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1. Virtual sensing network for statistical process monitoring

   https://doi.org/10.1080/24725854.2022.2148779  

   Krall, Alexander; Finke, Daniel; Yang, Hui (November 2023, IISE Transactions)

   Physical sensing is increasingly implemented in modern industries to improve information visibility, which generates real-time signals that are spatially distributed and temporally varying. These signals are often nonlinear and nonstationary in the high-dimensional space, which pose significant challenges to monitoring and control of complex systems. Therefore, this article presents a new “virtual sensing” approach that places imaginary sensors at different locations in signaling trajectories to monitor evolving dynamics within the signal space. First, we propose self-organizing principles to investigate distributional and topological features of nonlinear signals for optimal placement of imaginary sensors. Second, we design and develop the network model to represent real-time flux dynamics among these virtual sensors, in which each node represents a virtual sensor, while edges signify signal flux among sensors. Third, the establishment of a network model as well as the notion of transition uncertainty enable a fine-grained view into system dynamics and then extend a new Flux Rank (FR) algorithm for process monitoring. Experimental results show that the network FR methodology not only delineate real-time flux patterns in nonlinear signals, but also effectively monitor spatiotemporal changes in the dynamics of nonlinear dynamical systems. 

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2. Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models

   Randy Ardywibowo, Guang Zhao (April 2019, Proceedings of Machine Learning Research)

   Emerging wearable sensors have enabled the unprecedented ability to continuously monitor human activities for healthcare purposes. However, with so many ambient sensors collecting different measurements, it becomes important not only to maintain good monitoring accuracy, but also low power consumption to ensure sustainable monitoring. This power-efficient sensing scheme can be achieved by deciding which group of sensors to use at a given time, requiring an accurate characterization of the trade-off between sensor energy usage and the uncertainty in ignoring certain sensor signals while monitoring. To address this challenge in the context of activity monitoring, we have designed an adaptive activity monitoring framework. We first propose a switching Gaussian process to model the observed sensor signals emitting from the underlying activity states. To efficiently compute the Gaussian process model likelihood and quantify the context prediction uncertainty, we propose a block circulant embedding technique and use Fast Fourier Transforms (FFT) for inference. By computing the Bayesian loss function tailored to switching Gaussian processes, an adaptive monitoring procedure is developed to select features from available sensors that optimize the trade-off between sensor power consumption and the prediction performance quantified by state prediction entropy. We demonstrate the effectiveness of our framework on the popular benchmark of UCI Human Activity Recognition using Smartphones. 

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3. Energy-Efficient Node Deployment in Wireless Ad-hoc Sensor Networks

   https://doi.org/10.1109/ICC40277.2020.9149186  

   Guo, Jun; Karimi-Bidhendi, Saeed; Jafarkhani, Hamid (June 2020, IEEE International Conference on Communications (ICC-20))

   We study a wireless ad-hoc sensor network (WASN) where N sensors gather data from the surrounding environment and transmit their sensed information to M fusion centers (FCs) via multi-hop wireless communications. This node deployment problem is formulated as an optimization problem to make a trade-off between the sensing uncertainty and energy consumption of the network. Our primary goal is to find an optimal deployment of sensors and FCs that minimizes a Lagrangian combination of sensing uncertainty and energy consumption. To support arbitrary routing protocols in WASNs, the routing dependent necessary conditions for the optimal deployment are explored. Based on these necessary conditions, we propose a routing-aware Lloyd-like algorithm to optimize node deployment. Simulation results show that our proposed algorithm outperforms the existing deployment algorithms, on average. 

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4. Adaptive Federated Learning for Automatic Modulation Classification Under Class and Noise Imbalance

   https://doi.org/10.1609/aaaiss.v3i1.31223  

   Sanchez_Viloria, Jose Angel; Stripelis, Dimitris; Markopoulos, Panos P; Sklivanitis, George; Pados, Dimitris A (May 2024, Proceedings of the AAAI Symposium Series)

   The ability to rapidly understand and label the radio spectrum in an autonomous way is key for monitoring spectrum interference, spectrum utilization efficiency, protecting passive users, monitoring and enforcing compliance with regulations, detecting faulty radios, dynamic spectrum access, opportunistic mesh networking, and numerous NextG regulatory and defense applications. We consider the problem of automatic modulation classification (AMC) by a distributed network of wireless sensors that monitor the spectrum for signal transmissions of interest over a large deployment area. Each sensor receives signals under a specific channel condition depending on its location and trains an individual model of a deep neural network (DNN) accordingly to classify signals. To improve modulation classification accuracy, we consider federated learning (FL) where each individual sensor shares its trained model with a centralized controller, which, after aggregation, initializes its model for the next round of training. Without exchanging any spectrum data (such as in cooperative spectrum sensing), this process is repeated over time. A common DNN is built across the net- work while preserving the privacy associated with signals collected at different locations. Given their distributed nature, the statistics of the data across these sensors are likely to differ significantly. We propose the use of adaptive federated learning for AMC. Specifically, we use FEDADAM -an algorithm using Adam for server optimization – and ex- amine how it compares to the FEDAVG algorithm -one of the standard FL algorithms, which averages client parameters after some local iterations, in particular in challenging scenarios that include class imbalance and/or noise-level imbalance across the network. Our extensive numerical studies over 11 standard modulation classes corroborate the merit of adaptive FL, outperforming its standard alternatives in various challenging cases and for various network sizes. 

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5. High-sensitivity nanophotonic sensors with passive trapping of analyte molecules in hot spots

   https://doi.org/10.1038/s41377-020-00449-7  

   Miao, Xianglong; Yan, Lingyue; Wu, Yun; Liu, Peter Q. (January 2021, Light: Science & Applications)

   Abstract Nanophotonic resonators can confine light to deep-subwavelength volumes with highly enhanced near-field intensity and therefore are widely used for surface-enhanced infrared absorption spectroscopy in various molecular sensing applications. The enhanced signal is mainly contributed by molecules in photonic hot spots, which are regions of a nanophotonic structure with high-field intensity. Therefore, delivery of the majority of, if not all, analyte molecules to hot spots is crucial for fully utilizing the sensing capability of an optical sensor. However, for most optical sensors, simple and straightforward methods of introducing an aqueous analyte to the device, such as applying droplets or spin-coating, cannot achieve targeted delivery of analyte molecules to hot spots. Instead, analyte molecules are usually distributed across the entire device surface, so the majority of the molecules do not experience enhanced field intensity. Here, we present a nanophotonic sensor design with passive molecule trapping functionality. When an analyte solution droplet is introduced to the sensor surface and gradually evaporates, the device structure can effectively trap most precipitated analyte molecules in its hot spots, significantly enhancing the sensor spectral response and sensitivity performance. Specifically, our sensors produce a reflection change of a few percentage points in response to trace amounts of the amino-acid proline or glucose precipitate with a picogram-level mass, which is significantly less than the mass of a molecular monolayer covering the same measurement area. The demonstrated strategy for designing optical sensor structures may also be applied to sensing nano-particles such as exosomes, viruses, and quantum dots. 

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