Essays about: "Dynamic Time Warping"
Showing result 1 - 5 of 18 essays containing the words Dynamic Time Warping.
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1. Query By Example Keyword Spotting
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Voice user interfaces have been growing in popularity and with them an interest for open vocabulary keyword spotting. In this thesis we focus on one particular approach to open vocabulary keyword spotting, query by example keyword spotting. READ MORE
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2. Understanding Traffic Cruising Causation : Via Parking Data Enhancement
University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskapAbstract : Background. Some computer scientists have recently pointed out that it may be more effective for the computer science community to focus more on data preparation for performance improvements, rather than exclusively comparing modeling techniques. READ MORE
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3. Agriculture monitoring using satellite data
University essay from Luleå tekniska universitet/RymdteknikAbstract : As technology advances, the possibility of using satellite data and observations to aid inagricultural activities comes closer to reality. Swedish farmers can apply for subsidies for their land in which crop management and animal grazing occurs, and every year thousands of manual follow-up checks are conducted by Svenska Jordbruksverket (Swedish Board of Agriculture) to validate the farmers’ claims to financial aid. READ MORE
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4. Traffic Prediction From Temporal Graphs Using Representation Learning
University essay from KTH/Matematisk statistikAbstract : With the arrival of 5G networks, telecommunication systems are becoming more intelligent, integrated, and broadly used. This thesis focuses on predicting the upcoming traffic to efficiently promote resource allocation, guarantee stability and reliability of the network. READ MORE
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5. Time-series Generative Adversarial Networks for Telecommunications Data Augmentation
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Time- series Generative Adversarial Networks (TimeGAN) is proposed to overcome the GAN model’s insufficiency in producing synthetic samples that inherit the predictive ability of the original timeseries data. TimeGAN combines the unsupervised adversarial loss in the GAN framework with a supervised loss adopted from an autoregressive model. READ MORE
