Microcontroller Based System for Voice Disorders Detection

Penelitian ini terkait pada usaha untuk menciptakan alat monitoring aktivitas vokal berbasis mikrokontroler. Selanjutnya, alat ini dapat digunakan sebagai pendeteksi dini kekacauan suara (voice disorders). Penggunaan alat ini dirasa penting untuk pengguna yang berkepentingan. Aktivitas utama penelitian adalah mendesain sebuah sistem data logger, baik yang mencakup perangkat keras dan perangkat lunak. Perangkat keras mencakup sistem logger, untai pengkondisian sinyal, dan untai catu daya.Data yang diperoleh oleh mikrokontroler selanjutnya akan dianalisis menggunakan software MATLAB. Alat dirancang untuk bekerja pada mode normal dan kalibrasi. Analisis mencakup kalibrasi terhadap Sound Pressure Level (SPL) dan estimasi frekuensi fundamental (f0) yang merupakan parameter dasar untuk memonitor aktivitas vokal. Berdasarkan penelitian yang telah dilakukan, diperoleh konstanta kalibrasi K  sebesar 1.76 Pa/V (dengan standar deviasi 0.12%) dan rata-rata error untuk ECM sebesar -1.8dB (dengan standar deviasi 4.6 dB) terhadap mikrofon standar. Selanjutnya, setelah diperoleh data secara secara teknis, diperlukan kerjasama berbagai pihak yang terkait untuk merepresentasikan data tersebut kedalam tingkat kelelahan suara

Sub-1 GHz Wireless Nodes Performance Evaluation for Intelligent Greenhouse System

Greenhouses provide not only solution to problems faced by conventional farming systems but also play an important role to improve the energy efficiency and environmentally friendly awareness. To achieve benefits of greenhouse farming system in terms of energy efficiency, research related to this issue have been done by many researchers. However, resources that concern on how to practically implement the particular energy-saving technology for greenhouses need to be improved. In this research, field experiment results related to low-power communication between nodes have been reported by implementing universal prototype modules. The pros and cons of existing communication technology, the proposed architecture of network and module analysis, and the performance evaluation of the proposed module dedicated to intelligent greenhouse farming system were also discussed

Feature engineering and long short-term memory for energy use of appliances prediction

Electric energy consumption in a residential household is one of the key factors that affect the overall national electricity demand. Household appliances are one of the most electricity consumers in a residential household. Therefore, it is crucial to make a proper prediction for the electricity consumption of these appliances. This research implemented feature engineering technique and long short-term memory (LSTM) as a model predictor. Principal component analysis (PCA) was implemented as a feature extractor by reducing the final 62 features to 25 principal components for the LSTM inputs. Based on the experiments, the two-layered LSTM model (composed by 25 and 20 neurons for the first and second later respectively) with lookback number of 3 found to give the best performance with the error rates of 62.013 and 26.982 for RMSE and MAE, respectively

Penerapan Mikrokontroler Sebagai Sistem Kendali Perangkat Listrik Berbasis Android

Perkembangan teknologi seperti perangkat bergerak (ponsel) pintar yang yang dilengkapi dengan teknologi  jaringan  dapat  digunakan  untuk  mengatasi  masalah  penghemataan  listrik. Saklar  listrik  dapat  diganti  dengan  menggunakan  peralatan  relai  dan  dikendalikan  melalui  mikrokontroler  berbasis  jaringan  sehingga  dapat  terhubung  ke  ponsel  pintar  yang  sudah  terpasang  program  pengendali  akan  digunakan  untuk  mengatasi  masalah  tersebut.  Adanya peralatan Wifi- Shield dan sensor arus listrik yang dapat berkomunikasi dengan  mikrokontroler,  memungkinkan terbentuknya komunikasi secara serial.  Perintah  berupa  perintah  on/off  pada saklar dan informasi daya yang terpakai  akan  dikirim  dari  ponsel  pintar  dengan  koneksi  nirkabel menggunakan protocol Wifi. Sistem ini dapat menjadi pilihan solusi untuk system pengendalian jarak jauh. Dari hasil  percobaan  dengan  menggunakan  teknologi  tersebut,  ponsel  pintar  berbasis  Android  yang  terpasang  program  pengendali  saklar  listrik  dapat  mematikan  atau  menghidupkan  peralatan  listrik  dari  jarak  yang  cukup  jauh  (sejauh  koneksi  wifi  yang  tersedia). &nbsp

Optimising deep learning at the edge for accurate hourly air quality prediction

Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In this article, we address this challenge by: (1) designing a novel hybrid deep learning model for hourly PM2.5 pollutant prediction; (2) optimising the obtained model for edge devices; and (3) examining model performance running on the edge devices in terms of both accuracy and latency. The hybrid deep learning model in this work comprises a 1D Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to predict hourly PM2.5 concentration. The results show that our proposed model outperforms other deep learning models, evaluated by calculating RMSE and MAE errors. The proposed model was optimised for edge devices, the Raspberry Pi 3 Model B+ (RPi3B+) and Raspberry Pi 4 Model B (RPi4B). This optimised model reduced file size to a quarter of the original, with further size reduction achieved by implementing different post-training quantisation. In total, 8272 hourly samples were continuously fed to the edge device, with the RPi4B executing the model twice as fast as the RPi3B+ in all quantisation modes. Full-integer quantisation produced the lowest execution time, with latencies of 2.19 s and 4.73 s for RPi4B and RPi3B+, respectively. View Full-Text 10 selecting the best model, we optimise the model for edge devices, using Raspberry Pi 3 Model B+ 11 (RPi3B+) and Raspberry Pi 4 Model B boards (RPi4B). The lite version produced 4 times smaller 12 file size compared to the original version. From the lite version, further size reduction can be 13 achieved by implementing different post-training quantisations. About a 47% reduction can be 14 achieved by dynamic range quantisation, about 45% by full integer quantisation, and about 35% 15 by float16 quantisation. A total of 8272 hourly samples were continuously executed directly at the 16 edge. The RPi4B executed these data two times faster compared to the RPi3B+ in all quantisation 17 modes. Full-integer quantisation produced the most effective time execution, with latencies of 18 2.19 seconds and 4.73 seconds for RPi4B and RPi3B+, respectively

Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

A key challenge in building machine learning models for time series prediction is the incompleteness of the datasets. Missing data can arise for a variety of reasons, including sensor failure and network outages, resulting in datasets that can be missing significant periods of measurements. Models built using these datasets can therefore be biased. Although various methods have been proposed to handle missing data in many application areas, more air quality missing data prediction requires additional investigation. This study proposes an autoencoder model with spatiotemporal considerations to estimate missing values in air quality data. The model consists of one-dimensional convolution layers, making it flexible to cover spatial and temporal behaviours of air contaminants. This model exploits data from nearby stations to enhance predictions at the target station with missing data. This method does not require additional external features, such as weather and climate data. The results show that the proposed method effectively imputes missing data for discontinuous and long-interval interrupted datasets. Compared to univariate imputation techniques (most frequent, median and mean imputations), our model achieves up to 65% RMSE improvement and 20–40% against multivariate imputation techniques (decision tree, extra-trees, k-nearest neighbours and Bayesian ridge regressors). Imputation performance degrades when neighbouring stations are negatively correlated or weakly correlated

Classification of chili plant origin by using multilayer perceptron neural network

The geographical origin of the plants can affect the growth and hence the quality of the plants. In this research, the origin of the chili plants has been investigated by using Fourier transform infrared (FTIR) spectroscopy. The spectroscopy generated 3734 data with a wavenumber range from 4000–400 cm −1 . The pre-processing of the spectra was done by using baseline correction and vector normalization. The analysis was then taken in the biofingerprint area of 1800–900 cm −1 range which has 934 data points. Feature extraction for dimension reduction was achieved using principal component analysis (PCA). The PC scores from PCA were then fed into a k-means and a multilayer perceptron neural network (MLPNN). The k-means clustering shows that the samples can be distinguished into three different groups. Meanwhile, for the MLPNN, the number of the hidden layer's neurons and the learning rate of the system were optimized to get the best classification result. A hidden layer with twenty neurons had the highest accuracy, while a learning rate of 0.001 had the highest value of 100%

TEKNIK ANTARMUKA SECARA SERIAL PERIPHERAL INTERFACE (SPI) MENGGUNAKAN PLATFORM ARDUINO DAN MATLAB

Pada port serial mikrokontroler, komunikasi antara Transmitter (TX) dengan Receiver (RX) dikenal sebagai komunikasi secara asinkron (asynchronous). Karena operasi pada mikrokontroler umumnya berdasarkan detak yang disinkronkan menggunakan kristal (clock) tunggal, maka perbedaan kecepatan dapat menjadi masalah ketika dua perangkat yang sama-sama dilengkapi dengan sumber detak yang berbeda saling berkomunikasi. Ini adalah salah satu kelemahan komunikasi secara asinkron. Salah satu solusi yang digunakan untuk mengatasi masalah ini adalah dengan menggunakan komunikasi secara Serial Peripheral Interface (SPI) yang bersifat sinkron. Pada penelitian ini, antarmuka SPI pada sistem multi mikrokontroler dengan menggunakan platform Arduino dan MATLAB telah dipaparkan. Bedasarkan hasil penelitian, sistem komunikasi secara SPI yang terkontrol MATLAB telah berjalan, dan komunikasi master-slave berjalan baik

INTER-INTEGRATED CIRCUIT (I2C) SEBAGAI SISTEM KOMUNIKASI MULTI-MIKROKONTROLER MENGGUNAKAN PLATFORM ARDUINO DAN MATLAB

Pada aplikasi yang menggunakan mikrokontroler sebagai perangkat utama, pengguna sering dihadapkan pada masalah kurangnya jumlah pin yang tersedia pada suatu mikrokontoler. Terdapat dua alternatif yang dapat dilakukan ketika penggunaan pin menjadi masalah yang krusial, yaitu dengan mengganti jenis mikrokontroler atau menggunakan lebih dari satu buah mikrokontroler (multi-mikrokontroler). Kedua alternatif ini memiliki keunggulan dan kelemahannya masing-masing. Pada penelitian ini, penggunaan protokol Inter-integrated Circuit (I2C) akan diterapkan untuk sistem multi-mikrokontroler dan multi-sensor menggunakan Platform Arduino yang terkontrol MATLAB. Sebuah Master dan dua buah slave akan diuji pada penelitian ini. Master dan Slave akan sepenuhnya dikontrol menggunakan MATLAB. Kedua slave akan ditanamkan program Arduino, sedangkan Master akan menggunakan program MATLAB. Hasil dari penelitian ini menunjukkan bahwa kedua Slave dapat dikontrol dengan baik, baik membaca sensor yang terpasang maupun mengontrol LED. Sistem komunikasi secara I2C telah terbangun dengan baik