to a high content of fat, protein and carbohydrates. Principal component analysis was used to determine the accuracy of the experimental results. The research method is comprehensive as it covers light propagation from 350 nm to 1650 nm of wavelength range and non-intrusive as it does not affect the sample. Meanwhile, analysis of milk was also conducted based on random-laser properties such as multiple emission peaks and lasing threshold. Higher fat content in milk produces a lower random lasing threshold. Thus, we found that milk from animals, plants and humans can be analyzed using light absorption, fluorescence and random lasers. The research method might be useful for future study of milk contaminants that change the properties of milk.

Ruby is one of the most precious gemstones on Earth that is always high in demand especially in the jewelry industries. Due to its high value and very expensive, a lot of imitation of ruby has been made. This results in the rising of more complicated issues as gemologists need to perform the grading valuation very carefully and precisely. The current and common grading techniques mostly depend on human vision, which eventually leads to error. This paper aims to analyze the clarity of rubies gemstones using Charge-Coupled Device (CCD). The CCD detects the light intensity and then convert the light intensity value into the voltage value. The CCD sensor is very special in its architecture design, consisting of more than 1000 very small pixels that are sensitive to light sources. Based on the previous research, CCD has high sensitivity to laser light source with wavelength range within 430 nm to 650 nm. This research is going to prove that CCD is able to detect the clarity of various grading of the pink to blood-red ruby stones.

In assisted reproductive treatment, monitoring of follicular size using serial ultrasound is essential to access ovarian response. Traditional method requires doctors to measure the follicle size manually which will lead to inaccurate findings. As for more consistent and reliable parameter of follicular growth, an automated feature description may offer better accuracy in estimating to the response. In this study, by using two-dimensional ultrasound to acquire data from the ovaries, the ultrasound result will indicate the feature description automatically without manual calculation. This automated feature description is developed based on image processing technique using canny edge-detection method in MATLAB. It provides the analysis of the features based on area, perimeter, compactness, major and minor axis and centroid dataset to identify the follicle size. � Penerbit UTHM.

In assisted reproductive treatment, monitoring of follicular size using serial ultrasound is essential to access ovarian response. Traditional method requires doctors to measure the follicle size manually which will lead to inaccurate findings. As for more consistent and reliable parameter of follicular growth, an automated feature description may offer better accuracy in estimating to the response. In this study, by using two-dimensional ultrasound to acquire data from the ovaries, the ultrasound result will indicate the feature description automatically without manual calculation. This automated feature description is developed based on image processing technique using canny edge-detection method in MATLAB. It provides the analysis of the features based on area, perimeter, compactness, major and minor axis and centroid dataset to identify the follicle size

Ultrasonography (US) is one of the procedures to monitor the growth of renal size in diagnose kidney disease. However considering the complexity of renal size, this procedure leads to inter-observer variability and poor repeatability. Given images from Abdominal CT scan, a level set thresholding and combination of logical and arithmetic operation based method was developed to calculate the automated feature description of renal size. This is achieved by applying 2D CT scan image into image segmentation and feature extraction where thresholding and morphological segmentation method are conducted. Then, parameters of the kidney such as perimeter, area, major axis and minor axis were measured and analyzed in classification step. As a result, analysis on the kidney size between subjects who are normal and the results from the studies has shown capability to classify correctly the size of kidneys about accuracy of 80% to 81% in terms of the kidney's relative axis which is the ratio of right kidney and left kidneys. In addition, the method in measurement kidney size is compared between manual method and automated method and results shows that the accuracy of the automated method in terms of compactness is about 91% to 95.

Technology is a double-edged sword when it changes the way we perform everyday activities for the better. Unfortunately, misfits will always find new ways to misuse technology to facilitate their misdeeds. CCTV are great security measures that can be used to catch culprits in action, but other than banks, hospitals, shopping malls, academic institutes etc., a CCTV solution is beyond most household budgets and require continuous monitoring. Because mobile phones are the primary hand-held device, it is only logical to integrate these with a CCTV solution. Motion Detection Security System Notifier using Raspberry Pi with Telegram is as the name suggests. Using Raspberry Pi, a motion detection system is developed through the method of background subtraction, which works by subtracting the static from the background frame from the constantly changing foreground. The change of value in noise and pixel density between the background frame and the foreground indicates motion. Each time a motion is detected, the system will automatically send the frame to the user via Telegram to notify that there has been movement in the area as captured in the frames. A prototype is first developed in a virtual environment using VirtualBox software before running it on the Raspberry Pi. This paper will show the relevance of the project. � 2020 IEEE.

Endometriosis cyst or endometrium is commonly found in women with subfertility. Traditionally, medical technologies fail to detect the disease automatically and it is fully dependent on the doctors to determine the peritoneal disease where it may lead to inaccurate findings. A method of assessment may give more accurate detection without the need for surgical procedure, especially in monitoring disease recurrence. This will avoid surgical risk and will not delay the management. In this study, the feature description is developed using pattern recognition, involving image processing techniques; the ultrasonic images is used as input in which the region of interest of images, image segmentation, feature extraction are studied. � Penerbit UTHM.

Endometriosis cyst or endometrium is commonly found in women with subfertility. Traditionally, medical technologies fail to detect the disease automatically and it is fully dependent on the doctors to determine the peritoneal disease where it may lead to inaccurate findings. A method of assessment may give more accurate detection without the need for surgical procedure, especially in monitoring disease recurrence. This will avoid surgical risk and will not delay the management. In this study, the feature description is developed using pattern recognition, involving image processing techniques; the ultrasonic images is used as input in which the region of interest of images, image segmentation, feature extraction are studied

Diagnostic tools such as Point-of-Care (POC) analysis usually requires an on-site testing and measurements. Hence, to ease the fluid sample handling, microfluidics technology has been opted frequently to be incorporated with lab-on-chip (LOC). Therefore, an easy and conventional method of design and fabrication of the microfluidic device implemented. In this study, the device is fabricated with Polydimethylsiloxane (PDMS) and SU-8 using soft lithography technique. Herein, we focus on fluid transportation which is applied in clinical diagnostics and the transfer rate to the desired position as well as the fluid leakage during the transportation. Hence, the microfluidic sample transport channeling provides a practical solution for POC technological advances to study the biological systems that yield a better result in quantitative measurements and providing researchers new tools for life science research.

Water contamination is a critical issue in plant growth since the contaminated water can cause abnormality to the plants when phototoxicity occurs. Phytotoxicity can happen when plants use contaminated water for photosynthesis because anything chemical would cause an adverse reaction. For organic contaminants, samples from ammonium nitrate and pesticides are tested and the absorbance peak for contaminants are 363nm and 361nm respectively. Besides that, the heavy metal sample is prepared by mixing up white powder of zinc oxide with water to produce a concentrated heavy metal solution with an absorbance peak at 405nm. Lake water from Universiti Sains Islam Malaysia (USIM) that are used for irrigation systems also are collected to check any organic or heavy metal contaminants. The Ocean Optic spectrometer shows an absorbance peak at 361nm, while the low-cost spectrometer lies between 300nm to 400nm. The absorbance spectrum in this region shows the highest peak excitation of organic particles. It proves that pesticides are organic contaminants and the presence of this compound in water used for irrigation systems can cause abnormality in the plants growth. Therefore, appropriate, and systematic water supplies are indispensable in agricultural production systems to produce healthy growing plants for consumers.

Water pollution is a critical issue since it can severely affect health and the environment. The purpose of the study is to develop a portable spectrometer (ESP32-based spectrometer) to detect chemical contaminants in irrigation water by observing the light absorbance of contaminants. ESP32 and a light sensor (photodiode) were respectively, used as the main controller and detector of the portable spectrometer. It was developed based on optical dispersion and Beer–Lambert law theory. The light absorbance of different types of contaminants was displayed in a Blynk application for real-time monitoring. The samples were also tested using a lab-based spectroscopy method, ultraviolet-visible (UV-Vis) spectrometer. The spectral range of the measurement is from 350 nm to 700 nm and the standard error of the ESP32-based spectrometer is from 0.01 to 0.05. Five water samples were tested, consisting of ammonium nitrate, organic pesticide, zinc oxide and two different reservoirs used for irrigation. The absorption peaks of the ammonium nitrate and organic pesticide are 363 nm and 361 nm, respectively. Zinc oxide shows the absorbance peak at 405 nm, whereas both reservoirs show absorbance peaks lie in the region from 300 nm to 370 nm. Therefore, this study shows that different types of contaminants can absorb light only at specific wavelength regions by considering the concentration of samples. The developed ESP32-based spectrometer can be applied for on-site water quality monitoring as it is portable, light, simple and can be monitored in real time using multiple devices.

The exponential growth of the population and development comes in hand with excessive use of natural resources which leads to the depletion of the non-renewable material. The main goal of this Solar Decathlon Solar Home competition is to select the best home, whichcan be assembled within 10 days in the desert climate. The solution of four issues withcapacities and technologies are strategized to implement the benefit of the inhabitants of the Middle East region such as sobriety, innovation and mobility. The main objectives of this study are to describe the design philosophy, highlight the exclusive experimental analysis on thermal and sensor which embed the energy efficient and sustainability. The added value ofIslamic perspective of 'Mizan' and 'Sakinah' which mean balanced home and tranquillityare emphasizing the application of balance need for human and nature. The results showed the conductivity of plant waste material known as oil palm trunk is a medium of a heat transport and thermal insulator during high temperature while the innovative sobriety solution is achieved through wind and sandstorm sensor. A conclusion was drawn that the engineering of green product is performed the optimum human comfort in the dwelling of desert area,while from a social aspect, the principleof privacy protection is essential through the planningof a space in the solar home.

Nowadays, the innovation and production of prayer seats can widely be seen around the world particularly in a nation or region with a majority of Muslim population. Demands on this assistive device are everywhere in order to ease and help disabled Muslims to conduct their daily prayers. Most of the existing devices consist of basic systems and sometimes do not accurately meet disabled Muslims’ requirement. The main objective of this research is to design and develop a Solah chair using electronic sensor to assist disabled Muslims to perform Solah and other ritual activities comfortably. This research uses four types of electrical and electronic systems namely the ultrasonic system, DF player system, counter system and IR sensor system to assist disabled Muslims to perform Solah and other ritual activities such as zikir and listening to Quran recitation. Arduino Uno is used to integrate these four subsystems. Arduino as the main micro-controller for this system is chosen for its convenience and capabilities to read and run C-language. It is also a simple platform which is suitable for many IT projects in general.

Artificial intelligence (AI) and computer vision (CV) advancements have paved the way for more efficient agricultural activities such as predicting and estimating fruit yield. Durian, a fruit native to tropical regions, necessitated using high-tech solutions to keep up with its rising global demand. This work aimed to apply the image analysis technique using deep learning to identify and estimate the number of durian fruits using image recognition. A new dataset was specifically constructed in this work, consisting of 500 images split for training and testing the object detection model. Various pre-trained object detection models such as YOLOv3, YOLOv4, YOLOv3 tiny, and YOLOv4 tiny are used for performance comparison on the newly constructed dataset. The best model is then chosen as the inference model for the drone-captured video dataset, assisted by the DeepSORT algorithm as the counting mechanism. Our investigations showed that the YOLOv4 model significantly performs best among all four state-of-art detection networks where it computes the highest mean average precision (mAP) performance with 96.02% accuracy on the constructed dataset. This work enables more efficient and precise durian cultivation with less labour and higher-quality yields.

We study the properties of solid-based random lasers by observing spectral emission shape, emission intensity, emission line width, and lasing threshold. Random lasers based on solid polymer film are prepared by combining titania nanoparticles with rhodamine 640 in poly(vinyl alcohol) (PVA) water solution. We compare samples with and without scatterers. Adding nanoparticles to the samples narrows the emission spectrum, and the lasing threshold can be observed. The amount of titanium dioxide (TiO2) or titania nanoparticles is varied and affects the properties of random lasers. The spectral emission line width becomes narrower, and the lasing threshold decreases when the amount of titania increases. Results obtained show that the properties of solid-based random lasers can be improved by increasing scattering, which enhances the feedback mechanism. The enhanced properties of random lasers are very useful for creating a reliable optical device for future applications in medical and biosensing fields.

Orthogonal Frequency Division Multiplexing (OFDM) is one of the multicarrier transmission techniques used in wireless communication system. It has many benefits such as robust in channel fading and has high spectral density. The main objective of OFDM implementation in wireless communication system is to achieve less or zero Bit Error Rate (BER). However, OFDM design complexity, requirement and selection of the suitable modulation method are among the current issues. Thus, this paper aims to investigate the performance of OFDM in wireless communication by developing two OFDM based system designs. The transmitter, channel and receiver are designed based on OFDM system principles. Forward Error Correction (FEC) method is applied to reduce the BER. Both OFDM designs produce less BER with zero BER for the second OFDM design. The investigation study shows that the performance of OFDM can be enhanced by applying Fast Fourier Transform (FFT) technique. Zero BER can be achieved if the suitable modulation scheme is applied in the system. The developed designs are not complex, suitable to be applied for IEEE 802.11 standard. The BER performance can be influenced by the types of channels, signal to noise ratio (SNR) and various modulation schemes. Thus, this study can be used as a guidance to implement the OFDM in the current or future wireless communication system. � BEIESP.

Tipping or depositing large waste onto land using unauthorized and unlicensed methods are considered as illegal dumping. The increasing rate of illegal dumping becomes a crucial nation issue because this activity causes negative impacts to social, economy and environment. Thus, study on detecting the dumping activities is conducted to control the illegal dumping activities in Malaysia. Raspberry Pi with Python language is used as the microprocessor and a Raspberry Pi camera module with a microwave radar sensor are interfaced to it to capture the image of any vehicles entering the illegal dumping site. The image is captured to recognize the license plate of the vehicle. The method in this study is by using Open Automatic License Plate Recognition (ALPR), Open Computer Vision (CV) libraries and Optical Character Recognition (OCR) to detect the character of the plate registration number. The outcome of the study consists of recognition of Malaysia vehicles’ plate number and the automatic real time email notification on the illegal dumping case. The detection system can be used for case monitoring since the plate number recognition is done in real time. The system can be upgraded to ensure its sustainability in the harsh and isolated environment.

Water is an essential element for every plant to survive, absorb nutrients, and perform photosynthesis and respiration. If water is polluted, plant growth can be truncated. The aim of this research is to develop a water quality monitoring system for agriculture purposes based on integration of sensing framework with a smart decision support method. This research consists of three stages: (1) the first stage: developing sensing framework which has four different water quality parameter sensors such as potential hydrogen (pH), electrical conductivity (EC), temperature, and oxidation-reduction potential (ORP), (2) the second stage: developing a hardware platform that uses an Arduino for sensor array of data processing and acquisition, and finally (3) the third stage: developing soft computing framework for decision support which uses python applications and fuzzy logic. The system was tested using water from many sources such as rivers, lakes, tap water, and filtered machine. Filtered water shows the highest value of pH as the filtered machine produces alkaline water, whereas tap water shows the highest value of temperature because the water is trapped in a polyvinyl chloride (PVC) pipe. Lake water depicts the highest value of EC due to the highest amount of total suspended solids (TSS) in the water, whereas river water shows the highest value of ORP due to the highest amount of dissolved oxygen. The system can display three ranges of water quality: not acceptable (NA), adequate (ADE) and highly acceptable (HACC) ranges from 0 to 9. Filtered water is in HACC condition (ranges 7–9) because all water quality parameters are in highly acceptable ranges. Tap water shows ADE condition (ranges 4–7) because one of the water quality parameters is in adequate ranges. River and lake water depict NA conditions (ranges 0–4) as one of the water quality parameters is in not acceptable ranges. The research outcome shows that filtered water is the most reliable water source for plants due to the absence of dissolved solids and contaminants in the water. Filtered water can improve pH and reduce the risk of plant disease. This research can help farmers to monitor the quality of irrigated water which eventually prevents crop disease, enhances crop growth, and increases crop yield.