Tam Vu

A Lightweight and Inexpensive In-ear Sensing System For Automatic Whole-night Sleep Stage Monitoring

This paper introduces LIBS, a light-weight and inexpensive wearable sensing system, that can capture electrical activities of human brain, eyes, and facial muscles with two pairs of custom-built flexible electrodes each of which is embedded on an off-the-shelf foam earplug. A supervised non-negative matrix factorization algorithm to adaptively analyze and extract these bioelectrical signals from a single mixed in-ear channel collected by the sensor is also proposed. While LIBS can enable a wide class of low-cost self-care, human computer interaction, and health monitoring applications, we demonstrate its medical potential by developing an autonomous whole-night sleep staging system utilizing LIBSs outputs. We constructed a hardware prototype from off-the-shelf electronic components and used it to conduct 38 hours of sleep studies on 8 participants over a period of 30 days. Our evaluation results show that LIBS can monitor biosignals representing brain activities, eye movements, and muscle contractions with excellent fidelity such that it can be used for sleep stage classification with an average of more than 95% accuracy.

Capacitive Sensing 3D-printed Wristband for Enriched Hand Gesture Recognition

In this work, we design a wearable-form hand gesture recognition system using capacitive sensing technique. Our proposed system includes a 3D printed wristband, capacitive sensors arrays in a flexible circuit board, a low-cost micro-controller unit and wireless communication module (BLE). In particular, the wristband manipulates the changes in capacitance from multiple capacitive sensors to recognize and detect users hand gestures. The software stack translates the detected gestures into control command for application layer, together with an user-friendly web interface that supports both data communication and training between the wristband and the host PC. We also release an open API of our designed system for future applications. Lastly, we envision our system open API will be available for developers to customize vast range of hand gesture and integrate the wristband into various applications, from command on remote computer to video game controller.

Investigating Cost-effective RF-based Detection of Drones

Beyond their benign uses, civilian drones have increasingly been used in problematic ways that have stirred concern from the public and authorities. While many anti-drone systems have been proposed to take them down, such systems often rely on a fundamental assumption that the presence of the drone has already been detected and is known to the defender. However, there is a lack of an automated cost-effective drone detection system. In this paper, we investigate a drone detection system that is designed tao autonomously detect and characterize drones using radio frequency wireless signals. In particular, two technical approaches are proposed. The first approach is active tracking where the system sends a radio signal and then listens for its reflected component. The second approach is passive listening where it receives, extracts, and then analyzes observed wireless signal. We perform a set of preliminary experiments to explore the feasibility of the approaches using WARP and USRP software-defined platforms. Our preliminary results illustrate the feasibility of the proposed system and identify the challenges for future research.

Matthan: Drone Presence Detection by Identifying Physical Signatures in the Drone's RF Communication

Drones are increasingly flying in sensitive airspace where their presence may cause harm, such as near airports, forest fires, large crowded events, secure buildings, and even jails. This problem is likely to expand given the rapid proliferation of drones for commerce, monitoring, recreation, and other applications. A cost-effective detection system is needed to warn of the presence of drones in such cases. In this paper, we explore the feasibility of inexpensive RF-based detection of the presence of drones. We examine whether physical characteristics of the drone, such as body vibration and body shifting, can be detected in the wireless signal transmitted by drones during communication. We consider whether the received drone signals are uniquely differentiated from other mobile wireless phenomena such as cars equipped with Wi- Fi or humans carrying a mobile phone. The sensitivity of detection at distances of hundreds of meters as well as the accuracy of the overall detection system are evaluated using software defined radio (SDR) implementation.

In-ear Biosignal Recording System: A Wearable For Automatic Whole-night Sleep Staging

In this work, we present a low-cost and light-weight wearable sensing system that can monitor bioelectrical signals generated by electrically active tissues across the brain, the eyes, and the facial muscles from inside human ears. Our work presents two key aspects of the sensing, which include the construction of electrodes and the extraction of these biosignals using a supervised non-negative matrix factorization learning algorithm. To illustrate the usefulness of the system, we developed an autonomous sleep staging system using the output of our proposed in-ear sensing system. We prototyped the device and evaluated its sleep stage classification performance on 8 participants for a period of 1 month. With 94% accuracy on average, the evaluation results show that our wearable sensing system is promising to monitor brain, eyes, and facial muscle signals with reasonable fidelity from human ear canals.

Through-body Capacitive Touch Communication

To overcome the drawbacks and security issues of traditional wireless and RF-based communication techniques, multiple alternative wireless communication methods have been investigated. Among them, capacitive touch communication which utilizes the touchscreen process as communication channel has been proposed with the promising future since capacitive touchscreen has been widely known as an important component of pervasive touch-enabled smart devices. In this paper, we present for the first time the indirect method of capacitive touch communication that allows the users to transmit their information from the wearable devices to the touch-enabled devices using human body as a medium. The key idea is to modulate the signal emitted from a wearable device propagating through users body. These signal can be decoded to extract the transmitted data when the user touch to the touch-surface. In particular, we show a system design of through-body capacitive touch communication channel together with the circuit model analysis. We present our preliminary results of our system, and identify possible challenges for future research and development.

Outward Influence and Cascade Size Estimation in Billion-scale Networks

Estimating cascade size and nodes influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new influence measure, termed outward influence (OI), defined as the (expected) number of nodes that a subset of nodes S will activate, excluding the nodes in S. Thus, OI equals, the de facto standard measure, influence spread of S minus |S|. OI is not only more informative for nodes with small influence, but also, critical in designing new effective sampling and statistical estimation methods. Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence spread/outward influence at scale and with rigorous theoretical guarantees. The proposed methods are built on two novel components 1) IICP an important sampling method for outward influence; and 2) RSA, a robust mean estimation method that minimize the number of samples through analyzing variance and range of random variables. Compared to the state-of-the art for influence estimation, SIEA is Ω(log4 n) times faster in theory and up to several orders of magnitude faster in practice. For the first time, influence of nodes in the networks of billions of edges can be estimated with high accuracy within a few minutes. Our comprehensive experiments on real-world networks also give evidence against the popular practice of using a fixed number, e.g. 10K or 20K, of samples to compute the ``ground truth for influence spread.

Demo: Low-power Capacitive Sensing Wristband for Hand Gesture Recognition

Along with the rapid development of human-computer interaction (HCI), controlling objects and smart devices remotely from afar has become the trend to satisfy the needs of consumers. Hand gesture recognition is among the methods that highly attract the attention in this field yet there still exists many aspect to explore and improve. We propose a low-cost low-power wristband-form hand gesture recognition system utilizing capacitive sensing technique. We provide an open source system which includes low-power, low-cost hardware components and user-friendly software stack. This system will be available for users and developers to customize various hand gesture set and integrate into third part application, from computer remote command to video game controller.

EIR: Edge-aware inter-domain routing protocol for the future mobile internet

Abstract This work describes a clean-slate inter-domain routing protocol designed to meet the needs of the future mobile Internet. In particular, we describe the edge-aware inter-domain routing (EIR) protocol which provides new abstractions, such as aggregated-nodes (aNodes) and virtual-links (vLinks) for expressing network topologies and edge network properties necessary to address mobility related routing scenarios which are inadequately supported by the border gateway protocol (BGP) in use today. Specific use-cases addressed by EIR include emerging mobility service scenarios such as multi-homing across WiFi and cellular, multipath routing over several access networks, and anycast access from mobile devices to replicated cloud services. It is shown that EIR can be used to realize efficient routing strategies for the mobility use-cases under consideration, while also providing support for a range of inter-domain routing policies currently associated with BGP. Simulation results for protocol overhead are presented for a global-scale CAIDA topology, leading to an identification of parameters necessary to obtain a good balance between overhead and routing table convergence time. A Click-based proof-of-concept implementation of EIR on the ORBIT testbed is described and used to validate performance and functionality for selected mobility use-cases, including mobile data services with open WiFi access points and mobile platforms such as buses operating in an urban area.

Android Permission Recommendation Using Transitive Bayesian Inference Model

In current Android architecture, users have to decide whether an app is safe to use or not. Technical-savvy users can make correct decisions to avoid unnecessary privacy breach. However, most users may have difficulty to make correct decisions. DroidNet is an Android permission recommendation framework based on crowdsourcing. In this framework, DroidNet runs new apps under probation mode without granting their permission requests up-front. It provides recommendations on whether to accept or reject the permission requests based on decisions from peer expert users. To seek expert users, we propose an expertise rating algorithm using transitional Bayesian inference model. The recommendation is based on the aggregated expert responses and its confidence level. Our evaluation results demonstrate that given sufficient number of experts in the network, DroidNet can provide accurate recommendations and cover majority of app requests given a small coverage from a small set of initial experts.