Matching Points with Things

Given an ordered set of points and an ordered set of geometric objects in the plane, we are interested in finding a non-crossing matching between point-object pairs. We show that when the objects we match the points to are finite point sets, the problem is NP-complete in general, and polynomial when the objects are on a line or when their number is at most 2. When the objects are line segments, we show that the problem is NP-complete in general, and polynomial when the segments form a convex polygon or are all on a line. Finally, for objects that are straight lines, we show that the problem of finding a min-max non-crossing matching is NP-complete

No Match, No Vote

Responding to the loud wake-up call sounded in the 2000 election, Congress passed the Help America Vote Act (HAVA) in 2002, including provisions to streamline and modernize voter registration databases and establish identification requirements. However, in direct contravention of the intent of HAVA -- to impose fair and more uniform standards for state election administration -- some states have misinterpreted the law and passed onerous"No Match, No Vote" laws.Under such statutes, if a state is unable to match the information on a voter's registration application with information in an existing government database, the application is denied outright. Many of these non-matches, however, can be the result of errors outside of the applicant's control such as typographical data entry errors, flaws in existing governmental databases, and poor database matching protocols. By making it more difficult and sometimes impossible for applicants to register to vote, No Match, No Vote laws can and do disenfranchise qualified citizens. Shortly before the 2008 election, Time magazinedeclared the "Database Dilemma" number one on their list of "Things That Could Go Wrong on Election Day."As this paper will demonstrate, plenty of research exists to show that matching voter data with other government databases -- though required by HAVA -- is an unreliable, error-laden process, and that conditioning the right to vote on such a flawed system will inevitably disenfranchise eligible citizens. HAVA's verification provisions were put into place to improve state database management and facilitate accurate record keeping. These provisions were written to ensure that every voter's registration record has a unique number associated with it to allow states to easily identify duplicate registration records with greater confidence and determine and eliminate voters no longer eligible to vote in that jurisdiction.2 As the legislative history points out, it was not HAVA's intent in requiringa match to disenfranchise those otherwise eligible applicants whose data does not match exactly. Therefore, in order to comply with federal law while maintaining the rights of its citizens to vote, states should follow the best practices discussed below

AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed