TY - JOUR
T1 - Profile-splitting linearized bregman iterations for trend break detection applications
AU - Do Amaral, Gustavo Castro
AU - Calliari, Felipe
AU - Lunglmayr, Michael
PY - 2020
Y1 - 2020
N2 - Trend break detection is a fundamental problem that materializes in many areas of applied science, where being able to identify correctly, and in a timely manner, trend breaks in a noisy signal plays a central role in the success of the application. The linearized Bregman iterations algorithm is one of the methodologies that can solve such a problem in practical computation times with a high level of accuracy and precision. In applications such as fault detection in optical fibers, the length N of the dataset to be processed by the algorithm, however, may render the total processing time impracticable, since there is a quadratic increase on the latter with respect to N. To overcome this problem, the herewith proposed profile-splitting methodology enables blocks of data to be processed simultaneously, with significant gains in processing time and comparable performance. A thorough analysis of the efficiency of the proposed methodology stipulates optimized parameters for individual hardware units implementing the profile-splitting. These results pave the way for high performance linearized Bregman iteration algorithm hardware implementations capable of efficiently dealing with large datasets.
AB - Trend break detection is a fundamental problem that materializes in many areas of applied science, where being able to identify correctly, and in a timely manner, trend breaks in a noisy signal plays a central role in the success of the application. The linearized Bregman iterations algorithm is one of the methodologies that can solve such a problem in practical computation times with a high level of accuracy and precision. In applications such as fault detection in optical fibers, the length N of the dataset to be processed by the algorithm, however, may render the total processing time impracticable, since there is a quadratic increase on the latter with respect to N. To overcome this problem, the herewith proposed profile-splitting methodology enables blocks of data to be processed simultaneously, with significant gains in processing time and comparable performance. A thorough analysis of the efficiency of the proposed methodology stipulates optimized parameters for individual hardware units implementing the profile-splitting. These results pave the way for high performance linearized Bregman iteration algorithm hardware implementations capable of efficiently dealing with large datasets.
KW - FPGA
KW - Linearized Bregman iteration
KW - Optical time domain reflectometry
KW - Trend break detection
UR - http://www.scopus.com/inward/record.url?scp=85081011470&partnerID=8YFLogxK
U2 - 10.3390/electronics9030423
DO - 10.3390/electronics9030423
M3 - Article
AN - SCOPUS:85081011470
VL - 9
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 3
M1 - 423
ER -