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DRS-based measurements of tissue types encountered in orthopedic surgery are utilized to establish a wavelength selection methodology using machine learning techniques, which enables rapid development of clinically translatable optical systems

Two-dimensional scanning of a 0.6328 mum guided-light beam has been realized using noncolinear acousto-optic (AO) coplanar Bragg diffraction together with colinear AO guided-mode to substrate radiation-mode conversion in a Z-cut Xpropagation LiNbO3 proton-exchanged (PE) waveguide. The two surface acoustic (SAW) waves utilized are at the center frequencies of 500 and 200 MHz, propagating in the Y and X axes, respectively. Two-dimensional scanning of approximately 720 resolvable light beam spots, namely, 18 40 (horizontal vertical) scanning, has been demonstrated using a light beam of 1.0 mm aperture. The total number of resolvable beam spots can be greatly increased from 720 by simply utilizing SAW transducers of larger bandwidth and a light beam of greater aperture. It should also be possible to significantly increase the diffraction efficiency from 3 % by optimizing the parameters of the PE waveguide and the SAWs.

In this demo, we present an efficient hybrid indoor positioning solution that uses multi-sensory location-oriented observations, including WiFi, accelerometer, gyroscope and digital compass data, that are widely available on Android smartphones1. Our system mainly comprises three building blocks, namely the WiFi Fingerprinting, the Inertial Measurement Unit (IMU) Positioning and the Location Fusion components. The WiFi Fingerprinting module relies on existing WiFi infrastructure and exploits Received Signal Strength (RSS) values from neighboring Access Points (AP) to infer the unknown user location. Specifically, it utilizes a number of RSS fingerprints collected a priori to build the so-called radiomap. Subsequently, the WiFi-based location is estimated with a state-of-the-art algorithm that exploits the currently measured fingerprint and fingerprints in the radiomap [1]. The IMU Positioning module performs multi-dimensional (i.e., 3-axis accelerometer, gyroscope and digital compass) motion sensor fusion for calculating the user orientation in real-time and implements an in-house pedometer algorithm for pedestrian trajectory tracking. An interesting feature in our implementation is the use of raw magnetic data to detect magnetic anomalies, which are common inside buildings, e.g. due to power cables, electrical appliances or metal surfaces, in order to refine orientation. Moreover, a map-matching submodule performs error correction in order to handle inaccurate IMU location estimates (e.g., showing a user passing through a wall or moving into a restricted area). Finally, the WiFi-based and IMU-based location estimates and associated uncertainties are provided as inputs to the Location Fusion module that implements the hybridization scheme by means of a particle filter. Thus, our prototype system delivers a smooth final location estimate that is consistent with the actual travelled path see Fig. 1. We will demonstrate the real-time positioning capabilities of our hybrid system by allowing attendees to carry an Android smartphone running our tracking application and viewing their current location on a floorplan map, while walking around the demo area. In this interactive scenario, the participants will be able to appreciate the potential of our indoor geolocation system, which is reliable and attains a localization error below 3 meters through the integration and optimization of diverse technologies, while our software may run on any commercial Android smartphone. 503 504 Sponsors: ACM SIGMOBILE Conference code: 98247 Cited By :8

Identification of tumour margins during resection of the brain is critical for improving the post-operative outcomes. Present research aims to develop a miniaturized, optical system for simultaneous measurement of DRS and auto-fluorescence for brain tumour detection.