The increasing deployment of battery storage applications in both grid storage and electric vehicle fields is generating a vast used battery market. These batteries are typically recycled but could be reused in second-life applications. One of the challenges is to obtain an accurate remaining useful life (RUL) estimation algorithm, which determines whether a battery is suitable for reuse and estimates the number of second-life cycles the battery will last. In this article, the RUL estimation problem is considered. We propose several health indicators (HIs), some of which have not been explored before, along with simple yet effective estimation and classification algorithms. These algorithms include classification techniques such as regularized logistic regression (RLR), and regression techniques such as multivariable linear regression (MLR) and multilayer perceptron (MLP). As a more advanced solution, a multiple expert system combining said techniques is proposed. The performance of the algorithms and features is evaluated on a recent lithium iron phosphate (LFP) data set from Toyota Research Institute. We obtain satisfactory results in the estimation of RUL cycles with errors down to 49 root mean square error (RMSE) cycles for cells that live up to 1200 cycles, and 0.24% mean relative error (MRE) for the prediction of the evolution of capacity.

This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian (HTG) with structural geometry parameters that can be learned from the training data. The HTG measurement model provides an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Moreover, large-scale radar datasets can be leveraged to learn the geometry-related model parameters and offload the computationally demanding model parameter estimation from the state update step. The learned HTG measurement model is further incorporated into a random matrix based EOT approach with two (multi-sensor) measurement updates: one is based on a factorized Gaussian inverse-Wishart density representation and the other is based on a Rao-Blackwellized particle density representation. The effectiveness of the proposed approaches is verified on both synthetic data and real-world nuScenes dataset over 300 trajectories.

Wireless networked control systems (WNCSs) have the potential to revolutionize industrial automation in smart factories. Optimizing closed-loop performance while maintaining stability is a fundamental challenge in WNCS due to limited bandwidth and nondeterministic link quality of wireless networks. In order to bridge the gap between network design and control system performance, we propose an optimal dynamic transmission scheduling strategy that optimizes the performance of multiloop control systems by allocating network resources based on predictions of both link quality and control performance at run time. We formulate the optimal dynamic scheduling problem as a nonlinear integer programming problem, which is relaxed to a linear programming problem. We further extend the optimization problem to balance control performance and communication cost. The proposed optimal dynamic scheduling strategy renders the closed-loop system mean-square stable under mild assumptions. Its efficacy is demonstrated by simulating a four-loop control system over an IEEE 802.15.4 wireless network simulator--TOSSIM. The run-time network reconfiguration protocol tailored for optimal scheduling is designed and implemented on a real wireless network consisting of IEEE 802.15.4 devices. Hybrid simulations integrating a real wireless network and simulated physical plant control are performed. Simulation and experimental results show that the optimal dynamic scheduling can enhance control system performance and adapt to both constant and variable wireless interference and physical disturbance to the plant.

Point cloud is an emerging data format useful for various applications such has holographic display, autonomous vehicle, and augmented reality. Conventionally, communications of point cloud data have relied on digital compression and digital modulation for three-dimensional (3D) data streaming. However, such digital-based delivery schemes have fundamental issues called cliff and leveling effects, where the 3D reconstruction quality is a step function in terms of wireless channel quality. We propose a novel scheme of point cloud delivery, called HoloCast $+$ , to overcome cliff and leveling effects. Specifically, our method utilizes hybrid digital-analog coding, integrating digital compression and analog coding based on graph Fourier transform (GFT), to gracefully improve 3D reconstruction quality with the improvement of channel quality. We demonstrate that HoloCast $+$ offers better 3D reconstruction quality in terms of the symmetric mean square error (sMSE) by up to 18.3 dB and 10.5 dB, respectively, compared to conventional digital-based and analog-based delivery methods in wireless fading environments.

Complementary to the fine-grained channel state information (CSI) and coarse-grained received signal strength indicator (RSSI) measurements, the mid-grained spatial beam attributes (i.e., beam SNR) during the millimeter-wave (mmWave) beam training phase were recently repurposed for Wi-Fi sensing applications such as human activity recognition and indoor localization. This paper proposes a multi-band Wi-Fi sensing framework to fuse features from both CSI from 5 GHz bands and the mid-grained beam SNR at 60 GHz with feature granularity matching that pairs feature maps from the CSI and beam SNR at different granularity levels with learnable weights. To address the issue of limited labeled training data, we propose to pre-train an autoencoder-based multi-band Wi-Fi fusion network in an unsupervised fashion. For specific sensing tasks, separate sensing heads can be attached to the pre-trained fusion network with fine-tuning. The proposed framework is thoroughly validated for three sensing applications using in-house experimental datasets: 1) pose recognition; 2) occupancy sensing; and 3) indoor localization. Comparison to a list of baseline methods demonstrates the effectiveness of granularity matching. An ablation study is performed as a function of the amount of labeled data, the latent space dimension, and learning rates.