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Electric Vehicle (EV) charging has been a significant barrier to the widespread use of EVs. Traditional EV charging methods depend on cables, and there are concerns about safety, accessibility, convenience, and weather. A recent development, dynamic (or in-motion) wireless charging, enables EVs to charge wirelessly by incorporating charging infrastructure into roadways, allowing EVs to charge while moving. However, the energy transferred relies heavily on vehicle speed and time spent in the charging lane. This paper proposes an innovative solution that combines dynamic wire-less charging with Variable Speed Limit (VSL) control. This dynamic traffic control strategy adjusts speed limits based on real-time traffic, weather, and incidents. This integration of dynamic wireless charging and VSL has two potential benefits. First, it can motivate driver compliance with VSL through the incentive of charging. Second, it can promote smoother traffic flow and improve traffic safety by implementing lower speed limits at certain times. To verify these benefits, microscopic traffic simulations in SUMO were conducted under different EV penetration rates and VSL compliance rates. Simulation results reveal that the proposed approach can enhance dynamic wireless charging system performance while improving traffic flow and safety 

Abstract Phytophthora sansomeana is an emerging oomycete pathogen causing root rot in many agricultural species including soybean. However, as of now, only one potential resistance gene has been identified in soybean, and our understanding of how genetic and epigenetic regulation in soybean contributes to responses against this pathogen remains largely unknown. In this study, we performed whole genome bisulfite sequencing (WGBS) on two soybean lines, Colfax (resistant) and Williams 82 (susceptible) in response to P. sansomeana at two time points: 4 and 16 hours post inoculation to compare their methylation changes. Our findings revealed that there were no significant changes in genome-wide CG, CHG (H = A, T, or C), and CHH methylation. However, we observed local methylation changes, specially an increase in CHH methylation around genes and transposable elements (TEs) after inoculation, which occurred earlier in the susceptible line and later in the resistant line. After inoculation, we identified differentially methylated regions (DMRs) in both Colfax and Williams 82, with a predominant presence in TEs. Notably, our data also indicated that more TEs exhibited changes in their methylomes in the susceptible line compared to the resistant line. Furthermore, we discovered 837 DMRs within or flanking 772 differentially expressed genes (DEGs) in Colfax and 166 DMRs within or flanking 138 DEGs in Williams 82. These DEGs had diverse functions, with Colfax primarily showing involvement in metabolic process, defense response, plant and pathogen interaction, anion and nucleotide binding, and catalytic activity, while Williams 82 exhibited a significant association with photosynthesis. These findings suggest distinct molecular responses to P. sansomeana infection in the resistant and susceptible soybean lines. 

Basic vital signs such as heart and respiratory rates (HR and RR) are essential bio-indicators. Their longitudinal in-home collection enables prediction and detection of disease onset and change, providing for earlier health intervention. In this paper, we propose a robust, non-touch vital signs monitoring system using a pair of co-located Ultra-Wide Band (UWB) and depth sensors. By extensive manual examination, we identify four typical temporal and spectral signal patterns and their suitable vital signs estimators. We devise a probabilistic weighted framework (PWF) that quantifies evidence of these patterns to update the weighted combination of estimator output to track the vital signs robustly. We also design a “heatmap” based signal quality detector to exclude the disturbed signal from inadvertent motions. To monitor multiple co-habiting subjects in-home, we build a two-branch long short-term memory (LSTM) neural network to distinguish between individuals and their activities, providing activity context crucial to disambiguating critical from normal vital sign variability. To achieve reliable context annotation, we carefully devise the feature set of the consecutive skeletal poses from the depth data, and develop a probabilistic tracking model to tackle non-line-of-sight (NLOS) cases. Our experimental results demonstrate the robustness and superior performance of the individual modules as well as the end-to-end system for passive and context-aware vital signs monitoring. 

This demonstration presents a working prototype of VitalHub, a practical solution for longitudinal in-home vital signs monitoring. To balance the trade-offs between the challenges related to an individual’s efforts thus compliance, and robustness with vital signs monitoring, we introduce a passive monitoring solution, which is free of any on-body device or cooperative efforts from the user. By fusing the inputs from a pair of co-located UWB and depth sensors, VitalHub achieves robust, passive, context-aware and privacy-preserving sensing. We use a COTS UWB sensor to detect chest wall displacement due to the respiration and heartbeat for vital signs extraction. We use the depth information from Microsoft Kinect to detect and locate the users in the field of view and recognize the activities of the respective users for further analysis. We have tested the prototype extensively in engineering and medical lab environments. We will demonstrate the features and performance of VitalHub using real-world data in comparison with an FDA approved medical device. 

Best Paper Award 

This demonstration presents a working prototype of VitalHub, a practical solution for longitudinal in-home vital signs monitoring. To balance the trade-offs between the challenges related to an individual’s efforts thus compliance, and robustness with vital signs monitoring, we introduce a passive monitoring solution, which is free of any on-body device or cooperative efforts from the user. By fusing the inputs from a pair of co-located UWB and depth sensors, VitalHub achieves robust, passive, context-aware and privacy-preserving sensing. We use a COTS UWB sensor to detect chest wall displacement due to the respiration and heartbeat for vital signs extraction. We use the depth information from Microsoft Kinect to detect and locate the users in the field of view and recognize the activities of the respective users for further analysis.We have tested the prototype extensively in engineering and medical lab environments. We will demonstrate the features and performance of VitalHub using realworld data in comparison with an FDA approved medical device.