SENSYS

Firefighters still rely on coarse remote sensing and inaccurate eyewitness reports to localize spreading wildfires. Despite advances in sensing, UAVs, and computer vision, the community has yet to combine the right modalities to achieve effective ...

With the development of the Internet of Things and artificial intelligence, the deployment and inference of intelligent models have gradually raised concerns. To reduce the huge computation and storage overhead of modern deep neural networks, many ...

The ability of smart devices to determine their locations is the basis for many applications. We present LEAD, a system which can simultaneously Locate Everyday smArt Devices, such as smartphone, smartwatch, and headphone, with only one speaker. The ...

As society progresses, liquid identification plays an increasingly important role in human life. But for now, minority of existing liquid identification solutions on the market can meet daily requirements of being ubiquitous, cost-effective and non-...

The increasing popularity of virtual reality (VR) has stressed the importance of authenticating VR users while preserving their privacy. Behavioral biometrics, owing to their robustness and ease of collection, compared to traditional modes such as ...

Sonar radar captures visual representations of underwater objects and structures using sound wave reflections, making it essential for exploration, mapping, and continuous surveillance in wild ecosystems. Real-time analysis of sonar data is crucial for ...

The multi-camera capture system is an emerging visual sensing modality. It facilitates the production of various immersive contents ranging from regular to neural videos. Although the delivery of immersive content is popular and promising, it suffers ...

This paper introduces PhyMask, an adaptive masking paradigm designed to enhance the efficiency and interpretability of Masked Autoencoders (MAEs) in analyzing IoT sensing signals. Different from all mainstream MAEs, which rely on random masking ...

While federated learning leverages distributed client resources, it faces challenges due to heterogeneous client capabilities. This necessitates allocating models suited to clients' resources and careful parameter aggregation to accommodate this ...

On-device training Neural Networks (NNs) has been a crucial catalyst towards privacy-preserving and personalized mobile intelligence. Recently, a novel training paradigm, namely Parameter-Efficient Training (PET), is attracting attention in both the ...

Collaborative Vehicular Perception (CVP) enables connected and autonomous vehicles (CAVs) to cooperatively extend their views through wirelessly sharing their sensor data. Existing CVP systems employ either a vehicle-to-vehicle (V2V) or vehicle-to-...

Reliable cross medium (e.g., air-water) communication using radio frequency (RF) has remained an open-problem for decades. Currently, underwater devices cannot communicate directly with land-based or airborne devices. Typical solutions are inadequate ...

Human faces have been widely adopted in many applications and systems requiring a high-security standard. Although face authentication is deemed to be mature nowadays, many existing works have demonstrated not only the privacy leakage of facial ...

Millimeter-wave (mmWave) based human activity recognition (HAR) systems have demonstrated promising performance in various applications, leveraging the power of deep neural networks. However, these systems are suffering from the scarcity of available ...

The rapidly growing Internet of Things (IoT) can avoid the high cost and environmental burden of replacing trillions of batteries by using sustainable battery-free devices that operate maintenance-free for decades. To develop battery-free IoT systems, ...

Large-scale IoT sensor deployment calls for inexpensive, low-power sensor nodes that still perform long-range, large-scale networking at the system level. However, current sensor nodes are constructed according to the `one-size-fits-all' embedded design, ...

Soil moisture level sensing is essential for enabling smart irrigation, which is crucial for our food security and sustainable agriculture. Existing soil moisture sensing systems face limitations such as single-layer sensing, limited depth, power supply ...

Batteryless edge devices are extremely resource-constrained compared to traditional mobile platforms. Existing tiny deep neural network (DNN) inference solutions are problematic due to their slow and resource-intensive nature, rendering them unsuitable ...

While a large range of sensing applications such as activity sensing and vital sign monitoring have been realized with WiFi sensing, using commercial WiFi devices to obtain fine-grained size information of objects remains challenging due to the narrow ...

We present HUM-High-frequency UAV Messaging: an acoustic side channel communication system we design for localized drone-to-drone communications. We generate Pulse Width Modulated (PWM) signals from drone motors to carry information and improve ...

Unlike traditional data collection applications (e.g., environment monitoring) that are dominated by uplink transmissions, the newly emerging applications (e.g., device actuation, firmware update, packet reception acknowledgement) also pose ever-...

On-device adapting to continual, unpredictable domain shifts is essential for mobile applications like autonomous driving and augmented reality to deliver seamless user experiences in evolving environments. Test-time adaptation (TTA) emerges as a ...

This paper presents Mission, the first-of-this-kind cross-modal reidentification (ReID) design for mmWave Radar and RGB cameras. Given a person of interest detected by Radar in camera-restricted scenarios, Mission can identify the image of the person ...

In this work, we introduce SEESys, the first system to provide online pose error estimation for Simultaneous Localization and Mapping (SLAM). Unlike prior offline error estimation approaches, the SEESys framework efficiently collects real-time system ...

Sensors enable us to digitally capture stimuli like moisture, light, and force. Despite their low cost, reliability, and scalability, the lack of widespread adoption of IoT has hindered the realization of true ubiquitous sensing. A likely reason is that ...

LiDAR sensors measure the environment by emitting lasers and, when combined with deep neural networks (DNNs), can effectively identify surrounding obstacles such as vehicles and pedestrians. Given its crucial role in autonomous driving perception, the ...

Immersive telepresence has the potential to revolutionize remote communication by offering a highly interactive and engaging user experience. However, state-of-the-art exchanges large volumes of 3D content to achieve satisfactory visual quality, ...

LiDAR (Light Detection and Ranging) has been widely used in autonomous driving to perceive the surrounding environment of self-driving cars. Advanced LiDAR perception systems typically leverage deep neural networks (DNNs) to achieve high performance. ...

Federated Learning (FL) emerges as a new learning paradigm that enables multiple devices to collaboratively train a shared model while preserving data privacy. However, one fundamental and prevailing challenge that hinders the deployment of FL on mobile ...

We present TaDA, a system architecture enabling efficient execution of Internet of Things (IoT) applications across multiple computing units, powered by ambient energy harvesting. Low-power microcontroller units (MCUs) are increasingly specialized; for ...