This article presents the frequency-locked high-power RF oscillator using a gallium nitride (GaN) high electron mobility transistor (HEMT) amplifier and phase-locked loop (PLL) for 2.4-GHz industrial, scientific, and medical (ISM) band applications. The proposed architecture exploits the GaN power amplifier in the positive-feedback loop, whereas the desired phase shift for the target oscillating frequency is regulated from the PLL. To the best of our knowledge, this work is the first to employ the frequency locking scheme for a high-power solid-state RF oscillator. A detailed analysis of the oscillation conditions and the efficiency is provided. The prototype circuit is implemented with hybrid phase shifters and a fractional- N frequency synthesizer. The implemented RF oscillator circuit operates from 2.3 to 2.575 GHz and achieves a low phase noise of -131.8 dBc/Hz at a 1-MHz offset frequency. The power efficiency of the proposed oscillator reaches 58%, and the PLL incurs only 0.2% efficiency degradation.

One challenging problem for human-machine systems is to accurately estimate the position, velocity, and attitude of human foot motion, using an inertial measurement unit (IMU) sensor. This is particularly so in large environments affected by local magnetic disturbances. In this paper, we propose an algorithm that not only handles this problem, but also works efficiently in real time. The novelty of this paper lies mainly in two contributions: First, we propose a dynamic gait phase detection (GPD) method that can detect human foot gait phase with high accuracy (2.78% errors) in dynamic speeds of human foot motion, such as walking or running; second, we integrate an inertial navigation system, a GPD, a zero velocity update, and an extended Kalman filter in real time. The system can, thus, handle the IMU drift problem, as well as noise, for high-accuracy localization both indoors (0.375% errors) and outdoors (0.55% errors). To validate the proposed algorithm, we apply the motion-tracking system (MTS-ground truth), and the results show that 93.7% of the proposed algorithm's results converge on the MTS's results within a distance of less than 7.5 cm. Hence, the proposed algorithm can be embedded in wearable sensor devices for practical applications.

Bridge deck inspection is conducted to identify bridge condition deterioration and, thus, to facilitate implementation of appropriate maintenance or rehabilitation procedures. In this paper, we report the development of a robotic system for bridge deck data collection and analysis. The robotic system accurately localizes itself and autonomously maneuvers on the bridge deck to collect visual images and conduct nondestructive evaluation (NDE) measurements. The developed robotic system can reduce the cost and time of the bridge deck data collection. Crack detection and mapping algorithm to build the deck crack maps is presented in detail. The electrical resistivity (ER), impact-echo (IE) and ultrasonic surface waves (USW) data collected by the robot are analyzed to generate the corrosion, delamination and concrete elastic modulus maps of the deck. The presented robotic system has been successfully deployed to inspect numerous bridges.

Spoken language interfaces based on interactive semantic language models allow probabilities for hypothesized words to be conditioned on the semantic interpretation of these words in the context of some interfaced application environment. This conditioning may allow users to avoid recognition errors in an intuitive way, by adding extra, possibly redundant description. This paper evaluates the effect on error reduction of redundant descriptions in an interactive semantic language model. In order to evaluate the effect in natural use, the model is run on rich domains, supporting references to sets of individuals (instead of just individuals themselves) arranged in multiple continuous dimensions (a 2-D floorplan scene). Results of these experiments suggest that an interactive semantic language model allows users to achieve significantly higher recognition accuracy by providing additional redundant spoken description.

Recently, there has been an increasing deployment of content distribution networks (CDNs) that offer hosting services to Web content providers. In this paper, we first compare the uncooperative pulling of Web contents used by commercial CDNs with the cooperative pushing. Our results show that the latter can achieve comparable users' perceived performance with only 4%-5% of replication and update traffic compared with the former scheme. Therefore, we explore how to efficiently push content to CDN nodes. Using trace-driven simulation, we show that replicating content in units of URLs can yield 60%-70% reduction in clients' latency, compared with replicating in units of Websites. However, it is very expensive to perform such a fine-grained replication. To address this issue, we propose to replicate content in units of clusters, each containing objects which are likely to be requested by clients that are topologically close. To this end, we describe three clustering techniques and use various topologies and several large Web server traces to evaluate their performance. Our results show that the cluster-based replication achieves performance close to that of the URL-based scheme, but only at 1%-2% of computation and management cost. In addition, by adjusting the number of clusters, we can smoothly trade off management and computation cost for better client performance. To adapt to changes in users' access patterns, we also explore incremental clustering that adaptively adds new documents to the existing content clusters. We examine both offline and online incremental clustering, where the former assumes access history is available while the latter predicts access pattern based on the hyperlink structure. Our results show that the offline clustering yields performance close to that of the complete re-clustering at much lower overhead. The online incremental clustering and replication cut down the retrieval cost by 4.6 times compared with random and by 8 times compared with no replication. Therefore it is especially useful to improve document availability during flash crowds.

Recently, there has been an increasing deployment of content distribution networks (CDNs) that offer hosting services to Web content providers. We first compare uncooperative pulling of Web contents, used by commercial CDNs, with cooperative pushing. The latter can achieve user perceived performance comparable to the former scheme with only 4-5% of replication and update traffic. Therefore, we explore how to push content to CDN nodes efficiently. Using trace-driven simulation, we show that replicating content in units of URLs can yield 60-70% reduction in clients' latency, compared to replicating in units of Web sites. However, such a fine-grained replication is very expensive. We propose to replicate content in units of clusters, each containing objects which are likely to be requested by clients that are topologically close. We describe three clustering techniques, and use various topologies and several large Web server traces to evaluate their performance. Cluster-based replication achieves 40-60% improvement over per Web site based replication. By adjusting the number of clusters, we can smoothly trade off the management and computation cost for better client performance. We also explore incremental clusterings that adaptively add new documents to the existing content clusters. We examine both offline and online incremental clusterings. The offline clusterings yield close to the performance of the complete re-clustering at much lower overhead. The online incremental clustering and replication cut down the retrieval cost by 4.6-8 times compared to no replication and random replication, so it is especially useful for improving document availability during flash crowds.