Kaikai Liu

Towards accurate acoustic localization on a smartphone

Since our daily activities are dominantly indoor, as smart phones emerge as the most popular personal computing companions, major IT companies recently launched aggressive investment on mobile indoor location services and positioning systems, e.g., on iOS or Android mobile devices. However, one major hurdle has not been conquered yet: smart phone-based high-resolution indoor localization. In this paper, we propose a practical solution for accurate ranging and localization based on acoustic communication between anchor nodes with speakers and the microphone on a smartphone. To identify different anchor nodes and enable time-of-arrival (TOA) ranging, we propose approaches for signal modulation, symbol detection and demodulation, synchronization and ranging. Experimental results show that the communication bit-error-rate and ranging accuracy is sufficient for our target applications. The preliminary results of localization demonstrate that our algorithm could achieve highaccuracy of 23cm in the offline mode with a promising potential for realtime smartphone-based indoor localization.

Low Complexity Tri-Level Sampling Receiver Design for UWB Time-of-Arrival Estimation

In this paper, the effect of finite-level quantization on UWB time-of-arrival (TOA) estimation is investigated. The scheme of optimized quantization threshold combined with the post-quantization processing is derived, which is shown to provide satisfactory gains in the system performance. The TOA estimation errors of several low-resolution sampling approaches are compared via Monte Carlo simulation, where the tri-level quantizer is of particular interest due to its simplicity and capability. We demonstrate that the tri-level sampling receiver, with use of the proposed scheme provides an outstanding performance in TOA estimation with an affordable cost and low complexity.

Enabling Context-Aware Indoor Augmented Reality via Smartphone Sensing and Vision Tracking

Augmented reality (AR) aims to render the world that users see and overlay information that reflects the real physical dynamics. The digital view could be potentially projected near the Point-of-Interest (POI) in a way that makes the virtual view attached to the POI even when the camera moves. Achieving smooth support for movements is a subject of extensive studies. One of the key problems is where the augmented information should be added to the field of vision in real time. Existing solutions either leverage GPS location for rendering outdoor AR views (hundreds of kilometers away) or rely on image markers for small-scale presentation (only for the marker region). To realize AR applications under various scales and dynamics, we propose a suite of algorithms for fine-grained AR view tracking to improve the accuracy of attitude and displacement estimation, reduce the drift, eliminate the marker, and lower the computation cost. Instead of requiring extremely high, accurate, absolute locations, we propose multimodal solutions according to mobility levels without additional hardware requirement. Experimental results demonstrate significantly less error in projecting and tracking the AR view. These results are expected to make users excited to explore their surroundings with enriched content.

Improving GPS Service via Social Collaboration

The popularity of GPS-enabled smartphones enables a wide variety of new location-based or location-aware services and applications. However, the GPS module in a smartphone produces inaccurate position estimates and incurs high energy consumption, which inhibits the wide use of location-aware applications. To address this, we propose a social-aided cooperative location optimization (Coloc) scheme, which is capable of improving positioning accuracy and achieving low energy consumption. Specifically, our scheme enhances positioning accuracy by fusing the GPS positions of multiple co-located smartphones in a social network, or by neighborhood-based weighted least-squares estimation when relative distances between smartphones are available. The energy efficiency is achieved by sharing location information among co-located users and lower the update rate of the GPS module without sacrificing the accuracy. To validate our proposed approach, we conduct experiments in stationary and moving scenarios. Experimental results show that our proposed cooperative localization scheme can achieve sufficient performance gains in both indoor and outdoor environments.

IR-UWB radar signal sampling and reconstruction based on step-delay pulses

A novel sampling and recovering method based on step-delay pulses (SDP) for impulse radio ultra-wideband (IR-UWB) radar signal is presented in this paper. The SDP is used as the transmitted pulses of radar and the echoes from one group of SDP is used to reconstruct one pulse signal according to pulse delay relationship at the receiver. The SDP method is equivalent to increase the sampling rate and improve the performance of the reconstructing signal. Simulation results and discussion are given to evaluate the performance of the new approach. Related experiment are presented to demonstrate the theoretical feasibility of new method in signal sampling and recovering. The results show that the proposed method can sample and reconstruct IR-UWB signal and increase S NR effectively.

Hiding Media Data via Shaders: Enabling Private Sharing in the Clouds

In the era of Cloud and Social Networks, mobile devices exhibit much more powerful abilities for big media data storage and sharing. However, many users are still reluctant to share/store their data via clouds due to the potential leakage of confidential or private information. Although some cloud services provide storage encryption and access protection, privacy risks are still high since the protection is not always adequately conducted from end-to-end. Most customers are aware of the danger of letting data control out of their hands, e.g., Storing them to YouTube, Flickr, Facebook, Google+. Because of substantial practical and business needs, existing cloud services are restricted to the desired formats, e.g., Video and photo, without allowing arbitrary encrypted data. In this paper, we propose a format-compliant end-to-end privacy-preserving scheme for media sharing/storage issues with considerations for big data, clouds, and mobility. To realize efficient encryption for big media data, we jointly achieve format-compliant, compression-independent and correlation-preserving via multi-channel chained solutions under the guideline of Markov cipher. The encryption and decryption process is integrated into an image/video filter via GPU Shader for display-to-display full encryption. The proposed scheme makes big media data sharing/storage safer and easier in the clouds.

ExoApp: Performance Evaluation of Data-Intensive Applications on ExoGENI

ExoGENI is a new GENI-federated Infrastructureas- a-Service (IaaS) framework. In this paper, we evaluate the performance of data-intensive applications on ExoGENIs resources. To simplify experiments, we design an automatic provisioning system called ExoApp. This paper focuses on MapReduce-based applications. Users can easily deploy applications in ExoGENI using ExoApp, without having to manually configure cluster runtime environments. We then conduct a series of experiments using real-world data sets and standard benchmarks through ExoApp. Our result shows that ExoGENI demonstrates similar resource quality when hosting data-intensive applications and its Network-as-a-Service (NaaS) model maintains stable network performance. We finally identify the pros and cons of the ExoGENIs NaaS model in supporting data-intensive applications.