Development of ML model for triboelectric nanogenerator based sign language detection system
2026-04-09T08:51:42Z•25fa7d3ae1384c8187edbf879bcef359227ad1514ba574efdebb5c6ec9679258
4D-STEMCNN-LSTMLoRaWANMFCCMIMOTENGUAVautomatic-modulation-classificationbeamformingbloscchannel-knowledge-map (CKM)green-mlhdf5helikitelocalizationlossless-compressionmachine-learningmatrix-inversionnumerical-stabilitypreconditioningpropagation-modelingsignal-processingtime-serieswearable-sensorszstd
What happened
Feed of recent arXiv EESS/Signal Processing submissions (2026-04-09) covering advances in ML for wearable sensors (TENG glove for sign-language recognition with MFCC CNN-LSTM achieving ~93% accuracy), lossless-compression benchmarks for 4D-STEM (identifying Blosc/Zstd as fast/high-ratio options), numerical-stability improvements for long-term MIMO beamforming (subspace nulling preconditioning), a lightweight/energy-efficient automatic modulation classification pipeline, empirical LoRaWAN propagation measurements using vehicle/UAV/helikite platforms, a first-order optimality sensor-selection (F
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_eess_sp
- Record identifier
- 25fa7d3ae1384c8187edbf879bcef359227ad1514ba574efdebb5c6ec9679258
- Enrichment time
- 2026-04-09T08:51:42Z
- AI-assisted enrichment
- Yes
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