More Like this

1. Level Curve Tracking without Localization Enabled by Recurrent Neural Networks

   https://doi.org/10.1109/CACRE50138.2020.9230272  

   Zhang, Ziqiao; Al-Abri, Said; Wu, Wencen; Zhang, Fumin (September 2020, 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE))

   null (Ed.)

   Recursive neural networks can be trained to serve as a memory for robots to perform intelligent behaviors when localization is not available. This paper develops an approach to convert a spatial map, represented as a scalar field, into a trained memory represented by the long short-term memory (LSTM) neural network. The trained memory can be retrieved through sensor measurements collected by robots to achieve intelligent behaviors, such as tracking level curves in the map. Memory retrieval does not require robot locations. The retrieved information is combined with sensor measurements through a Kalman filter enabled by the LSTM (LSTM-KF). Furthermore, a level curve tracking control law is designed. Simulation results show that the LSTM-KF and the control law are effective to generate level curve tracking behaviors for single-robot and multi-robot teams. 

   more » « less
2. LSTM-Enabled Level Curve Tracking in Scalar Fields Using Multiple Mobile Robots

   https://doi.org/10.1115/DETC2021-68554  

   Parikh, Kunj J.; Wu, Wencen (November 2021, ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   In this work, we investigate the problem of level curve tracking in unknown scalar fields using a limited number of mobile robots. We design and implement a long short-term memory (LSTM) enabled control strategy for a mobile sensor network to detect and track desired level curves. Based on the existing work of cooperative Kalman filter, we design an LSTM-enhanced Kalman filter that utilizes the sensor measurements and a sequence of past fields and gradients to estimate the current field value and gradient. We also design an LSTM model to estimate the Hessian of the field. The LSTM-enabled strategy has some benefits such as it can be trained offline on a collection of level curves in known fields prior to deployment, where the trained model will enable the mobile sensor network to track level curves in unknown fields for various applications. Another benefit is that we can train using larger resources to get more accurate models while utilizing a limited number of resources when the mobile sensor network is deployed in production. Simulation results show that this LSTM-enabled control strategy successfully tracks the level curve using a mobile multi-robot sensor network. 

   more » « less
3. Test beam performance of a CBC3-based mini-module for the Phase-2 CMS Outer Tracker before and after neutron irradiation

   https://doi.org/10.1088/1748-0221/18/04/P04001  

   Adam, W.; Bergauer, T.; Damanakis, K.; Dragicevic, M.; Frühwirth, R.; Steininger, H.; Beaumont, W.; Darwish, M.R.; Janssen, T.; Kello, T.; et al (April 2023, Journal of Instrumentation)

   Abstract The Large Hadron Collider (LHC) at CERN will undergo major upgrades to increase the instantaneous luminosity up to 5–7.5×10 34 cm -2 s -1 . This High Luminosity upgrade of the LHC (HL-LHC) will deliver a total of 3000–4000 fb -1 of proton-proton collisions at a center-of-mass energy of 13–14 TeV. To cope with these challenging environmental conditions, the strip tracker of the CMS experiment will be upgraded using modules with two closely-spaced silicon sensors to provide information to include tracking in the Level-1 trigger selection. This paper describes the performance, in a test beam experiment, of the first prototype module based on the final version of the CMS Binary Chip front-end ASIC before and after the module was irradiated with neutrons. Results demonstrate that the prototype module satisfies the requirements, providing efficient tracking information, after being irradiated with a total fluence comparable to the one expected through the lifetime of the experiment. 

   more » « less
4. Development of a Laser Vibrometer-Based Shear Wave Sensing System for Characterizing Mechanical Properties of Viscoelastic Materials

   https://doi.org/10.1115/SMASIS2023-110811  

   Cai, Bowen; Shen, Liang; Pei, Zhe; Li, Teng; Li, Jiali; Bo, Luyu; Du, Yingshan; Tian, Zhenhua (September 2023, American Society of Mechanical Engineers)

   Abstract Characterizing the mechanical properties of viscoelastic materials is critical in biomedical applications such as detecting breast cancer, skin diseases, myocardial diseases, and hepatic fibrosis. Current methods lack the consideration of dispersion curves that depend on material properties and shear wave frequency. This paper presents a novel method that combines noncontact shear wave sensing and dispersion analysis to characterize the mechanical properties of viscoelastic materials. Our shear wave sensing system uses a piezoelectric stack (PZT stack) to generate shear waves and a laser Doppler vibrometer (LDV) integrated with a 3D robotic stage to acquire time-space wavefields. Next, an inverse method is employed for the wavefield analysis. This method leverages multi-dimensional Fourier transform and frequency-wavenumber dispersion curve regression. Through proof-of-concept experiments, our sensing system successfully generated shear waves and acquired its timespace wavefield in a customized viscoelastic phantom. After dispersion curve analysis, we successfully characterized two material properties (shear elasticity and shear viscosity) and measured shear wave velocities at different frequencies. 

   more » « less
5. SPICER: Self‐supervised learning for MRI with automatic coil sensitivity estimation and reconstruction

   https://doi.org/10.1002/mrm.30121  

   Hu, Yuyang; Gan, Weijie; Ying, Chunwei; Wang, Tongyao; Eldeniz, Cihat; Liu, Jiaming; Chen, Yasheng; An, Hongyu; Kamilov, Ulugbek S (September 2024, Magnetic Resonance in Medicine)

   Abstract PurposeTo introduce a novel deep model‐based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k‐space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation. MethodsSPICER consists of two modules to simultaneously reconstructs accurate MR images and estimates high‐quality coil sensitivity maps (CSMs). The first module, CSM estimation module, uses a convolutional neural network (CNN) to estimate CSMs from the raw measurements. The second module, DMBA‐based MRI reconstruction module, forms reconstructed images from the input measurements and the estimated CSMs using both the physical measurement model and learned CNN prior. With the benefit of our self‐supervised learning strategy, SPICER can be efficiently trained without any fully sampled reference data. ResultsWe validate SPICER on both open‐access datasets and experimentally collected data, showing that it can achieve state‐of‐the‐art performance in highly accelerated data acquisition settings (up to ). Our results also highlight the importance of different modules of SPICER—including the DMBA, the CSM estimation, and the SPICER training loss—on the final performance of the method. Moreover, SPICER can estimate better CSMs than pre‐estimation methods especially when the ACS data is limited. ConclusionDespite being trained on noisy undersampled data, SPICER can reconstruct high‐quality images and CSMs in highly undersampled settings, which outperforms other self‐supervised learning methods and matches the performance of the well‐known E2E‐VarNet trained on fully sampled ground‐truth data. 

   more » « less