Query-by-Example Spoken Term Detection ALBAYZIN 2012 evaluation: overview, systems, results, and discussion

Query-by-Example Spoken Term Detection (QbE STD) aims at retrieving data from a speech data repository given an acoustic query containing the term of interest as input. Nowadays, it has been receiving much interest due to the high volume of information stored in audio or audiovisual format. QbE STD differs from automatic speech recognition (ASR) and keyword spotting (KWS)/spoken term detection (STD) since ASR is interested in all the terms/words that appear in the speech signal and KWS/STD relies on a textual transcription of the search term to retrieve the speech data. This paper presents the systems submitted to the ALBAYZIN 2012 QbE STD evaluation held as a part of ALBAYZIN 2012 evaluation campaign within the context of the IberSPEECH 2012 Conference(a). The evaluation consists of retrieving the speech files that contain the input queries, indicating their start and end timestamps within the appropriate speech file. Evaluation is conducted on a Spanish spontaneous speech database containing a set of talks from MAVIR workshops(b), which amount at about 7 h of speech in total. We present the database metric systems submitted along with all results and some discussion. Four different research groups took part in the evaluation. Evaluation results show the difficulty of this task and the limited performance indicates there is still a lot of room for improvement. The best result is achieved by a dynamic time warping-based search over Gaussian posteriorgrams/posterior phoneme probabilities. This paper also compares the systems aiming at establishing the best technique dealing with that difficult task and looking for defining promising directions for this relatively novel task.

Technologies and Methodologies for the Detection of Harmful Algae and Their Toxins, St Petersburg, Florida, Oct 22-24 2008 : workshop proceedings

The Alliance for Coastal Technologies (ACT) Workshop "Technologies and Methodologies for the Detection of Harmful Algae and their Toxins" convened in St. Petersburg, Florida, October 22- 24, 2008 and was co-sponsored by ACT (http://act-us.info); the Cooperative Institute for Coastal and Estuarine Environmental Technology (CICEET, http://ciceet.unh.edu); and the Florida Fish and Wildlife Conservation Commission (FWC, http://www.myfwc.com). Participants from various sectors, including researchers, coastal decision makers, and technology vendors, collaborated to exchange information and build consensus. They focused on the status of currently available detection technologies and methodologies for harmful algae (HA) and their toxins, provided direction for developing operational use of existing technology, and addressed requirements for future technology developments in this area. Harmful algal blooms (HABs) in marine and freshwater systems are increasingly common worldwide and are known to cause extensive ecological, economic, and human health problems. In US waters, HABs are encountered in a growing number of locations and are also increasing in duration and severity. This expansion in HABs has led to elevated incidences of poisonous seafood, toxin-contaminated drinking water, mortality of fish and other animals dependent upon aquatic resources (including protected species), public health and economic impacts in coastal and lakeside communities, losses to aquaculture enterprises, and long-term aquatic ecosystem changes. This meeting represented the fourth ACT sponsored workshop that has addressed technology developments for improved monitoring of water-born pathogens and HA species in some form. A primary motivation was to assess the need and community support for an ACT-led Performance Demonstration of Harmful Algae Detection Technologies and Methodologies in order to facilitate their integration into regional ocean observing systems operations. The workshop focused on the identification of region-specific monitoring needs and available technologies and methodologies for detection/quantification of harmful algal species and their toxins along the US marine and freshwater coasts. To address this critical environmental issue, several technologies and methodologies have been, or are being, developed to detect and quantify various harmful algae and their associated toxins in coastal marine and freshwater environments. There are many challenges to nationwide adoption of HAB detection as part of a core monitoring infrastructure: the geographic uniqueness of primary algal species of concern around the country, the variety of HAB impacts, and the need for a clear vision of the operational requirements for monitoring the various species. Nonetheless, it was a consensus of the workshop participants that ACT should support the development of HA detection technology performance demonstrations but that these would need to be tuned regionally to algal species and toxins of concern in order to promote the adoption of state of the art technologies into HAR monitoring networks. [PDF contains 36 pages]

A Self-Referencing Intensity Based Polymer Optical Fiber Sensor for Liquid Detection

A novel self-referencing fiber optic intensity sensor based on bending losses of a partially polished polymer optical fiber (POF) coupler is presented. The coupling ratio (K) depends on the external liquid in which the sensor is immersed. It is possible to distinguish between different liquids and to detect their presence. Experimental results for the most usual liquids found in industry, like water and oil, are given. K value increases up to 10% from the nominal value depending on the liquid. Sensor temperature dependence has also been studied for a range from 25 degrees C (environmental condition) to 50 degrees C. Any sector requiring liquid level measurements in flammable atmospheres can benefit from this intrinsically safe technology.

Soluble Angiopoietin Receptor Tie-2 In Patients With Acute Myocardial Infarction And Its Detection By Optical Protein-Chip

The Tie-2 receptor has been shown to play a role in angiogenesis in atherosclerosis. The conventional method assaying the level of soluble Tie-2 (sTie-2) was ELISA. However, this method has some disadvantages. The aims of this research are to establish a more simple detection method, the optical protein-chip based on imaging ellipsomtry (OPC-IE) applying to Tie-2 assay. The sTie-2 biosensor surface on silicon wafer was prepared first, and then serum levels of sTie-2 in 38 patients with AMI were measured on admission (day 1), day 2, day 3 and day 7 after onset of chest pain and 41 healthy controls by ELISA and OPC-IE in parallel. Median level of sTie-2 increased significantly in the AMI patients when compared with the controls. Statistics showed there was a significant correlation in sTie-2 results between the two methods (r=0.923, P0.01). The result of this study showed that the level of sTie-2 increased in AMI, and OPC-IE assay was a fast, reliable, and convenient technique to measure sTie-2 in serum.

Phage M13Ko7 Detection With Biosensor Based On Imaging Ellipsometry And Afm Microscopic Confirmation

A rapid detection and identification of pathogens is important for minimizing transfer and spread of disease. A label-free and multiplex biosensor based on imaging ellipsometry (BIE) had been developed for the detection of phage M13KO7. The surface of silicon wafer is modified with aldehyde, and proteins can be patterned homogeneously and simultaneously on the surface of silicon wafer in an array format by a microfluidic system. Avidin is immobilized on the surface for biotin-anti-M13 immobilization by means of interaction between avidin and biotin, which will serve as ligand against phage M13KO7. Phages M13KO7 are specifically captured by the ligand when phage M13KO7 solution passes over the surface, resulting in a significant increase of mass surface concentration of the anti-M13 binding phage M13KO7 layer, which could be detected by imaging ellipsometry with a sensitivity of 10(9) pfu/ml. Moreover, atomic force microscopy is also used to confirm the fact that phage M13KO7 has been directly captured by ligands on the surface. It indicates that BIE is competent for direct detection of phage M13KO7 and has potential in the field of virus detection. (C) 2008 Elsevier B.V. All rights reserved.

Detection of hepatitis B virus markers using a biosensor based on imaging ellipsometry

A biosensor based on imaging ellipsometry (BIE) has been developed and validated in 169 patients for detecting five markers of hepatitis B virus (HBV) infection. The methodology has been established to pave the way for clinical diagnosis, including ligand screening, determination of the sensitivity, set-up of cut-off values (CoVs) and comparison with other clinical methods. A matrix assay method was established for ligand screening. The CoVs of HBV markers were derived with the help of receiver operating characteristic curves. Enzyme-linked immunosorbent assay (ELISA) was the reference method. Ligands with high bioactivity were selected and sensitivities of 1 ng/mL and 1 IU/mL for hepatitis B surface antigen (HBsAg) and surface antibody (anti-HBs) were obtained respectively. The CoVs of HBsAg, anti-HBs, hepatitis B e antigen, hepatitis B e antibody and core antibody were as follows: 15%, 18%, 15%, 20% and 15%, respectively, which were the percentages over the values of corresponding ligand controls. BIE can simultaneously detect up to five markers within 1 h with results in acceptable agreement with ELISA, and thus shows a potential for diagnosing hepatitis B with high throughput.

Sensor networks for geospatial event detection - theory and applications

This thesis presents theories, analyses, and algorithms for detecting and estimating parameters of geospatial events with today's large, noisy sensor networks. A geospatial event is initiated by a significant change in the state of points in a region in a 3-D space over an interval of time. After the event is initiated it may change the state of points over larger regions and longer periods of time. Networked sensing is a typical approach for geospatial event detection. In contrast to traditional sensor networks comprised of a small number of high quality (and expensive) sensors, trends in personal computing devices and consumer electronics have made it possible to build large, dense networks at a low cost. The changes in sensor capability, network composition, and system constraints call for new models and algorithms suited to the opportunities and challenges of the new generation of sensor networks. This thesis offers a single unifying model and a Bayesian framework for analyzing different types of geospatial events in such noisy sensor networks. It presents algorithms and theories for estimating the speed and accuracy of detecting geospatial events as a function of parameters from both the underlying geospatial system and the sensor network. Furthermore, the thesis addresses network scalability issues by presenting rigorous scalable algorithms for data aggregation for detection. These studies provide insights to the design of networked sensing systems for detecting geospatial events. In addition to providing an overarching framework, this thesis presents theories and experimental results for two very different geospatial problems: detecting earthquakes and hazardous radiation. The general framework is applied to these specific problems, and predictions based on the theories are validated against measurements of systems in the laboratory and in the field.

Universal Greenberger-Horne-Zeilinger-state analyzer based on two-photon polarization parity detection

We present a universal analyzer for the three-particle Greenberger-Horne-Zeilinger (GHZ) states with quantum nondemolition parity detectors and linear-optics elements. In our scheme, all of the three-photon GHZ states can be discriminated with nearly unity probability in the regime of weak nonlinearity feasible at the present state of the art experimentally. We also show that our scheme can be easily extended to the analysis of the multi-particle GHZ states.