In this paper, we propose a quantized YCbCr color space (QYCbCr) technique which is employed in standard JPEG. The objective of this work is to accelerate computational time of the standard JPEG image compression algorithm. This is a development of the standard JPEG which is named QYCBCr algorithm. It merges two processes i.e., YCbCr color space conversion and Q quantization in which in the standar JPEG they were performed separately. The merger forms a new single integrated process of color conversion which is employed prior to DCT process by subsequently eliminating the quantization process. The equation formula of QYCbCr color coversion is built based on the chrominance and luminance properties of the human visual system which derived from quatization matrices. Experiment results performed on images of different sizes show that the computational running time of QYCbCr algorithm gives 4 up to 8 times faster than JPEG standard, and also provides higher compression ratio and better image quality.
This research was aims to design a prototype and application for early detection of Happy Hypoxia symptoms in COVID-19 patients based on the Internet of Things (IoT).The experimental board consists of a microcontroller integrated with a pulse oximetry sensor, a heart rate sensor and a body temperature sensor. To determine the feasibility of the tool, an accuracy test is carried out. Accuracy shows the value of the proximity of the measurement results to the reference value or standard value. Accuracy is obtained by calculating the error value. The accuracy of the sensors used in this study refers to standard medical equipment that has been calibrated. In this study, the accuracy value was obtained by referring to the mean of error rate of each sensor. The sensor reading value is sent from the microcontroller to firebase as a cloud database and then displayed on the application dashboard as a user interface. From this data, it is hoped that it can help COVID-19 sufferers to detect the symptoms of Happy Hypoxia and help medical experts for prognostic or therapeutic purposes.
Kasus kehilangan bayi sering terjadi di rumah sakit/bersalin yang biasanya pelaku sering menyamar sebagai tenaga medis. Hal ini terjadi karena belum maksimalnya keamanan bayi di rumah sakit/bersalin sehingga perlunya dibuat sistem yang dapat mencegah terjadinya penculikan ini. Sistem keamanan pintu inkubator bayi telah di rancang menggunakan solenoid sebagai media pengunci dan kamera web untuk mendeteksi dan mengenali wajah yang ingin mengakses/membuka pintu inkubator bayi menggunakan OpenCV library dengan Histogram Pola Biner Lokal dan Pengklasifikasi Kaskade Haar. Hasil ujicoba menunjukkan bahwa sistem keamanan inkubator bayi mampu melakukan pendeteksian sekaligus pengenalan wajah pengguna berdasarkan database wajah yang sudah tersimpan di sistem. Pintu inkubator bayi akan terbuka jika wajah pengguna sudah terdaftar di sistem. Prototipe Sistem keamanan inkubator bayi dengan pengenalan wajah ini menjadi solusi untuk mencegah penculikan/kehilangan atau tertukarnya bayi di rumah sakit/bersalin.
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