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We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States. The architecture enables real-time monitoring and control and supports electric vehicle (EV) charging at scale. The ACN adopts a flexible Adaptive Scheduling Algorithm based on convex optimization and model predictive control and allows for significant over-subscription of electrical infrastructure. We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals. We demonstrate how the Adaptive Scheduling Algorithm handles these challenges, and compare its performance against baseline algorithms from the deadline scheduling literature using real workloads recorded from the Caltech ACN and accurate system models. We find that in these realistic settings, our scheduling algorithm can improve operator profit by 3.4 times over uncontrolled charging and consistently outperforms baseline algorithms when delivering energy in highly congested systems. [ABSTRACT FROM AUTHOR]

A modular arm-swinging human body phantom model is developed to facilitate the investigation of on-body wave propagation mechanisms. Transmission data between antennas are collected over the phantom model, and compared with computer simulations and human volunteer measurements. The motion capture technique is applied to correlate measured transmission data with body motion characteristics. A parametric study of various body antenna parameters is also performed. [ABSTRACT FROM AUTHOR]

The characteristics of a nonline-of-sight on-body creeping wave are utilized to classify human motion activities. Creeping wave propagation around the torso of a nonmoving human is reviewed. Its associated complex transmission data are measured for six different motion activities on both male and female subjects. A dynamic time warping algorithm is applied on the signal magnitude and phase data for pattern recognition. The study is extended to different on-body channels and frequencies for comparisons. [ABSTRACT FROM PUBLISHER]

Quantitative histomorphometry (QH) refers to the process of computationally modeling disease appearance on digital pathology images by extracting hundreds of image features and using them to predict disease presence or outcome. Since constructing a robust and interpretable classifier is challenging in a high dimensional feature space, dimensionality reduction (DR) is often implemented prior to classifier construction. However, when DR is performed it can be challenging to quantify the contribution of each of the original features to the final classification result. We have previously presented a method for scoring features based on their importance for classification on an embedding derived via principal components analysis (PCA). However, nonlinear DR involves the eigen-decomposition of a kernel matrix rather than the data itself, compounding the issue of classifier interpretability. In this paper we present feature importance in nonlinear embeddings (FINE), an extension of our PCA-based feature scoring method to kernel PCA (KPCA), as well as several NLDR algorithms that can be cast as variants of KPCA. FINE is applied to four digital pathology datasets to identify key QH features for predicting the risk of breast and prostate cancer recurrence. Measures of nuclear and glandular architecture and clusteredness were found to play an important role in predicting the likelihood of recurrence of both breast and prostate cancers. Compared to the t-test, Fisher score, and Gini index, FINE was able to identify a stable set of features that provide good classification accuracy on four publicly available datasets from the NIPS 2003 Feature Selection Challenge. [ABSTRACT FROM AUTHOR]

In this work, we present a new methodology to facilitate prediction of recurrent prostate cancer (CaP) following radical prostatectomy (RP) via the integration of quantitative image features and protein expression in the excised prostate. Creating a fused predictor from high-dimensional data streams is challenging because the classifier must 1) account for the “curse of dimensionality” problem, which hinders classifier performance when the number of features exceeds the number of patient studies and 2) balance potential mismatches in the number of features across different channels to avoid classifier bias towards channels with more features. Our new data integration methodology, supervised Multi-view Canonical Correlation Analysis (sMVCCA), aims to integrate infinite views of highdimensional data to provide more amenable data representations for disease classification. Additionally, we demonstrate sMVCCA using Spearman’s rank correlation which, unlike Pearson’s correlation, can account for nonlinear correlations and outliers. Forty CaP patients with pathological Gleason scores 6–8 were considered for this study. 21 of these men revealed biochemical recurrence (BCR) following RP, while 19 did not. For each patient, 189 quantitative histomorphometric attributes and 650 protein expression levels were extracted from the primary tumor nodule. The fused histomorphometric/proteomic representation via sMVCCA combined with a random forest classifier predicted BCR with a mean AUC of 0.74 and a maximum AUC of 0.9286. We found sMVCCA to perform statistically significantly (p < 0.05) better than comparative state-of-the-art data fusion strategies for predicting BCR. Furthermore, Kaplan-Meier analysis demonstrated improved BCR-free survival prediction for the sMVCCA-fused classifier as compared to histology or proteomic features alone. [ABSTRACT FROM AUTHOR]

The design and performance of a low-cost, energy-efficient carbon nanotube sensor interface circuit (SIC) is presented that is aimed at deployable/expendable applications with the sensor operating at ambient temperatures. The submicrometer SIC is designed and fabricated in CMOS 180-nm technology and consumes a maximum of 122 \muW, including a decoder and voltage follower. The SIC achieves a measurement accuracy of 1.4% over a 2.5 k\Omega to 25 M\Omega dynamic range of sensor resistance. A critical issue associated with a submicrometer integrated circuit implementation is satisfactory performance of the SIC for fabrication process variations that range from the slow, to typical, to fast process corners and simultaneously meeting the requirement for the very large dynamic range for sensor resistances. A robust design considering the full range of potential process variations becomes more critical as feature size is reduced. The SIC design includes an added degree for freedom for counteracting process variations during post fabrication calibration. In addition, a new design procedure is outlined and implemented to permit an accurate and efficient first order design to ensure satisfactory performance for typical, fast, and slow model parameters. The first order design is verified by complete model simulation performance, and finally selected measured results are included for a fabricated circuit. [ABSTRACT FROM PUBLISHER]

Alerts readers to changes that are to be implemented for the 2006 International Conference on Robotics and Automation. Organizers' acceptance of poster submissions for the first time; Process for reviewing papers for the conference; Call for papers, posters, videos, and tutorial/workshop proposals.

With a wide array of multi-modal, multi-protocol, and multi-scale biomedical data available for disease diagnosis and prognosis, there is a need for quantitative tools to combine such varied channels of information, especially imaging and non-imaging data (e.g. spectroscopy, proteomics). The major problem in such quantitative data integration lies in reconciling the large spread in the range of dimensionalities and scales across the different modalities. The primary goal of quantitative data integration is to build combined meta-classifiers; however these efforts are thwarted by challenges in (1) homogeneous representation of the data channels, (2) fusing the attributes to construct an integrated feature vector, and (3) the choice of learning strategy for training the integrated classifier. In this paper, we seek to (a) define the characteristics that guide the 4 independent methods for quantitative data fusion that use the idea of a meta-space for building integrated multi-modal, multi-scale meta-classifiers, and (b) attempt to understand the key components which allowed each method to succeed. These methods include (1) Generalized Embedding Concatenation (GEC), (2) Consensus Embedding (CE), (3) Semi-Supervised Multi-Kernel Graph Embedding (SeSMiK), and (4) Boosted Embedding Combination (BEC). In order to evaluate the optimal scheme for fusing imaging and non-imaging data, we compared these 4 schemes for the problems of combining (a) multi-parametric MRI with spectroscopy for prostate cancer (CaP) diagnosis in vivo , and (b) histological image with proteomic signatures (obtained via mass spectrometry) for predicting prognosis in CaP patients. The kernel combination approach (SeSMiK) marginally outperformed the embedding combination schemes. Additionally, intelligent weighting of the data channels (based on their relative importance) appeared to outperform unweighted strategies. All 4 strategies easily outperformed a naïve decision fusion approach, suggesting that data integration methods will play an important role in the rapidly emerging field of integrated diagnostics and personalized healthcare.