Abstract Image memorability (IM) estimation typically relies on learning generic semantic features from large-scale datasets; however, memorability is intrinsically individual-dependent and personalized visual viewing behavior, but overlooking this variability can undermine model performance in downstream cognitive and human–machine interaction tasks, leading toward suboptimal performance. To address this, we propose PerMem , a unified framework that integrates knowledge distillation (KD) and imitation learning (IL) to jointly learn generic and personalized salient representations for memorability estimation by leveraging both image content and gaze-derived heatmaps. PerMem employs a teacher network built upon a pre-trained ResNet-50 backbone, followed by an encoder–decoder architecture coupled with spatial and channel attention mechanisms to generate generic saliency-aware memorability maps. A lightweight, attention-guided student encoder–decoder is then optimized through a composite imitation-guided distillation process, where knowledge is distilled from the teacher while simultaneously imitating user-specific gaze fixation heatmaps. Through this joint training process, the student network learns to produce personalized memorability estimates while achieving substantial reductions in computational complexity. We validate PerMem on two public IM estimation datasets (LaMem and SUN) and a in-house WoM dataset comprising of 45 participants (UMBC IRB #670) engaged in visual search and navigation tasks that reflect individualized visual attention patterns. PerMem outperforms nine state-of-the-art IM models by capturing coarse-to-fine saliency and adapting to individual attention, achieving $$\approx $$ ≈ 6% improvement in memorability prediction. Lastly, we evaluate PerMem on heterogeneous embedded edge devices, including Jetson Nano, Jetson Xavier NX, and Raspberry Pi, demonstrating efficient on-device inference with consistent reductions in memory usage (25.6–33.4%), power consumption (19.0–25.0%), and inference latency (34.9–39.8%) relative to the teacher model, highlighting its practical robustness for resource-constrained, human-centric applications.

Image memorability (IM) estimation typically relies on learning generic semantic features from large-scale datasets; however, memorability is intrinsically individual-dependent and personalized visual viewing behavior, but overlooking this variability can undermine model performance in downstream cognitive and human-machine interaction tasks, leading towards suboptimal performance. To address this, we propose MemGaze, a unified framework that integrates knowledge distillation (KD) and imitation learning (IL) to jointly learn generic and personalized salient representations for memorability estimation by leveraging both image content and gaze-derived heatmaps. MemGaze employs a teacher network built upon a pretrained ResNet-50 backbone, followed by an encoder-decoder architecture coupled with spatial and channel attention mechanisms to generate generic saliency-aware memorability maps. A lightweight, attention-guided student encoder-decoder is then optimized through a composite imitation-guided distillation process, where knowledge is distilled from the teacher while simultaneously imitating user-specific gaze fixation heatmaps. Through this joint training process, the student network learns to produce personalized memorability estimates while achieving substantial reductions in computational complexity. We validate MemGaze on two public IM estimation datasets (LaMem and SUN) and a inhouse WoM dataset comprising of 45 participants (UMBC IRB #670) engaged in visual search and navigation tasks that reflect individualized visual attention patterns. MemGaze outperforms nine state-of-the-art IM models by capturing coarse-to-fine saliency and adapting to individual attention, achieving ≈6% improvement in memorability prediction.

Monitoring respiratory rate (RR) is essential for early identification of respiratory and metabolic abnormalities. However, the limitations of contact-based sensors and the lack of reliability in many contactless methods make continuous and accurate monitoring difficult in non-clinical settings. To address these challenges, we introduce RespFormer, an edge-optimized, motion-guided temporal-frequency multimodal fusion transformer framework for real-time, contactless RR estimation and breathing pattern classification. RespFormer integrates dense optical flow analysis with temporal, statistical, and frequency-domain features derived from video sequences and enhances them through a multi-stage signal processing pipeline. These features are modeled using an ensemble of three time series transformer architectures (ETSformer, Temporal Fusion Transformer, and Informer) to capture distinct aspects of temporal dynamics. A shared attention-based refinement module enhances the feature representations, and final predictions are fused using a stacking-based meta-learner. We validate RespFormer on a multimodal dataset comprising synchronized RGB, NIR, and IR video data, including a custom in-house dataset captured under various conditions. Experimental results demonstrate that RespFormer achieves a mean absolute error (MAE) ≈ 0.98 bpm, improving prediction accuracy by ≈ 11% and reducing memory usage by ≈ 26%, while maintaining real-time inference (≈ 1.22 seconds) on resource-constrained devices. Furthermore, RespFormer accurately classifies breathing patterns (normal, bradypnea, tachypnea, and apnea) with 95% accuracy, underscoring it’s potential for practical application in telemedicine, clinical screening, and low-resource healthcare settings.

Recent Internet-of-Things (IoT) networks span across a multitude of stationary and robotic devices, namely unmanned ground vehicles, surface vessels, and aerial drones, to carry out mission-critical services such as search and rescue operations, wildfire monitoring, and flood/hurricane impact assessment. Achieving communication synchrony, reliability, and minimal communication jitter among these devices is a key challenge both at the simulation and system levels of implementation due to the underpinning differences between a physics-based robot operating system (ROS) simulator that is time-based and a network-based wireless simulator that is event-based, in addition to the complex dynamics of mobile and heterogeneous IoT devices deployed in a real environment. Nevertheless, synchronization between physics (robotics) and network simulators is one of the most difficult issues to address in simulating a heterogeneous multi-robot system before transitioning it into practice. The existing TCP/IP communication protocol-based synchronizing middleware mostly relied on Robot Operating System 1 (ROS1), which expends a significant portion of communication bandwidth and time due to its master-based architecture. To address these issues, we design a novel synchronizing middleware between robotics and traditional wireless network simulators, relying on the newly released real-time ROS2 architecture with a masterless packet discovery mechanism. Additionally, we propose a ground and aerial agents' velocity-aware customized QoS policy for Data Distribution Service (DDS) to minimize the packet loss and transmission latency between a diverse set of robotic agents, and we offer the theoretical guarantee of our proposed QoS policy. We performed extensive network performance evaluations both at the simulation and system levels in terms of packet loss probability and average latency with line-of-sight (LOS) and non-line-of-sight (NLOS) and TCP/UDP communication protocols over our proposed ROS2-based synchronization middleware. Moreover, for a comparative study, we presented a detailed ablation study replacing NS-3 with a real-time wireless network simulator, EMANE, and masterless ROS2 with master-based ROS1. Our proposed middleware attests to the promise of building a large-scale IoT infrastructure with a diverse set of stationary and robotic devices that achieve low-latency communications (12% and 11% reduction in simulation and reality, respectively) while satisfying the reliability (10% and 15% packet loss reduction in simulation and reality, respectively) and high-fidelity requirements of mission-critical applications.

Millimeter-wave (MMW) radar is becoming an essential sensing technology in smart environments due to its light and weather-independent sensing capability. Such capabilities have been widely explored and integrated with intelligent vehicle systems, often deployed in industry-grade MMW radars. However, industry-grade MMW radars are often expensive and difficult to attain for deployable community-purpose smart environment applications. On the other hand, commercially available MMW radars pose hidden underpinning challenges that are yet to be well investigated for tasks such as recognizing objects, and activities, real-time person tracking, object localization, etc. Such tasks are frequently accompanied by image and video data, which are relatively easy for an individual to obtain, interpret, and annotate. However, image and video data are light and weather-dependent, vulnerable to the occlusion effect, and inherently raise privacy concerns for individuals. It is crucial to investigate the performance of an alternative sensing mechanism where commercially available MMW radars can be a viable alternative to eradicate the dependencies and preserve privacy issues. Before championing MMW radar, several questions need to be answered regarding MMW radar’s practical feasibility and performance under different operating environments. To answer the concerns, we have collected a dataset using commercially available MMW radar, Automotive mmWave Radar (AWR2944) from Texas Instruments, and reported the optimum experimental settings for object recognition performance using several deep learning algorithms in this study. Moreover, our robust data collection procedure allows us to systematically study and identify potential challenges in the object recognition task under a cross-ambience scenario. We have explored the potential approaches to overcome the underlying challenges and reported extensive experimental results.