scholarly journals Rancangan dan Implementasi Sistem Absensi dengan Sensor Fingerprint dan Sensor Suhu Non–Contact Berbasis IoT Menggunakan Google Sheets

2021 ◽  
Vol 2 (1) ◽  
pp. 28-35
Author(s):  
SOTYOHADI SOTYOHADI
Keyword(s):  
Fingerprint Data

Virus Corona atau Covid-19 merupakan virus baru dari Wuhan, Provinsi Hubei yang menyebar secara contagious. Pada bulan Juni 2020, Kementrian Pendidikan, Nadiem Makarim, mengatakan bahwa pada bulan November murid Taman Kanak-kanak telah diperbolehkan masuk sekolah. Namun, dengan jumlah murid tiap kelas sebanyak 5 anak serta harus mematuhi protokol kesehatan.  Gejala dari Virus Corona yaitu seperti flu yang ditandai dengan kenaikan suhu tubuh pada manusia. Sehingga, pandemi Covid-19 telah mengubah aspek pada dunia pendidikan, karena mengharuskan semua elemen pendidikan untuk beradaptasi dan melanjutkan sisa semester. Sebagai salah satu upaya pendeteksian dini dan pencegahan penularan pada dunia pendidikan yang melangsungkan kegiatan belajar mengajar secara offline, yaitu dengan membuat alat absensi sekaligus pendeteksi suhu tubuh siswa sekolah. Penelitian ini merancang dan mengimplementasikan sensor suhu MLX90614 yang terintegrasi dengan sensor fingerprint. Data suhu tubuh dan kehadiran siswa nantinya akan dikirim dengan ESP8266 menuju internet, sehingga internet berfungsi sebagai penyimpanan database pengukuran suhu dan kehadiran siswa. Oleh karena itu, user dapat dengan mudah memonitoring kehadiran siswa sekaligus kondisi suhu tubuh siswa melalui sebuah aplikasi yang dapat diunduh pada smartphone.

scholarly journals Assessing Geographical Origin of Gentiana Rigescens Using Untargeted Chromatographic Fingerprint, Data Fusion and Chemometrics

Molecules ◽  
2019 ◽  
Vol 24 (14) ◽  
pp. 2562 ◽  
Author(s):  
Shen ◽  
Yu ◽  
Wang
Keyword(s):  
Data Fusion ◽  
Geographical Origin ◽  
Data Sets ◽  
Classification Models ◽  
Hplc Fingerprint ◽  
Low Level ◽  
Level Data ◽  
Stems And Leaves ◽  
Gentiana Rigescens ◽  
Fingerprint Data

Gentiana rigescens Franchet, which is famous for its bitter properties, is a traditional drug of chronic hepatitis and important raw materials for the pharmaceutical industry in China. In the study, high-performance liquid chromatography (HPLC), coupled with diode array detector (DAD) and chemometrics, were used to investigate the chemical geographical variation of G. rigescens and to classify medicinal materials, according to their grown latitudes. The chromatographic fingerprints of 280 individuals and 840 samples from rhizomes, stems, and leaves of four different latitude areas were recorded and analyzed for tracing the geographical origin of medicinal materials. At first, HPLC fingerprints of underground and aerial parts were generated while using reversed-phase liquid chromatography. After the preliminary data exploration, two supervised pattern recognition techniques, random forest (RF) and orthogonal partial least-squares discriminant analysis (OPLS-DA), were applied to the three HPLC fingerprint data sets of rhizomes, stems, and leaves, respectively. Furthermore, fingerprint data sets of aerial and underground parts were separately processed and joined while using two data fusion strategies (“low-level” and “mid-level”). The results showed that classification models that are based OPLS-DA were more efficient than RF models. The classification models using low-level data fusion method built showed considerably good recognition and prediction abilities (the accuracy is higher than 99% and sensibility, specificity, Matthews correlation coefficient, and efficiency range from 0.95 to 1.00). Low-level data fusion strategy combined with OPLS-DA could provide the best discrimination result. In summary, this study explored the latitude variation of phytochemical of G. rigescens and developed a reliable and accurate identification method for G. rigescens that were grown at different latitudes based on untargeted HPLC fingerprint, data fusion, and chemometrics. The study results are meaningful for authentication and the quality control of Chinese medicinal materials.

MODIFIED PRINCIPAL CURVES BASED FINGERPRINT MINUTIAE EXTRACTION AND PSEUDO MINUTIAE DETECTION

2011 ◽  
Vol 25 (08) ◽  
pp. 1243-1260 ◽  
Author(s):  
HONGYUN ZHANG ◽  
DUOQIAN MIAO ◽  
CAIMING ZHONG
Keyword(s):  
Extraction Method ◽  
Detection Method ◽  
Polygonal Line ◽  
Experimental Results ◽  
Data Sets ◽  
Gray Level ◽  
Principal Curves ◽  
Minutiae Extraction ◽  
Fingerprint Minutiae ◽  
Fingerprint Data

It is difficult but crucial for minutiae extraction and pseudo minutiae deletion of low quality fingerprint images in auto fingerprint identification systems. Traditional methods based on thinning images or gray-level images are, however, susceptible to noise. Reference 14 indicated that principal curves based fingerprint minutiae extraction was feasible to overcome the drawback, but the extended polygonal line (EPL) principal curves algorithm used in the paper extracted the principal curves ineffectively. As the fingerprint data sets are usually large, the original EPL principal curves algorithm is time-consuming. Meanwhile, scattered fingerprint data lead to the deviation of fingerprint skeleton. In this paper, the algorithm is modified, and a fingerprint minutiae extraction and pseudo minutiae detection method based on principal curves is proposed. Experimental results show that the modified EPL principal curves algorithm outperforms the original EPL algorithm both in efficiency and quality, and the proposed minutiae extraction method outperforms the methods proposed by Miao under noise conditions.

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