Hari Arief Dharmawan

The accuracy level is important in shooting activities and depends on many factors, such as hand tremors as body vibration and shooting position. Achieving high accuracy in different shooters is challenging, especially in the case of different shooting positions. However, there is a lack of information about the influence of shooting positions and experiences on a shooter’s body vibration and accuracy levels. Thus, this study aims to develop a portable and low-cost hand tremor measurement device (as a function of body vibration) to identify the influence of hand movement on shooting accuracy. For this purpose, low-cost accelerometer sensors and a microcontroller were used as the measurement kit. Three different shooting positions (squatting, standing, and prone) were analyzed. The shooters were classified into novice and expert groups. Each group had five participants with standard fire guns and accelerometer kits. These participants were asked to shoot the target to get their best accuracy. Besides, the hand tremor level data from the self-developed kit were recorded to investigate the hand tremors. The results show that the novice participants have more hand tremors in all shooting positions. There are significant differences between the squatting, standing, and prone positions in hand tremors for novice and expert participants. In the expert group, the prone and squatting positions have the least vibration level, indicated by the least acceleration (0.01 - 0.04 m/s2 for the expert group and 0.02 - 0.11 m/s2 for the novice group). The best accuracy for all positions is also obtained from expert shooters. It can be concluded that different shooting positions are related to the body vibrations. The expert shooters have a lower body vibration than the novice participants. The hand tremor levels may influence the accuracy level since different shooting positions and experiences have different vibration and accuracy levels

Formaldehyde is a kind of air pollutant with strong volatility at room temperature. Formaldehyde is emitted from many sources, such as furnishing, building sect

A frequency counter is essential for resonance-based sensors like quartz crystal microbalance. An electronic nose or tongue using a QCM sensor array requires a multichannel frequency counter to detect the frequency shift of the sensors simultaneously. The frequency counter’s resolution, precision, and sampling speed are important factors. Board size, energy consumption, and rapid deployment are also considered in the design. This work shows the development of an independent multichannel frequency counter using a commercial Xilinx Spartan 6 series XC6SLX9 board module and a microcontroller board. Both modules are general-purpose modules; therefore, there is no need for a printed circuit board design, resulting in a quick implementation: the use of FPGA results in a compact size and low energy consumption. The developed counter is designed based on a reciprocal counter utilizing the internal logic block of the FPGA. The FPGA module has a built-in 50 MHz TCXO clock and is the reference clock. The high-resolution timing of the counter is realized by multiplying the 50 MHz clock by 6 to reach 300 MHz. The multiplication utilizes the PLL modules in the FPGA. The high precision and accuracy of the counter are achieved by calibrating the timing clock to a 10 MHz rubidium oscillator. The data communication to the microcontroller is done via the SPI by implementing the SPI protocol in the FPGA. The resource is optimized by utilizing PLL and DSP blocks for the counter. Only 5% registers and 5% LUTs of the FPGA resource are used to build a four-channel frequency counter. The result shows that the counter can measure the frequency of incoming signals with a resolution of 0.033 Hz at 10 MHz with a sampling time of 1 second. The system has been tested to monitor the frequency changes of a QCM sensor array.

A practical monitoring system implementing an asynchronous interrupt driven sampling technique has been developed in this research. This system is intended to acquire power-line single-phase voltage and current signals as well as their related information such as RMS values and harmonic contents. The system uses a voltage transformer and a current transducer to acquire the signals of the voltage and current. A microcontroller is used in the system to perform acquisition processes, calculation, and wireless data transmission. The design activities include the development of hardware of the system and software in the microcontroller as well as monitoring software with Bluetooth technology that will run on portable devices. As part of the research, a new sampling technique was developed to provide constant and precise timings during the acquisition processes. In this technique, some peripherals related to the acquisition process were driven asynchronously using interrupt mechanisms. Some experimentation has been carried out, and the result showed that the system implementing this technique could successfully manage the timings for the synchronous acquisition sequences. Furthermore, the use of the Bluetooth interfaces in the system allowed practical monitoring process to be performed using portable devices.

Carbon dioxide, or CO2 gas, is an important atmospheric gas in the environment. An increase in CO2 concentration affects environmental damage. On the other side, CO2 concentration increases from year to year. In line with this, there is a need to develop a measurement system of CO2 concentration with good performance. Thus, this study aims to develop a quartz crystal microbalance (QCM)-based CO2 sensor using titanium dioxide nanoparticles and to identify the response and reversibility levels. For this purpose, this study used five sensors with different thicknesses: Q1, Q2, Q3, Q4, and Q5, to identify the response and reversibility responses. The results show that the sensors had a frequency shift of 5.14 kHz (Q1), 5.19 kHz (Q2), 5.70 kHz (Q3), 5.78 kHz (Q4), and 6.05 kHz (Q5). The response times are 80.1 s (Q1), 82.8 s (Q2), 84.6 s (Q3), 85.5 s (Q4), and 247.5 s (Q5). The frequency shifts and the response times indicate that Q5 has the best response for CO2. All sensors have fast recovery times, 10.8 s to 21.6 s. It can be concluded that the developed sensors have good response times, recovery times, and reversibility levels for CO2 gas detection. This sensor can be used as an alternative to a CO2 gas concentration measurement system, providing a novel and rapid detection method and fast recovery time.

This study aims to develop a real-time QCM (quartz crystal microbalance)-based method for a fungal spore mass measurement system as the indoor air pollutants. Penicillium camemberti (B2), Penicillium caseifulvum (B3), and a mixture of both P. camemberti and P. caseivulfum (B4) were used as the bioaerosol samples in the form of fungal spores. These samples were collected and cultured inside an isolated chamber and mixed with fresh air (filtered by a filter paper) to generate bioaerosol with a diameter of less than 1 μm (fine particles). These bioaerosols were filtered to produce different particle diameters using a particulate cyclone (a filter paper and a suction pump). The developed system consisted of a GO (graphene oxide, a graphene derrivative)-coated QCM (Q1), a bare QCM (Q2), a crystal oscillator, and a frequency counter to process the output signal, QCM’s frequency, and bioaerosol mass. The system shows a good performance with the sensitivity of 27x10-2 to 29x10-2 Hz/ng and 23x10-2 to 29x10-2 Hz/ng for coated and uncoated sensors, respectively. The best performance is obtained from the coated QCM sensor Q1. The system works well in measuring bioaerosol concentrations with an accuracy of 82% for the coated QCM and 66% for the uncoated QCM. The coated QCM has the potentials of being developed as a fungal spore sensor.

The complexity of the electric power network causes a lot of distortion, such as a decrease in power quality (PQ) in the form of voltage variations, harmonics, and frequency fluctuations. Monitoring the distortion source is important to ensure the availability of clean and quality electric power. Therefore, this study aims to classify power quality using a neural network with empirical mode decomposition-based feature extraction. The proposed method consists of 2 main steps, namely feature extraction, and classification. Empirical Mode Decomposition (EMD) was also applied to categorize the PQ disturbances into several intrinsic mode functions (IMF) components, which were extracted using statistical parameters and the Hilbert transformation. The statistical parameters consist of mean, root mean squared, range, standard deviation, kurtosis, crest factor, energy, and skewness, while the Hilbert transformation consists of instantaneous frequency and amplitude. The feature extraction results from both parameters were combined into a set of PQ disturbances and classified using Multi-Layer Feedforward Neural Networks (MLFNN). Training and testing were carried out on 3 feature datasets, namely statistical parameters, Hilbert transforms, and a combination of both as inputs from 3 different MLFNN architectures. The best results were obtained from the combined feature input on the network architecture with 2 layers of ten neurons, by 98.4 %, 97.75, and 97.4 % for precision, recall, and overall accuracy, respectively. The implemented method is used to classify PQ signals reliably for pure sinusoids, harmonics with sag and swell, as well as flicker with 100 % precision

This paper presents a design of a PM_{2 5} monitoring system based on the Samyoung sensor type DSM501A with the HC-05 Bluetooth data communication module. The sensor is integrated into an Arduino Nano. The measured data are not only displayed on an LCD but also are sent to a computer using a Bluetooth module for the storage. The data are stored in the excel data format and viewed by using the PLX-DAQ add-on. The system was calibrated by using a standard PM₂₅ measurement device (Kanomax dust monitor type 3443). The calibration procedure was conducted by measuring the PM₂ 5 concentration of the sources. The performance of the system is presented in the optimum communication distance, the concentration range, and the system sensitivity. The result shows that the system works well. The optimum distance between the system and the computer is in the range up to 16 m. The sensitivity of the system is found of 600 μg/m³/minute with the range of the concentration measurement up to 1400 μg/m³ at the room temperature and humidity.

During this time, Single Board Computers (SBCs) are widely used in monitoring and control applications. Many relatively low-cost SBCs are available. This paper explains the implementation of an SBC LattePanda for online monitoring of powers through Local Area Networks (LANs). The SBC was used as the main part of the monitoring system that mainly performed acquisition of voltage and current signals sent by some sensors. A program for the microcontroller on the SBC was written for the acquisition processes. Furthermore, a client/server application was developed using Delphi to perform online monitoring of the signals. Some experimentation was performed to mainly ensure that the microcontroller could capture the voltage and current signals with precise and constant sampling timing as well as send the data to the client/server application successfully. A PicoScope was utilized during the experimentation to gain the timing information performed by the microcontroller. For calibration of the voltage and current, a 4.5-digit digital multimeter was implemented. The final stage of the experimentation revealed that the intended signals could be sampled with a constant timing and in this case 128 samples per channel was chosen. The developed application could also show the expected parameters including the signal of powers using an additional remote computers connected to the LAN.