More Like this

1. Motile cells as probes for characterizing acoustofluidic devices

   https://doi.org/10.1039/D0LC01025A  

   Kim, Minji ; Bayly, Philip V. ; Meacham, J. Mark ( February 2021 , Lab on a Chip)

   Acoustic microfluidics has emerged as a versatile solution for particle manipulation in medicine and biology. However, current technologies are largely confined to specialized research laboratories. The translation of acoustofluidics from research to clinical and industrial settings requires improved consistency and repeatability across different platforms. Performance comparisons will require straightforward experimental assessment tools that are not yet available. We introduce a method for characterizing acoustofluidic devices in real-time by exploiting the capacity of swimming microorganisms to respond to changes in their environment. The unicellular alga, Chlamydomonas reinhardtii , is used as an active probe to visualize the evolving acoustic pressure field within microfluidic channels and chambers. In contrast to more familiar mammalian cells, C. reinhardtii are simple to prepare and maintain, and exhibit a relatively uniform size distribution that more closely resembles calibration particles; however, unlike passive particles, these motile cells naturally fill complex chamber geometries and redistribute when the acoustic field changes or is turned off. In this way, C. reinhardtii cells offer greater flexibility than conventional polymer or glass calibration beads for in situ determination of device operating characteristics. To illustrate the technique, the varying spatial density and distribution of swimming cells are correlated to the acoustic potential tomore »automatically locate device resonances within a specified frequency range. Peaks in the correlation coefficient of successive images not only identify the resonant frequencies for various geometries, but the peak shape can be related to the relative strength of the resonances. Qualitative mapping of the acoustic field strength with increasing voltage amplitude is also shown. Thus, we demonstrate that dynamically responsive C. reinhardtii enable real-time measurement and continuous monitoring of acoustofluidic device performance.« less
2. A Barrier Certificate-based Simplex Architecture with Application to Microgrids

   Damare, Amol ; Roy, Shouvik ; Smolka, Scott A. ; Stoller, Scott D. ( January 2022 , 22nd International Conference on Runtime Verification (RV 2022))

   Dang, Thao ; Stolz, Volker (Ed.)

   We present Barrier-based Simplex (Bb-Simplex), a new, provably correct design for runtime assurance of continuous dynamical systems. Bb-Simplex is centered around the Simplex Control Architecture, which consists of a high-performance advanced controller which is not guaranteed to maintain safety of the plant, a verified-safe baseline controller, and a decision module that switches control of the plant between the two controllers to ensure safety without sacrificing performance. In Bb-Simplex, Barrier certificates are used to prove that the baseline controller ensures safety. Furthermore, Bb-Simplex features a new automated method for deriving, from the barrier certificate, the conditions for switching between the controllers. Our method is based on the Taylor expansion of the barrier certificate and yields computationally inexpensive switching conditions. We consider a significant application of Bb-Simplex to a microgrid featuring an advanced controller in the form of a neural network trained using reinforcement learning. The microgrid is modeled in RTDS, an industry-standard high-fidelity, real-time power systems simulator. Our results demonstrate that Bb-Simplex can automatically derive switching conditions for complex systems, the switching conditions are not overly conservative, and Bb-Simplex ensures safety even in the presence of adversarial attacks on the neural controller.
3. SmartDet: Context-Aware Dynamic Control of Edge Task Offloading for Mobile Object Detection

   https://doi.org/10.1109/WoWMoM54355.2022.00034  

   Callegaro, Davide ; Levorato, Marco ; Restuccia, Francesco ( June 2022 , IEEE WoWMoM)

   Mobile devices such as drones and autonomous vehicles increasingly rely on object detection (OD) through deep neural networks (DNNs) to perform critical tasks such as navigation, target-tracking and surveillance, just to name a few. Due to their high complexity, the execution of these DNNs requires excessive time and energy. Low-complexity object tracking (OT) is thus used along with OD, where the latter is periodically applied to generate "fresh" references for tracking. However, the frames processed with OD incur large delays, which does not comply with real-time applications requirements. Offloading OD to edge servers can mitigate this issue, but existing work focuses on the optimization of the offloading process in systems where the wireless channel has a very large capacity. Herein, we consider systems with constrained and erratic channel capacity, and establish parallel OT (at the mobile device) and OD (at the edge server) processes that are resilient to large OD latency. We propose Katch-Up, a novel tracking mechanism that improves the system resilience to excessive OD delay. We show that this technique greatly improves the quality of the reference available to tracking, and boosts performance up to 33%. However, while Katch-Up significantly improves performance, it also increases the computing loadmore »of the mobile device. Hence, we design SmartDet, a low-complexity controller based on deep reinforcement learning (DRL) that learns to achieve the right trade-off between resource utilization and OD performance. SmartDet takes as input highly-heterogeneous context-related information related to the current video content and the current network conditions to optimize frequency and type of OD offloading, as well as Katch-Up utilization. We extensively evaluate SmartDet on a real-world testbed composed by a JetSon Nano as mobile device and a GTX 980 Ti as edge server, connected through a Wi-Fi link, to collect several network-related traces, as well as energy measurements. We consider a state-of-the-art video dataset (ILSVRC 2015 - VID) and state-of-the-art OD models (EfficientDet 0, 2 and 4). Experimental results show that SmartDet achieves an optimal balance between tracking performance – mean Average Recall (mAR) and resource usage. With respect to a baseline with full Katch-Up usage and maximum channel usage, we still increase mAR by 4% while using 50% less of the channel and 30% power resources associated with Katch-Up. With respect to a fixed strategy using minimal resources, we increase mAR by 20% while using Katch-Up on 1/3 of the frames.« less
4. Reinforcement Learning-Based Home Energy Management System for Resiliency

   https://doi.org/10.23919/ACC50511.2021.9483162  

   Raman, Naren Srivaths ; Gaikwad, Ninad ; Barooah, Prabir ; Meyn, Sean P. ( May 2021 , American Control Conference)

   With increase in the frequency of natural disasters such as hurricanes that disrupt the supply from the grid, there is a greater need for resiliency in electric supply. Rooftop solar photovoltaic (PV) panels along with batteries can provide resiliency to a house in a blackout due to a natural disaster. Our previous work showed that intelligence can reduce the size of a PV+battery system for the same level of post-blackout service compared to a conventional system that does not employ intelligent control. The intelligent controller proposed is based on model predictive control (MPC), which has two main challenges. One, it requires simple yet accurate models as it involves real-time optimization. Two, the discrete actuation for residential loads (on/off) makes the underlying optimization problem a mixed-integer program (MIP) which is challenging to solve. An attractive alternative to MPC is reinforcement learning (RL) as the real-time control computation is both model-free and simple. These points of interest accompany certain trade-offs; RL requires computationally expensive offline learning, and its performance is sensitive to various design choices. In this work, we propose an RL-based controller. We compare its performance with the MPC controller proposed in our prior work and a non-intelligent baseline controller. Themore »RL controller is found to provide a resiliency performance — by commanding critical loads and batteries—similar to MPC with a significant reduction in computational effort.« less
5. Acoustofluidic rotational tweezing enables high-speed contactless morphological phenotyping of zebrafish larvae

   https://doi.org/10.1038/s41467-021-21373-3  

   Chen, Chuyi ; Gu, Yuyang ; Philippe, Julien ; Zhang, Peiran ; Bachman, Hunter ; Zhang, Jinxin ; Mai, John ; Rufo, Joseph ; Rawls, John F. ; Davis, Erica E. ; et al ( February 2021 , Nature Communications)

   Modern biomedical research and preclinical pharmaceutical development rely heavily on the phenotyping of small vertebrate models for various diseases prior to human testing. In this article, we demonstrate an acoustofluidic rotational tweezing platform that enables contactless, high-speed, 3D multispectral imaging and digital reconstruction of zebrafish larvae for quantitative phenotypic analysis. The acoustic-induced polarized vortex streaming achieves contactless and rapid (~1 s/rotation) rotation of zebrafish larvae. This enables multispectral imaging of the zebrafish body and internal organs from different viewing perspectives. Moreover, we develop a 3D reconstruction pipeline that yields accurate 3D models based on the multi-view images for quantitative evaluation of basic morphological characteristics and advanced combinations of metrics. With its contactless nature and advantages in speed and automation, our acoustofluidic rotational tweezing system has the potential to be a valuable asset in numerous fields, especially for developmental biology, small molecule screening in biochemistry, and pre-clinical drug development in pharmacology.