Analytical Analysis of Flexible Microfluidic Based Pressure Sensor Based on Triple-Channel Design

In designing a flexible microfluidic-based pressure sensor, the microchannel plays an important role in maximizing the sensor's performance. Similarly, the material used for the sensor's membrane is crucial in achieving optimal performance. This study presents an analytical analysis and FEA simulation of the membrane and microchannel of the flexible pressure sensor, aimed at optimizing it design and material selection. Different types of materials, including two commonly used polymers, Polyimide (PI) and Polydimethylsiloxane (PDMS) were evaluated. Moreover, different designs of the microchannel, including single-channel, double-channel, and triple-channel, were analyzed. The applied pressure, width of the microchannel, and length of the microchannel were varied to study the normalized resistance of the microchannel and maximize the performance of the pressure sensor. The results showed that the triple-channel design produced the highest normalized resistance. To achieve maximum performance, it is found that using a membrane with a large area facing the applied pressure was optimal in terms of dimensions. In conclusion, optimizing the microchannel and membrane design and material selection is crucial in improving the overall performance of flexible microfluidic-based pressure sensors

Deep Learning Approaches for Automatic Drum Transcription

Drum transcription is the task of transcribing audio or music into drum notation. Drum notation is helpful to help drummers as instruction in playing drums and could also be useful for students to learn about drum music theories. Unfortunately, transcribing music is not an easy task. A good transcription can usually be obtained only by an experienced musician. On the other side, musical notation is beneficial not only for professionals but also for amateurs. This study develops an Automatic Drum Transcription (ADT) application using the segment and classify method with Deep Learning as the classification method. The segment and classify method is divided into two steps. First, the segmentation step achieved a score of 76.14% in macro F1 after doing a grid search to tune the parameters. Second, the spectrogram feature is extracted on the detected onsets as the input for the classification models. The models are evaluated using the multi-objective optimization (MOO) of macro F1 score and time consumption for prediction. The result shows that the LSTM model outperformed the other models with MOO scores of 77.42%, 86.97%, and 82.87% on MDB Drums, IDMT-SMT Drums, and combined datasets, respectively. The model is then used in the ADT application. The application is built using the FastAPI framework, which delivers the transcription result as a drum tab

Planar Microwave Sensor with High Sensitivity for Material Characterization Based on Square Split Ring Resonator (SSRR) for Solid and Liquid

Microwave resonator sensors are the most extensively used sensors in the food industries, quality assurance, medical, and manufacturing. Planar resonant technique is chosen as the medium for characterizing dielectric properties of material due to its compact in size, low cost and easy to fabricate. But these techniques have a low Q-factor and little sensitivity. This work uses the perturbation approach to overcome this technique's flaw, which is that Q-factor and resonant frequency are affected by the resonator's dielectric properties. This suggested sensor operated at 2.5GHz between 1GHz and 4GHz for material characterisation of solid and liquid samples. These sensors were constructed on a substrate made of RT/Duroid Roger 5880, which has a copper layer that is 0.0175 mm thick and has a dielectric constant of 2.2. This square split ring resonator (SSRR) sensor thus generates narrower resonant, low insertion loss, and a high Q-factor value of 430 at 2.5GHz. The SSRR sensor's sensitivity is 98.59%, which is higher than that of past studies. The application of the suggested sensor as a tool for material characterisation, particularly for identifying material attributes, is supported by this findings

Numerical Analysis of Wave Load Characteristics on Jack-Up Production Platform Structure Using Modified k-ω SST Turbulence Model

One of the important stages in the offshore structure design process is the evaluation of the marine hydrodynamic load in which the structure operates, this is to ensure an appropriate design and improve the safety of the structure. Therefore, accurate modeling of the marine environment is needed to produce good evaluation data, one of the methods that can accurately model the marine environment is through the Computational Fluid Dynamic (CFD) method. This research aims to analyze the ocean wave load of pressure and force characteristics on the jack-up production platform hull structure using the (CFD) method. The foam-extend 4.0 (the fork of the OpenFOAM) software with waveFoam solver is utilized to predict the free surface flow phenomena as its capability to predict with accurate results. The Reynold Averaged Navier Stokes (RANS) turbulence model of k-ω SST is applied to predict the turbulence effect in the flow field. Five variations of incident wave direction type are carried out to examine its effect on the pressure and force characteristics on the jack-up production platform hull. The wave model shows inaccurate results with the decrease in wave height caused by excessive turbulence in the water surface area. Excessive turbulence levels can be overcome by incorporating density variable and buoyancy terms based on the Standard Gradient Diffusion Hypothesis (SGDH) into the turbulent kinetic energy equation. The k-ω SST Buoyancy turbulence model shows accurate results when verified to predict wave run-up and horizontal force loads on monopile structures. Furthermore, test results of the wave load on the jack-up production platform hull structure shows that the most significant wave load is obtained in variations with the wave arrival direction relatively opposite to the platform wall. Especially in the direction of 90° because it also has the most expansive impact surface area. Meanwhile, the lower wave load is obtained in variations 45° and 135°, which have the relatively oblique direction of wave arrival to the surface

Performance Analysis of MIMO-OFDM System Using Predistortion Neural Network with Convolutional Coding Addition to Reduce SDR-Based HPA Nonlinearity

In recent years, the development of communication technology has advanced at an accelerated rate. Communication technologies such as 4G, 5G, Wi-Fi 5 (802.11ac), and Wi-Fi 6 (802.11ax) are extensively used today due to their excellent system quality and extremely high data transfer rates. Some of these technologies incorporate MIMO-OFDM into their protocol. MIMO-OFDM is widely used in modern communication systems due to its benefits, which include high data rates, spectral efficiency, and fading resistance. Despite these benefits, MIMO-OFDM has disadvantages, with the use of a nonlinear HPA being one of them. Nonlinear HPA causes in-band and out-of-band distortions in MIMO-OFDM signals. Utilizing predistortion (PD) is one way of solving this issue. PD is a technique that uses the inverse distortion of the HPA to compensate for the nonlinear characteristics of the HPA. To enhance the quality of MIMO-OFDM systems that the use of HPA has degraded, the convolutional coding (CC) method can be combined with the help of PD. Convolutional coding is a type of channel coding that can be used for error detection and correction. This study will evaluate a combined technique of PD neural networks (PDNN) and CC on the MIMO-OFDM system using Software Defined Radio (SDR) devices. The evaluation of this system led to the use of a technique that combines PDNN and CC to improve SNR and minimise BER on MIMO-OFDM systems that HPA on SDR devices has degraded. In addition, at code rates 1/2, 2/3, and 3/4, using PDNN reduces the SNR value required to achieve BER equal to 0 by 12.037%, 37.8%, and 4.10% when compared to Digital Predistortion (DPD)

An Implementation of blood Glucose and cholesterol monitoring device using non-invasive technique

Invasive testing of glucose and cholesterol levels in the blood is the most prevalent procedure, which is uncomfortable, expensive, and risky since it can spread infections and harm skin cells. Diabetes and cholesterol are two of the most common diseases in the world, and they require constant monitoring to avoid health issues and organ damage. As a result, a non-invasive approach will allow for more regular testing and painless monitoring. The blood glucose and cholesterol levels can be assessed using the principle of reflecting and refractive properties of NIR light source against blood components. The MAX30100 sensor circuit gives SPO2 (Saturated Peripheral Oxygen Level) and BPM (beats per minute, or heart rate) information to the regression model, which is used to forecast blood glucose and cholesterol levels. The polynomial regression model is trained using preset datasets, and the trained model yields regression co-efficient values. For the fresh sample inputs from the sensor, the co-efficient values are used to estimate the new needed parameter value. The projected blood glucose and cholesterol levels are displayed on the LCD Display and delivered through Bluetooth HC-05 module via Serial communication to the mobile application

Modified Deep Pattern Classifier on Indonesian Traditional Dance Spatio-Temporal Data

Traditional dances, like those of Indonesia, have complex and unique patterns requiring accurate cultural preservation and documentation classification. However, traditional dance classification methods often rely on manual analysis and subjective judgment, which leads to inconsistencies and limitations. This research explores a modified deep pattern classifier of traditional dance movements in videos, including Gambyong, Remo, and Topeng, using a Convolutional Neural Network (CNN). Evaluation model's performance using a testing spatio-temporal dataset in Indonesian traditional dance videos is performed. The videos are processed through frame-level segmentation, enabling the CNN to capture nuances in posture, footwork, and facial expressions exhibited by dancers. Then, the obtained confusion matrix enables the calculation of performance metrics such as accuracy, precision, sensitivity, and F1-score. The results showcase a high accuracy of 97.5%, indicating the reliable classification of the dataset. Furthermore, future research directions are suggested, including investigating advanced CNN architectures, incorporating temporal information through recurrent neural networks, exploring transfer learning techniques, and integrating user feedback for iterative refinement of the model. The proposed method has the potential to advance dance analysis and find applications in dance education, choreography, and cultural preservation

A Combination of Lexicon-based and Distributional Representations for Classification of Indonesian Vaccine Acceptance Rates

When the COVID-19 pandemic hit, the use of vaccines was advertised as the end of the pandemic by the entire world. However, the chances of vaccination depended on the sentiments of society and individuals about the vaccine. People's acceptance of vaccines can change depending on conditions and events. Social media platforms such as Twitter can be used as a source of information to find out the conditions and attitudes of the community toward the program. By implementing a machine learning technique on the COVID-19 vaccine dataset, we hope to impact the classification result with text. This study suggests three distinct machine learning models for classifying texts of the COVID-19 vaccination, namely a model based on the first lexicon using the feature extraction method; second, using the word insertion technique to utilize distribution representation; and third, a combination model of distribution representation and feature extraction based on the lexicon. From the evaluation that has been carried out, we found that a combination of lexicon-based and distributional representation methods succeeded in giving the best results for classifying the level of acceptance of the COVID-19 vaccine in Indonesia with an accuracy score of 71.44% and an F1-score of 71.43%

IRAWNET: A Method for Transcribing Indonesian Classical Music Notes Directly from Multichannel Raw Audio

A challenging task when developing real-time Automatic Music Transcription (AMT) methods is directly leveraging inputs from multichannel raw audio without any handcrafted signal transformation and feature extraction steps. The crucial problems are that raw audio only contains an amplitude in each timestamp, and the signals of the left and right channels have different amplitude intensities and onset times. Thus, this study addressed these issues by proposing the IRawNet method with fused feature layers to merge different amplitude from multichannel raw audio. IRawNet aims to transcribe Indonesian classical music notes. It was validated with the Gamelan music dataset. The Synthetic Minority Oversampling Technique (SMOTE) overcame the class imbalance of the Gamelan music dataset. Under various experimental scenarios, the performance effects of oversampled data, hyperparameters tuning, and fused feature layers are analyzed. Furthermore, the performance of the proposed method was compared with Temporal Convolutional Network (TCN), Deep WaveNet, and the monochannel IRawNet. The results proved that proposed method almost achieves superior results in entire metric performances with 0.871 of accuracy, 0.988 of AUC, 0.927 of precision, 0.896 of recall, and 0.896 of F1 score

Human-machine Translation Model Evaluation Based on Artificial Intelligence Translation

As artificial intelligence (AI) translation technology advances, big data, cloud computing, and emerging technologies have enhanced the progress of the data industry over the past several decades. Human-machine translation becomes a new interactive mode between humans and machines and plays an essential role in transmitting information. Nevertheless, several translation models have their drawbacks and limitations, such as error rates and inaccuracy, and they are not able to adapt to the various demands of different groups. Taking the AI-based translation model as the research object, this study conducted an analysis of attention mechanisms and relevant technical means, examined the setbacks of conventional translation models, and proposed an AI-based translation model that produced a clear and high quality translation and presented a reference to further perfect AI-based translation models. The values of the manual and automated evaluation have demonstrated that the human-machine translation model improved the mismatchings between texts and contexts and enhanced the accurate and efficient intelligent recognition and expressions. It is set to a score of 1-10 for evaluation comparison with 30 language users as participants, and the achieved 6 points or above is considered effective. The research results suggested that the language fluency score rose from 4.9667 for conventional Statistical Machine Translation to 6.6333 for the AI-based translation model. As a result, the human-machine translation model improved the efficiency, speed, precision, and accuracy of language input to a certain degree, strengthened the correlation between semantic characteristics and intelligent recognition, and pushed the advancement of intelligent recognition. It can provide accurate and high-quality translation for language users and achieve an understanding of natural language input and output and automatic processing