Organic Indoor Location Discovery

We describe an indoor, room-level location discovery method based on spatial variations in "wifi signatures," i.e., MAC addresses and signal strengths of existing wireless access points. The principal novelty of our system is its organic nature; it builds signal strength maps from the natural mobility and lightweight contributions of ordinary users, rather than dedicated effort by a team of site surveyors. Whenever a user's personal device observes an unrecognized signature, a GUI solicits the user's location. The resulting location-tagged signature or "bind" is then shared with other clients through a common database, enabling devices subsequently arriving there to discover location with no further user contribution. Realizing a working system deployment required three novel elements: (1) a human-computer interface for indicating location over intervals of varying duration; (2) a client-server protocol for pre-fetching signature data for use in localization; and (3) a location-estimation algorithm incorporating highly variable signature data. We describe an experimental deployment of our method in a nine-story building with more than 1,400 distinct spaces served by more than 200 wireless access points. At the conclusion of the deployment, users could correctly localize to within 10 meters 92 percent of the time

BlueDBM: An Appliance for Big Data Analytics

Complex data queries, because of their need for random accesses, have proven to be slow unless all the data can be accommodated in DRAM. There are many domains, such as genomics, geological data and daily twitter feeds where the datasets of interest are 5TB to 20 TB. For such a dataset, one would need a cluster with 100 servers, each with 128GB to 256GBs of DRAM, to accommodate all the data in DRAM. On the other hand, such datasets could be stored easily in the flash memory of a rack-sized cluster. Flash storage has much better random access performance than hard disks, which makes it desirable for analytics workloads. In this paper we present BlueDBM, a new system architecture which has flash-based storage with in-store processing capability and a low-latency high-throughput inter-controller network. We show that BlueDBM outperforms a flash-based system without these features by a factor of 10 for some important applications. While the performance of a ram-cloud system falls sharply even if only 5%~10% of the references are to the secondary storage, this sharp performance degradation is not an issue in BlueDBM. BlueDBM presents an attractive point in the cost-performance trade-off for Big Data analytics.Quanta Computer (Firm)Samsung (Firm)Lincoln Laboratory (PO7000261350)Intel Corporatio

H.264 Decoder: A Case Study in Multiple Design Points

H.264, a state-of-the-art video compression standard, is used across a range of products from cell phones to HDTV. These products have vastly different performance, power and cost requirements, necessitating different hardware-software solutions for H.264 decoding. We show that a de-sign methodology and associated tools which support syn-thesis from high-level descriptions and which allow mod-ular refinement throughout the design cycle, can share the majority of design effort across multiple design points. Us-ing Bluespec SystemVerilog, we have created a variety of designs for the H.264 decoder tuned to support decod-ing at resolutions ranging from QCIF video (176 × 14

Approved for External Publication Integrating Presence and Location Services using SIP

internet telephony, location awareness, session initiation protocol, wireless trackin

Growing an organic indoor location system

Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments.Nokia Research Cente

Hemolytic uremic syndrome as the presenting manifestation of WT1 mutation and Denys-Drash syndrome: a case report

Abstract Background Hemolytic uremic syndrome (HUS) can occur as a primary process due to mutations in complement genes or secondary to another underlying disease. HUS sometimes occurs in the setting of glomerular diseases, and it has been described in association with Denys-Drash syndrome (DDS), which is characterized by the triad of abnormal genitourinary development; a pathognomonic glomerulopathy, diffuse mesangial sclerosis; and the development of Wilms tumor. Case presentation We report the case of a 46, XX female infant who presented with HUS and biopsy-proven thrombotic microangiopathy. Next generation sequencing of genes with known mutations causative of atypical HUS found that she was homozygous for the Complement Factor H H3 haplotype and heterozygous for a variant of unknown significance in the DGKE gene. Whole exome sequencing identified a de novo heterozygous WT1 c.1384C > T; p.R394W mutation, which is classically associated with Denys-Drash syndrome (DDS). At the time of bilateral nephrectomy five months after her initial biopsy, she had diffuse mesangial sclerosis, typical of Denys-Drash syndrome, without evidence of thrombotic microangiopathy. Conclusion This unique case highlights HUS as a rare but important manifestation of WT1 mutation and provides new insight into the genetics underlying this association