首页|New Support Vector Machines Study Findings Have Been Reported from University of Klagenfurt (Observations and Considerations for Implementing Vibration Signals as an Input Technique for Mobile Devices)
New Support Vector Machines Study Findings Have Been Reported from University of Klagenfurt (Observations and Considerations for Implementing Vibration Signals as an Input Technique for Mobile Devices)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on support vector machines are presented in a new report. According to news reporting from the University o f Klagenfurt by NewsRx journalists, research stated, “This work examines swipe-b ased interactions on smart devices, like smartphones and smartwatches, that dete ct vibration signals through defined swipe surfaces.” Our news reporters obtained a quote from the research from University of Klagenf urt: “We investigate how these devices, held in users’ hands or worn on their wr ists, process vibration signals from swipe interactions and ambient noise using a support vector machine (SVM). The work details the signal processing workflow involving filters, sliding windows, feature vectors, SVM kernels, and ambient no ise management. It includes how we separate the vibration signal from a potentia l swipe surface and ambient noise. We explore both software and human factors in fluencing the signals: the former includes the computational techniques mentione d, while the latter encompasses swipe orientation, contact, and movement. Our fi ndings show that the SVM classifies swipe surface signals with an accuracy of 69 .61% when both devices are used, 97.59% with only th e smartphone, and 99.79% with only the smartwatch.”
University of KlagenfurtMachine Learni ngSupport Vector Machines
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