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1. Towards scalable soft e-skin: Flexible event-based tactile-sensors using wireless sensor elements embedded in soft elastomer

   https://doi.org/10.1109/BioRob49111.2020.9224353  

   Slepyan, Ariel; Thakor, Nitish (November 2020, IEEE BIOROB Conference)

   null (Ed.)

   Scalable, high-density electronic skins (e-skins) are a desirable goal of tactile sensing. However, a realization of this goal has been elusive due to the trade-off between spatial and temporal resolution that current tactile sensors suffer from. Additionally, as tactile sensing grids become large, wiring becomes unmanageable, and there is a need for a wireless approach. In this work, a scalable, event-based, passive tactilesensing system is proposed that is based on radio-frequency identification (RFID) technology. An RFID-based tactile sensing hand is developed with 19 pressure sensing taxels. The taxels are read wirelessly using a single ‘hand-shaped’ RFID antenna. Each RFID tag is transformed into a pressure sensor by disconnecting the RFID chip from its antenna and embedding the chip and antenna into soft elastomer with an air gap introduced between the RFID chip and its antenna. When a pressure event occurs, the RFID chip contacts its antenna and receives power and communicates with the RFID reader. Thus, the sensor is transformed into a biomimetic event-based sensor, whose response is activated only when used. Further, this work demonstrates the feasibility of constructing event-based, passive sensing grids that can be read wirelessly. Future tactile sensing e-skins can utilize this approach to become scalable and dense, while retaining high temporal resolution. Moreover, this approach can be applied beyond tactile sensing, for the development of scalable and high-density sensors of any modality. 

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2. Intensity-modulated nanoplasmonic interferometric sensor for MMP-9 detection

   https://doi.org/10.1039/C8LC01391H  

   Qian, Yifeng; Zeng, Xie; Gao, Yongkang; Li, Hang; Kumar, Sushil; Gan, Qiaoqiang; Cheng, Xuanhong; Bartoli, Filbert J. (March 2019, Lab on a Chip)

   To elucidate the secretary function of immune cells, we develop a nanoplasmonic circular interferometric biosensor based on intensity interrogation for label-free and dynamic sensing of molecular secretion. Exceptional sensitivity has been demonstrated through coupling free light and surface plasmon polariton (SPPs) waves, which generates a constructive and deconstructive interference pattern with high contrast and narrow linewidth when illuminated by white light. Alternatively, by adopting a narrow-band LED source and a CCD camera in this work, the transmission intensity of multiple sensing units is monitored simultaneously with a simple collinear optical setup. This intensity-modulated sensing platform yields a resolution of 4.1 × 10 −5 refractive index unit (RIU) with a high temporal resolution of 1 s and a miniaturized footprint as small as 9.8 × 9.8 μm 2 for a single sensing unit. By integrating the signals from multiple sensor units, the resolution of a 12 × 12 sensor array was found to reach 7.3 × 10 −6 RIU. We apply this sensor array to detect matrix metalloproteinase 9 (MMP-9) secretion from human monocytic cells, THP-1, at different time points after lipopolysaccharide (LPS) simulation and the results are in good agreement with enzyme-linked immunosorbent assay (ELISA) tests, but without the need for labeling. The spatial, temporal and mass resolutions of the sensor array are found to exceed other label-free technologies. These biomolecular arrays, incorporated in a microfluidic sensor platform, hold great potential for the study the dynamics and interplay of cell secretion signals and achieving a better understanding of single cell functions. 

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3. An integrated cyberGIS and machine learning framework for fine-scale prediction of Urban Heat Island using satellite remote sensing and urban sensor network data

   https://doi.org/10.1007/s44212-022-00002-4  

   Lyu, Fangzheng; Wang, Shaohua; Han, Su Yeon; Catlett, Charlie; Wang, Shaowen (September 2022, Urban Informatics)

   Abstract Due to climate change and rapid urbanization, Urban Heat Island (UHI), featuring significantly higher temperature in metropolitan areas than surrounding areas, has caused negative impacts on urban communities. Temporal granularity is often limited in UHI studies based on satellite remote sensing data that typically has multi-day frequency coverage of a particular urban area. This low temporal frequency has restricted the development of models for predicting UHI. To resolve this limitation, this study has developed a cyber-based geographic information science and systems (cyberGIS) framework encompassing multiple machine learning models for predicting UHI with high-frequency urban sensor network data combined with remote sensing data focused on Chicago, Illinois, from 2018 to 2020. Enabled by rapid advances in urban sensor network technologies and high-performance computing, this framework is designed to predict UHI in Chicago with fine spatiotemporal granularity based on environmental data collected with the Array of Things (AoT) urban sensor network and Landsat-8 remote sensing imagery. Our computational experiments revealed that a random forest regression (RFR) model outperforms other models with the prediction accuracy of 0.45 degree Celsius in 2020 and 0.8 degree Celsius in 2018 and 2019 with mean absolute error as the evaluation metric. Humidity, distance to geographic center, and PM_2.5concentration are identified as important factors contributing to the model performance. Furthermore, we estimate UHI in Chicago with 10-min temporal frequency and 1-km spatial resolution on the hottest day in 2018. It is demonstrated that the RFR model can accurately predict UHI at fine spatiotemporal scales with high-frequency urban sensor network data integrated with satellite remote sensing data. 

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4. A 28-GHz Hybrid Beamforming Transmitter With Spatial Notch Steering Enabling Concurrent Dual Data Streams for 5G MIMO Applications

   Hu, Yaolong; Zhang, Xiaohan; Chi, Taiyun (May 2024, IEEE journal of solidstate circuits)

   This article presents a new notch steering scheme for hybrid beamforming transmitters (TXs) aimed at suppressing spatial interference, thereby enhancing the signal-to-interference-plus-noise ratio (SINR) to support spatial multiplexing. Built upon existing phased arrays, this scheme integrates an auxiliary-path vector modulator (VM) into each antenna element, which in turn, forms an interference-canceling beam. By spatially combining the array factors (AFs) of the main beam and the interference-canceling beam, a deep spatial notch is created while ensuring minimal main-beam power degradation. Unlike the conventional zero-forcing method that requires matrix inversion in digital for spatial notch creation, our scheme enables the computation of antenna weights in analog, significantly reducing the computational cost and latency. Leveraging this new notch steering scheme, we develop a 28-GHz four-element fully connected (FC) hybrid beamforming TX array using the GlobalFoundries 45-nm CMOS Silicon-on-Insulator (SOI) process. It is capable of simultaneously transmitting two independent, wideband data streams (DSs) in the same polarization toward two directions. In probing-based measurements, each TX channel delivers 19.7-dBm OP1 dB, 20.4-dBm PSAT , and 30.6% peak power-added efficiency (PAE) at 29 GHz, demonstrating state-of-the-art TX linearity and efficiency. In over-the-air (OTA) measurements, the packaged TX array achieves 29.8-dBm EIRP1 dB and is able to steer a spatial notch outside the −10-dB beamwidth of the main beam, with a notch depth of >35 dB and a main-beam power degradation of < 0.8 dB. Moreover, in spatial multiplexing demonstrations, the TX array is capable of transmitting a 400-MHz 64-quadrature amplitude modulation (QAM) signal to the intended receiver (RX) in the first DS, while suppressing the co-channel continuous-wave or wideband modulated interference created by the second DS with a high SINR. 

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5. ShapeMap 3-D: Efficient shape mapping through dense touch and vision

   https://doi.org/10.1109/ICRA46639.2022.9812040  

   Suresh, Sudharshan; Si, Zilin; Mangelson, Joshua G.; Yuan, Wenzhen; Kaess, Michael (May 2022, IEEE International Conference on Robotics and Automation)

   Knowledge of 3-D object shape is of great importance to robot manipulation tasks, but may not be readily available in unstructured environments. While vision is often occluded during robot-object interaction, high-resolution tactile sensors can give a dense local perspective of the object. However, tactile sensors have limited sensing area and the shape representation must faithfully approximate non-contact areas. In addition, a key challenge is efficiently incorporating these dense tactile measurements into a 3-D mapping framework. In this work, we propose an incremental shape mapping method using a GelSight tactile sensor and a depth camera. Local shape is recovered from tactile images via a learned model trained in simulation. Through efficient inference on a spatial factor graph informed by a Gaussian process, we build an implicit surface representation of the object. We demonstrate visuo-tactile mapping in both simulated and real-world experiments, to incrementally build 3-D reconstructions of household objects. 

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