{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:36:47Z","timestamp":1760233007570,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,11]],"date-time":"2022-12-11T00:00:00Z","timestamp":1670716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Kumoh National Institute of Technology","award":["2021"],"award-info":[{"award-number":["2021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Ultrasound systems have been widely used for consultation; however, they are susceptible to cyberattacks. Such ultrasound systems use random bits to protect patient information, which is vital to the stability of information-protecting systems used in ultrasound machines. The stability of the random bit must satisfy its unpredictability. To create a random bit, noise generated in hardware is typically used; however, extracting sufficient noise from systems is challenging when resources are limited. There are various methods for generating noises but most of these studies are based on hardware. Compared with hardware-based methods, software-based methods can be easily accessed by the software developer; therefore, we applied a mathematically generated noise function to generate random bits for ultrasound systems. Herein, we compared the performance of random bits using a newly proposed mathematical function and using the frequency of the central processing unit of the hardware. Random bits are generated using a raw bitmap image measuring 1000 \u00d7 663 bytes. The generated random bit analyzes the sampling data in generation time units as time-series data and then verifies the mean, median, and mode. To further apply the random bit in an ultrasound system, the image is randomized by applying exclusive mixing to a 1000 \u00d7 663 ultrasound phantom image; subsequently, the comparison and analysis of statistical data processing using hardware noise and the proposed algorithm were provided. The peak signal-to-noise ratio and mean square error of the images are compared to evaluate their quality. As a result of the test, the min entropy estimate (estimated value) was 7.156616\/8 bit in the proposed study, which indicated a performance superior to that of GetSystemTime. These results show that the proposed algorithm outperforms the conventional method used in ultrasound systems.<\/jats:p>","DOI":"10.3390\/s22249709","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T05:10:19Z","timestamp":1670821819000},"page":"9709","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Mathematically Generated Noise Technique for Ultrasound Systems"],"prefix":"10.3390","volume":"22","author":[{"given":"Hojong","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Gachon University, Seongnam 13120, Republic of Korea"}]},{"given":"Seung-Hyeok","family":"Shin","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Big-Data Science, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,11]]},"reference":[{"key":"ref_1","unstructured":"Shung, K.K., and Thieme, G.A. 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