Youngjune Gwon

Robust indoor location estimation of stationary and mobile users

We present algorithms for estimating the location of stationary and mobile users based on heterogeneous indoor RF technologies. We propose two location algorithms, selective fusion location estimation (SELFLOC) and region of confidence (RoC), which can be used in conjunction with classical location algorithms such as triangulation, or with third-party commercial location estimation systems. The SELFLOC algorithm infers the user location by selectively fusing location information from multiple wireless technologies and/or multiple classical location algorithms in a theoretically optimal manner. The RoC algorithm attempts to overcome the problem of aliasing in the signal domain, where different physical locations have similar RF characteristics, which is particularly acute when users are mobile. We have empirically validated the proposed algorithms using wireless LAN and Bluetooth technology. Our experimental results show that applying SELFLOC for stationary users when using multiple wireless technologies and multiple classical location algorithms can improve location accuracy significantly, with mean distance errors as low as 1.6 m. For mobile users we find that using RoC can allow us to obtain mean errors as low as 3.7 m. Both algorithms can be used in conjunction with a commercial location estimation system and improve its accuracy further.

Error characteristics and calibration-free techniques for wireless LAN-based location estimation

Using wireless LAN technology for location estimation provides alternate means to enable location-based applications without investment in sensor network infrastructure and special hardware. However, the main drawback of wireless LAN-based location systems is calibration of signal strength as a function of location in spatially high-density, which consumes manual labor and needs to be carried out repeatedly. In this paper, we analyze empirical error characteristics of calibration-based location algorithms such as triangulation in various spatial densities of calibration, using commercially available wireless LAN products. Then, we propose Triangular Interpolation and eXtrapolation (TIX), a calibration-free location algorithm, and present empirical performance evaluation. TIX can achieve mean distance error within 5.4 m, which is comparable to within 4.7 m errors of the calibration-based algorithms. We also present theoretical analysis on error characteristics of the location algorithms deriving accuracy limits and quantifying the effect of RF measurement and calibration.

Determining RF angle of arrival using COTS antenna arrays: A field evaluation

We are interested in estimating the angle of arrival of an RF signal by using commercial-off-the-shelf (COTS) software-defined radios (SDRs). The proposed COTS-based approach has the advantages of flexibility, low cost and ease of deployment, but-unlike traditional phased antenna arrays in which elements are already phase-aligned - we face the challenge of aligning individual SDRs during field deployment in order to ensure coherent phase detection. We propose a strategy to relax the requirement of tight phase synchronization between distributed oscillators by using a novel phase difference of arrival mechanism based on a field-deployable reference transmitter. This approach enables flexible and inexpensive COTS phased-array designs. We evaluate our method in an outdoor, 20m×20m open field and observe localization errors below 3m. We conclude that a COTS-based approach to RF source localization is amenable to rapid and low-cost deployment of sensing infrastructure and could potentially be of interest to the Intelligence, Surveillance and Reconnaissance (ISR) community at the tactical edge.

Competing Mobile Network Game: Embracing antijamming and jamming strategies with reinforcement learning

We introduce Competing Mobile Network Game (CMNG), a stochastic game played by cognitive radio networks that compete for dominating an open spectrum access. Differentiated from existing approaches, we incorporate both communicator and jamming nodes to form a network for friendly coalition, integrate antijamming and jamming subgames into a stochastic framework, and apply Q-learning techniques to solve for an optimal channel access strategy. We empirically evaluate our Q-learning based strategies and find that Minimax-Q learning is more suitable for an aggressive environment than Nash-Q while Friend-or-foe Q-learning can provide the best solution under distributed mobile ad hoc networking scenarios in which the centralized control can hardly be available.

Multi-Modal Audio, Video and Physiological Sensor Learning for Continuous Emotion Prediction

The automatic determination of emotional state from multimedia content is an inherently challenging problem with a broad range of applications including biomedical diagnostics, multimedia retrieval, and human computer interfaces. The Audio Video Emotion Challenge (AVEC) 2016 provides a well-defined framework for developing and rigorously evaluating innovative approaches for estimating the arousal and valence states of emotion as a function of time. It presents the opportunity for investigating multimodal solutions that include audio, video, and physiological sensor signals. This paper provides an overview of our AVEC Emotion Challenge system, which uses multi-feature learning and fusion across all available modalities. It includes a number of technical contributions, including the development of novel high- and low-level features for modeling emotion in the audio, video, and physiological channels. Low-level features include modeling arousal in audio with minimal prosodic-based descriptors. High-level features are derived from supervised and unsupervised machine learning approaches based on sparse coding and deep learning. Finally, a state space estimation approach is applied for score fusion that demonstrates the importance of exploiting the time-series nature of the arousal and valence states. The resulting system outperforms the baseline systems [10] on the test evaluation set with an achieved Concordant Correlation Coefficient (CCC) for arousal of 0.770 vs 0.702 (baseline) and for valence of 0.687 vs 0.638. Future work will focus on exploiting the time-varying nature of individual channels in the multi-modal framework.

Adaptive approach for locally optimized IP handoffs across heterogeneous wireless networks

Seamless IP handoff is a crucial requirement to support real-time applications in mobile networks. In this paper, we aim to achieve seamless IP handoffs that are optimized for low latency and low packet loss in heterogeneous mobile wireless networks. We propose adaptive mechanisms that are distributed among heterogeneous network elements and mobile nodes. Adaptive process runs among geographically adjacent heterogeneous network elements and mobile nodes in the vicinity. The proposed mechanisms are devised to optimize IP handoff performances by attempting adaptive adjustment based on the local properties extracted through statistical processing.

Fast handoffs in wireless LAN networks using mobile initiated tunneling handoff protocol for IPv4 (MITHv4)

We investigate fast IP handoffs in wireless LAN networks. As a simple mobile-controlled approach, we introduce mobile initiated tunneling handoff protocol for IPv4 (MITHv4). Our experimental results show that MITHv4 can achieve optimized low latency and low loss IP handoffs in wireless LAN networks. Furthermore, MITHv4 significantly reduces the link layer trigger requirements and substantial access network support to synchronize link layer and IP layer handoffs that fast Mobile IPv4 (FMIPv4) protocols heavily rely on. These benefits of MITHv4 are crucial for wireless LAN networks where the required link layer triggers for FMIPv4 are not feasible due to limited access network control.

Detecting Depression using Vocal, Facial and Semantic Communication Cues

Major depressive disorder (MDD) is known to result in neurophysiological and neurocognitive changes that affect control of motor, linguistic, and cognitive functions. MDDs impact on these processes is reflected in an individuals communication via coupled mechanisms: vocal articulation, facial gesturing and choice of content to convey in a dialogue. In particular, MDD-induced neurophysiological changes are associated with a decline in dynamics and coordination of speech and facial motor control, while neurocognitive changes influence dialogue semantics. In this paper, biomarkers are derived from all of these modalities, drawing first from previously developed neurophysiologically-motivated speech and facial coordination and timing features. In addition, a novel indicator of lower vocal tract constriction in articulation is incorporated that relates to vocal projection. Semantic features are analyzed for subject/avatar dialogue content using a sparse coded lexical embedding space, and for contextual clues related to the subjects present or past depression status. The features and depression classification system were developed for the 6th International Audio/Video Emotion Challenge (AVEC), which provides data consisting of audio, video-based facial action units, and transcribed text of individuals communicating with the human-controlled avatar. A clinical Patient Health Questionnaire (PHQ) score and binary depression decision are provided for each participant. PHQ predictions were obtained by fusing outputs from a Gaussian staircase regressor for each feature set, with results on the development set of mean F1=0.81, RMSE=5.31, and MAE=3.34. These compare favorably to the challenge baseline development results of mean F1=0.73, RMSE=6.62, and MAE=5.52. On test set evaluation, our system obtained a mean F1=0.70, which is similar to the challenge baseline test result. Future work calls for consideration of joint feature analyses across modalities in an effort to detect neurological disorders based on the interplay of motor, linguistic, affective, and cognitive components of communication.

Compressive sensing with optimal sparsifying basis and applications in spectrum sensing

We describe a method of integrating Karhunen-Loève Transform (KLT) into compressive sensing, which can as a result improve the compression ratio without affecting the accuracy of decoding. We present two complementary results: 1) by using KLT to find an optimal basis for decoding we can drastically reduce the number of measurements for compressive sensing used in applications such as radio spectrum analysis; 2) by using compressive sensing we can estimate and recover the KLT basis from compressive measurements of an input signal. In particular, we propose CS-KLT, an online estimation algorithm to cope with nonstationarity of wireless channels in reality. We validate our results with empirical data collected from a wideband UHF spectrum and field experiments to detect multiple radio transmitters, using software-defined radios.

Enhanced forwarding from the previous care-of address (EFWD) for fast handovers in mobile IPv6

We introduce enhanced forwarding from the previous care-of address (EFWD) for fast handovers in mobile IPv6. EFWD enables a mobile node to directly control a bi-directional tunnel that is used to redirect data from the previous subnets access router to new subnets access router. As a result, data loss during a handover can be prevented. EFWD also reduces handover latency by expediting the mobile nodes movement detection and new subnets access router discovery, and by eliminating time to acquire a new care-of address. Main benefit of EFWD is the removal of link layer pre-triggers that are required by fast mobile IPv6 and generally infeasible for wireless technologies other than cellular systems. Empirical results indicate that performance of EFWD is nearly optimal as fast mobile IPv6, which incurs 30-40 msec handover latency with 3-4 lost UDP packets.