In-Sensor Computing

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10 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Analog Computing revisited: A fully analog and minimalistic Damage Detector for Ultrasonic Testing enabling Material-Integrated Structural Health Monitoring
Sep 28, 2026Stefan BosseAnalog Computing Fault Detection
University of Koblenz, Practical Computer Science, Koblenz, Germany
Hardware-Aware Learned Representation Compression for Distributed In-Sensor Vision
Sep 12, 2026Chengwei Zhou, Abu Masum, Xuming Chen +7In-Sensor Computing Edge Computing
Electrical, Computer, and Systems Engineering (ECSE) Department, Case Western Reserve University, Cleveland, OH 44106, USA · School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70503, USA · Department of Electrical and Computer Engineering (ECE), University of Southern California, Los Angeles, CA 90089, USA +2
STEMPix: A Phase-Transition-Material-Based Pixel Sensor for Resolving Edge-Movement Direction
Sep 3, 2026Md Rahatul Islam Udoy, Sumeet Kumar Gupta, Deep Jariwala +1Edge Detection In-Sensor Computing
Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA · Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47906, USA · Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
FrequencyFormer: A Co-Designed Sensor-to-Processor Pipeline for Frequency-Domain Vision Transformer Inference
Jun 17, 2026Chengwei Zhou, Ovishake Sen, Xuming Chen +5Edge Inference Efficient ViTs
Case Western Reserve University, USA · University of Florida, USA · New Jersey Institute of Technology, USA
PixVOD: Pixel-Distributed Direct Visual Odometry and Depth Estimation
Jun 2, 2026Shinjeong Kim, Ignacio Alzugaray, Callum Rhodes +2Depth Estimation Visual Odometry
Department of Computing, Imperial College London
Exploiting In-Sensor Computing for Energy-Efficient Earth Observation
May 31, 2026Luigi Capogrosso, Pietro Bonazzi, Loris Hoxhaj +1Remote Sensing Image Understanding Energy-Efficient ML
Interdisciplinary Transformation University of Austria · ETH Zurich · University of Verona
Design and Characteristics of a Thin-Film ThermoMesh for the Efficient Embedded Sensing of a Spatio-Temporally Sparse Heat Source
Apr 30, 2026Sajjad Boorghan Farahan, Ahmed Alajlouni, Jingzhou ZhaoThermal Imaging In-Sensor Computing
Department of Mechanical Engineering State University of New York at Binghamton, Binghamton, NY
Lightweight True In-Pixel Encryption with FeFET Enabled Pixel Design for Secure Imaging
Apr 6, 2026Md Rahatul Islam Udoy, Diego Ferrer, Wantong Li +3In-Sensor Computing
PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation
Mar 12, 2026Pietro Bonazzi, Nicola Farronato, Stefan Zihlmann +2In-Sensor Computing Promptable Image Segmentation
Equal contribution · ETH Zürich, Zürich, Switzerland. · IBM Research, Zürich, Switzerland.
Performance Analysis of Edge and In-Sensor AI Processors: A Comparative Review
Feb 18, 2026Luigi Capogrosso, Pietro Bonazzi, Michele MagnoIn-Sensor Computing Edge AI
Interdisciplinary Transformation University of Austria · ETH Zurich

Sep 28, 2026J/K move · Enter open · S save

Analog Computing revisited: A fully analog and minimalistic Damage Detector for Ultrasonic Testing enabling Material-Integrated Structural Health Monitoring

Stefan Bosse

University of Koblenz, Practical Computer Science, Koblenz, Germany

Analog Computing Fault Detection In-Sensor Computing Structural Health Monitoring

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Ultrasonic Testing (UT) is commonly used to detect damage in structures, e.g., metal plates. A sensor acquires Ultrasonic waves, e.g., by using PZT transducers. The time-resolved sensor signal must be processed with analog electronics, e.g., amplified and filtered. Commonly a digitalization follows using an Analog-to-Digital converter, finally processing the digital sensor signal, applying digital signal processing, feature extraction, and Machine Learning by using powerful microprocessor systems. The disadvantages of digital processing systems are their high number of transistors (microchip area), energy consumption, state-dependent processing and therefore sensitivity to energy supply interruption. Beyond silicon electronics, printed organic electronics gains interest. But printed electronics is still limited to low transistor and electronic component counts (typically 100). We will investigate and demonstrate a fully analog signal processing and feature extraction system consisting of an analog Hilbert transform deriving the signal envelope, simple analog arithmetic calculations for feature extraction, and finally damage classification and regression using an analog Artificial Neural Network. We expect a full damage detection system with less than 100 transistors. We will test our damage detection system with PZT transducer signals from Steel plates with circular defects. The focus of this work is the analog computation of the signal envelope (using all-pass filter networks for approximation of the Hilbert transform) and the analog feature extraction as well as the prediction of damage, forming an analog computer which can perform in-sensor computation, computing without a digital computer.