Robotic tactile sensors based on Electrical Impedance Tomography (EIT) have gained great attention in robotic sensing applications due to their features such as no internal wiring, "all-in-one" structure, and continuous sensing capabilities. However, the effectiveness of EIT-based tactile sensors is hampered by limited spatial resolution and artifacts in the reconstructed images. To address these challenges, various iterative optimization methods based on spatial regularizations and model-based methods have been proposed. In this study, a new EIT reconstruction method using null-space decomposition based on a diffusion model (NSDM) is proposed. Specifically, NSDM consists of a forward diffusion process that first gradually adds Gaussian noise to a clean conductivity image, followed by a backward process that learns to predict the noise that should be removed during each sampling step, utilizing a prior to ensure that the denoising process does not deviate from the correct direction. NSDM requires no training, no optimization, and only requires a pre-prepared diffusion model. Experimental results (both simulation and actual tests) demonstrate that the proposed method outperforms existing generation methods and provides higher quality reconstruction, providing a new solution for robotic tactile sensing in real scenarios.

Electrical Impedance Tomography (EIT) is a highly ill-posed inverse problem, with the challenge of reconstructing internal conductivities using only boundary voltage measurements. Although Physics-Informed Neural Networks (PINNs) have shown potential in solving inverse problems, existing approaches are limited in their applicability to EIT, as they often rely on impractical prior knowledge and assumptions that cannot be satisfied in real-world scenarios. To address these limitations, we propose a two-stage hybrid learning framework that combines Convolutional Neural Networks (CNNs) and PINNs. This framework integrates data-driven and model-driven paradigms, blending supervised and unsupervised learning to reconstruct conductivity distributions while ensuring adherence to the underlying physical laws, thereby overcoming the constraints of existing methods.

Electrical Impedance Tomography (EIT)-based tactile sensors offer durability, scalability, and cost-effective manufacturing. However, simultaneously reconstructing force and shape from boundary measurements remains challenging due to EIT’s inherent location dependencies and image artifacts. This study presents a model-driven multimodal convolutional neural network (MM-CNN) for joint EIT-based force and shape sensing. The hybrid approach combines physics-inspired voltage preprocessing with an attention-based network to overcome EIT’s limitations. The preprocessing network applies a linearized one-step inverse solution with Tikhonov regularization to convert raw boundary voltage into a noise-reduced 2D image. The image reconstruction network uses an attention mechanism to focus on salient features, addressing location dependency issues. Quantitative metrics show that MM-CNN outperforms traditional EIT algorithms like NOSER and TV, reducing location dependency and improving shape discrimination. MM-CNN enables unified force and shape modalities, validated through real-contact experiments, enhancing EIT tactile systems for human-robot interaction by incorporating physical knowledge with deep learning.

This paper propose a novel transfer learning approach, Pseudo-Domain Adversarial Network (PDAN), to tackle the issue of electrode displacement in Electrical Impedance Tomography (EIT). Electrode displacement, caused by human movement or improper operation, significantly affects the accuracy of EIT by introducing data errors. Existing solutions either modify the electrode assembly at a high cost or employ recognition algorithms that require retraining from scratch. To overcome these limitations, our work leverages the power of transfer learning to enhance model performance in the target domain by utilizing knowledge from a related task in the source domain. PDAN extends the capabilities of deep adversarial learning by incorporating noisy images to simulate post-electrode rotation scenarios, aiding in the reduction of negative impacts caused by minor electrode displacements. Our method demonstrates superior performance in classifying leg posture data, achieving around 90% accuracy, and proving robust against sensor electrode offset. Experimental results across various datasets validate the effectiveness of PDAN, indicating its potential in addressing complex real-world situations with improved generalization capabilities.

In this paper, an opportunistic cooperative HARQ system is analyzed. Different from prior analyses, time correlated fading channels are considered. Based on moment generation function and Laplace transform, the outage probability and throughput in terms of long-term average transmission rate (LATR) of this opportunistic cooperative HARQ system are derived in closed-forms. The accuracy of the analytical results is verified by computer simulations. From the analytical results, the impacts of the time correlation and other system parameters on the performance are investigated and the optimal packet rate selection to maximize the LATR is discussed.