Wireless sensing offers a non-destructive method to estimate fruit moisture content, which is essential for assessing internal fruit quality. While fruit quality is often judged by external appearance, this can be misleading, as fruits may look perfect externally but be spoiled internally. Therefore, accurate measurement methods are needed to assess the internal conditions of fruits. Current techniques for measuring fruit moisture, crucial for evaluating quality, are often destructive or expensive, limiting their practicality for everyday use. This paper presents FSense, a novel, non-destructive, and cost-effective system for estimating fruit moisture utilizing millimeter-wave (mmWave) technology. To preprocess radar antenna signals, we introduce a twin-domain model based on the discrete wavelet transform to remove fruit-structure dependencies. Additionally, we propose a Knowledge-Based Transfer Learning Model (KB-TLM) to address variability among fruits with thin and thick pericarps and to leverage transfer learning to apply source-domain knowledge to target domains. This model effectively predicts moisture content across various fruits by employing customized convolutional and generative adversarial networks, which extract stable, moisture-dependent features independent of fruit type. Over one month, extensive experiments were conducted on 240 fruits across four categories, namely apple, pear, peach, and watermelon, in two representative environments, including an empty laboratory and a busy library. Under the original random split setting, FSense achieves an RMSE as low as 0.219. More importantly, under the stricter leave-one-fruit-out evaluation designed to avoid leakage from repeated longitudinal measurements of the same fruit, FSense maintains robust moisture estimation, with RMSE values of 0.276, 0.319, 0.348, and 0.392 for apple, pear, peach, and watermelon, respectively. Additionally, leaving one batch out and leaving one environment out in evaluations further confirm the generalization capability of FSense under fruit identity, batch, and environmental shifts. These results show that commodity 76–81 GHz mmWave radar provides a repeatable moisture-sensitive effective response that can be calibrated for non-destructive fruit moisture estimation, supporting potential applications in fruit preservation, quality monitoring, and post-harvest management.
Taste is not shaped by the tongue alone: smell and vision also influence how a beverage is experienced. We present Kaolid+, a lid-type olfactory interface that routes aroma either toward the nose or through a straw, while illuminating the drink with RGB light. This form factor allowed us to compare orthonasal and retronasal aroma delivery under a closely matched setup, and to compare subjective taste ratings across no-light, aroma-congruent orange-light, and aroma-incongruent blue-light conditions under retronasal orange aroma delivery. In a within-subjects design with unflavored carbonated water and orange aroma, the retronasal condition received higher Perceived Sweetness and Flavor Change ratings than the orthonasal condition; overall acceptability did not differ significantly between the two methods. Under retronasal delivery, the orange-light condition was associated with higher Perceived Sweetness ratings than the no-light condition, and the orange-light condition was rated higher than the blue-light condition for overall acceptability, sweetness, and naturalness. Kaolid+ received a mean SUS score of 77.63, indicating good usability. The results position Kaolid+ as a smart beverage interface for studying lid-integrated olfactory actuation and visual cueing during everyday drinking, and provide initial evidence that these device conditions may be associated with changes in sweetness-related ratings within the tested orange-aroma carbonated-water scenario.
Past decades have witnessed the rapid development of battery-free backscatter-based cameras, opening up promising long-term and sustainable surveillance applications by harvesting wireless energy. However, limited by the fluctuations of the ambient energy, existing backscatter systems suffer from unreliability issues, e.g., stalls and frame losses in video transmission. To tackle this problem, we propose EATag, the first energy-aware backscatter system that can provide reliable battery-free video frame transmission through adaptation. EATag provides hardware and software co-designs to achieve energy budgeting, prediction, capacity estimation, and adaptation for reliable video transmission. By leveraging the reader to monitor the operation duty cycles and estimate the channel fluctuation, the tag only needs to consume a little energy to achieve efficient adaptation. We implement the EATag system with off-the-shelf components. We conduct extensive experiments to evaluate the performance of EATag. Results demonstrate that EATag can achieve a stalling rate reduction of 57.3%-89.2% when compared to the existing battery-free video systems. We also present an IC schematic design of the tag with TSMC 90 nm standard cells. Circuit simulation results show that it can potentially consume only 58 μ W to provide reliable battery-free video transmission.
The rapid growth of IoT-powered smart communities has created valuable, but siloed data, limited by interoperability and data-sovereignty concerns. We present ComDeX , a context-aware federated IoT middleware that enables selective, intelligent collaboration across communities. ComDeX uses a property-graph data model to encode IoT entities as context-rich pub/sub messages, discovered and filtered via an advertisement-based federation, so communities share only what is relevant while retaining control. To support collaborative intelligence, ComDeX adds a cross-organization Discovery → Eligibility → Enrollment (DEE) layer that enables discovery of FL tasks and clients, independent of the chosen FL runtime. We prototype ComDeX on MQTT and NGSI-LD and evaluate in a smart-port scenario, demonstrating faster and more efficient cross-community exchange than existing NGSI-LD federation approaches. In non-IID FL experiments, we showcase cross-community enrollment capabilities and expose different DEE coordination tradeoffs. Overall, ComDeX enables scalable, adaptive collaboration for data sharing and federated model training.
Autonomous Driving (AD) has garnered significant attention in recent years across multiple domains. Despite notable advancements in algorithms and hardware, large-scale deployment of autonomous vehicles remains constrained by the lack of adequate real-time guarantees. This article comprehensively investigates real-time assurance challenges in AD systems from theoretical and practical perspectives. First, we introduce foundational concepts of real-time systems and analyze common modeling approaches, including the multi-rate DAG and processing chain DAG models. We then delve into the task scheduling and communication mechanisms of three representative middleware systems–ROS2, Cyber, and ERDOS–and the seL4 operating system. Our analysis reveals their underlying design philosophies and optimization strategies for real-time performance. The findings highlight the critical difficulty of meeting stringent real-time requirements in autonomous systems. By offering insights into current limitations and opportunities for improvement, this article aims to establish a deeper understanding of real-time assurance issues and encourage greater focus on system-level guarantees within the AD community.
