Alfred G. Brandstein Interview (MORS)

Interviewers: Bruggeman, John; Stephens, Cortez D. Interview location(s): Woodbridge, Virgini

A general framework for online audio source separation

We consider the problem of online audio source separation. Existing algorithms adopt either a sliding block approach or a stochastic gradient approach, which is faster but less accurate. Also, they rely either on spatial cues or on spectral cues and cannot separate certain mixtures. In this paper, we design a general online audio source separation framework that combines both approaches and both types of cues. The model parameters are estimated in the Maximum Likelihood (ML) sense using a Generalised Expectation Maximisation (GEM) algorithm with multiplicative updates. The separation performance is evaluated as a function of the block size and the step size and compared to that of an offline algorithm.Comment: International conference on Latente Variable Analysis and Signal Separation (2012

Weighted and unweighted network of amino acids within protein

The information regarding the structure of a single protein is encoded in the network of interacting amino acids. Considering each protein as a weighted and unweighted network of amino acids we have analyzed a total of forty nine protein structures that covers the three branches of life on earth. Our results show that the probability degree distribution of network connectivity follows Poisson's distribution; whereas the probability strength distribution does not follow any known distribution. However, the average strength of amino acid node depends on its degree (k). For some of the proteins, the strength of a node increases linearly with k. On the other hand, for a set of other proteins, although the strength increases linaerly with k for smaller values of k, we have not obtained any clear functional relationship of strength with degree at higher values of k. The results also show that the weight of the amino acid nodes belonging to the highly connected nodes tend to have a higher value. The result that the average clustering coefficient of weighted network is less than that of unweighted network implies that the topological clustering is generated by edges with low weights. The ratio of average clustering coefficients of protein network to that of the corresponding classical random network varies linearly with the number (N) of amino acids of a protein; whereas the ratio of characteristic path lengths varies logarithmically with N. The power law behaviour of clustering coefficients of weighted and unweighted network as a function of degree k indicates that the network has a signature of hierarchical network. It has also been observed that the network is of assortative type

Exercises in Emergency Preparedness for Health Professionals in Community Clinics

Health professionals in community settings are generally unprepared for disasters. From 2006 to 2008 the California Statewide Area Health Education Center (AHEC) program conducted 90 table top exercises in community practice sites in 18 counties. The exercises arranged and facilitated by AHEC trained local coordinators and trainers were designed to assist health professionals in developing and applying their practice site emergency plans using simulated events about pandemic influenza or other emergencies. Of the 1,496 multidisciplinary health professionals and staff participating in the exercises, 1,176 (79%) completed learner evaluation forms with 92–98% of participants rating the training experiences as good to excellent. A few reported helpful effects when applying their training to a real time local disaster. Assessments of the status of clinic emergency plans using 15 criteria were conducted at three intervals: when the exercises were scheduled, immediately before the exercises, and for one-third of sites, three months after the exercise. All sites made improvements in their emergency plans with some or all of the plan criteria. Of the sites having follow up, most (N = 23) were community health centers that made statistically significant changes in two-thirds of the plan criteria (P = .001–.046). Following the exercises, after action reports were completed for 88 sites and noted strengths, weaknesses, and plans for improvements in their emergency plans Most sites (72–90%) showed improvements in how to activate their plans, the roles of their staff, and how to participate in a coordinated response. Challenges in scheduling exercises included time constraints and lack of resources among busy health professionals. Technical assistance and considerations of clinic schedules mitigated these issues. The multidisciplinary table top exercises proved to be an effective means to develop or improve clinic emergency plans and enhance the dialogue and coordination among health professionals before an emergency happens

Speech Processing Research Program

Contains an introduction and reports on five research projects.National Science Foundation Grant MIP 87-14969National Science Foundation FellowshipU.S. Air Force - Electronic Systems Division Contract F1 9628-89-K-0041U.S. Navy - Office of Naval Research Contract N00014-89-J-148

Speech Processing Research Program

Contains an introduction and reports on five research projects.National Science Foundation FellowshipNational Science Foundation Grant MIP 87-14969U.S. Navy - Office of Naval Research Contract N00014-89-J-1489U.S. Air Force - Electronic Systems Division Contract F19628-89-K-0041National Science Foundation Fellowshi

A Hybrid Global Minimization Scheme for Accurate Source Localization in Sensor Networks

We consider the localization problem of multiple wideband sources in a multi-path environment by coherently taking into account the attenuation characteristics and the time delays in the reception of the signal. Our proposed method leaves the space for unavailability of an accurate signal attenuation model in the environment by considering the model as an unknown function with reasonable prior assumptions about its functional space. Such approach is capable of enhancing the localization performance compared to only utilizing the signal attenuation information or the time delays. In this paper, the localization problem is modeled as a cost function in terms of the source locations, attenuation model parameters and the multi-path parameters. To globally perform the minimization, we propose a hybrid algorithm combining the differential evolution algorithm with the Levenberg-Marquardt algorithm. Besides the proposed combination of optimization schemes, supporting the technical details such as closed forms of cost function sensitivity matrices are provided. Finally, the validity of the proposed method is examined in several localization scenarios, taking into account the noise in the environment, the multi-path phenomenon and considering the sensors not being synchronized

Trust Index Based Fault Tolerant Multiple Event Localization Algorithm for WSNs

This paper investigates the use of wireless sensor networks for multiple event source localization using binary information from the sensor nodes. The events could continually emit signals whose strength is attenuated inversely proportional to the distance from the source. In this context, faults occur due to various reasons and are manifested when a node reports a wrong decision. In order to reduce the impact of node faults on the accuracy of multiple event localization, we introduce a trust index model to evaluate the fidelity of information which the nodes report and use in the event detection process, and propose the Trust Index based Subtract on Negative Add on Positive (TISNAP) localization algorithm, which reduces the impact of faulty nodes on the event localization by decreasing their trust index, to improve the accuracy of event localization and performance of fault tolerance for multiple event source localization. The algorithm includes three phases: first, the sink identifies the cluster nodes to determine the number of events occurred in the entire region by analyzing the binary data reported by all nodes; then, it constructs the likelihood matrix related to the cluster nodes and estimates the location of all events according to the alarmed status and trust index of the nodes around the cluster nodes. Finally, the sink updates the trust index of all nodes according to the fidelity of their information in the previous reporting cycle. The algorithm improves the accuracy of localization and performance of fault tolerance in multiple event source localization. The experiment results show that when the probability of node fault is close to 50%, the algorithm can still accurately determine the number of the events and have better accuracy of localization compared with other algorithms