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1. Moisture Estimation in Woodchips Using IIoT Wi-Fi and Machine Learning Techniques

   https://doi.org/10.1016/B978-0-323-85159-6.50276-1  

   Suthar, K.; He, Q.P. (January 2022, Computer aided chemical engineering)

   For the pulping process in a pulp & paper plant that uses woodchips as raw material, the moisture content (MC) of the woodchips is a major process disturbance that affects product quality and consumption of energy, water, and chemicals. Existing woodchip MC sensing technologies have not been widely adopted by the industry due to unreliable performance and/or high maintenance requirements that can hardly be met in a manufacturing environment. To address these limitations, we propose a non-destructive, economic, and robust woodchip MC sensing approach utilizing channel state information (CSI) from industrial Internet-of-Things (IIoT) based Wi-Fi. While these IIoT devices are small, low-cost, and rugged to stand for harsh environment, they do have their limitations such as the raw CSI data are often very noisy and sensitive to woodchip packing. Thus, direct application of machine learning (ML) algorithms leads to poor performance. To address this, statistics pattern analysis (SPA) is utilized to extract physically and statistically meaningful features from the raw CSI data, which are sensitive to woodchip MC but not to packing. The SPA features are then used for developing multiclass classification models as well as regression models using various linear and nonlinear ML techniques to provide potential solutions to woodchip MC estimation for the pulp and paper industry. 

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2. Feature Learning for Neural-Network-Based Positioning with Channel State Information

   https://doi.org/10.1109/IEEECONF53345.2021.9723124  

   Gonultas, Emre; Taner, Sueda; Huang, Howard; Studer, Christoph (October 2021, Asilomar Conference on Signals, Systems, and Computers)

   Recent channel state information (CSI)-based positioning pipelines rely on deep neural networks (DNNs) in order to learn a mapping from estimated CSI to position. Since real-world communication transceivers suffer from hardware impairments, CSI-based positioning systems typically rely on features that are designed by hand. In this paper, we propose a CSI-based positioning pipeline that directly takes raw CSI measurements and learns features using a structured DNN in order to generate probability maps describing the likelihood of the transmitter being at pre-defined grid points. To further improve the positioning accuracy of moving user equipments, we propose to fuse a time-series of learned CSI features or a time-series of probability maps. To demonstrate the efficacy of our methods, we perform experiments with real-world indoor line-of-sight (LoS) and nonLoS channel measurements. We show that CSI feature learning and time-series fusion can reduce the mean distance error by up to 2.5× compared to the state-of-the-art. 

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3. Non-destructive ZooMS identification reveals strategic bone tool raw material selection by Neandertals

   https://doi.org/10.1038/s41598-020-64358-w  

   Martisius, Naomi L.; Welker, Frido; Dogandžić, Tamara; Grote, Mark N.; Rendu, William; Sinet-Mathiot, Virginie; Wilcke, Arndt; McPherron, Shannon J.; Soressi, Marie; Steele, Teresa E. (December 2020, Scientific Reports)

   Abstract Five nearly identical fragments of specialized bone tools, interpreted as lissoirs (French for “smoothers”), have been found at two Middle Paleolithic sites in southwest France. The finds span three separate archaeological deposits, suggesting continuity in the behavior of late Neandertals. Using standard morphological assessments, we determined that the lissoirs were produced on ribs of medium-sized ungulates. However, since these bones are highly fragmented and anthropogenically modified, species determinations were challenging. Also, conservative curation policy recommends minimizing destructive sampling of rare, fragile, or small artifacts for molecular identification methods. To better understand raw material selection for these five lissoirs , we reassess their taxonomy using a non-destructive ZooMS methodology based on triboelectric capture of collagen. We sampled four storage containers and obtained identifiable MALDI-TOF MS collagen fingerprints, all indicative of the same taxonomic clade, which includes aurochs and bison ( Bos sp. and Bison sp.). The fifth specimen, which was stored in a plastic bag, provided no useful MALDI-TOF MS spectra. We show that the choice of large bovid ribs in an archaeological layer dominated by reindeer ( Rangifer tarandus ) demonstrates strategic selection by these Neandertals. Furthermore, our results highlight the value of a promising technique for the non-destructive analysis of bone artifacts. 

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4. Robust discriminant analysis using multi-directional projection pursuit

   https://doi.org/10.1016/j.patrec.2020.09.013  

   Huang, Hsin-Hsiung; Zhang, Teng (October 2020, Pattern Recognition Letters)

   While linear discriminant analysis (LDA) is a widely used classification method, it is highly affected by outliers which commonly occur in various real datasets. Therefore, several robust LDA methods have been proposed. However, they either rely on robust estimation of the sample means and covariance matrix which may have noninvertible Hessians or can only handle binary classes or low dimensional cases. The proposed robust discriminant analysis is a multi-directional projection-pursuit approach which can classify multiple classes without estimating the covariance or Hessian matrix and work for high dimensional cases. The weight function effectively gives smaller weights to the points more deviant from the class center. The discriminant vectors and scoring vectors are solved by the proposed iterative algorithm. It inherits good properties of the weight function and multi-directional projection pursuit for reducing the influence of outliers on estimating the discriminant directions and producing robust classification which is less sensitive to outliers. We show that when a weight function is appropriately chosen, then the influence function is bounded and discriminant vectors and scoring vectors are both consistent as the percentage of outliers goes to zero. The experimental results show that the robust optimal scoring discriminant analysis is effective and efficient. 

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5. Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer

   https://doi.org/10.1109/ISWCS49558.2021.9562215  

   Rappaport, Brian; Gonultas, Emre; Hoydis, Jakob; Arnold, Maximilian; Srinath, Pavan Koteshwar; Studer, Christoph (September 2021, 17th International Symposium on Wireless Communication Systems (ISWCS))

   Channel charting is an emerging technology that enables self-supervised pseudo-localization of user equipments by performing dimensionality reduction on large channel-state information (CSI) databases that are passively collected at infrastructure base stations or access points. In this paper, we introduce a new dimensionality reduction method specifically designed for channel charting using a novel split triplet loss, which utilizes physical information available during the CSI acquisition process. In addition, we propose a novel regularizer that exploits the physical concept of inertia, which significantly improves the quality of the learned channel charts. We provide an experimental verification of our methods using synthetic and real-world measured CSI datasets, and we demonstrate that our methods are able to outperform the state-of-the-art in channel charting based on the triplet loss. 

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