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Stały URI dla kolekcjihttp://hdl.handle.net/11652/4775
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Pozycja Grounded HyperSymbolic Representations Learned through Gradient-Based Optimization(Wydawnictwo Politechniki Łódzkiej, 2023) Łuczak, Piotr; Ślot, Krzysztof; Kucharski, JacekHyperdimensional computing is a novel paradigm, capable of processing complex data structures with simple operations. Its main limitations lie in the conversion of real world data onto hyperdimensional space, which due to lack of a universal translation scheme, oftentimes requires application-specific methods. This work presents a novel method for unsupervised hyperdimensional conversion of arbitrary image data. Additionally, this method is augmented by the ability of creating HyperSymbols, or class prototypes, provided that such class labels are available. The proposed method achieves promising performance on MNIST dataset, both in translating individual samples as well as producing HyperSymbols for downstream classification task.Pozycja RNN-based Phase Unwrapping for Enabling Vital Parameter Monitoring with FMCW Radars(Wydawnictwo Politechniki Łódzkiej, 2023) Łuczak, Piotr; Hausman, Sławomir; Ślot, KrzysztofApplication of radar technology enables remote breathing and heart rate monitoring by analyzing motion waveforms, which are reconstructed from phase signals extracted from radar-delivered data. However, nonlinear deformations introduced by phase recovery procedure make accurate motion reconstruction highly challenging, especially for millimeter-long waves that are commonly generated by state-of-the-art radar devices. In the presented paper we show that a GRU-based neural predictor is capable of correct phase unwrapping under presence of noise (originating e.g. from random subject’s movements), enabling vital parameter monitoring in realistic scenarios, which cannot be accomplished using standard approaches.