Complete gradient-LC-ESI system on a chip for protein analysis

This paper presents the first fully integrated gradient-elution liquid chromatography-electrospray ionization (LC-ESI) system on a chip. This chip integrates a pair of high-pressure gradient pumps, a sample injection pump, a passive mixer, a packed separation column, and an ESI nozzle. We also present the successful on-chip separation of protein digests by reverse phase (RP)-LC coupled with on-line mass spectrometer (MS) analysis

Microbubble Cavitation Imaging

Ultrasound cavitation of microbubble contrast agents has a potential for therapeutic applications such as sonothrombolysis (STL) in acute ischemic stroke. For safety, efficacy, and reproducibility of treatment, it is critical to evaluate the cavitation state (moderate oscillations, stable cavitation, and inertial cavitation) and activity level in and around a treatment area. Acoustic passive cavitation detectors (PCDs) have been used to this end but do not provide spatial information. This paper presents a prototype of a 2-D cavitation imager capable of producing images of the dominant cavitation state and activity level in a region of interest. Similar to PCDs, the cavitation imaging described here is based on the spectral analysis of the acoustic signal radiated by the cavitating microbubbles: ultraharmonics of the excitation frequency indicate stable cavitation, whereas elevated noise bands indicate inertial cavitation; the absence of both indicates moderate oscillations. The prototype system is a modified commercially available ultrasound scanner with a sector imaging probe. The lateral resolution of the system is 1.5 mm at a focal depth of 3 cm, and the axial resolution is 3 cm for a therapy pulse length of 20 mu s. The maximum frame rate of the prototype is 2 Hz. The system has been used for assessing and mapping the relative importance of the different cavitation states of a microbubble contrast agent. In vitro (tissue-mimicking flow phantom) and in vivo (heart, liver, and brain of two swine) results for cavitation states and their changes as a function of acoustic amplitude are presented

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction