eess.SP · 2026-06-26 · No. 35

Signal Processing, 2026-06-26.

2 new papers in eess.SP. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

2 entries
  1. 01

    Inverse Design of Compact and Wideband Inverted Doherty Power Amplifiers Using Deep Learning

    Han Zhou, Haojie Chang, David Widen, Christian Fager

    eess.SP · cs.AI · cs.AR · eess.SY

    This paper presents a deep learning-assisted methodology for the inverse synthesis of a compact, wideband inverted Doherty power amplifier (PA). Convolutional neural networks (CNNs) and genetic algorithms (GAs) are jointly employed to generate pixelated Doherty combiner networks that integrate load modulation, impedance matching, power combining, and phase compensation into a single structure. As a proof of concept, we design and fabricate a...

    arxiv.org/abs/2606.27002 · PDF

  2. 02

    State-Specific Respiratory Signatures for Affective and Stress Recognition: Interpretable Respiratory Markers, Autocorrelation Lags, and Compact CNN Models

    Andrei Velichko, Mehmet Tahir Huyut

    eess.SP · cs.LG · q-bio.QM

    Respiratory activity is a direct and interpretable physiological channel for wearable stress and affective-state recognition, yet many studies emphasize classification accuracy without identifying which respiratory properties separate different states. This work reframes RESP-based recognition as a joint predictive and explanatory problem. Using the chest respiratory channel of the WESAD dataset, we analyze 60 s windows under...

    arxiv.org/abs/2606.26723 · PDF

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